7491:. A properly conducted regression analysis will include an assessment of how well the assumed form is matched by the observed data, but it can only do so within the range of values of the independent variables actually available. This means that any extrapolation is particularly reliant on the assumptions being made about the structural form of the regression relationship. If this knowledge includes the fact that the dependent variable cannot go outside a certain range of values, this can be made use of in selecting the model â even if the observed dataset has no values particularly near such bounds. The implications of this step of choosing an appropriate functional form for the regression can be great when extrapolation is considered. At a minimum, it can ensure that any extrapolation arising from a fitted model is "realistic" (or in accord with what is known).
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31:
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7380:(or polyserial correlations) between the categorical variables. Such procedures differ in the assumptions made about the distribution of the variables in the population. If the variable is positive with low values and represents the repetition of the occurrence of an event, then count models like the
7766:
applications and on some calculators. While many statistical software packages can perform various types of nonparametric and robust regression, these methods are less standardized. Different software packages implement different methods, and a method with a given name may be implemented differently
1533:
in 1809. Legendre and Gauss both applied the method to the problem of determining, from astronomical observations, the orbits of bodies about the Sun (mostly comets, but also later the then newly discovered minor planets). Gauss published a further development of the theory of least squares in 1821,
1508:
between the independent and dependent variables. Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset. To use regressions for prediction or to infer causal relationships, respectively, a researcher
7304:
are sometimes more difficult to interpret if the model's assumptions are violated. For example, if the error term does not have a normal distribution, in small samples the estimated parameters will not follow normal distributions and complicate inference. With relatively large samples, however, a
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In the middle, the interpolated straight line represents the best balance between the points above and below this line. The dotted lines represent the two extreme lines. The first curves represent the estimated values. The outer curves represent a prediction for a new
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in the class of linear unbiased estimators. Practitioners have developed a variety of methods to maintain some or all of these desirable properties in real-world settings, because these classical assumptions are unlikely to hold exactly. For example, modeling
1509:
must carefully justify why existing relationships have predictive power for a new context or why a relationship between two variables has a causal interpretation. The latter is especially important when researchers hope to estimate causal relationships using
7467:. Performing extrapolation relies strongly on the regression assumptions. The further the extrapolation goes outside the data, the more room there is for the model to fail due to differences between the assumptions and the sample data or the true values.
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By itself, a regression is simply a calculation using the data. In order to interpret the output of regression as a meaningful statistical quantity that measures real-world relationships, researchers often rely on a number of classical
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4383:
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is the number of observations needed to reach the desired precision if the model had only one independent variable. For example, a researcher is building a linear regression model using a dataset that contains 1000 patients
6019:{\displaystyle {\hat {\sigma }}_{\beta _{0}}={\hat {\sigma }}_{\varepsilon }{\sqrt {{\frac {1}{n}}+{\frac {{\bar {x}}^{2}}{\sum (x_{i}-{\bar {x}})^{2}}}}}={\hat {\sigma }}_{\beta _{1}}{\sqrt {\frac {\sum x_{i}^{2}}{n}}}.}
3122:
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The response variable may be non-continuous ("limited" to lie on some subset of the real line). For binary (zero or one) variables, if analysis proceeds with least-squares linear regression, the model is called the
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that represents the uncertainty may accompany the point prediction. Such intervals tend to expand rapidly as the values of the independent variable(s) moved outside the range covered by the observed data.
1595:, regression in which the predictor (independent variable) or response variables are curves, images, graphs, or other complex data objects, regression methods accommodating various types of missing data,
2255:
1545:
in the 19th century to describe a biological phenomenon. The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon also known as
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2004:
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is largely focused on developing techniques that allow researchers to make reasonable real-world conclusions in real-world settings, where classical assumptions do not hold exactly.
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There are no generally agreed methods for relating the number of observations versus the number of independent variables in the model. One method conjectured by Good and Hardin is
7402:
When the model function is not linear in the parameters, the sum of squares must be minimized by an iterative procedure. This introduces many complications which are summarized in
2901:
1473:) of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative
4728:
Returning our attention to the straight line case: Given a random sample from the population, we estimate the population parameters and obtain the sample linear regression model:
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Interpretations of these diagnostic tests rest heavily on the model's assumptions. Although examination of the residuals can be used to invalidate a model, the results of a
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type models may be used when the sample is not randomly selected from the population of interest. An alternative to such procedures is linear regression based on
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methods for regression, regression in which the predictor variables are measured with error, regression with more predictor variables than observations, and
1573:
of the response variable is
Gaussian, but the joint distribution need not be. In this respect, Fisher's assumption is closer to Gauss's formulation of 1821.
7483:
However, this does not cover the full set of modeling errors that may be made: in particular, the assumption of a particular form for the relation between
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Under the further assumption that the population error term is normally distributed, the researcher can use these estimated standard errors to create
13705:
3445:{\displaystyle \sum _{i}{\hat {e}}_{i}^{2}=\sum _{i}({\hat {Y}}_{i}-({\hat {\beta }}_{0}+{\hat {\beta }}_{1}X_{1i}+{\hat {\beta }}_{2}X_{2i}))^{2}=0}
9449:
7694:
Although the parameters of a regression model are usually estimated using the method of least squares, other methods which have been used include:
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1133:
2515:
to distinguish the estimate from the true (unknown) parameter value that generated the data. Using this estimate, the researcher can then use the
2591:
for prediction or to assess the accuracy of the model in explaining the data. Whether the researcher is intrinsically interested in the estimate
2818:
It is important to note that there must be sufficient data to estimate a regression model. For example, suppose that a researcher has access to
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model is a standard method of estimating a joint relationship between several binary dependent variables and some independent variables. For
4015:
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1461:) that minimizes the sum of squared differences between the true data and that line (or hyperplane). For specific mathematical reasons (see
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be able to reconstruct any of the independent variables by adding and multiplying the remaining independent variables. As discussed in
1380:
1183:
8748:"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation"
7767:
in different packages. Specialized regression software has been developed for use in fields such as survey analysis and neuroimaging.
12683:
5046:
7403:
5542:(MSE) of the regression. The denominator is the sample size reduced by the number of model parameters estimated from the same data,
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357:
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808:
17:
8454:
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8654:
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7639:
5807:{\displaystyle {\hat {\sigma }}_{\beta _{1}}={\hat {\sigma }}_{\varepsilon }{\sqrt {\frac {1}{\sum (x_{i}-{\bar {x}})^{2}}}}}
2521:
2260:
1492:
Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for
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10371:
9823:
9638:
3993:
A handful of conditions are sufficient for the least-squares estimator to possess desirable properties: in particular, the
3807:
1317:
1080:
615:
347:
6702:{\displaystyle \sum _{i=1}^{n}\sum _{k=1}^{p}x_{ij}x_{ik}{\hat {\beta }}_{k}=\sum _{i=1}^{n}x_{ij}y_{i},\ j=1,\dots ,p.\,}
2391:
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This is still linear regression; although the expression on the right hand side is quadratic in the independent variable
1789:
1335:
8370:
Fotheringham, AS; Wong, DWS (1 January 1991). "The modifiable areal unit problem in multivariate statistical analysis".
5462:
Under the assumption that the population error term has a constant variance, the estimate of that variance is given by:
5261:{\displaystyle {\widehat {\beta }}_{1}={\frac {\sum (x_{i}-{\bar {x}})(y_{i}-{\bar {y}})}{\sum (x_{i}-{\bar {x}})^{2}}}}
4080:
standard errors, among other techniques. When rows of data correspond to locations in space, the choice of how to model
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10163:
9813:
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Regression methods continue to be an area of active research. In recent decades, new methods have been developed for
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to calculate regressions. Before 1970, it sometimes took up to 24 hours to receive the result from one regression.
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512:
311:
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In practice, researchers first select a model they would like to estimate and then use their chosen method (e.g.,
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1941:
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120:
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Distance metric learning, which is learned by the search of a meaningful distance metric in a given input space.
5043:, a set of simultaneous linear equations in the parameters, which are solved to yield the parameter estimators,
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In multiple linear regression, there are several independent variables or functions of independent variables.
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13578:
13349:
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1453:) that most closely fits the data according to a specific mathematical criterion. For example, the method of
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722:
181:
2105:
must be specified. Sometimes the form of this function is based on knowledge about the relationship between
13960:
13443:
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12088:
12073:
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11771:
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10211:
10201:
10120:
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9668:
9065:
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7748:
4072:. Correlated errors that exist within subsets of the data or follow specific patterns can be handled using
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1138:
1048:
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869:
859:
440:
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13394:
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10932:
10396:
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9719:
9015:
8941:
8803:
8798:
6984:
5691:
3591:, then there does not generally exist a set of parameters that will perfectly fit the data. The quantity
1352:
1264:
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710:
532:
383:
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4896:
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13585:
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11636:
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9833:
9343:
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7317:
4555:{\displaystyle y_{i}=\beta _{0}+\beta _{1}x_{i}+\beta _{2}x_{i}^{2}+\varepsilon _{i},\ i=1,\dots ,n.\!}
2037:
1546:
1312:
1239:
989:
884:
672:
605:
565:
352:
321:
248:
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3860:
3245:{\displaystyle {\hat {Y}}_{i}={\hat {\beta }}_{0}+{\hat {\beta }}_{1}X_{1i}+{\hat {\beta }}_{2}X_{2i}}
13782:
13777:
13645:
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13051:
13009:
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that does not rely on the data. If no such knowledge is available, a flexible or convenient form for
2046:
1592:
1366:
972:
740:
610:
342:
331:
295:
202:
7613:). If the researcher decides that five observations are needed to precisely define a straight line (
6205:{\displaystyle y_{i}=\beta _{1}x_{i1}+\beta _{2}x_{i2}+\cdots +\beta _{p}x_{ip}+\varepsilon _{i},\,}
2717:
2623:
13620:
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11898:
11863:
11827:
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10963:
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10525:
10364:
10158:
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10000:
9928:
9818:
9800:
9643:
8900:
7823:
7759:
7733:
7723:
7333:
7274:
6510:{\displaystyle \varepsilon _{i}=y_{i}-{\hat {\beta }}_{1}x_{i1}-\cdots -{\hat {\beta }}_{p}x_{ip}.}
4172:
4126:
4077:
3994:
1596:
1570:
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994:
914:
837:
755:
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90:
69:
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For such reasons and others, some tend to say that it might be unwise to undertake extrapolation.
7020:
6816:
1485:) or estimate the conditional expectation across a broader collection of non-linear models (e.g.,
13891:
13818:
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12492:
12105:
12045:
11982:
11620:
11604:
11342:
11204:
11194:
11044:
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790:
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326:
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1619:) to estimate the parameters of that model. Regression models involve the following components:
13914:
13886:
13665:
13389:
12875:
12786:
12530:
12460:
12253:
12190:
11945:
11832:
10829:
10726:
10633:
10512:
10411:
10252:
10078:
9943:
9933:
9884:
9648:
9408:
9127:
9122:
8332:
7919:
7818:
7377:
7258:
7143:
7088:
7042:
4957:
4394:
4006:
3844:
3710:
3483:
2657:
2365:
1823:
1616:
1549:). For Galton, regression had only this biological meaning, but his work was later extended by
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9628:
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9084:
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8205:"The goodness of fit of regression formulae, and the distribution of regression coefficients"
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7306:
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897:
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35:
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Data
Fitting and Uncertainty (A practical introduction to weighted least squares and beyond)
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that most closely fits the data. To carry out regression analysis, the form of the function
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3998:
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2135:
2108:
2012:
1911:
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1004:
954:
398:
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269:
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192:
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79:
8406:
Principles and
Procedures of Statistics with Special Reference to the Biological Sciences.
7983:, Firmin Didot, Paris, 1805. âSur la MĂŠthode des moindres quarrĂŠsâ appears as an appendix.
8:
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Meade, Nigel; Islam, Towhidul (1995). "Prediction intervals for growth curve forecasts".
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can be invoked such that hypothesis testing may proceed using asymptotic approximations.
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are assumed to be free of error. This important assumption is often overlooked, although
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8106:(Galton uses the term "regression" in this paper, which discusses the height of humans.)
7633:), then the maximum number of independent variables the model can support is 4, because
6793:
4893:, is the difference between the value of the dependent variable predicted by the model,
4817:{\displaystyle {\widehat {y}}_{i}={\widehat {\beta }}_{0}+{\widehat {\beta }}_{1}x_{i}.}
3486:. Alternatively, one can visualize infinitely many 3-dimensional planes that go through
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7381:
7373:
7242:{\displaystyle \mathbf {{\hat {\boldsymbol {\beta }}}=(X^{\top }X)^{-1}X^{\top }Y} .\,}
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6888:
6868:
6378:
6288:
6268:
6248:
6064:
5577:
5442:
5393:
4708:
4378:{\displaystyle y_{i}=\beta _{0}+\beta _{1}x_{i}+\varepsilon _{i},\quad i=1,\dots ,n.\!}
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6285:-th independent variable. If the first independent variable takes the value 1 for all
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9168:
9039:
9032:
8830:â how linear regression mistakes can appear when Y-range is much smaller than X-range
8779:
8717:
8675:
8650:
8621:
8594:
8525:
8474:
8446:
8391:
8350:
8268:
8257:
8029:
7938:
7884:
7874:
7803:
7778:
7385:
7353:
6056:
5539:
4120:
3982:
3857:
Deviations from the model have an expected value of zero, conditional on covariates:
3744:
3621:
in the model. Moreover, to estimate a least squares model, the independent variables
2903:. Suppose further that the researcher wants to estimate a bivariate linear model via
2337:
1584:
1462:
1446:
1402:
1394:
1178:
1021:
934:
730:
700:
645:
640:
595:
537:
416:
207:
110:
64:
8555:
6780:{\displaystyle \mathbf {(X^{\top }X){\hat {\boldsymbol {\beta }}}={}X^{\top }Y} ,\,}
5347:{\displaystyle {\widehat {\beta }}_{0}={\bar {y}}-{\widehat {\beta }}_{1}{\bar {x}}}
13805:
13558:
13517:
13329:
13304:
13116:
13046:
12890:
12675:
12570:
12525:
12289:
12276:
12169:
12144:
12078:
12010:
11888:
11496:
11389:
11322:
11235:
11182:
11001:
10872:
10666:
10550:
10465:
10432:
9979:
9846:
9469:
9459:
9266:
9060:
9010:
9005:
8948:
8936:
8769:
8759:
8694:
8515:
8507:
8379:
8347:
Geographically weighted regression: the analysis of spatially varying relationships
8304:
8252:
8232:
8216:
8177:
8133:
8094:(Galton uses the term "reversion" in this paper, which discusses the size of peas.)
8062:
7894:
7834:
7357:
5112:
In the case of simple regression, the formulas for the least squares estimates are
3944:
1632:
1628:
1604:
1566:
1501:
1206:
959:
909:
819:
803:
773:
635:
630:
580:
570:
468:
232:
161:
8821:
2340:, different forms of regression analysis provide tools to estimate the parameters
2333:
to be a reasonable approximation for the statistical process generating the data.
13866:
13568:
13371:
13364:
13299:
13239:
13121:
13111:
13041:
13014:
12999:
12954:
12944:
12895:
12487:
12231:
12093:
12020:
11695:
11569:
11542:
11519:
11488:
11115:
11110:
11064:
10794:
10445:
10262:
10168:
10109:
10104:
9582:
9526:
9348:
8990:
8910:
8575:
7978:
7698:
7451:
the range of values in the dataset used for model-fitting is known informally as
7277:
of the estimated parameters. Commonly used checks of goodness of fit include the
7270:
7269:
Once a regression model has been constructed, it may be important to confirm the
6034:
5387:
4014:
can lead to reasonable estimates independent variables are measured with errors.
3986:
3124:
that explain the data equally well: any combination can be chosen that satisfies
1234:
1038:
904:
844:
393:
100:
11977:
13553:
13339:
13276:
13261:
13244:
13202:
13004:
12885:
12753:
12730:
12720:
12436:
12431:
10894:
10824:
10470:
10206:
9556:
9521:
9511:
9336:
9094:
8920:
8732:
8309:
8089:
7859:
7365:
3456:. To understand why there are infinitely many options, note that the system of
1542:
1254:
785:
522:
145:
4132:
In linear regression, the model specification is that the dependent variable,
13954:
13861:
13740:
13548:
13522:
13399:
13359:
13324:
13314:
13294:
13036:
12994:
12969:
12917:
12905:
12900:
12809:
12593:
12560:
12423:
12384:
12195:
12164:
11628:
11582:
11187:
10889:
10716:
10480:
10475:
10278:
9808:
9788:
9501:
9481:
9398:
9077:
8613:
8181:
7798:
7755:
7463:
7453:
7361:
7324:
or are variables constrained to fall only in a certain range, often arise in
3516:
3117:{\displaystyle ({\hat {\beta }}_{0},{\hat {\beta }}_{1},{\hat {\beta }}_{2})}
2904:
2361:
1470:
1173:
1102:
984:
715:
600:
264:
140:
27:
Set of statistical processes for estimating the relationships among variables
8158:
8067:
8050:
1557:
to a more general statistical context. In the work of Yule and
Pearson, the
13881:
13309:
13158:
13148:
13126:
13056:
12949:
12863:
12535:
12468:
12445:
12360:
11690:
10986:
10884:
10819:
10761:
10746:
10683:
10638:
9587:
9418:
8833:
8783:
8698:
8602:
8579:
8466:
8159:
8117:
8092:. "Typical laws of heredity", Nature 15 (1877), 492â495, 512â514, 532â533.
7337:
7325:
4960:. This method obtains parameter estimates that minimize the sum of squared
4108:
3978:
1554:
130:
8552:
Proc. International
Conference on Computer Analysis of Images and Patterns
7726:, requires a large number of observations and is computationally intensive
2040:
can be used when the independent variables are assumed to contain errors.
13876:
13573:
13271:
13180:
13143:
12707:
12578:
12540:
12223:
12124:
11986:
11799:
11766:
11258:
11175:
11170:
10814:
10771:
10751:
10731:
10721:
10490:
9866:
9683:
9454:
9363:
9358:
8980:
8958:
8764:
8511:
8409:
7808:
7763:
4107:
within geographic units can have important consequences. The subfield of
1588:
1497:
979:
473:
176:
125:
8318:
7372:
may be used when the dependent variable is only sometimes observed, and
3005:{\displaystyle Y_{i}=\beta _{0}+\beta _{1}X_{1i}+\beta _{2}X_{2i}+e_{i}}
13502:
13224:
11424:
10904:
10604:
10535:
10485:
10460:
10380:
9577:
9536:
9531:
9444:
9353:
9261:
9173:
9153:
8228:
8189:
8168:
8145:
8076:
3814: in this section. Unsourced material may be challenged and removed.
1493:
1458:
1128:
824:
750:
8423:
8104:
Francis Galton. Presidential address, Section H, Anthropology. (1885)
7958:
7285:
and hypothesis testing. Statistical significance can be checked by an
5528:{\displaystyle {\hat {\sigma }}_{\varepsilon }^{2}={\frac {SSR}{n-2}}}
1504:. Second, in some situations regression analysis can be used to infer
30:
13600:
13419:
12989:
12870:
11577:
11429:
11049:
10844:
10756:
10741:
10736:
10701:
9572:
9541:
9439:
9283:
9246:
9183:
9137:
9132:
9117:
8264:
7278:
5594:
1550:
1505:
1287:
1068:
8520:
8383:
8345:
Fotheringham, A. Stewart; Brunsdon, Chris; Charlton, Martin (2002).
8220:
8163:
8137:
8121:
7424:
5104:
3789:
13466:
13219:
12853:
12823:
11093:
10711:
10588:
10583:
10578:
9474:
9306:
8815:
3452:
and are therefore valid solutions that minimize the sum of squared
2466:. A given regression method will ultimately provide an estimate of
2179:
is chosen. For example, a simple univariate regression may propose
13138:
12598:
12299:
9597:
9434:
9388:
9311:
9211:
9206:
9158:
8545:"Human age estimation by metric learning for regression problems"
7828:
3482:
equations is to be solved for 3 unknowns, which makes the system
1753:
directly observed in data and are often denoted using the scalar
1063:
12778:
7980:
Nouvelles mÊthodes pour la dÊtermination des orbites des comètes
7762:
and multiple regression using least squares can be done in some
3617:
appears often in regression analysis, and is referred to as the
1715:, which are observed in data and often denoted using the scalar
13433:
12520:
11501:
11475:
11455:
10706:
10497:
9612:
9592:
9464:
9256:
8746:
Chicco, Davide; Warrens, Matthijs J.; Jurman, Giuseppe (2021).
8665:
Applied
Regression Analysis, Linear Models and Related Methods.
7717:, which is more robust in the presence of outliers, leading to
7409:
7301:
7297:
7290:
7286:
5094:{\displaystyle {\widehat {\beta }}_{0},{\widehat {\beta }}_{1}}
3038:
data points, then they could find infinitely many combinations
2838:
rows of data with one dependent and two independent variables:
1661:, which are observed in data and are often denoted as a vector
814:
8011:
Theoria combinationis observationum erroribus minimis obnoxiae
7336:. Nonlinear models for binary dependent variables include the
2660:, least squares is widely used because the estimated function
13595:
10349:
9413:
9393:
9383:
9378:
9373:
9368:
9331:
9163:
8707:
Regression
Analysis — Theory, Methods, and Applications
1058:
1053:
780:
8344:
2773:) are useful when researchers want to model other functions
13721:
Committee on the
Environment, Public Health and Food Safety
13527:
10440:
9403:
7995:
Chapter 1 of: Angrist, J. D., & Pischke, J. S. (2008).
1561:
of the response and explanatory variables is assumed to be
8740:
Operations and
Production Systems with Multiple Objectives
4129:
for a derivation of these formulas and a numerical example
2250:{\displaystyle f(X_{i},\beta )=\beta _{0}+\beta _{1}X_{i}}
1500:, where its use has substantial overlap with the field of
9787:
8473:(3rd ed.). Hoboken, New Jersey: Wiley. p. 211.
6061:
In the more general multiple regression model, there are
4074:
clustered standard errors, geographic weighted regression
1421:
in machine learning parlance) and one or more error-free
8729:
Many
Regression Algorithms, One Unified Model: A Review.
6712:
In matrix notation, the normal equations are written as
6375:
The least squares parameter estimates are obtained from
3851:
The sample is representative of the population at large.
2656:
will depend on context and their goals. As described in
1346:
List of datasets in computer vision and image processing
8455:
page 274 section 9.7.4 "interpolation vs extrapolation"
7997:
Mostly Harmless Econometrics: An Empiricist's Companion
7678:{\displaystyle {\frac {\log 1000}{\log 5}}\approx 4.29}
7404:
Differences between linear and non-linear least squares
3997:
assumptions imply that the parameter estimates will be
2584:{\displaystyle {\hat {Y_{i}}}=f(X_{i},{\hat {\beta }})}
1569:
in his works of 1922 and 1925. Fisher assumed that the
8731:
Neural Networks, vol. 69, Sept. 2015, pp. 60â79.
8245:
2326:{\displaystyle Y_{i}=\beta _{0}+\beta _{1}X_{i}+e_{i}}
8369:
8349:(Reprint ed.). Chichester, England: John Wiley.
8333:
Regressions: Why Are Economists Obessessed with Them?
7707:
Percentage regression, for situations where reducing
7642:
7619:
7599:
7578:
7558:
7538:
7505:
7494:
7175:
7146:
7117:
7091:
7071:
7045:
7023:
6987:
6958:
6938:
6911:
6891:
6871:
6841:
6819:
6796:
6721:
6533:
6404:
6381:
6347:
6311:
6291:
6271:
6251:
6221:
6090:
6067:
5823:
5703:
5667:
5641:
5603:
5580:
5548:
5471:
5445:
5416:
5396:
5363:
5275:
5121:
5049:
4977:
4935:
4899:
4837:
4737:
4711:
4684:
4652:
4625:
4598:
4571:
4433:
4397:
4286:
4255:
4228:
4201:
4181:
4138:
4086:
4051:
4024:
3956:
3922:
3863:
3854:
The independent variables are measured with no error.
3753:
3719:
3627:
3597:
3571:
3545:
3525:
3492:
3462:
3258:
3130:
3044:
3018:
2913:
2844:
2824:
2779:
2720:
2666:
2626:
2597:
2524:
2492:
2472:
2394:
2374:
2346:
2263:
2185:
2165:
2138:
2111:
2091:
2049:
2015:
1944:
1914:
1883:
1863:
1836:
1801:
1759:
1721:
1694:
1667:
1640:
12705:
12262:
Autoregressive conditional heteroskedasticity (ARCH)
8162:; Yule, G.U.; Blanchard, Norman; Lee, Alice (1903).
7774:
2459:{\displaystyle \sum _{i}(Y_{i}-f(X_{i},\beta ))^{2}}
1587:, regression involving correlated responses such as
8745:
8471:
Common Errors in Statistics (And How to Avoid Them)
11724:
8256:
7677:
7625:
7605:
7584:
7564:
7544:
7524:
7241:
7158:
7132:
7103:
7077:
7057:
7031:
7009:
6973:
6944:
6924:
6897:
6877:
6857:
6827:
6805:
6779:
6701:
6509:
6387:
6360:
6333:
6297:
6277:
6257:
6237:
6204:
6073:
6018:
5806:
5679:
5653:
5627:
5586:
5566:
5527:
5451:
5431:
5402:
5378:
5346:
5260:
5093:
5039:Minimization of this function results in a set of
5028:
4948:
4921:
4885:
4816:
4717:
4697:
4668:
4638:
4611:
4584:
4554:
4415:
4377:
4268:
4241:
4214:
4187:
4151:
4099:
4064:
4037:
3969:
3935:
3906:
3768:
3735:
3693:
3609:
3583:
3557:
3531:
3504:
3474:
3444:
3244:
3116:
3030:
3004:
2895:
2830:
2807:
2757:
2703:
2648:
2612:
2583:
2507:
2478:
2458:
2380:
2352:
2325:
2249:
2171:
2151:
2124:
2097:
2077:
2043:The researchers' goal is to estimate the function
2028:
1998:
1927:
1896:
1869:
1849:
1814:
1772:
1734:
1700:
1680:
1646:
1445:). The most common form of regression analysis is
7930:
7209:
7192:
7183:
6748:
6739:
6723:
6395:normal equations. The residual can be written as
4551:
4374:
1908:that may stand in for un-modeled determinants of
1449:, in which one finds the line (or a more complex
13952:
8251:
7754:All major statistical software packages perform
4929:, and the true value of the dependent variable,
11810:Multivariate adaptive regression splines (MARS)
8709:, Springer-Verlag, Berlin, 2011 (4th printing).
8605:(1987). "Regression and correlation analysis,"
8028:. Kendall/Hunt Publishing Company. p. 59.
5108:Illustration of linear regression on a data set
4195:data points there is one independent variable:
1788:, different terminologies are used in place of
8591:Evan J. Williams, "I. Regression," pp. 523â41.
7959:Criticism and Influence Analysis in Regression
7312:
4886:{\displaystyle e_{i}=y_{i}-{\widehat {y}}_{i}}
1465:), this allows the researcher to estimate the
1341:List of datasets for machine-learning research
12794:
12691:
10365:
9773:
8849:
8542:
8500:Journal of Modern Applied Statistical Methods
7831:(a linear least squares estimation algorithm)
7410:Prediction (interpolation and extrapolation)
7133:{\displaystyle {\hat {\boldsymbol {\beta }}}}
6974:{\displaystyle {\hat {\boldsymbol {\beta }}}}
4016:Heteroscedasticity-consistent standard errors
1374:
441:
8863:
8733:https://doi.org/10.1016/j.neunet.2015.05.005
8636:Journal of Business and Economic Statistics,
5029:{\displaystyle SSR=\sum _{i=1}^{n}e_{i}^{2}}
8644:
7924:
7572:is the number of independent variables and
2336:Once researchers determine their preferred
1999:{\displaystyle Y_{i}=f(X_{i},\beta )+e_{i}}
13756:Centers for Disease Control and Prevention
12801:
12787:
12698:
12684:
10410:
10372:
10358:
9780:
9766:
8856:
8842:
8684:
8597:, "II. Analysis of Variance," pp. 541â554.
8465:
8051:"Kinship and Correlation (reprinted 1989)"
3694:{\displaystyle (X_{1i},X_{2i},...,X_{ki})}
2257:, suggesting that the researcher believes
1381:
1367:
448:
434:
34:Regression line for 50 random points in a
13716:Centre for Disease Prevention and Control
13706:Center for Disease Control and Prevention
11023:
8773:
8763:
8519:
8308:
8236:
8066:
7360:with more than two values, there are the
7238:
6776:
6698:
6201:
3830:Learn how and when to remove this message
3779:
2388:that minimizes the sum of squared errors
8584:International Encyclopedia of Statistics
8493:
8259:Statistical Methods for Research Workers
8209:Journal of the Royal Statistical Society
8126:Journal of the Royal Statistical Society
7423:
7320:, which are response variables that are
5694:of the parameter estimates are given by
5103:
2704:{\displaystyle f(X_{i},{\hat {\beta }})}
1576:In the 1950s and 1960s, economists used
1521:The earliest form of regression was the
29:
13761:Health departments in the United States
10289:Numerical smoothing and differentiation
8607:New Palgrave: A Dictionary of Economics
8287:
7991:
7989:
7968:
7934:Statistical Models: Theory and Practice
7845:Multivariate adaptive regression spline
7391:
7352:with more than two values there is the
7180:
7121:
6962:
6745:
6044:
5635:if an intercept is used. In this case,
3012:. If the researcher only has access to
2765:. However, alternative variants (e.g.,
14:
13953:
13766:Council on Education for Public Health
12336:KaplanâMeier estimator (product limit)
8425:Probability, Statistics and Estimation
8421:
8202:
8048:
8023:
3981:with one another. Mathematically, the
1401:is a set of statistical processes for
13824:Professional degrees of public health
13731:Ministry of Health and Family Welfare
12782:
12679:
12409:
11976:
11723:
11022:
10792:
10409:
10353:
9761:
8837:
8822:What is multiple regression used for?
8496:"Least Squares Percentage Regression"
8002:
3747:and therefore that a unique solution
2896:{\displaystyle (Y_{i},X_{1i},X_{2i})}
38:around the line y=1.5x+2 (not shown)
13921:
13814:Bachelor of Science in Public Health
12646:
12346:Accelerated failure time (AFT) model
9824:Iteratively reweighted least squares
9694:Generative adversarial network (GAN)
8828:Regression of Weakly Correlated Data
8116:
7986:
4114:
3812:adding citations to reliable sources
3783:
2364:(including its most common variant,
2009:Note that the independent variables
1795:Most regression models propose that
1541:The term "regression" was coined by
13933:
13082:Workers' right to access the toilet
12923:Human right to water and sanitation
12658:
11941:Analysis of variance (ANOVA, anova)
10793:
8582:, ed. (1978), "Linear Hypotheses,"
7931:David A. Freedman (27 April 2009).
7758:regression analysis and inference.
7747:For a more comprehensive list, see
7461:this range of the data is known as
7443:variable given known values of the
7010:{\displaystyle {\hat {\beta }}_{j}}
4705:is an error term and the subscript
4423:to the preceding regression gives:
3847:. These assumptions often include:
3539:distinct parameters, one must have
1790:dependent and independent variables
1610:
1336:Glossary of artificial intelligence
24:
12036:CochranâMantelâHaenszel statistics
10662:Pearson product-moment correlation
9842:Pearson product-moment correlation
8727:Stulp, Freek, and Olivier Sigaud.
8569:
8026:Second-Semester Applied Statistics
7711:errors is deemed more appropriate.
7495:Power and sample size calculations
7226:
7200:
6764:
6731:
4922:{\displaystyle {\widehat {y}}_{i}}
4725:indexes a particular observation.
1578:electromechanical desk calculators
1565:. This assumption was weakened by
25:
13972:
13355:Commercial determinants of health
12808:
8791:
8618:Alternative Methods of Regression
8404:Steel, R.G.D, and Torrie, J. H.,
7957:R. Dennis Cook; Sanford Weisberg
4592:, it is linear in the parameters
3943:is constant across observations (
13932:
13920:
13909:
13908:
12938:National public health institute
12657:
12645:
12633:
12620:
12619:
12410:
10322:
9732:
9731:
9711:
8672:Applied Nonparametric Regression
8645:Draper, N.R.; Smith, H. (1998).
8335:March 2006. Accessed 2011-12-03.
7850:Multivariate normal distribution
7814:Fraction of variance unexplained
7777:
7689:
7289:of the overall fit, followed by
7231:
7222:
7216:
7213:
7205:
7196:
7189:
7025:
6821:
6769:
6760:
6754:
6736:
6727:
4698:{\displaystyle \varepsilon _{i}}
3907:{\displaystyle E(e_{i}|X_{i})=0}
3788:
415:
13335:Open-source healthcare software
13077:Sociology of health and illness
12751:Associative (causal) forecasts
12295:Least-squares spectral analysis
8536:
8487:
8459:
8443:Statistical methods of analysis
8435:
8415:
8398:
8363:
8338:
8325:
8281:
8196:
8164:"The Law of Ancestral Heredity"
8152:
8110:
8098:
7855:Pearson correlation coefficient
7265:Category:Regression diagnostics
4349:
4167:(but need not be linear in the
3799:needs additional citations for
3769:{\displaystyle {\hat {\beta }}}
2808:{\displaystyle f(X_{i},\beta )}
2613:{\displaystyle {\hat {\beta }}}
2508:{\displaystyle {\hat {\beta }}}
2078:{\displaystyle f(X_{i},\beta )}
363:Least-squares spectral analysis
301:Generalized estimating equation
121:Multinomial logistic regression
96:Vector generalized linear model
13696:Caribbean Public Health Agency
13508:Sexually transmitted infection
13405:Statistical hypothesis testing
13166:Occupational safety and health
13067:Sexual and reproductive health
12980:Occupational safety and health
11276:Mean-unbiased minimum-variance
10379:
9644:Recurrent neural network (RNN)
9634:Differentiable neural computer
8818:â basic history and references
8632:Calculating Interval Forecasts
8122:"On the Theory of Correlation"
8083:
8042:
8017:
7965:, Vol. 13. (1982), pp. 313â361
7951:
7937:. Cambridge University Press.
7913:
7252:
7124:
6995:
6965:
6609:
6479:
6438:
5965:
5941:
5934:
5912:
5896:
5860:
5831:
5791:
5784:
5762:
5740:
5711:
5622:
5604:
5561:
5549:
5479:
5423:
5370:
5338:
5304:
5246:
5239:
5217:
5209:
5203:
5181:
5178:
5172:
5150:
4956:. One method of estimation is
3916:The variance of the residuals
3895:
3881:
3867:
3760:
3713:, this condition ensures that
3688:
3628:
3515:More generally, to estimate a
3427:
3423:
3398:
3363:
3341:
3331:
3316:
3306:
3276:
3217:
3182:
3160:
3138:
3111:
3099:
3077:
3055:
3045:
2890:
2845:
2802:
2783:
2758:{\displaystyle E(Y_{i}|X_{i})}
2752:
2738:
2724:
2698:
2692:
2670:
2649:{\displaystyle {\hat {Y_{i}}}}
2640:
2604:
2578:
2572:
2550:
2538:
2499:
2447:
2443:
2424:
2405:
2208:
2189:
2072:
2053:
1980:
1961:
756:Relevance vector machine (RVM)
13:
1:
13350:Social determinants of health
12589:Geographic information system
11805:Simultaneous equations models
9689:Variational autoencoder (VAE)
9649:Long short-term memory (LSTM)
8916:Computational learning theory
7999:. Princeton University Press.
7906:
7840:Modifiable areal unit problem
7281:, analyses of the pattern of
6885:element of the column vector
6055:For a numerical example, see
1935:or random statistical noise:
1457:computes the unique line (or
1245:Computational learning theory
809:Expectationâmaximization (EM)
182:Nonlinear mixed-effects model
13410:Analysis of variance (ANOVA)
13171:Human factors and ergonomics
12736:Decomposition of time series
11772:Coefficient of determination
11383:Uniformly most powerful test
10312:Regression analysis category
10202:Response surface methodology
9669:Convolutional neural network
8649:(3rd ed.). John Wiley.
7920:Necessary Condition Analysis
7749:List of statistical software
7032:{\displaystyle \mathbf {X} }
6828:{\displaystyle \mathbf {X} }
1483:Necessary Condition Analysis
1405:the relationships between a
1202:Coefficient of determination
1049:Convolutional neural network
761:Support vector machine (SVM)
7:
13591:Good manufacturing practice
13395:Randomized controlled trial
12341:Proportional hazards models
12285:Spectral density estimation
12267:Vector autoregression (VAR)
11701:Maximum posterior estimator
10933:Randomized controlled trial
10184:FrischâWaughâLovell theorem
10154:Mean and predicted response
9664:Multilayer perceptron (MLP)
8804:Encyclopedia of Mathematics
8647:Applied Regression Analysis
7770:
7742:
7318:Limited dependent variables
7313:Limited dependent variables
4669:{\displaystyle \beta _{2}.}
4045:to change across values of
1534:including a version of the
1353:Outline of machine learning
1250:Empirical risk minimization
384:Mean and predicted response
10:
13977:
13661:Theory of planned behavior
13586:Good agricultural practice
13491:Public health surveillance
13383:epidemiological statistics
13027:Public health intervention
12717:Historical data forecasts
12101:Multivariate distributions
10521:Average absolute deviation
9834:Correlation and dependence
9740:Artificial neural networks
9654:Gated recurrent unit (GRU)
8880:Differentiable programming
8372:Environment and Planning A
8310:10.1214/088342305000000331
8024:Mogull, Robert G. (2004).
7746:
7703:Bayesian linear regression
7413:
7395:
7370:Censored regression models
7293:of individual parameters.
7262:
7256:
6361:{\displaystyle \beta _{1}}
6054:
6048:
5432:{\displaystyle {\bar {y}}}
5379:{\displaystyle {\bar {x}}}
4639:{\displaystyle \beta _{1}}
4612:{\displaystyle \beta _{0}}
4269:{\displaystyle \beta _{1}}
4242:{\displaystyle \beta _{0}}
4124:
4118:
3983:varianceâcovariance matrix
2038:errors-in-variables models
1547:regression toward the mean
1516:
990:Feedforward neural network
741:Artificial neural networks
177:Linear mixed-effects model
13904:
13839:
13798:
13783:World Toilet Organization
13778:World Health Organization
13685:
13674:
13611:
13536:
13452:
13380:
13345:Public health informatics
13285:
13090:
13052:Right to rest and leisure
12881:Globalization and disease
12816:
12749:
12715:
12615:
12569:
12506:
12459:
12422:
12418:
12405:
12377:
12359:
12326:
12317:
12275:
12222:
12183:
12132:
12123:
12089:Structural equation model
12044:
12001:
11997:
11972:
11931:
11897:
11851:
11818:
11780:
11747:
11743:
11719:
11659:
11568:
11487:
11451:
11442:
11425:Score/Lagrange multiplier
11410:
11363:
11308:
11234:
11225:
11035:
11031:
11018:
10977:
10951:
10903:
10858:
10840:Sample size determination
10805:
10801:
10788:
10692:
10647:
10621:
10603:
10559:
10511:
10431:
10422:
10418:
10405:
10387:
10307:
10271:
10220:
10192:
10179:Minimum mean-square error
10146:
10092:
10066:Decomposition of variance
10064:
10029:
9988:
9970:Growth curve (statistics)
9957:
9939:Generalized least squares
9919:
9908:
9875:
9832:
9799:
9707:
9621:
9565:
9494:
9427:
9299:
9199:
9192:
9146:
9110:
9073:Artificial neural network
9053:
8929:
8896:Automatic differentiation
8869:
8816:Earliest Uses: Regression
7734:interval predictor models
7715:Least absolute deviations
7159:{\displaystyle p\times 1}
7104:{\displaystyle n\times 1}
7058:{\displaystyle n\times p}
4416:{\displaystyle x_{i}^{2}}
3565:distinct data points. If
2767:least absolute deviations
1525:, which was published by
973:Artificial neural network
343:Least absolute deviations
13829:Schools of public health
13621:Diffusion of innovations
13320:Health impact assessment
13032:Public health laboratory
12928:Management of depression
12759:Simple linear regression
12584:Environmental statistics
12106:Elliptical distributions
11899:Generalized linear model
11828:Simple linear regression
11598:HodgesâLehmann estimator
11055:Probability distribution
10964:Stochastic approximation
10526:Coefficient of variation
10037:Generalized linear model
9929:Simple linear regression
9819:Non-linear least squares
9801:Computational statistics
8901:Neuromorphic engineering
8864:Differentiable computing
8742:. John Wiley & Sons.
8609:, v. 4, pp. 120â23.
8469:; Hardin, J. W. (2009).
8422:Rouaud, Mathieu (2013).
8049:Galton, Francis (1989).
7963:Sociological Methodology
7824:Generalized linear model
7760:Simple linear regression
7724:Nonparametric regression
7334:linear probability model
7275:statistical significance
6334:{\displaystyle x_{i1}=1}
4173:simple linear regression
4127:simple linear regression
1597:nonparametric regression
1571:conditional distribution
1487:nonparametric regression
1282:Journals and conferences
1229:Mathematical foundations
1139:Temporal difference (TD)
995:Recurrent neural network
915:Conditional random field
838:Dimensionality reduction
586:Dimensionality reduction
548:Quantum machine learning
543:Neuromorphic engineering
503:Self-supervised learning
498:Semi-supervised learning
91:Generalized linear model
13892:Social hygiene movement
13819:Doctor of Public Health
13651:Social cognitive theory
13453:Infectious and epidemic
13235:Fecalâoral transmission
12244:Cross-correlation (XCF)
11852:Non-standard predictors
11286:LehmannâScheffĂŠ theorem
10959:Adaptive clinical trial
9674:Residual neural network
9090:Artificial Intelligence
8705:A. Sen, M. Srivastava,
8554:: 74â82. Archived from
8290:"Fisher and Regression"
7900:Linear trend estimation
7525:{\displaystyle N=m^{n}}
6265:-th observation on the
6081:independent variables:
5628:{\displaystyle (n-p-1)}
3558:{\displaystyle N\geq k}
3252:, all of which lead to
2713:conditional expectation
2620:or the predicted value
1708:denotes a row of data).
1523:method of least squares
1467:conditional expectation
691:Apprenticeship learning
13887:Germ theory of disease
13666:Transtheoretical model
12640:Mathematics portal
12461:Engineering statistics
12369:NelsonâAalen estimator
11946:Analysis of covariance
11833:Ordinary least squares
11757:Pearson product-moment
11161:Statistical functional
11072:Empirical distribution
10905:Controlled experiments
10634:Frequency distribution
10412:Descriptive statistics
10329:Mathematics portal
10253:Orthogonal polynomials
10079:Analysis of covariance
9944:Weighted least squares
9934:Ordinary least squares
9885:Ordinary least squares
8752:PeerJ Computer Science
8738:Malakooti, B. (2013).
8699:10.1002/for.3980140502
8687:Journal of Forecasting
8630:Chatfield, C. (1993) "
8543:YangJing Long (2009).
8288:Aldrich, John (2005).
8182:10.1093/biomet/2.2.211
7819:Function approximation
7679:
7627:
7607:
7586:
7566:
7546:
7526:
7447:variables. Prediction
7430:
7378:polychoric correlation
7259:Regression diagnostics
7243:
7160:
7134:
7105:
7079:
7059:
7033:
7011:
6975:
6946:
6926:
6899:
6879:
6859:
6858:{\displaystyle x_{ij}}
6829:
6807:
6781:
6703:
6644:
6575:
6554:
6511:
6389:
6362:
6335:
6299:
6279:
6259:
6239:
6238:{\displaystyle x_{ij}}
6206:
6075:
6049:For a derivation, see
6020:
5808:
5681:
5661:so the denominator is
5655:
5629:
5588:
5568:
5529:
5453:
5433:
5404:
5380:
5348:
5262:
5109:
5095:
5030:
5010:
4958:ordinary least squares
4950:
4923:
4887:
4818:
4719:
4699:
4670:
4640:
4613:
4586:
4556:
4417:
4379:
4270:
4243:
4222:, and two parameters,
4216:
4189:
4153:
4101:
4066:
4039:
4018:allow the variance of
3971:
3937:
3908:
3780:Underlying assumptions
3770:
3737:
3736:{\displaystyle X^{T}X}
3711:ordinary least squares
3695:
3611:
3585:
3584:{\displaystyle N>k}
3559:
3533:
3506:
3476:
3446:
3246:
3118:
3032:
3006:
2897:
2832:
2809:
2759:
2705:
2658:ordinary least squares
2650:
2614:
2585:
2509:
2480:
2479:{\displaystyle \beta }
2460:
2382:
2381:{\displaystyle \beta }
2366:ordinary least squares
2354:
2353:{\displaystyle \beta }
2327:
2251:
2173:
2153:
2126:
2099:
2079:
2030:
2000:
1929:
1898:
1871:
1870:{\displaystyle \beta }
1851:
1816:
1774:
1736:
1702:
1682:
1648:
1647:{\displaystyle \beta }
1617:ordinary least squares
1455:ordinary least squares
1240:Biasâvariance tradeoff
1122:Reinforcement learning
1098:Spiking neural network
508:Reinforcement learning
422:Mathematics portal
348:Iteratively reweighted
39:
18:Statistical regression
13771:Public Health Service
13656:Social norms approach
13646:PRECEDEâPROCEED model
13092:Preventive healthcare
12985:Pharmaceutical policy
12834:Chief Medical Officer
12726:Exponential smoothing
12556:Population statistics
12498:System identification
12232:Autocorrelation (ACF)
12160:Exponential smoothing
12074:Discriminant analysis
12069:Canonical correlation
11933:Partition of variance
11795:Regression validation
11639:(JonckheereâTerpstra)
11538:Likelihood-ratio test
11227:Frequentist inference
11139:Locationâscale family
11060:Sampling distribution
11025:Statistical inference
10992:Cross-sectional study
10979:Observational studies
10938:Randomized experiment
10767:Stem-and-leaf display
10569:Central limit theorem
10294:System identification
10258:Chebyshev polynomials
10243:Numerical integration
10194:Design of experiments
10138:Regression validation
9965:Polynomial regression
9890:Partial least squares
9629:Neural Turing machine
9217:Human image synthesis
8824:â Multiple regression
8799:"Regression analysis"
8494:Tofallis, C. (2009).
8203:Fisher, R.A. (1922).
8068:10.1214/ss/1177012581
7870:Regression validation
7730:Scenario optimization
7680:
7628:
7608:
7587:
7567:
7547:
7527:
7427:
7414:Further information:
7350:categorical variables
7322:categorical variables
7307:central limit theorem
7273:of the model and the
7244:
7161:
7135:
7106:
7080:
7060:
7034:
7012:
6976:
6947:
6927:
6925:{\displaystyle y_{i}}
6900:
6880:
6860:
6830:
6808:
6782:
6704:
6624:
6555:
6534:
6512:
6390:
6363:
6336:
6300:
6280:
6260:
6240:
6207:
6076:
6039:population parameters
6021:
5809:
5682:
5656:
5630:
5589:
5569:
5567:{\displaystyle (n-p)}
5530:
5454:
5434:
5405:
5381:
5349:
5263:
5107:
5096:
5031:
4990:
4951:
4949:{\displaystyle y_{i}}
4924:
4888:
4819:
4720:
4700:
4671:
4641:
4614:
4587:
4585:{\displaystyle x_{i}}
4557:
4418:
4380:
4271:
4244:
4217:
4215:{\displaystyle x_{i}}
4190:
4169:independent variables
4154:
4152:{\displaystyle y_{i}}
4102:
4100:{\displaystyle e_{i}}
4067:
4065:{\displaystyle X_{i}}
4040:
4038:{\displaystyle e_{i}}
3972:
3970:{\displaystyle e_{i}}
3938:
3936:{\displaystyle e_{i}}
3909:
3771:
3738:
3696:
3612:
3586:
3560:
3534:
3507:
3477:
3447:
3247:
3119:
3033:
3007:
2898:
2833:
2810:
2760:
2706:
2651:
2615:
2586:
2510:
2481:
2461:
2383:
2368:) finds the value of
2355:
2328:
2252:
2174:
2154:
2152:{\displaystyle X_{i}}
2127:
2125:{\displaystyle Y_{i}}
2100:
2080:
2031:
2029:{\displaystyle X_{i}}
2001:
1930:
1928:{\displaystyle Y_{i}}
1899:
1897:{\displaystyle e_{i}}
1872:
1852:
1850:{\displaystyle X_{i}}
1817:
1815:{\displaystyle Y_{i}}
1786:fields of application
1775:
1773:{\displaystyle e_{i}}
1737:
1735:{\displaystyle Y_{i}}
1703:
1683:
1681:{\displaystyle X_{i}}
1659:independent variables
1649:
1627:, often denoted as a
1439:explanatory variables
1423:independent variables
1076:Neural radiance field
898:Structured prediction
621:Structured prediction
493:Unsupervised learning
379:Regression validation
358:Bayesian multivariate
75:Polynomial regression
36:Gaussian distribution
33:
13847:Sara Josephine Baker
13746:Public Health Agency
13631:Health communication
13496:Disease surveillance
13462:Asymptomatic carrier
13444:Statistical software
13132:Preventive nutrition
12960:Medical anthropology
12849:Environmental health
12479:Probabilistic design
12064:Principal components
11907:Exponential families
11859:Nonlinear regression
11838:General linear model
11800:Mixed effects models
11790:Errors and residuals
11767:Confounding variable
11669:Bayesian probability
11647:Van der Waerden test
11637:Ordered alternative
11402:Multiple comparisons
11281:RaoâBlackwellization
11244:Estimating equations
11200:Statistical distance
10918:Factorial experiment
10451:Arithmetic-Geometric
10299:Moving least squares
10238:Approximation theory
10174:Studentized residual
10164:Errors and residuals
10159:GaussâMarkov theorem
10074:Analysis of variance
9996:Nonlinear regression
9975:Segmented regression
9949:General linear model
9867:Confounding variable
9814:Linear least squares
9720:Computer programming
9699:Graph neural network
9274:Text-to-video models
9252:Text-to-image models
9100:Large language model
9085:Scientific computing
8891:Statistical manifold
8886:Information geometry
8765:10.7717/peerj-cs.623
8586:. Free Press, v. 1,
8512:10.2139/ssrn.1406472
8445:, World Scientific.
8441:Chiang, C.L, (2003)
8263:(Twelfth ed.).
7880:Segmented regression
7640:
7617:
7597:
7576:
7556:
7552:is the sample size,
7536:
7503:
7398:Nonlinear regression
7392:Nonlinear regression
7173:
7144:
7115:
7089:
7069:
7043:
7021:
6985:
6956:
6936:
6909:
6889:
6869:
6839:
6817:
6794:
6719:
6531:
6402:
6379:
6370:regression intercept
6345:
6309:
6289:
6269:
6249:
6219:
6088:
6065:
6051:linear least squares
6045:General linear model
6031:confidence intervals
5821:
5701:
5665:
5639:
5601:
5578:
5546:
5469:
5443:
5414:
5394:
5361:
5273:
5119:
5047:
4975:
4933:
4897:
4835:
4735:
4709:
4682:
4650:
4623:
4596:
4569:
4431:
4395:
4284:
4253:
4226:
4199:
4179:
4136:
4084:
4049:
4022:
3954:
3920:
3861:
3808:improve this article
3751:
3717:
3703:linearly independent
3625:
3595:
3569:
3543:
3523:
3490:
3460:
3256:
3128:
3042:
3016:
2911:
2842:
2822:
2777:
2718:
2664:
2624:
2595:
2522:
2490:
2470:
2392:
2372:
2344:
2261:
2183:
2163:
2136:
2109:
2089:
2047:
2013:
1942:
1912:
1881:
1861:
1834:
1799:
1757:
1719:
1692:
1665:
1638:
1536:GaussâMarkov theorem
1506:causal relationships
1395:statistical modeling
1265:Statistical learning
1163:Learning with humans
955:Local outlier factor
404:GaussâMarkov theorem
399:Studentized residual
389:Errors and residuals
223:Principal components
193:Nonlinear regression
80:General linear model
13961:Regression analysis
13857:Carl Rogers Darnall
13852:Samuel Jay Crumbine
13626:Health belief model
13479:Notifiable diseases
13415:Regression analysis
13250:Waterborne diseases
12839:Cultural competence
12764:Regression analysis
12551:Official statistics
12474:Methods engineering
12155:Seasonal adjustment
11923:Poisson regressions
11843:Bayesian regression
11782:Regression analysis
11762:Partial correlation
11734:Regression analysis
11333:Prediction interval
11328:Likelihood interval
11318:Confidence interval
11310:Interval estimation
11271:Unbiased estimators
11089:Model specification
10969:Up-and-down designs
10657:Partial correlation
10613:Index of dispersion
10531:Interquartile range
10317:Statistics category
10248:Gaussian quadrature
10133:Model specification
10100:Stepwise regression
9958:Predictor structure
9895:Total least squares
9877:Regression analysis
9862:Partial correlation
9793:regression analysis
9066:In-context learning
8906:Pattern recognition
8641:. pp. 121â135.
8297:Statistical Science
8267:: Oliver and Boyd.
8055:Statistical Science
7890:Stepwise regression
7865:Prediction interval
7719:quantile regression
7473:prediction interval
7420:Prediction interval
7388:model may be used.
7346:multivariate probit
6005:
5680:{\displaystyle n-2}
5654:{\displaystyle p=1}
5538:This is called the
5495:
5439:is the mean of the
5025:
4507:
4412:
4171:). For example, in
4012:errors-in-variables
3610:{\displaystyle N-k}
3505:{\displaystyle N=2}
3475:{\displaystyle N=2}
3292:
3031:{\displaystyle N=2}
2771:quantile regression
1906:additive error term
1828:regression function
1479:quantile regression
1475:location parameters
1399:regression analysis
1108:Electrochemical RAM
1015:reservoir computing
746:Logistic regression
665:Supervised learning
651:Multimodal learning
626:Feature engineering
571:Generative modeling
533:Rule-based learning
528:Curriculum learning
488:Supervised learning
463:Part of a series on
249:Errors-in-variables
116:Logistic regression
106:Binomial regression
51:Regression analysis
45:Part of a series on
13455:disease prevention
13390:Caseâcontrol study
13062:Security of person
12911:Health care reform
12571:Spatial statistics
12451:Medical statistics
12351:First hitting time
12305:Whittle likelihood
11956:Degrees of freedom
11951:Multivariate ANOVA
11884:Heteroscedasticity
11696:Bayesian estimator
11661:Bayesian inference
11510:KolmogorovâSmirnov
11395:Randomization test
11365:Testing hypotheses
11338:Tolerance interval
11249:Maximum likelihood
11144:Exponential family
11077:Density estimation
11037:Statistical theory
10997:Natural experiment
10943:Scientific control
10860:Survey methodology
10546:Standard deviation
10334:Statistics outline
10233:Numerical analysis
9659:Echo state network
9547:JĂźrgen Schmidhuber
9242:Facial recognition
9237:Speech recognition
9147:Software libraries
8716:. Vieweg+Teubner,
8612:Birkes, David and
8576:William H. Kruskal
8331:Rodney Ramcharan.
7794:Anscombe's quartet
7785:Mathematics portal
7675:
7623:
7603:
7582:
7562:
7542:
7522:
7433:Regression models
7431:
7416:Predicted response
7382:Poisson regression
7374:Heckman correction
7239:
7166:. The solution is
7156:
7130:
7101:
7075:
7055:
7029:
7007:
6971:
6942:
6922:
6895:
6875:
6855:
6825:
6806:{\displaystyle ij}
6803:
6777:
6699:
6507:
6385:
6358:
6331:
6295:
6275:
6255:
6235:
6202:
6071:
6016:
5991:
5804:
5677:
5651:
5625:
5584:
5564:
5525:
5472:
5449:
5429:
5400:
5376:
5344:
5258:
5110:
5091:
5026:
5011:
4946:
4919:
4883:
4814:
4715:
4695:
4666:
4636:
4609:
4582:
4552:
4493:
4413:
4398:
4375:
4266:
4239:
4212:
4185:
4161:linear combination
4149:
4097:
4062:
4035:
3967:
3933:
3904:
3766:
3733:
3691:
3619:degrees of freedom
3607:
3581:
3555:
3529:
3502:
3472:
3442:
3305:
3269:
3268:
3242:
3114:
3028:
3002:
2893:
2828:
2805:
2755:
2701:
2646:
2610:
2581:
2505:
2486:, usually denoted
2476:
2456:
2404:
2378:
2350:
2323:
2247:
2169:
2149:
2122:
2095:
2075:
2026:
1996:
1925:
1894:
1867:
1847:
1812:
1770:
1732:
1713:dependent variable
1698:
1678:
1644:
1625:unknown parameters
1559:joint distribution
1511:observational data
1451:linear combination
1409:(often called the
1407:dependent variable
676: •
591:Density estimation
136:Multinomial probit
40:
13948:
13947:
13900:
13899:
13810:Higher education
13641:Positive deviance
13636:Health psychology
13612:Health behavioral
13539:safety management
13513:Social distancing
13287:Population health
13267:Smoking cessation
13215:Pharmacovigilance
13186:Injury prevention
13154:Infection control
13072:Social psychology
13022:Prisoners' rights
12965:Medical sociology
12933:Public health law
12829:Biological hazard
12776:
12775:
12769:Econometric model
12673:
12672:
12611:
12610:
12607:
12606:
12546:National accounts
12516:Actuarial science
12508:Social statistics
12401:
12400:
12397:
12396:
12393:
12392:
12328:Survival function
12313:
12312:
12175:Granger causality
12016:Contingency table
11991:Survival analysis
11968:
11967:
11964:
11963:
11820:Linear regression
11715:
11714:
11711:
11710:
11686:Credible interval
11655:
11654:
11438:
11437:
11254:Method of moments
11123:Parametric family
11084:Statistical model
11014:
11013:
11010:
11009:
10928:Random assignment
10850:Statistical power
10784:
10783:
10780:
10779:
10629:Contingency table
10599:
10598:
10466:Generalized/power
10347:
10346:
10339:Statistics topics
10284:Calibration curve
10093:Model exploration
10060:
10059:
10030:Non-normal errors
9921:Linear regression
9912:statistical model
9755:
9754:
9517:Stephen Grossberg
9490:
9489:
8722:978-3-8348-1022-9
8663:Fox, J. (1997).
8656:978-0-471-17082-2
8595:Julian C. Stanley
8480:978-0-470-45798-6
8412:, 1960, page 288.
8356:978-0-471-49616-8
8274:978-0-05-002170-5
8035:978-0-7575-1181-3
7944:978-1-139-47731-4
7885:Signal processing
7875:Robust regression
7804:Estimation theory
7667:
7626:{\displaystyle m}
7606:{\displaystyle N}
7585:{\displaystyle m}
7565:{\displaystyle n}
7545:{\displaystyle N}
7386:negative binomial
7358:ordinal variables
7354:multinomial logit
7186:
7127:
7078:{\displaystyle Y}
6998:
6968:
6945:{\displaystyle j}
6898:{\displaystyle Y}
6878:{\displaystyle i}
6751:
6673:
6612:
6482:
6441:
6388:{\displaystyle p}
6298:{\displaystyle i}
6278:{\displaystyle j}
6258:{\displaystyle i}
6074:{\displaystyle p}
6057:linear regression
6011:
6010:
5968:
5953:
5951:
5937:
5899:
5882:
5863:
5834:
5802:
5801:
5787:
5743:
5714:
5587:{\displaystyle p}
5540:mean square error
5523:
5482:
5452:{\displaystyle y}
5426:
5403:{\displaystyle x}
5390:(average) of the
5373:
5341:
5323:
5307:
5286:
5256:
5242:
5206:
5175:
5132:
5082:
5060:
4910:
4874:
4792:
4770:
4748:
4718:{\displaystyle i}
4526:
4391:Adding a term in
4188:{\displaystyle n}
4121:Linear regression
4115:Linear regression
3985:of the errors is
3840:
3839:
3832:
3763:
3745:invertible matrix
3532:{\displaystyle k}
3401:
3366:
3344:
3319:
3296:
3279:
3259:
3220:
3185:
3163:
3141:
3102:
3080:
3058:
2831:{\displaystyle N}
2711:approximates the
2695:
2643:
2607:
2575:
2541:
2502:
2395:
2338:statistical model
2172:{\displaystyle f}
2098:{\displaystyle f}
1701:{\displaystyle i}
1607:with regression.
1585:robust regression
1463:linear regression
1447:linear regression
1391:
1390:
1196:Model diagnostics
1179:Human-in-the-loop
1022:Boltzmann machine
935:Anomaly detection
731:Linear regression
646:Ontology learning
641:Grammar induction
616:Semantic analysis
611:Association rules
596:Anomaly detection
538:Neuro-symbolic AI
458:
457:
111:Binary regression
70:Simple regression
65:Linear regression
16:(Redirected from
13968:
13936:
13935:
13924:
13923:
13912:
13911:
13806:Health education
13683:
13682:
13537:Food hygiene and
13518:Tropical disease
13330:Infant mortality
13305:Community health
13181:Controlled Drugs
13117:Health promotion
13047:Right to housing
12891:Health economics
12803:
12796:
12789:
12780:
12779:
12700:
12693:
12686:
12677:
12676:
12661:
12660:
12649:
12648:
12638:
12637:
12623:
12622:
12526:Crime statistics
12420:
12419:
12407:
12406:
12324:
12323:
12290:Fourier analysis
12277:Frequency domain
12257:
12204:
12170:Structural break
12130:
12129:
12079:Cluster analysis
12026:Log-linear model
11999:
11998:
11974:
11973:
11915:
11889:Homoscedasticity
11745:
11744:
11721:
11720:
11640:
11632:
11624:
11623:(KruskalâWallis)
11608:
11593:
11548:Cross validation
11533:
11515:AndersonâDarling
11462:
11449:
11448:
11420:Likelihood-ratio
11412:Parametric tests
11390:Permutation test
11373:1- & 2-tails
11264:Minimum distance
11236:Point estimation
11232:
11231:
11183:Optimal decision
11134:
11033:
11032:
11020:
11019:
11002:Quasi-experiment
10952:Adaptive designs
10803:
10802:
10790:
10789:
10667:Rank correlation
10429:
10428:
10420:
10419:
10407:
10406:
10374:
10367:
10360:
10351:
10350:
10327:
10326:
10084:Multivariate AOV
9980:Local regression
9917:
9916:
9909:Regression as a
9900:Ridge regression
9847:Rank correlation
9782:
9775:
9768:
9759:
9758:
9745:Machine learning
9735:
9734:
9715:
9470:Action selection
9460:Self-driving car
9267:Stable Diffusion
9232:Speech synthesis
9197:
9196:
9061:Machine learning
8937:Gradient descent
8858:
8851:
8844:
8835:
8834:
8812:
8787:
8777:
8767:
8702:
8660:
8563:
8562:
8560:
8549:
8540:
8534:
8533:
8523:
8491:
8485:
8484:
8463:
8457:
8439:
8433:
8432:
8430:
8419:
8413:
8402:
8396:
8395:
8378:(7): 1025â1044.
8367:
8361:
8360:
8342:
8336:
8329:
8323:
8322:
8312:
8294:
8285:
8279:
8278:
8262:
8253:Ronald A. Fisher
8249:
8243:
8242:
8240:
8200:
8194:
8193:
8156:
8150:
8149:
8114:
8108:
8102:
8096:
8087:
8081:
8080:
8070:
8046:
8040:
8039:
8021:
8015:
8006:
8000:
7993:
7984:
7972:
7966:
7955:
7949:
7948:
7928:
7922:
7917:
7895:Taxicab geometry
7835:Local regression
7787:
7782:
7781:
7699:Bayesian methods
7684:
7682:
7681:
7676:
7668:
7666:
7655:
7644:
7632:
7630:
7629:
7624:
7612:
7610:
7609:
7604:
7591:
7589:
7588:
7583:
7571:
7569:
7568:
7563:
7551:
7549:
7548:
7543:
7531:
7529:
7528:
7523:
7521:
7520:
7248:
7246:
7245:
7240:
7234:
7230:
7229:
7220:
7219:
7204:
7203:
7188:
7187:
7179:
7165:
7163:
7162:
7157:
7139:
7137:
7136:
7131:
7129:
7128:
7120:
7110:
7108:
7107:
7102:
7084:
7082:
7081:
7076:
7064:
7062:
7061:
7056:
7038:
7036:
7035:
7030:
7028:
7016:
7014:
7013:
7008:
7006:
7005:
7000:
6999:
6991:
6980:
6978:
6977:
6972:
6970:
6969:
6961:
6951:
6949:
6948:
6943:
6931:
6929:
6928:
6923:
6921:
6920:
6904:
6902:
6901:
6896:
6884:
6882:
6881:
6876:
6864:
6862:
6861:
6856:
6854:
6853:
6834:
6832:
6831:
6826:
6824:
6812:
6810:
6809:
6804:
6786:
6784:
6783:
6778:
6772:
6768:
6767:
6758:
6753:
6752:
6744:
6735:
6734:
6708:
6706:
6705:
6700:
6671:
6667:
6666:
6657:
6656:
6643:
6638:
6620:
6619:
6614:
6613:
6605:
6601:
6600:
6588:
6587:
6574:
6569:
6553:
6548:
6522:normal equations
6516:
6514:
6513:
6508:
6503:
6502:
6490:
6489:
6484:
6483:
6475:
6462:
6461:
6449:
6448:
6443:
6442:
6434:
6427:
6426:
6414:
6413:
6394:
6392:
6391:
6386:
6367:
6365:
6364:
6359:
6357:
6356:
6340:
6338:
6337:
6332:
6324:
6323:
6304:
6302:
6301:
6296:
6284:
6282:
6281:
6276:
6264:
6262:
6261:
6256:
6244:
6242:
6241:
6236:
6234:
6233:
6211:
6209:
6208:
6203:
6197:
6196:
6184:
6183:
6171:
6170:
6152:
6151:
6139:
6138:
6126:
6125:
6113:
6112:
6100:
6099:
6080:
6078:
6077:
6072:
6035:hypothesis tests
6025:
6023:
6022:
6017:
6012:
6006:
6004:
5999:
5986:
5985:
5983:
5982:
5981:
5980:
5970:
5969:
5961:
5954:
5952:
5950:
5949:
5948:
5939:
5938:
5930:
5924:
5923:
5907:
5906:
5901:
5900:
5892:
5888:
5883:
5875:
5873:
5871:
5870:
5865:
5864:
5856:
5849:
5848:
5847:
5846:
5836:
5835:
5827:
5813:
5811:
5810:
5805:
5803:
5800:
5799:
5798:
5789:
5788:
5780:
5774:
5773:
5754:
5753:
5751:
5750:
5745:
5744:
5736:
5729:
5728:
5727:
5726:
5716:
5715:
5707:
5686:
5684:
5683:
5678:
5660:
5658:
5657:
5652:
5634:
5632:
5631:
5626:
5593:
5591:
5590:
5585:
5573:
5571:
5570:
5565:
5534:
5532:
5531:
5526:
5524:
5522:
5511:
5500:
5494:
5489:
5484:
5483:
5475:
5458:
5456:
5455:
5450:
5438:
5436:
5435:
5430:
5428:
5427:
5419:
5409:
5407:
5406:
5401:
5385:
5383:
5382:
5377:
5375:
5374:
5366:
5353:
5351:
5350:
5345:
5343:
5342:
5334:
5331:
5330:
5325:
5324:
5316:
5309:
5308:
5300:
5294:
5293:
5288:
5287:
5279:
5267:
5265:
5264:
5259:
5257:
5255:
5254:
5253:
5244:
5243:
5235:
5229:
5228:
5212:
5208:
5207:
5199:
5193:
5192:
5177:
5176:
5168:
5162:
5161:
5145:
5140:
5139:
5134:
5133:
5125:
5100:
5098:
5097:
5092:
5090:
5089:
5084:
5083:
5075:
5068:
5067:
5062:
5061:
5053:
5041:normal equations
5035:
5033:
5032:
5027:
5024:
5019:
5009:
5004:
4955:
4953:
4952:
4947:
4945:
4944:
4928:
4926:
4925:
4920:
4918:
4917:
4912:
4911:
4903:
4892:
4890:
4889:
4884:
4882:
4881:
4876:
4875:
4867:
4860:
4859:
4847:
4846:
4823:
4821:
4820:
4815:
4810:
4809:
4800:
4799:
4794:
4793:
4785:
4778:
4777:
4772:
4771:
4763:
4756:
4755:
4750:
4749:
4741:
4724:
4722:
4721:
4716:
4704:
4702:
4701:
4696:
4694:
4693:
4675:
4673:
4672:
4667:
4662:
4661:
4645:
4643:
4642:
4637:
4635:
4634:
4618:
4616:
4615:
4610:
4608:
4607:
4591:
4589:
4588:
4583:
4581:
4580:
4561:
4559:
4558:
4553:
4524:
4520:
4519:
4506:
4501:
4492:
4491:
4479:
4478:
4469:
4468:
4456:
4455:
4443:
4442:
4422:
4420:
4419:
4414:
4411:
4406:
4384:
4382:
4381:
4376:
4345:
4344:
4332:
4331:
4322:
4321:
4309:
4308:
4296:
4295:
4275:
4273:
4272:
4267:
4265:
4264:
4248:
4246:
4245:
4240:
4238:
4237:
4221:
4219:
4218:
4213:
4211:
4210:
4194:
4192:
4191:
4186:
4158:
4156:
4155:
4150:
4148:
4147:
4106:
4104:
4103:
4098:
4096:
4095:
4071:
4069:
4068:
4063:
4061:
4060:
4044:
4042:
4041:
4036:
4034:
4033:
3976:
3974:
3973:
3968:
3966:
3965:
3945:homoscedasticity
3942:
3940:
3939:
3934:
3932:
3931:
3913:
3911:
3910:
3905:
3894:
3893:
3884:
3879:
3878:
3835:
3828:
3824:
3821:
3815:
3792:
3784:
3775:
3773:
3772:
3767:
3765:
3764:
3756:
3742:
3740:
3739:
3734:
3729:
3728:
3700:
3698:
3697:
3692:
3687:
3686:
3659:
3658:
3643:
3642:
3616:
3614:
3613:
3608:
3590:
3588:
3587:
3582:
3564:
3562:
3561:
3556:
3538:
3536:
3535:
3530:
3511:
3509:
3508:
3503:
3481:
3479:
3478:
3473:
3451:
3449:
3448:
3443:
3435:
3434:
3422:
3421:
3409:
3408:
3403:
3402:
3394:
3387:
3386:
3374:
3373:
3368:
3367:
3359:
3352:
3351:
3346:
3345:
3337:
3327:
3326:
3321:
3320:
3312:
3304:
3291:
3286:
3281:
3280:
3272:
3267:
3251:
3249:
3248:
3243:
3241:
3240:
3228:
3227:
3222:
3221:
3213:
3206:
3205:
3193:
3192:
3187:
3186:
3178:
3171:
3170:
3165:
3164:
3156:
3149:
3148:
3143:
3142:
3134:
3123:
3121:
3120:
3115:
3110:
3109:
3104:
3103:
3095:
3088:
3087:
3082:
3081:
3073:
3066:
3065:
3060:
3059:
3051:
3037:
3035:
3034:
3029:
3011:
3009:
3008:
3003:
3001:
3000:
2988:
2987:
2975:
2974:
2962:
2961:
2949:
2948:
2936:
2935:
2923:
2922:
2902:
2900:
2899:
2894:
2889:
2888:
2873:
2872:
2857:
2856:
2837:
2835:
2834:
2829:
2814:
2812:
2811:
2806:
2795:
2794:
2764:
2762:
2761:
2756:
2751:
2750:
2741:
2736:
2735:
2710:
2708:
2707:
2702:
2697:
2696:
2688:
2682:
2681:
2655:
2653:
2652:
2647:
2645:
2644:
2639:
2638:
2629:
2619:
2617:
2616:
2611:
2609:
2608:
2600:
2590:
2588:
2587:
2582:
2577:
2576:
2568:
2562:
2561:
2543:
2542:
2537:
2536:
2527:
2514:
2512:
2511:
2506:
2504:
2503:
2495:
2485:
2483:
2482:
2477:
2465:
2463:
2462:
2457:
2455:
2454:
2436:
2435:
2417:
2416:
2403:
2387:
2385:
2384:
2379:
2359:
2357:
2356:
2351:
2332:
2330:
2329:
2324:
2322:
2321:
2309:
2308:
2299:
2298:
2286:
2285:
2273:
2272:
2256:
2254:
2253:
2248:
2246:
2245:
2236:
2235:
2223:
2222:
2201:
2200:
2178:
2176:
2175:
2170:
2158:
2156:
2155:
2150:
2148:
2147:
2131:
2129:
2128:
2123:
2121:
2120:
2104:
2102:
2101:
2096:
2084:
2082:
2081:
2076:
2065:
2064:
2035:
2033:
2032:
2027:
2025:
2024:
2005:
2003:
2002:
1997:
1995:
1994:
1973:
1972:
1954:
1953:
1934:
1932:
1931:
1926:
1924:
1923:
1904:representing an
1903:
1901:
1900:
1895:
1893:
1892:
1876:
1874:
1873:
1868:
1856:
1854:
1853:
1848:
1846:
1845:
1821:
1819:
1818:
1813:
1811:
1810:
1779:
1777:
1776:
1771:
1769:
1768:
1741:
1739:
1738:
1733:
1731:
1730:
1707:
1705:
1704:
1699:
1687:
1685:
1684:
1679:
1677:
1676:
1653:
1651:
1650:
1645:
1611:Regression model
1605:causal inference
1529:in 1805, and by
1502:machine learning
1383:
1376:
1369:
1330:Related articles
1207:Confusion matrix
960:Isolation forest
905:Graphical models
684:
683:
636:Learning to rank
631:Feature learning
469:Machine learning
460:
459:
450:
443:
436:
420:
419:
327:Ridge regression
162:Multilevel model
42:
41:
21:
13976:
13975:
13971:
13970:
13969:
13967:
13966:
13965:
13951:
13950:
13949:
13944:
13896:
13867:Margaret Sanger
13835:
13794:
13678:
13676:
13670:
13613:
13607:
13579:Safety scandals
13538:
13532:
13454:
13448:
13382:
13376:
13372:Social medicine
13365:Race and health
13300:Child mortality
13281:
13240:Open defecation
13122:Human nutrition
13112:Family planning
13100:Behavior change
13086:
13042:Right to health
12955:Maternal health
12945:Health politics
12896:Health literacy
12812:
12807:
12777:
12772:
12745:
12711:
12704:
12674:
12669:
12632:
12603:
12565:
12502:
12488:quality control
12455:
12437:Clinical trials
12414:
12389:
12373:
12361:Hazard function
12355:
12309:
12271:
12255:
12218:
12214:BreuschâGodfrey
12202:
12179:
12119:
12094:Factor analysis
12040:
12021:Graphical model
11993:
11960:
11927:
11913:
11893:
11847:
11814:
11776:
11739:
11738:
11707:
11651:
11638:
11630:
11622:
11606:
11591:
11570:Rank statistics
11564:
11543:Model selection
11531:
11489:Goodness of fit
11483:
11460:
11434:
11406:
11359:
11304:
11293:Median unbiased
11221:
11132:
11065:Order statistic
11027:
11006:
10973:
10947:
10899:
10854:
10797:
10795:Data collection
10776:
10688:
10643:
10617:
10595:
10555:
10507:
10424:Continuous data
10414:
10401:
10383:
10378:
10348:
10343:
10321:
10303:
10267:
10263:Chebyshev nodes
10216:
10212:Bayesian design
10188:
10169:Goodness of fit
10142:
10115:
10105:Model selection
10088:
10056:
10025:
9984:
9953:
9910:
9904:
9871:
9828:
9795:
9786:
9756:
9751:
9703:
9617:
9583:Google DeepMind
9561:
9527:Geoffrey Hinton
9486:
9423:
9349:Project Debater
9295:
9193:Implementations
9188:
9142:
9106:
9049:
8991:Backpropagation
8925:
8911:Tensor calculus
8865:
8862:
8797:
8794:
8657:
8580:Judith M. Tanur
8572:
8570:Further reading
8567:
8566:
8558:
8547:
8541:
8537:
8492:
8488:
8481:
8464:
8460:
8440:
8436:
8428:
8420:
8416:
8403:
8399:
8384:10.1068/a231025
8368:
8364:
8357:
8343:
8339:
8330:
8326:
8292:
8286:
8282:
8275:
8250:
8246:
8221:10.2307/2341124
8201:
8197:
8157:
8153:
8138:10.2307/2979746
8115:
8111:
8103:
8099:
8088:
8084:
8047:
8043:
8036:
8022:
8018:
8007:
8003:
7994:
7987:
7973:
7969:
7956:
7952:
7945:
7929:
7925:
7918:
7914:
7909:
7904:
7783:
7776:
7773:
7752:
7745:
7692:
7656:
7645:
7643:
7641:
7638:
7637:
7618:
7615:
7614:
7598:
7595:
7594:
7577:
7574:
7573:
7557:
7554:
7553:
7537:
7534:
7533:
7516:
7512:
7504:
7501:
7500:
7497:
7439:a value of the
7422:
7412:
7400:
7394:
7315:
7271:goodness of fit
7267:
7261:
7255:
7225:
7221:
7212:
7208:
7199:
7195:
7178:
7177:
7176:
7174:
7171:
7170:
7145:
7142:
7141:
7119:
7118:
7116:
7113:
7112:
7090:
7087:
7086:
7070:
7067:
7066:
7044:
7041:
7040:
7024:
7022:
7019:
7018:
7001:
6990:
6989:
6988:
6986:
6983:
6982:
6960:
6959:
6957:
6954:
6953:
6937:
6934:
6933:
6916:
6912:
6910:
6907:
6906:
6890:
6887:
6886:
6870:
6867:
6866:
6846:
6842:
6840:
6837:
6836:
6820:
6818:
6815:
6814:
6795:
6792:
6791:
6763:
6759:
6757:
6743:
6742:
6730:
6726:
6722:
6720:
6717:
6716:
6662:
6658:
6649:
6645:
6639:
6628:
6615:
6604:
6603:
6602:
6593:
6589:
6580:
6576:
6570:
6559:
6549:
6538:
6532:
6529:
6528:
6495:
6491:
6485:
6474:
6473:
6472:
6454:
6450:
6444:
6433:
6432:
6431:
6422:
6418:
6409:
6405:
6403:
6400:
6399:
6380:
6377:
6376:
6352:
6348:
6346:
6343:
6342:
6316:
6312:
6310:
6307:
6306:
6290:
6287:
6286:
6270:
6267:
6266:
6250:
6247:
6246:
6226:
6222:
6220:
6217:
6216:
6192:
6188:
6176:
6172:
6166:
6162:
6144:
6140:
6134:
6130:
6118:
6114:
6108:
6104:
6095:
6091:
6089:
6086:
6085:
6066:
6063:
6062:
6059:
6053:
6047:
6000:
5995:
5987:
5984:
5976:
5972:
5971:
5960:
5959:
5958:
5944:
5940:
5929:
5928:
5919:
5915:
5908:
5902:
5891:
5890:
5889:
5887:
5874:
5872:
5866:
5855:
5854:
5853:
5842:
5838:
5837:
5826:
5825:
5824:
5822:
5819:
5818:
5794:
5790:
5779:
5778:
5769:
5765:
5758:
5752:
5746:
5735:
5734:
5733:
5722:
5718:
5717:
5706:
5705:
5704:
5702:
5699:
5698:
5692:standard errors
5666:
5663:
5662:
5640:
5637:
5636:
5602:
5599:
5598:
5579:
5576:
5575:
5547:
5544:
5543:
5512:
5501:
5499:
5490:
5485:
5474:
5473:
5470:
5467:
5466:
5444:
5441:
5440:
5418:
5417:
5415:
5412:
5411:
5395:
5392:
5391:
5365:
5364:
5362:
5359:
5358:
5333:
5332:
5326:
5315:
5314:
5313:
5299:
5298:
5289:
5278:
5277:
5276:
5274:
5271:
5270:
5249:
5245:
5234:
5233:
5224:
5220:
5213:
5198:
5197:
5188:
5184:
5167:
5166:
5157:
5153:
5146:
5144:
5135:
5124:
5123:
5122:
5120:
5117:
5116:
5085:
5074:
5073:
5072:
5063:
5052:
5051:
5050:
5048:
5045:
5044:
5020:
5015:
5005:
4994:
4976:
4973:
4972:
4940:
4936:
4934:
4931:
4930:
4913:
4902:
4901:
4900:
4898:
4895:
4894:
4877:
4866:
4865:
4864:
4855:
4851:
4842:
4838:
4836:
4833:
4832:
4805:
4801:
4795:
4784:
4783:
4782:
4773:
4762:
4761:
4760:
4751:
4740:
4739:
4738:
4736:
4733:
4732:
4710:
4707:
4706:
4689:
4685:
4683:
4680:
4679:
4678:In both cases,
4657:
4653:
4651:
4648:
4647:
4630:
4626:
4624:
4621:
4620:
4603:
4599:
4597:
4594:
4593:
4576:
4572:
4570:
4567:
4566:
4515:
4511:
4502:
4497:
4487:
4483:
4474:
4470:
4464:
4460:
4451:
4447:
4438:
4434:
4432:
4429:
4428:
4407:
4402:
4396:
4393:
4392:
4340:
4336:
4327:
4323:
4317:
4313:
4304:
4300:
4291:
4287:
4285:
4282:
4281:
4280:straight line:
4260:
4256:
4254:
4251:
4250:
4233:
4229:
4227:
4224:
4223:
4206:
4202:
4200:
4197:
4196:
4180:
4177:
4176:
4143:
4139:
4137:
4134:
4133:
4130:
4123:
4117:
4091:
4087:
4085:
4082:
4081:
4056:
4052:
4050:
4047:
4046:
4029:
4025:
4023:
4020:
4019:
3961:
3957:
3955:
3952:
3951:
3927:
3923:
3921:
3918:
3917:
3889:
3885:
3880:
3874:
3870:
3862:
3859:
3858:
3836:
3825:
3819:
3816:
3805:
3793:
3782:
3755:
3754:
3752:
3749:
3748:
3724:
3720:
3718:
3715:
3714:
3679:
3675:
3651:
3647:
3635:
3631:
3626:
3623:
3622:
3596:
3593:
3592:
3570:
3567:
3566:
3544:
3541:
3540:
3524:
3521:
3520:
3491:
3488:
3487:
3484:underdetermined
3461:
3458:
3457:
3430:
3426:
3414:
3410:
3404:
3393:
3392:
3391:
3379:
3375:
3369:
3358:
3357:
3356:
3347:
3336:
3335:
3334:
3322:
3311:
3310:
3309:
3300:
3287:
3282:
3271:
3270:
3263:
3257:
3254:
3253:
3233:
3229:
3223:
3212:
3211:
3210:
3198:
3194:
3188:
3177:
3176:
3175:
3166:
3155:
3154:
3153:
3144:
3133:
3132:
3131:
3129:
3126:
3125:
3105:
3094:
3093:
3092:
3083:
3072:
3071:
3070:
3061:
3050:
3049:
3048:
3043:
3040:
3039:
3017:
3014:
3013:
2996:
2992:
2980:
2976:
2970:
2966:
2954:
2950:
2944:
2940:
2931:
2927:
2918:
2914:
2912:
2909:
2908:
2881:
2877:
2865:
2861:
2852:
2848:
2843:
2840:
2839:
2823:
2820:
2819:
2790:
2786:
2778:
2775:
2774:
2746:
2742:
2737:
2731:
2727:
2719:
2716:
2715:
2687:
2686:
2677:
2673:
2665:
2662:
2661:
2634:
2630:
2628:
2627:
2625:
2622:
2621:
2599:
2598:
2596:
2593:
2592:
2567:
2566:
2557:
2553:
2532:
2528:
2526:
2525:
2523:
2520:
2519:
2494:
2493:
2491:
2488:
2487:
2471:
2468:
2467:
2450:
2446:
2431:
2427:
2412:
2408:
2399:
2393:
2390:
2389:
2373:
2370:
2369:
2360:. For example,
2345:
2342:
2341:
2317:
2313:
2304:
2300:
2294:
2290:
2281:
2277:
2268:
2264:
2262:
2259:
2258:
2241:
2237:
2231:
2227:
2218:
2214:
2196:
2192:
2184:
2181:
2180:
2164:
2161:
2160:
2143:
2139:
2137:
2134:
2133:
2116:
2112:
2110:
2107:
2106:
2090:
2087:
2086:
2060:
2056:
2048:
2045:
2044:
2020:
2016:
2014:
2011:
2010:
1990:
1986:
1968:
1964:
1949:
1945:
1943:
1940:
1939:
1919:
1915:
1913:
1910:
1909:
1888:
1884:
1882:
1879:
1878:
1862:
1859:
1858:
1841:
1837:
1835:
1832:
1831:
1806:
1802:
1800:
1797:
1796:
1764:
1760:
1758:
1755:
1754:
1726:
1722:
1720:
1717:
1716:
1693:
1690:
1689:
1672:
1668:
1666:
1663:
1662:
1639:
1636:
1635:
1613:
1519:
1469:(or population
1417:variable, or a
1387:
1358:
1357:
1331:
1323:
1322:
1283:
1275:
1274:
1235:Kernel machines
1230:
1222:
1221:
1197:
1189:
1188:
1169:Active learning
1164:
1156:
1155:
1124:
1114:
1113:
1039:Diffusion model
975:
965:
964:
937:
927:
926:
900:
890:
889:
845:Factor analysis
840:
830:
829:
813:
776:
766:
765:
686:
685:
669:
668:
667:
656:
655:
561:
553:
552:
518:Online learning
483:
471:
454:
414:
394:Goodness of fit
101:Discrete choice
28:
23:
22:
15:
12:
11:
5:
13974:
13964:
13963:
13946:
13945:
13943:
13942:
13930:
13918:
13905:
13902:
13901:
13898:
13897:
13895:
13894:
13889:
13884:
13879:
13874:
13869:
13864:
13859:
13854:
13849:
13843:
13841:
13837:
13836:
13834:
13833:
13832:
13831:
13826:
13821:
13816:
13808:
13802:
13800:
13796:
13795:
13793:
13792:
13785:
13780:
13775:
13774:
13773:
13768:
13763:
13758:
13750:
13749:
13748:
13743:
13735:
13734:
13733:
13725:
13724:
13723:
13718:
13710:
13709:
13708:
13700:
13699:
13698:
13689:
13687:
13680:
13675:Organizations,
13672:
13671:
13669:
13668:
13663:
13658:
13653:
13648:
13643:
13638:
13633:
13628:
13623:
13617:
13615:
13609:
13608:
13606:
13605:
13604:
13603:
13598:
13588:
13583:
13582:
13581:
13576:
13571:
13566:
13561:
13556:
13551:
13542:
13540:
13534:
13533:
13531:
13530:
13525:
13520:
13515:
13510:
13505:
13500:
13499:
13498:
13488:
13487:
13486:
13476:
13475:
13474:
13464:
13458:
13456:
13450:
13449:
13447:
13446:
13441:
13440:
13439:
13431:
13422:
13417:
13412:
13402:
13397:
13392:
13386:
13384:
13381:Biological and
13378:
13377:
13375:
13374:
13369:
13368:
13367:
13362:
13357:
13347:
13342:
13340:Multimorbidity
13337:
13332:
13327:
13322:
13317:
13312:
13307:
13302:
13297:
13291:
13289:
13283:
13282:
13280:
13279:
13277:Vector control
13274:
13269:
13264:
13262:School hygiene
13259:
13258:
13257:
13252:
13247:
13245:Sanitary sewer
13242:
13237:
13232:
13222:
13217:
13212:
13211:
13210:
13203:Patient safety
13200:
13199:
13198:
13193:
13188:
13183:
13178:
13173:
13163:
13162:
13161:
13156:
13151:
13146:
13136:
13135:
13134:
13129:
13119:
13114:
13109:
13108:
13107:
13096:
13094:
13088:
13087:
13085:
13084:
13079:
13074:
13069:
13064:
13059:
13054:
13049:
13044:
13039:
13034:
13029:
13024:
13019:
13018:
13017:
13012:
13007:
13002:
12997:
12987:
12982:
12977:
12967:
12962:
12957:
12952:
12947:
12942:
12941:
12940:
12935:
12925:
12920:
12915:
12914:
12913:
12908:
12898:
12893:
12888:
12886:Harm reduction
12883:
12878:
12873:
12868:
12867:
12866:
12861:
12851:
12846:
12841:
12836:
12831:
12826:
12820:
12818:
12814:
12813:
12806:
12805:
12798:
12791:
12783:
12774:
12773:
12771:
12766:
12761:
12756:
12754:Moving average
12750:
12747:
12746:
12744:
12743:
12741:NaĂŻve approach
12738:
12733:
12731:Trend analysis
12728:
12723:
12721:Moving average
12716:
12713:
12712:
12703:
12702:
12695:
12688:
12680:
12671:
12670:
12668:
12667:
12655:
12643:
12629:
12616:
12613:
12612:
12609:
12608:
12605:
12604:
12602:
12601:
12596:
12591:
12586:
12581:
12575:
12573:
12567:
12566:
12564:
12563:
12558:
12553:
12548:
12543:
12538:
12533:
12528:
12523:
12518:
12512:
12510:
12504:
12503:
12501:
12500:
12495:
12490:
12481:
12476:
12471:
12465:
12463:
12457:
12456:
12454:
12453:
12448:
12443:
12434:
12432:Bioinformatics
12428:
12426:
12416:
12415:
12403:
12402:
12399:
12398:
12395:
12394:
12391:
12390:
12388:
12387:
12381:
12379:
12375:
12374:
12372:
12371:
12365:
12363:
12357:
12356:
12354:
12353:
12348:
12343:
12338:
12332:
12330:
12321:
12315:
12314:
12311:
12310:
12308:
12307:
12302:
12297:
12292:
12287:
12281:
12279:
12273:
12272:
12270:
12269:
12264:
12259:
12251:
12246:
12241:
12240:
12239:
12237:partial (PACF)
12228:
12226:
12220:
12219:
12217:
12216:
12211:
12206:
12198:
12193:
12187:
12185:
12184:Specific tests
12181:
12180:
12178:
12177:
12172:
12167:
12162:
12157:
12152:
12147:
12142:
12136:
12134:
12127:
12121:
12120:
12118:
12117:
12116:
12115:
12114:
12113:
12098:
12097:
12096:
12086:
12084:Classification
12081:
12076:
12071:
12066:
12061:
12056:
12050:
12048:
12042:
12041:
12039:
12038:
12033:
12031:McNemar's test
12028:
12023:
12018:
12013:
12007:
12005:
11995:
11994:
11970:
11969:
11966:
11965:
11962:
11961:
11959:
11958:
11953:
11948:
11943:
11937:
11935:
11929:
11928:
11926:
11925:
11909:
11903:
11901:
11895:
11894:
11892:
11891:
11886:
11881:
11876:
11871:
11869:Semiparametric
11866:
11861:
11855:
11853:
11849:
11848:
11846:
11845:
11840:
11835:
11830:
11824:
11822:
11816:
11815:
11813:
11812:
11807:
11802:
11797:
11792:
11786:
11784:
11778:
11777:
11775:
11774:
11769:
11764:
11759:
11753:
11751:
11741:
11740:
11737:
11736:
11731:
11725:
11717:
11716:
11713:
11712:
11709:
11708:
11706:
11705:
11704:
11703:
11693:
11688:
11683:
11682:
11681:
11676:
11665:
11663:
11657:
11656:
11653:
11652:
11650:
11649:
11644:
11643:
11642:
11634:
11626:
11610:
11607:(MannâWhitney)
11602:
11601:
11600:
11587:
11586:
11585:
11574:
11572:
11566:
11565:
11563:
11562:
11561:
11560:
11555:
11550:
11540:
11535:
11532:(ShapiroâWilk)
11527:
11522:
11517:
11512:
11507:
11499:
11493:
11491:
11485:
11484:
11482:
11481:
11473:
11464:
11452:
11446:
11444:Specific tests
11440:
11439:
11436:
11435:
11433:
11432:
11427:
11422:
11416:
11414:
11408:
11407:
11405:
11404:
11399:
11398:
11397:
11387:
11386:
11385:
11375:
11369:
11367:
11361:
11360:
11358:
11357:
11356:
11355:
11350:
11340:
11335:
11330:
11325:
11320:
11314:
11312:
11306:
11305:
11303:
11302:
11297:
11296:
11295:
11290:
11289:
11288:
11283:
11268:
11267:
11266:
11261:
11256:
11251:
11240:
11238:
11229:
11223:
11222:
11220:
11219:
11214:
11209:
11208:
11207:
11197:
11192:
11191:
11190:
11180:
11179:
11178:
11173:
11168:
11158:
11153:
11148:
11147:
11146:
11141:
11136:
11120:
11119:
11118:
11113:
11108:
11098:
11097:
11096:
11091:
11081:
11080:
11079:
11069:
11068:
11067:
11057:
11052:
11047:
11041:
11039:
11029:
11028:
11016:
11015:
11012:
11011:
11008:
11007:
11005:
11004:
10999:
10994:
10989:
10983:
10981:
10975:
10974:
10972:
10971:
10966:
10961:
10955:
10953:
10949:
10948:
10946:
10945:
10940:
10935:
10930:
10925:
10920:
10915:
10909:
10907:
10901:
10900:
10898:
10897:
10895:Standard error
10892:
10887:
10882:
10881:
10880:
10875:
10864:
10862:
10856:
10855:
10853:
10852:
10847:
10842:
10837:
10832:
10827:
10825:Optimal design
10822:
10817:
10811:
10809:
10799:
10798:
10786:
10785:
10782:
10781:
10778:
10777:
10775:
10774:
10769:
10764:
10759:
10754:
10749:
10744:
10739:
10734:
10729:
10724:
10719:
10714:
10709:
10704:
10698:
10696:
10690:
10689:
10687:
10686:
10681:
10680:
10679:
10674:
10664:
10659:
10653:
10651:
10645:
10644:
10642:
10641:
10636:
10631:
10625:
10623:
10622:Summary tables
10619:
10618:
10616:
10615:
10609:
10607:
10601:
10600:
10597:
10596:
10594:
10593:
10592:
10591:
10586:
10581:
10571:
10565:
10563:
10557:
10556:
10554:
10553:
10548:
10543:
10538:
10533:
10528:
10523:
10517:
10515:
10509:
10508:
10506:
10505:
10500:
10495:
10494:
10493:
10488:
10483:
10478:
10473:
10468:
10463:
10458:
10456:Contraharmonic
10453:
10448:
10437:
10435:
10426:
10416:
10415:
10403:
10402:
10400:
10399:
10394:
10388:
10385:
10384:
10377:
10376:
10369:
10362:
10354:
10345:
10344:
10342:
10341:
10336:
10331:
10319:
10314:
10308:
10305:
10304:
10302:
10301:
10296:
10291:
10286:
10281:
10275:
10273:
10269:
10268:
10266:
10265:
10260:
10255:
10250:
10245:
10240:
10235:
10229:
10227:
10218:
10217:
10215:
10214:
10209:
10207:Optimal design
10204:
10198:
10196:
10190:
10189:
10187:
10186:
10181:
10176:
10171:
10166:
10161:
10156:
10150:
10148:
10144:
10143:
10141:
10140:
10135:
10130:
10129:
10128:
10123:
10118:
10113:
10102:
10096:
10094:
10090:
10089:
10087:
10086:
10081:
10076:
10070:
10068:
10062:
10061:
10058:
10057:
10055:
10054:
10049:
10044:
10039:
10033:
10031:
10027:
10026:
10024:
10023:
10018:
10013:
10008:
10006:Semiparametric
10003:
9998:
9992:
9990:
9986:
9985:
9983:
9982:
9977:
9972:
9967:
9961:
9959:
9955:
9954:
9952:
9951:
9946:
9941:
9936:
9931:
9925:
9923:
9914:
9906:
9905:
9903:
9902:
9897:
9892:
9887:
9881:
9879:
9873:
9872:
9870:
9869:
9864:
9859:
9853:
9851:Spearman's rho
9844:
9838:
9836:
9830:
9829:
9827:
9826:
9821:
9816:
9811:
9805:
9803:
9797:
9796:
9785:
9784:
9777:
9770:
9762:
9753:
9752:
9750:
9749:
9748:
9747:
9742:
9729:
9728:
9727:
9722:
9708:
9705:
9704:
9702:
9701:
9696:
9691:
9686:
9681:
9676:
9671:
9666:
9661:
9656:
9651:
9646:
9641:
9636:
9631:
9625:
9623:
9619:
9618:
9616:
9615:
9610:
9605:
9600:
9595:
9590:
9585:
9580:
9575:
9569:
9567:
9563:
9562:
9560:
9559:
9557:Ilya Sutskever
9554:
9549:
9544:
9539:
9534:
9529:
9524:
9522:Demis Hassabis
9519:
9514:
9512:Ian Goodfellow
9509:
9504:
9498:
9496:
9492:
9491:
9488:
9487:
9485:
9484:
9479:
9478:
9477:
9467:
9462:
9457:
9452:
9447:
9442:
9437:
9431:
9429:
9425:
9424:
9422:
9421:
9416:
9411:
9406:
9401:
9396:
9391:
9386:
9381:
9376:
9371:
9366:
9361:
9356:
9351:
9346:
9341:
9340:
9339:
9329:
9324:
9319:
9314:
9309:
9303:
9301:
9297:
9296:
9294:
9293:
9288:
9287:
9286:
9281:
9271:
9270:
9269:
9264:
9259:
9249:
9244:
9239:
9234:
9229:
9224:
9219:
9214:
9209:
9203:
9201:
9194:
9190:
9189:
9187:
9186:
9181:
9176:
9171:
9166:
9161:
9156:
9150:
9148:
9144:
9143:
9141:
9140:
9135:
9130:
9125:
9120:
9114:
9112:
9108:
9107:
9105:
9104:
9103:
9102:
9095:Language model
9092:
9087:
9082:
9081:
9080:
9070:
9069:
9068:
9057:
9055:
9051:
9050:
9048:
9047:
9045:Autoregression
9042:
9037:
9036:
9035:
9025:
9023:Regularization
9020:
9019:
9018:
9013:
9008:
8998:
8993:
8988:
8986:Loss functions
8983:
8978:
8973:
8968:
8963:
8962:
8961:
8951:
8946:
8945:
8944:
8933:
8931:
8927:
8926:
8924:
8923:
8921:Inductive bias
8918:
8913:
8908:
8903:
8898:
8893:
8888:
8883:
8875:
8873:
8867:
8866:
8861:
8860:
8853:
8846:
8838:
8832:
8831:
8825:
8819:
8813:
8793:
8792:External links
8790:
8789:
8788:
8758:(e623): e623.
8743:
8736:
8725:
8710:
8703:
8693:(5): 413â430.
8682:
8668:
8661:
8655:
8642:
8628:
8610:
8599:
8598:
8592:
8588:
8587:
8571:
8568:
8565:
8564:
8561:on 2010-01-08.
8535:
8486:
8479:
8458:
8434:
8414:
8397:
8362:
8355:
8337:
8324:
8303:(4): 401â417.
8280:
8273:
8244:
8215:(4): 597â612.
8195:
8176:(2): 211â236.
8151:
8109:
8097:
8090:Francis Galton
8082:
8041:
8034:
8016:
8001:
7985:
7967:
7950:
7943:
7923:
7911:
7910:
7908:
7905:
7903:
7902:
7897:
7892:
7887:
7882:
7877:
7872:
7867:
7862:
7860:Quasi-variance
7857:
7852:
7847:
7842:
7837:
7832:
7826:
7821:
7816:
7811:
7806:
7801:
7796:
7790:
7789:
7788:
7772:
7769:
7744:
7741:
7740:
7739:
7736:
7732:, leading to
7727:
7721:
7712:
7705:
7691:
7688:
7687:
7686:
7674:
7671:
7665:
7662:
7659:
7654:
7651:
7648:
7622:
7602:
7581:
7561:
7541:
7519:
7515:
7511:
7508:
7496:
7493:
7460:
7450:
7411:
7408:
7396:Main article:
7393:
7390:
7366:ordered probit
7314:
7311:
7257:Main article:
7254:
7251:
7250:
7249:
7237:
7233:
7228:
7224:
7218:
7215:
7211:
7207:
7202:
7198:
7194:
7191:
7185:
7182:
7155:
7152:
7149:
7126:
7123:
7100:
7097:
7094:
7074:
7054:
7051:
7048:
7027:
7004:
6997:
6994:
6967:
6964:
6941:
6919:
6915:
6894:
6874:
6852:
6849:
6845:
6823:
6802:
6799:
6788:
6787:
6775:
6771:
6766:
6762:
6756:
6750:
6747:
6741:
6738:
6733:
6729:
6725:
6710:
6709:
6697:
6694:
6691:
6688:
6685:
6682:
6679:
6676:
6670:
6665:
6661:
6655:
6652:
6648:
6642:
6637:
6634:
6631:
6627:
6623:
6618:
6611:
6608:
6599:
6596:
6592:
6586:
6583:
6579:
6573:
6568:
6565:
6562:
6558:
6552:
6547:
6544:
6541:
6537:
6518:
6517:
6506:
6501:
6498:
6494:
6488:
6481:
6478:
6471:
6468:
6465:
6460:
6457:
6453:
6447:
6440:
6437:
6430:
6425:
6421:
6417:
6412:
6408:
6384:
6368:is called the
6355:
6351:
6330:
6327:
6322:
6319:
6315:
6294:
6274:
6254:
6232:
6229:
6225:
6213:
6212:
6200:
6195:
6191:
6187:
6182:
6179:
6175:
6169:
6165:
6161:
6158:
6155:
6150:
6147:
6143:
6137:
6133:
6129:
6124:
6121:
6117:
6111:
6107:
6103:
6098:
6094:
6070:
6046:
6043:
6027:
6026:
6015:
6009:
6003:
5998:
5994:
5990:
5979:
5975:
5967:
5964:
5957:
5947:
5943:
5936:
5933:
5927:
5922:
5918:
5914:
5911:
5905:
5898:
5895:
5886:
5881:
5878:
5869:
5862:
5859:
5852:
5845:
5841:
5833:
5830:
5815:
5814:
5797:
5793:
5786:
5783:
5777:
5772:
5768:
5764:
5761:
5757:
5749:
5742:
5739:
5732:
5725:
5721:
5713:
5710:
5676:
5673:
5670:
5650:
5647:
5644:
5624:
5621:
5618:
5615:
5612:
5609:
5606:
5583:
5563:
5560:
5557:
5554:
5551:
5536:
5535:
5521:
5518:
5515:
5510:
5507:
5504:
5498:
5493:
5488:
5481:
5478:
5448:
5425:
5422:
5399:
5372:
5369:
5355:
5354:
5340:
5337:
5329:
5322:
5319:
5312:
5306:
5303:
5297:
5292:
5285:
5282:
5268:
5252:
5248:
5241:
5238:
5232:
5227:
5223:
5219:
5216:
5211:
5205:
5202:
5196:
5191:
5187:
5183:
5180:
5174:
5171:
5165:
5160:
5156:
5152:
5149:
5143:
5138:
5131:
5128:
5088:
5081:
5078:
5071:
5066:
5059:
5056:
5037:
5036:
5023:
5018:
5014:
5008:
5003:
5000:
4997:
4993:
4989:
4986:
4983:
4980:
4943:
4939:
4916:
4909:
4906:
4880:
4873:
4870:
4863:
4858:
4854:
4850:
4845:
4841:
4825:
4824:
4813:
4808:
4804:
4798:
4791:
4788:
4781:
4776:
4769:
4766:
4759:
4754:
4747:
4744:
4714:
4692:
4688:
4665:
4660:
4656:
4633:
4629:
4606:
4602:
4579:
4575:
4563:
4562:
4550:
4547:
4544:
4541:
4538:
4535:
4532:
4529:
4523:
4518:
4514:
4510:
4505:
4500:
4496:
4490:
4486:
4482:
4477:
4473:
4467:
4463:
4459:
4454:
4450:
4446:
4441:
4437:
4410:
4405:
4401:
4386:
4385:
4373:
4370:
4367:
4364:
4361:
4358:
4355:
4352:
4348:
4343:
4339:
4335:
4330:
4326:
4320:
4316:
4312:
4307:
4303:
4299:
4294:
4290:
4263:
4259:
4236:
4232:
4209:
4205:
4184:
4146:
4142:
4119:Main article:
4116:
4113:
4094:
4090:
4059:
4055:
4032:
4028:
3991:
3990:
3964:
3960:
3950:The residuals
3948:
3930:
3926:
3914:
3903:
3900:
3897:
3892:
3888:
3883:
3877:
3873:
3869:
3866:
3855:
3852:
3838:
3837:
3796:
3794:
3787:
3781:
3778:
3762:
3759:
3732:
3727:
3723:
3690:
3685:
3682:
3678:
3674:
3671:
3668:
3665:
3662:
3657:
3654:
3650:
3646:
3641:
3638:
3634:
3630:
3606:
3603:
3600:
3580:
3577:
3574:
3554:
3551:
3548:
3528:
3512:fixed points.
3501:
3498:
3495:
3471:
3468:
3465:
3441:
3438:
3433:
3429:
3425:
3420:
3417:
3413:
3407:
3400:
3397:
3390:
3385:
3382:
3378:
3372:
3365:
3362:
3355:
3350:
3343:
3340:
3333:
3330:
3325:
3318:
3315:
3308:
3303:
3299:
3295:
3290:
3285:
3278:
3275:
3266:
3262:
3239:
3236:
3232:
3226:
3219:
3216:
3209:
3204:
3201:
3197:
3191:
3184:
3181:
3174:
3169:
3162:
3159:
3152:
3147:
3140:
3137:
3113:
3108:
3101:
3098:
3091:
3086:
3079:
3076:
3069:
3064:
3057:
3054:
3047:
3027:
3024:
3021:
2999:
2995:
2991:
2986:
2983:
2979:
2973:
2969:
2965:
2960:
2957:
2953:
2947:
2943:
2939:
2934:
2930:
2926:
2921:
2917:
2892:
2887:
2884:
2880:
2876:
2871:
2868:
2864:
2860:
2855:
2851:
2847:
2827:
2804:
2801:
2798:
2793:
2789:
2785:
2782:
2754:
2749:
2745:
2740:
2734:
2730:
2726:
2723:
2700:
2694:
2691:
2685:
2680:
2676:
2672:
2669:
2642:
2637:
2633:
2606:
2603:
2580:
2574:
2571:
2565:
2560:
2556:
2552:
2549:
2546:
2540:
2535:
2531:
2501:
2498:
2475:
2453:
2449:
2445:
2442:
2439:
2434:
2430:
2426:
2423:
2420:
2415:
2411:
2407:
2402:
2398:
2377:
2349:
2320:
2316:
2312:
2307:
2303:
2297:
2293:
2289:
2284:
2280:
2276:
2271:
2267:
2244:
2240:
2234:
2230:
2226:
2221:
2217:
2213:
2210:
2207:
2204:
2199:
2195:
2191:
2188:
2168:
2146:
2142:
2119:
2115:
2094:
2074:
2071:
2068:
2063:
2059:
2055:
2052:
2023:
2019:
2007:
2006:
1993:
1989:
1985:
1982:
1979:
1976:
1971:
1967:
1963:
1960:
1957:
1952:
1948:
1922:
1918:
1891:
1887:
1866:
1844:
1840:
1809:
1805:
1782:
1781:
1767:
1763:
1743:
1729:
1725:
1709:
1697:
1675:
1671:
1655:
1643:
1612:
1609:
1543:Francis Galton
1518:
1515:
1425:(often called
1389:
1388:
1386:
1385:
1378:
1371:
1363:
1360:
1359:
1356:
1355:
1350:
1349:
1348:
1338:
1332:
1329:
1328:
1325:
1324:
1321:
1320:
1315:
1310:
1305:
1300:
1295:
1290:
1284:
1281:
1280:
1277:
1276:
1273:
1272:
1267:
1262:
1257:
1255:Occam learning
1252:
1247:
1242:
1237:
1231:
1228:
1227:
1224:
1223:
1220:
1219:
1214:
1212:Learning curve
1209:
1204:
1198:
1195:
1194:
1191:
1190:
1187:
1186:
1181:
1176:
1171:
1165:
1162:
1161:
1158:
1157:
1154:
1153:
1152:
1151:
1141:
1136:
1131:
1125:
1120:
1119:
1116:
1115:
1112:
1111:
1105:
1100:
1095:
1090:
1089:
1088:
1078:
1073:
1072:
1071:
1066:
1061:
1056:
1046:
1041:
1036:
1031:
1030:
1029:
1019:
1018:
1017:
1012:
1007:
1002:
992:
987:
982:
976:
971:
970:
967:
966:
963:
962:
957:
952:
944:
938:
933:
932:
929:
928:
925:
924:
923:
922:
917:
912:
901:
896:
895:
892:
891:
888:
887:
882:
877:
872:
867:
862:
857:
852:
847:
841:
836:
835:
832:
831:
828:
827:
822:
817:
811:
806:
801:
793:
788:
783:
777:
772:
771:
768:
767:
764:
763:
758:
753:
748:
743:
738:
733:
728:
720:
719:
718:
713:
708:
698:
696:Decision trees
693:
687:
673:classification
663:
662:
661:
658:
657:
654:
653:
648:
643:
638:
633:
628:
623:
618:
613:
608:
603:
598:
593:
588:
583:
578:
573:
568:
566:Classification
562:
559:
558:
555:
554:
551:
550:
545:
540:
535:
530:
525:
523:Batch learning
520:
515:
510:
505:
500:
495:
490:
484:
481:
480:
477:
476:
465:
464:
456:
455:
453:
452:
445:
438:
430:
427:
426:
425:
424:
409:
408:
407:
406:
401:
396:
391:
386:
381:
373:
372:
368:
367:
366:
365:
360:
355:
350:
345:
337:
336:
335:
334:
329:
324:
319:
314:
306:
305:
304:
303:
298:
293:
288:
280:
279:
278:
277:
272:
267:
259:
258:
254:
253:
252:
251:
243:
242:
241:
240:
235:
230:
225:
220:
215:
210:
205:
203:Semiparametric
200:
195:
187:
186:
185:
184:
179:
174:
172:Random effects
169:
164:
156:
155:
154:
153:
148:
146:Ordered probit
143:
138:
133:
128:
123:
118:
113:
108:
103:
98:
93:
85:
84:
83:
82:
77:
72:
67:
59:
58:
54:
53:
47:
46:
26:
9:
6:
4:
3:
2:
13973:
13962:
13959:
13958:
13956:
13941:
13940:
13931:
13929:
13928:
13919:
13917:
13916:
13907:
13906:
13903:
13893:
13890:
13888:
13885:
13883:
13880:
13878:
13875:
13873:
13870:
13868:
13865:
13863:
13862:Joseph Lister
13860:
13858:
13855:
13853:
13850:
13848:
13845:
13844:
13842:
13838:
13830:
13827:
13825:
13822:
13820:
13817:
13815:
13812:
13811:
13809:
13807:
13804:
13803:
13801:
13797:
13790:
13786:
13784:
13781:
13779:
13776:
13772:
13769:
13767:
13764:
13762:
13759:
13757:
13754:
13753:
13751:
13747:
13744:
13742:
13741:Health Canada
13739:
13738:
13736:
13732:
13729:
13728:
13726:
13722:
13719:
13717:
13714:
13713:
13711:
13707:
13704:
13703:
13701:
13697:
13694:
13693:
13691:
13690:
13688:
13686:Organizations
13684:
13681:
13673:
13667:
13664:
13662:
13659:
13657:
13654:
13652:
13649:
13647:
13644:
13642:
13639:
13637:
13634:
13632:
13629:
13627:
13624:
13622:
13619:
13618:
13616:
13610:
13602:
13599:
13597:
13594:
13593:
13592:
13589:
13587:
13584:
13580:
13577:
13575:
13572:
13570:
13567:
13565:
13562:
13560:
13557:
13555:
13552:
13550:
13547:
13546:
13544:
13543:
13541:
13535:
13529:
13526:
13524:
13523:Vaccine trial
13521:
13519:
13516:
13514:
13511:
13509:
13506:
13504:
13501:
13497:
13494:
13493:
13492:
13489:
13485:
13482:
13481:
13480:
13477:
13473:
13470:
13469:
13468:
13465:
13463:
13460:
13459:
13457:
13451:
13445:
13442:
13438:
13436:
13432:
13430:
13428:
13423:
13421:
13418:
13416:
13413:
13411:
13408:
13407:
13406:
13403:
13401:
13400:Relative risk
13398:
13396:
13393:
13391:
13388:
13387:
13385:
13379:
13373:
13370:
13366:
13363:
13361:
13360:Health equity
13358:
13356:
13353:
13352:
13351:
13348:
13346:
13343:
13341:
13338:
13336:
13333:
13331:
13328:
13326:
13325:Health system
13323:
13321:
13318:
13316:
13315:Global health
13313:
13311:
13308:
13306:
13303:
13301:
13298:
13296:
13295:Biostatistics
13293:
13292:
13290:
13288:
13284:
13278:
13275:
13273:
13270:
13268:
13265:
13263:
13260:
13256:
13253:
13251:
13248:
13246:
13243:
13241:
13238:
13236:
13233:
13231:
13228:
13227:
13226:
13223:
13221:
13218:
13216:
13213:
13209:
13206:
13205:
13204:
13201:
13197:
13194:
13192:
13189:
13187:
13184:
13182:
13179:
13177:
13174:
13172:
13169:
13168:
13167:
13164:
13160:
13157:
13155:
13152:
13150:
13147:
13145:
13142:
13141:
13140:
13137:
13133:
13130:
13128:
13125:
13124:
13123:
13120:
13118:
13115:
13113:
13110:
13106:
13103:
13102:
13101:
13098:
13097:
13095:
13093:
13089:
13083:
13080:
13078:
13075:
13073:
13070:
13068:
13065:
13063:
13060:
13058:
13055:
13053:
13050:
13048:
13045:
13043:
13040:
13038:
13037:Right to food
13035:
13033:
13030:
13028:
13025:
13023:
13020:
13016:
13013:
13011:
13008:
13006:
13003:
13001:
12998:
12996:
12993:
12992:
12991:
12988:
12986:
12983:
12981:
12978:
12975:
12971:
12970:Mental health
12968:
12966:
12963:
12961:
12958:
12956:
12953:
12951:
12948:
12946:
12943:
12939:
12936:
12934:
12931:
12930:
12929:
12926:
12924:
12921:
12919:
12918:Housing First
12916:
12912:
12909:
12907:
12906:Health system
12904:
12903:
12902:
12901:Health policy
12899:
12897:
12894:
12892:
12889:
12887:
12884:
12882:
12879:
12877:
12874:
12872:
12869:
12865:
12862:
12860:
12857:
12856:
12855:
12852:
12850:
12847:
12845:
12842:
12840:
12837:
12835:
12832:
12830:
12827:
12825:
12822:
12821:
12819:
12815:
12811:
12810:Public health
12804:
12799:
12797:
12792:
12790:
12785:
12784:
12781:
12770:
12767:
12765:
12762:
12760:
12757:
12755:
12752:
12748:
12742:
12739:
12737:
12734:
12732:
12729:
12727:
12724:
12722:
12719:
12718:
12714:
12709:
12706:Quantitative
12701:
12696:
12694:
12689:
12687:
12682:
12681:
12678:
12666:
12665:
12656:
12654:
12653:
12644:
12642:
12641:
12636:
12630:
12628:
12627:
12618:
12617:
12614:
12600:
12597:
12595:
12594:Geostatistics
12592:
12590:
12587:
12585:
12582:
12580:
12577:
12576:
12574:
12572:
12568:
12562:
12561:Psychometrics
12559:
12557:
12554:
12552:
12549:
12547:
12544:
12542:
12539:
12537:
12534:
12532:
12529:
12527:
12524:
12522:
12519:
12517:
12514:
12513:
12511:
12509:
12505:
12499:
12496:
12494:
12491:
12489:
12485:
12482:
12480:
12477:
12475:
12472:
12470:
12467:
12466:
12464:
12462:
12458:
12452:
12449:
12447:
12444:
12442:
12438:
12435:
12433:
12430:
12429:
12427:
12425:
12424:Biostatistics
12421:
12417:
12413:
12408:
12404:
12386:
12385:Log-rank test
12383:
12382:
12380:
12376:
12370:
12367:
12366:
12364:
12362:
12358:
12352:
12349:
12347:
12344:
12342:
12339:
12337:
12334:
12333:
12331:
12329:
12325:
12322:
12320:
12316:
12306:
12303:
12301:
12298:
12296:
12293:
12291:
12288:
12286:
12283:
12282:
12280:
12278:
12274:
12268:
12265:
12263:
12260:
12258:
12256:(BoxâJenkins)
12252:
12250:
12247:
12245:
12242:
12238:
12235:
12234:
12233:
12230:
12229:
12227:
12225:
12221:
12215:
12212:
12210:
12209:DurbinâWatson
12207:
12205:
12199:
12197:
12194:
12192:
12191:DickeyâFuller
12189:
12188:
12186:
12182:
12176:
12173:
12171:
12168:
12166:
12165:Cointegration
12163:
12161:
12158:
12156:
12153:
12151:
12148:
12146:
12143:
12141:
12140:Decomposition
12138:
12137:
12135:
12131:
12128:
12126:
12122:
12112:
12109:
12108:
12107:
12104:
12103:
12102:
12099:
12095:
12092:
12091:
12090:
12087:
12085:
12082:
12080:
12077:
12075:
12072:
12070:
12067:
12065:
12062:
12060:
12057:
12055:
12052:
12051:
12049:
12047:
12043:
12037:
12034:
12032:
12029:
12027:
12024:
12022:
12019:
12017:
12014:
12012:
12011:Cohen's kappa
12009:
12008:
12006:
12004:
12000:
11996:
11992:
11988:
11984:
11980:
11975:
11971:
11957:
11954:
11952:
11949:
11947:
11944:
11942:
11939:
11938:
11936:
11934:
11930:
11924:
11920:
11916:
11910:
11908:
11905:
11904:
11902:
11900:
11896:
11890:
11887:
11885:
11882:
11880:
11877:
11875:
11872:
11870:
11867:
11865:
11864:Nonparametric
11862:
11860:
11857:
11856:
11854:
11850:
11844:
11841:
11839:
11836:
11834:
11831:
11829:
11826:
11825:
11823:
11821:
11817:
11811:
11808:
11806:
11803:
11801:
11798:
11796:
11793:
11791:
11788:
11787:
11785:
11783:
11779:
11773:
11770:
11768:
11765:
11763:
11760:
11758:
11755:
11754:
11752:
11750:
11746:
11742:
11735:
11732:
11730:
11727:
11726:
11722:
11718:
11702:
11699:
11698:
11697:
11694:
11692:
11689:
11687:
11684:
11680:
11677:
11675:
11672:
11671:
11670:
11667:
11666:
11664:
11662:
11658:
11648:
11645:
11641:
11635:
11633:
11627:
11625:
11619:
11618:
11617:
11614:
11613:Nonparametric
11611:
11609:
11603:
11599:
11596:
11595:
11594:
11588:
11584:
11583:Sample median
11581:
11580:
11579:
11576:
11575:
11573:
11571:
11567:
11559:
11556:
11554:
11551:
11549:
11546:
11545:
11544:
11541:
11539:
11536:
11534:
11528:
11526:
11523:
11521:
11518:
11516:
11513:
11511:
11508:
11506:
11504:
11500:
11498:
11495:
11494:
11492:
11490:
11486:
11480:
11478:
11474:
11472:
11470:
11465:
11463:
11458:
11454:
11453:
11450:
11447:
11445:
11441:
11431:
11428:
11426:
11423:
11421:
11418:
11417:
11415:
11413:
11409:
11403:
11400:
11396:
11393:
11392:
11391:
11388:
11384:
11381:
11380:
11379:
11376:
11374:
11371:
11370:
11368:
11366:
11362:
11354:
11351:
11349:
11346:
11345:
11344:
11341:
11339:
11336:
11334:
11331:
11329:
11326:
11324:
11321:
11319:
11316:
11315:
11313:
11311:
11307:
11301:
11298:
11294:
11291:
11287:
11284:
11282:
11279:
11278:
11277:
11274:
11273:
11272:
11269:
11265:
11262:
11260:
11257:
11255:
11252:
11250:
11247:
11246:
11245:
11242:
11241:
11239:
11237:
11233:
11230:
11228:
11224:
11218:
11215:
11213:
11210:
11206:
11203:
11202:
11201:
11198:
11196:
11193:
11189:
11188:loss function
11186:
11185:
11184:
11181:
11177:
11174:
11172:
11169:
11167:
11164:
11163:
11162:
11159:
11157:
11154:
11152:
11149:
11145:
11142:
11140:
11137:
11135:
11129:
11126:
11125:
11124:
11121:
11117:
11114:
11112:
11109:
11107:
11104:
11103:
11102:
11099:
11095:
11092:
11090:
11087:
11086:
11085:
11082:
11078:
11075:
11074:
11073:
11070:
11066:
11063:
11062:
11061:
11058:
11056:
11053:
11051:
11048:
11046:
11043:
11042:
11040:
11038:
11034:
11030:
11026:
11021:
11017:
11003:
11000:
10998:
10995:
10993:
10990:
10988:
10985:
10984:
10982:
10980:
10976:
10970:
10967:
10965:
10962:
10960:
10957:
10956:
10954:
10950:
10944:
10941:
10939:
10936:
10934:
10931:
10929:
10926:
10924:
10921:
10919:
10916:
10914:
10911:
10910:
10908:
10906:
10902:
10896:
10893:
10891:
10890:Questionnaire
10888:
10886:
10883:
10879:
10876:
10874:
10871:
10870:
10869:
10866:
10865:
10863:
10861:
10857:
10851:
10848:
10846:
10843:
10841:
10838:
10836:
10833:
10831:
10828:
10826:
10823:
10821:
10818:
10816:
10813:
10812:
10810:
10808:
10804:
10800:
10796:
10791:
10787:
10773:
10770:
10768:
10765:
10763:
10760:
10758:
10755:
10753:
10750:
10748:
10745:
10743:
10740:
10738:
10735:
10733:
10730:
10728:
10725:
10723:
10720:
10718:
10717:Control chart
10715:
10713:
10710:
10708:
10705:
10703:
10700:
10699:
10697:
10695:
10691:
10685:
10682:
10678:
10675:
10673:
10670:
10669:
10668:
10665:
10663:
10660:
10658:
10655:
10654:
10652:
10650:
10646:
10640:
10637:
10635:
10632:
10630:
10627:
10626:
10624:
10620:
10614:
10611:
10610:
10608:
10606:
10602:
10590:
10587:
10585:
10582:
10580:
10577:
10576:
10575:
10572:
10570:
10567:
10566:
10564:
10562:
10558:
10552:
10549:
10547:
10544:
10542:
10539:
10537:
10534:
10532:
10529:
10527:
10524:
10522:
10519:
10518:
10516:
10514:
10510:
10504:
10501:
10499:
10496:
10492:
10489:
10487:
10484:
10482:
10479:
10477:
10474:
10472:
10469:
10467:
10464:
10462:
10459:
10457:
10454:
10452:
10449:
10447:
10444:
10443:
10442:
10439:
10438:
10436:
10434:
10430:
10427:
10425:
10421:
10417:
10413:
10408:
10404:
10398:
10395:
10393:
10390:
10389:
10386:
10382:
10375:
10370:
10368:
10363:
10361:
10356:
10355:
10352:
10340:
10337:
10335:
10332:
10330:
10325:
10320:
10318:
10315:
10313:
10310:
10309:
10306:
10300:
10297:
10295:
10292:
10290:
10287:
10285:
10282:
10280:
10279:Curve fitting
10277:
10276:
10274:
10270:
10264:
10261:
10259:
10256:
10254:
10251:
10249:
10246:
10244:
10241:
10239:
10236:
10234:
10231:
10230:
10228:
10226:
10225:approximation
10223:
10219:
10213:
10210:
10208:
10205:
10203:
10200:
10199:
10197:
10195:
10191:
10185:
10182:
10180:
10177:
10175:
10172:
10170:
10167:
10165:
10162:
10160:
10157:
10155:
10152:
10151:
10149:
10145:
10139:
10136:
10134:
10131:
10127:
10124:
10122:
10119:
10117:
10116:
10108:
10107:
10106:
10103:
10101:
10098:
10097:
10095:
10091:
10085:
10082:
10080:
10077:
10075:
10072:
10071:
10069:
10067:
10063:
10053:
10050:
10048:
10045:
10043:
10040:
10038:
10035:
10034:
10032:
10028:
10022:
10019:
10017:
10014:
10012:
10009:
10007:
10004:
10002:
10001:Nonparametric
9999:
9997:
9994:
9993:
9991:
9987:
9981:
9978:
9976:
9973:
9971:
9968:
9966:
9963:
9962:
9960:
9956:
9950:
9947:
9945:
9942:
9940:
9937:
9935:
9932:
9930:
9927:
9926:
9924:
9922:
9918:
9915:
9913:
9907:
9901:
9898:
9896:
9893:
9891:
9888:
9886:
9883:
9882:
9880:
9878:
9874:
9868:
9865:
9863:
9860:
9857:
9856:Kendall's tau
9854:
9852:
9848:
9845:
9843:
9840:
9839:
9837:
9835:
9831:
9825:
9822:
9820:
9817:
9815:
9812:
9810:
9809:Least squares
9807:
9806:
9804:
9802:
9798:
9794:
9790:
9789:Least squares
9783:
9778:
9776:
9771:
9769:
9764:
9763:
9760:
9746:
9743:
9741:
9738:
9737:
9730:
9726:
9723:
9721:
9718:
9717:
9714:
9710:
9709:
9706:
9700:
9697:
9695:
9692:
9690:
9687:
9685:
9682:
9680:
9677:
9675:
9672:
9670:
9667:
9665:
9662:
9660:
9657:
9655:
9652:
9650:
9647:
9645:
9642:
9640:
9637:
9635:
9632:
9630:
9627:
9626:
9624:
9622:Architectures
9620:
9614:
9611:
9609:
9606:
9604:
9601:
9599:
9596:
9594:
9591:
9589:
9586:
9584:
9581:
9579:
9576:
9574:
9571:
9570:
9568:
9566:Organizations
9564:
9558:
9555:
9553:
9550:
9548:
9545:
9543:
9540:
9538:
9535:
9533:
9530:
9528:
9525:
9523:
9520:
9518:
9515:
9513:
9510:
9508:
9505:
9503:
9502:Yoshua Bengio
9500:
9499:
9497:
9493:
9483:
9482:Robot control
9480:
9476:
9473:
9472:
9471:
9468:
9466:
9463:
9461:
9458:
9456:
9453:
9451:
9448:
9446:
9443:
9441:
9438:
9436:
9433:
9432:
9430:
9426:
9420:
9417:
9415:
9412:
9410:
9407:
9405:
9402:
9400:
9399:Chinchilla AI
9397:
9395:
9392:
9390:
9387:
9385:
9382:
9380:
9377:
9375:
9372:
9370:
9367:
9365:
9362:
9360:
9357:
9355:
9352:
9350:
9347:
9345:
9342:
9338:
9335:
9334:
9333:
9330:
9328:
9325:
9323:
9320:
9318:
9315:
9313:
9310:
9308:
9305:
9304:
9302:
9298:
9292:
9289:
9285:
9282:
9280:
9277:
9276:
9275:
9272:
9268:
9265:
9263:
9260:
9258:
9255:
9254:
9253:
9250:
9248:
9245:
9243:
9240:
9238:
9235:
9233:
9230:
9228:
9225:
9223:
9220:
9218:
9215:
9213:
9210:
9208:
9205:
9204:
9202:
9198:
9195:
9191:
9185:
9182:
9180:
9177:
9175:
9172:
9170:
9167:
9165:
9162:
9160:
9157:
9155:
9152:
9151:
9149:
9145:
9139:
9136:
9134:
9131:
9129:
9126:
9124:
9121:
9119:
9116:
9115:
9113:
9109:
9101:
9098:
9097:
9096:
9093:
9091:
9088:
9086:
9083:
9079:
9078:Deep learning
9076:
9075:
9074:
9071:
9067:
9064:
9063:
9062:
9059:
9058:
9056:
9052:
9046:
9043:
9041:
9038:
9034:
9031:
9030:
9029:
9026:
9024:
9021:
9017:
9014:
9012:
9009:
9007:
9004:
9003:
9002:
8999:
8997:
8994:
8992:
8989:
8987:
8984:
8982:
8979:
8977:
8974:
8972:
8969:
8967:
8966:Hallucination
8964:
8960:
8957:
8956:
8955:
8952:
8950:
8947:
8943:
8940:
8939:
8938:
8935:
8934:
8932:
8928:
8922:
8919:
8917:
8914:
8912:
8909:
8907:
8904:
8902:
8899:
8897:
8894:
8892:
8889:
8887:
8884:
8882:
8881:
8877:
8876:
8874:
8872:
8868:
8859:
8854:
8852:
8847:
8845:
8840:
8839:
8836:
8829:
8826:
8823:
8820:
8817:
8814:
8810:
8806:
8805:
8800:
8796:
8795:
8785:
8781:
8776:
8771:
8766:
8761:
8757:
8753:
8749:
8744:
8741:
8737:
8734:
8730:
8726:
8723:
8719:
8715:
8711:
8708:
8704:
8700:
8696:
8692:
8688:
8683:
8681:
8680:0-521-42950-1
8677:
8673:
8669:
8666:
8662:
8658:
8652:
8648:
8643:
8640:
8637:
8633:
8629:
8627:
8626:0-471-56881-3
8623:
8619:
8615:
8611:
8608:
8604:
8603:Lindley, D.V.
8601:
8600:
8596:
8593:
8590:
8589:
8585:
8581:
8577:
8574:
8573:
8557:
8553:
8546:
8539:
8531:
8527:
8522:
8517:
8513:
8509:
8505:
8501:
8497:
8490:
8482:
8476:
8472:
8468:
8462:
8456:
8452:
8451:981-238-310-7
8448:
8444:
8438:
8431:. p. 60.
8427:
8426:
8418:
8411:
8407:
8401:
8393:
8389:
8385:
8381:
8377:
8373:
8366:
8358:
8352:
8348:
8341:
8334:
8328:
8320:
8316:
8311:
8306:
8302:
8298:
8291:
8284:
8276:
8270:
8266:
8261:
8260:
8254:
8248:
8239:
8234:
8230:
8226:
8222:
8218:
8214:
8210:
8206:
8199:
8191:
8187:
8183:
8179:
8175:
8171:
8170:
8165:
8161:
8160:Pearson, Karl
8155:
8147:
8143:
8139:
8135:
8132:(4): 812â54.
8131:
8127:
8123:
8119:
8118:Yule, G. Udny
8113:
8107:
8101:
8095:
8091:
8086:
8078:
8074:
8069:
8064:
8060:
8056:
8052:
8045:
8037:
8031:
8027:
8020:
8014:. (1821/1823)
8013:
8012:
8005:
7998:
7992:
7990:
7982:
7981:
7976:
7975:A.M. Legendre
7971:
7964:
7960:
7954:
7946:
7940:
7936:
7935:
7927:
7921:
7916:
7912:
7901:
7898:
7896:
7893:
7891:
7888:
7886:
7883:
7881:
7878:
7876:
7873:
7871:
7868:
7866:
7863:
7861:
7858:
7856:
7853:
7851:
7848:
7846:
7843:
7841:
7838:
7836:
7833:
7830:
7827:
7825:
7822:
7820:
7817:
7815:
7812:
7810:
7807:
7805:
7802:
7800:
7799:Curve fitting
7797:
7795:
7792:
7791:
7786:
7780:
7775:
7768:
7765:
7761:
7757:
7756:least squares
7750:
7737:
7735:
7731:
7728:
7725:
7722:
7720:
7716:
7713:
7710:
7706:
7704:
7700:
7697:
7696:
7695:
7690:Other methods
7672:
7669:
7663:
7660:
7657:
7652:
7649:
7646:
7636:
7635:
7634:
7620:
7600:
7579:
7559:
7539:
7517:
7513:
7509:
7506:
7492:
7490:
7486:
7481:
7478:
7475:
7474:
7468:
7466:
7465:
7464:extrapolation
7458:
7457:. Prediction
7456:
7455:
7454:interpolation
7448:
7446:
7442:
7438:
7437:
7426:
7421:
7417:
7407:
7405:
7399:
7389:
7387:
7383:
7379:
7375:
7371:
7367:
7363:
7362:ordered logit
7359:
7355:
7351:
7347:
7343:
7339:
7335:
7329:
7327:
7323:
7319:
7310:
7308:
7303:
7299:
7294:
7292:
7288:
7284:
7280:
7276:
7272:
7266:
7260:
7235:
7169:
7168:
7167:
7153:
7150:
7147:
7098:
7095:
7092:
7072:
7052:
7049:
7046:
7002:
6992:
6939:
6917:
6913:
6892:
6872:
6850:
6847:
6843:
6800:
6797:
6773:
6715:
6714:
6713:
6695:
6692:
6689:
6686:
6683:
6680:
6677:
6674:
6668:
6663:
6659:
6653:
6650:
6646:
6640:
6635:
6632:
6629:
6625:
6621:
6616:
6606:
6597:
6594:
6590:
6584:
6581:
6577:
6571:
6566:
6563:
6560:
6556:
6550:
6545:
6542:
6539:
6535:
6527:
6526:
6525:
6523:
6504:
6499:
6496:
6492:
6486:
6476:
6469:
6466:
6463:
6458:
6455:
6451:
6445:
6435:
6428:
6423:
6419:
6415:
6410:
6406:
6398:
6397:
6396:
6382:
6373:
6371:
6353:
6349:
6328:
6325:
6320:
6317:
6313:
6292:
6272:
6252:
6230:
6227:
6223:
6198:
6193:
6189:
6185:
6180:
6177:
6173:
6167:
6163:
6159:
6156:
6153:
6148:
6145:
6141:
6135:
6131:
6127:
6122:
6119:
6115:
6109:
6105:
6101:
6096:
6092:
6084:
6083:
6082:
6068:
6058:
6052:
6042:
6040:
6036:
6032:
6013:
6007:
6001:
5996:
5992:
5988:
5977:
5973:
5962:
5955:
5945:
5931:
5925:
5920:
5916:
5909:
5903:
5893:
5884:
5879:
5876:
5867:
5857:
5850:
5843:
5839:
5828:
5817:
5816:
5795:
5781:
5775:
5770:
5766:
5759:
5755:
5747:
5737:
5730:
5723:
5719:
5708:
5697:
5696:
5695:
5693:
5688:
5674:
5671:
5668:
5648:
5645:
5642:
5619:
5616:
5613:
5610:
5607:
5596:
5581:
5558:
5555:
5552:
5541:
5519:
5516:
5513:
5508:
5505:
5502:
5496:
5491:
5486:
5476:
5465:
5464:
5463:
5460:
5446:
5420:
5397:
5389:
5367:
5335:
5327:
5320:
5317:
5310:
5301:
5295:
5290:
5283:
5280:
5269:
5250:
5236:
5230:
5225:
5221:
5214:
5200:
5194:
5189:
5185:
5169:
5163:
5158:
5154:
5147:
5141:
5136:
5129:
5126:
5115:
5114:
5113:
5106:
5102:
5086:
5079:
5076:
5069:
5064:
5057:
5054:
5042:
5021:
5016:
5012:
5006:
5001:
4998:
4995:
4991:
4987:
4984:
4981:
4978:
4971:
4970:
4969:
4967:
4963:
4959:
4941:
4937:
4914:
4907:
4904:
4878:
4871:
4868:
4861:
4856:
4852:
4848:
4843:
4839:
4830:
4811:
4806:
4802:
4796:
4789:
4786:
4779:
4774:
4767:
4764:
4757:
4752:
4745:
4742:
4731:
4730:
4729:
4726:
4712:
4690:
4686:
4676:
4663:
4658:
4654:
4631:
4627:
4604:
4600:
4577:
4573:
4548:
4545:
4542:
4539:
4536:
4533:
4530:
4527:
4521:
4516:
4512:
4508:
4503:
4498:
4494:
4488:
4484:
4480:
4475:
4471:
4465:
4461:
4457:
4452:
4448:
4444:
4439:
4435:
4426:
4425:
4424:
4408:
4403:
4399:
4389:
4371:
4368:
4365:
4362:
4359:
4356:
4353:
4350:
4346:
4341:
4337:
4333:
4328:
4324:
4318:
4314:
4310:
4305:
4301:
4297:
4292:
4288:
4279:
4278:
4277:
4261:
4257:
4234:
4230:
4207:
4203:
4182:
4175:for modeling
4174:
4170:
4166:
4162:
4144:
4140:
4128:
4122:
4112:
4110:
4092:
4088:
4079:
4075:
4057:
4053:
4030:
4026:
4017:
4013:
4008:
4004:
4000:
3996:
3988:
3984:
3980:
3962:
3958:
3949:
3946:
3928:
3924:
3915:
3901:
3898:
3890:
3886:
3875:
3871:
3864:
3856:
3853:
3850:
3849:
3848:
3846:
3834:
3831:
3823:
3820:December 2020
3813:
3809:
3803:
3802:
3797:This section
3795:
3791:
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3517:least squares
3513:
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2905:least squares
2885:
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2400:
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2362:least squares
2347:
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2197:
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2117:
2113:
2092:
2069:
2066:
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2057:
2050:
2041:
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2021:
2017:
1991:
1987:
1983:
1977:
1974:
1969:
1965:
1958:
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1950:
1946:
1938:
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1920:
1916:
1907:
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1598:
1594:
1593:growth curves
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1476:
1472:
1471:average value
1468:
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1174:Crowdsourcing
1172:
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1103:Memtransistor
1101:
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1013:
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1003:
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986:
985:Deep learning
983:
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968:
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949:
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921:
920:Hidden Markov
918:
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513:Meta-learning
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265:Least squares
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167:Fixed effects
165:
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147:
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141:Ordered logit
139:
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63:
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61:
60:
56:
55:
52:
49:
48:
44:
43:
37:
32:
19:
13937:
13925:
13913:
13882:Radium Girls
13877:Typhoid Mary
13564:Microbiology
13434:
13426:
13414:
13310:Epidemiology
13208:Organization
13159:Oral hygiene
13149:Hand washing
13127:Healthy diet
13057:Right to sit
12950:Labor rights
12763:
12662:
12650:
12631:
12624:
12536:Econometrics
12486: /
12469:Chemometrics
12446:Epidemiology
12439: /
12412:Applications
12254:ARIMA model
12201:Q-statistic
12150:Stationarity
12046:Multivariate
11989: /
11985: /
11983:Multivariate
11981: /
11921: /
11917: /
11781:
11733:
11691:Bayes factor
11590:Signed rank
11502:
11476:
11468:
11456:
11151:Completeness
10987:Cohort study
10885:Opinion poll
10820:Missing data
10807:Study design
10762:Scatter plot
10684:Scatter plot
10677:Spearman's Ď
10639:Grouped data
10272:Applications
10111:
9989:Non-standard
9876:
9792:
9588:Hugging Face
9552:David Silver
9200:Audioâvisual
9054:Applications
9033:Augmentation
8953:
8878:
8802:
8755:
8751:
8728:
8713:
8706:
8690:
8686:
8671:
8670:Hardle, W.,
8664:
8646:
8638:
8635:
8617:
8583:
8556:the original
8551:
8538:
8503:
8499:
8489:
8470:
8461:
8442:
8437:
8424:
8417:
8405:
8400:
8375:
8371:
8365:
8346:
8340:
8327:
8300:
8296:
8283:
8258:
8247:
8212:
8208:
8198:
8173:
8167:
8154:
8129:
8125:
8112:
8105:
8100:
8093:
8085:
8061:(2): 80â86.
8058:
8054:
8044:
8025:
8019:
8010:
8008:C.F. Gauss.
8004:
7996:
7979:
7970:
7962:
7953:
7933:
7926:
7915:
7753:
7708:
7693:
7498:
7488:
7484:
7482:
7479:
7471:
7469:
7462:
7452:
7444:
7440:
7435:
7434:
7432:
7429:measurement.
7401:
7330:
7326:econometrics
7316:
7295:
7268:
6789:
6711:
6521:
6519:
6374:
6214:
6060:
6033:and conduct
6028:
5689:
5537:
5461:
5356:
5111:
5038:
4826:
4727:
4677:
4564:
4390:
4387:
4168:
4164:
4131:
4109:econometrics
4073:
3995:GaussâMarkov
3992:
3979:uncorrelated
3841:
3826:
3817:
3806:Please help
3801:verification
3798:
3706:
3514:
2817:
2517:fitted value
2516:
2335:
2042:
2008:
1827:
1794:
1783:
1750:
1749:, which are
1746:
1712:
1658:
1624:
1614:
1582:
1575:
1555:Karl Pearson
1540:
1520:
1491:
1442:
1438:
1434:
1430:
1426:
1418:
1414:
1410:
1398:
1392:
1260:PAC learning
947:
796:
791:Hierarchical
723:
678:
677:
671:
575:
322:Non-negative
50:
13939:WikiProject
13679:and history
13559:Engineering
13272:Vaccination
13144:Food safety
12708:forecasting
12664:WikiProject
12579:Cartography
12541:Jurimetrics
12493:Reliability
12224:Time domain
12203:(LjungâBox)
12125:Time-series
12003:Categorical
11987:Time-series
11979:Categorical
11914:(Bernoulli)
11749:Correlation
11729:Correlation
11525:JarqueâBera
11497:Chi-squared
11259:M-estimator
11212:Asymptotics
11156:Sufficiency
10923:Interaction
10835:Replication
10815:Effect size
10772:Violin plot
10752:Radar chart
10732:Forest plot
10722:Correlogram
10672:Kendall's Ď
9736:Categories
9684:Autoencoder
9639:Transformer
9507:Alex Graves
9455:OpenAI Five
9359:IBM Watsonx
8981:Convolution
8959:Overfitting
8712:T. Strutz:
8506:: 526â534.
8467:Good, P. I.
8410:McGraw Hill
7809:Forecasting
7764:spreadsheet
7342:logit model
7253:Diagnostics
6952:element of
6813:element of
5410:values and
3845:assumptions
3705:: one must
3519:model with
1784:In various
1747:error terms
1589:time series
1567:R.A. Fisher
1498:forecasting
1144:Multi-agent
1081:Transformer
980:Autoencoder
736:Naive Bayes
474:data mining
332:Regularized
296:Generalized
228:Least angle
126:Mixed logit
13692:Caribbean
13569:Processing
13503:Quarantine
13425:Student's
13225:Sanitation
12859:History of
12531:Demography
12249:ARMA model
12054:Regression
11631:(Friedman)
11592:(Wilcoxon)
11530:Normality
11520:Lilliefors
11467:Student's
11343:Resampling
11217:Robustness
11205:divergence
11195:Efficiency
11133:(monotone)
11128:Likelihood
11045:Population
10878:Stratified
10830:Population
10649:Dependence
10605:Count data
10536:Percentile
10513:Dispersion
10446:Arithmetic
10381:Statistics
10147:Background
10110:Mallows's
9725:Technology
9578:EleutherAI
9537:Fei-Fei Li
9532:Yann LeCun
9445:Q-learning
9428:Decisional
9354:IBM Watson
9262:Midjourney
9154:TensorFlow
9001:Activation
8954:Regression
8949:Clustering
8169:Biometrika
7907:References
7709:percentage
7263:See also:
6932:, and the
6790:where the
6037:about the
5595:regressors
4427:parabola:
4165:parameters
4078:NeweyâWest
4003:consistent
1494:prediction
1459:hyperplane
1435:covariates
1431:predictors
1427:regressors
1403:estimating
1129:Q-learning
1027:Restricted
825:Mean shift
774:Clustering
751:Perceptron
679:regression
581:Clustering
576:Regression
371:Background
275:Non-linear
257:Estimation
13872:John Snow
13799:Education
13789:Full list
13677:education
13601:ISO 22000
13554:Chemistry
13467:Epidemics
13420:ROC curve
13230:Emergency
13010:Radiation
12990:Pollution
12974:Ministers
12871:Euthenics
11912:Logistic
11679:posterior
11605:Rank sum
11353:Jackknife
11348:Bootstrap
11166:Bootstrap
11101:Parameter
11050:Statistic
10845:Statistic
10757:Run chart
10742:Pie chart
10737:Histogram
10727:Fan chart
10702:Bar chart
10584:L-moments
10471:Geometric
10222:Numerical
9608:MIT CSAIL
9573:Anthropic
9542:Andrew Ng
9440:AlphaZero
9284:VideoPoet
9247:AlphaFold
9184:MindSpore
9138:SpiNNaker
9133:Memristor
9040:Diffusion
9016:Rectifier
8996:Batchnorm
8976:Attention
8971:Adversary
8809:EMS Press
8614:Dodge, Y.
8392:153979055
8265:Edinburgh
7670:≈
7661:
7650:
7283:residuals
7279:R-squared
7227:⊤
7214:−
7201:⊤
7184:^
7181:β
7151:×
7125:^
7122:β
7096:×
7050:×
6996:^
6993:β
6966:^
6963:β
6765:⊤
6749:^
6746:β
6732:⊤
6687:…
6626:∑
6610:^
6607:β
6557:∑
6536:∑
6480:^
6477:β
6470:−
6467:⋯
6464:−
6439:^
6436:β
6429:−
6407:ε
6350:β
6190:ε
6164:β
6157:⋯
6132:β
6106:β
5989:∑
5974:β
5966:^
5963:σ
5935:¯
5926:−
5910:∑
5897:¯
5868:ε
5861:^
5858:σ
5840:β
5832:^
5829:σ
5785:¯
5776:−
5760:∑
5748:ε
5741:^
5738:σ
5720:β
5712:^
5709:σ
5672:−
5617:−
5611:−
5556:−
5517:−
5487:ε
5480:^
5477:σ
5424:¯
5371:¯
5339:¯
5321:^
5318:β
5311:−
5305:¯
5284:^
5281:β
5240:¯
5231:−
5215:∑
5204:¯
5195:−
5173:¯
5164:−
5148:∑
5130:^
5127:β
5080:^
5077:β
5058:^
5055:β
4992:∑
4962:residuals
4908:^
4872:^
4862:−
4790:^
4787:β
4768:^
4765:β
4746:^
4687:ε
4655:β
4628:β
4601:β
4540:…
4513:ε
4485:β
4462:β
4449:β
4363:…
4338:ε
4315:β
4302:β
4258:β
4231:β
4007:efficient
3761:^
3758:β
3602:−
3550:≥
3454:residuals
3399:^
3396:β
3364:^
3361:β
3342:^
3339:β
3329:−
3317:^
3298:∑
3277:^
3261:∑
3218:^
3215:β
3183:^
3180:β
3161:^
3158:β
3139:^
3100:^
3097:β
3078:^
3075:β
3056:^
3053:β
2968:β
2942:β
2929:β
2800:β
2693:^
2690:β
2641:^
2605:^
2602:β
2573:^
2570:β
2539:^
2500:^
2497:β
2474:β
2441:β
2419:−
2397:∑
2376:β
2348:β
2292:β
2279:β
2229:β
2216:β
2206:β
2070:β
1978:β
1865:β
1642:β
1551:Udny Yule
1288:ECML PKDD
1270:VC theory
1217:ROC curve
1149:Self-play
1069:DeepDream
910:Bayes net
701:Ensembles
482:Paradigms
238:Segmented
13955:Category
13915:Category
13614:sciences
13549:Additive
13220:Safe sex
13191:Medicine
13105:Theories
12876:Genomics
12854:Eugenics
12844:Deviance
12824:Auxology
12626:Category
12319:Survival
12196:Johansen
11919:Binomial
11874:Isotonic
11461:(normal)
11106:location
10913:Blocking
10868:Sampling
10747:QâQ plot
10712:Box plot
10694:Graphics
10589:Skewness
10579:Kurtosis
10551:Variance
10481:Heronian
10476:Harmonic
10052:Logistic
10042:Binomial
10021:Isotonic
10016:Quantile
9716:Portals
9475:Auto-GPT
9307:Word2vec
9111:Hardware
9028:Datasets
8930:Concepts
8784:34307865
8674:(1990),
8521:2299/965
8319:20061201
8255:(1954).
8120:(1897).
7771:See also
7743:Software
7532:, where
7368:models.
5459:values.
4829:residual
3999:unbiased
3987:diagonal
3776:exists.
3701:must be
1824:function
1601:Bayesian
1563:Gaussian
1527:Legendre
1443:features
1415:response
711:Boosting
560:Problems
353:Bayesian
291:Weighted
286:Ordinary
218:Isotonic
213:Quantile
13927:Commons
13840:History
13737:Canada
13712:Europe
13196:Nursing
13176:Hygiene
13139:Hygiene
12864:Liberal
12817:General
12710:methods
12652:Commons
12599:Kriging
12484:Process
12441:studies
12300:Wavelet
12133:General
11300:Plug-in
11094:L space
10873:Cluster
10574:Moments
10392:Outline
10047:Poisson
9598:Meta AI
9435:AlphaGo
9419:PanGu-ÎŁ
9389:ChatGPT
9364:Granite
9312:Seq2seq
9291:Whisper
9212:WaveNet
9207:AlexNet
9179:Flux.jl
9159:PyTorch
9011:Sigmoid
9006:Softmax
8871:General
8811:, 2001
8775:8279135
8530:1406472
8238:1084801
8229:2341124
8190:2331683
8146:2979746
8077:2245330
7829:Kriging
7701:, e.g.
7459:outside
7436:predict
7384:or the
7291:t-tests
7017:. Thus
6341:, then
6245:is the
5386:is the
4163:of the
1877:, with
1688:(where
1517:History
1477:(e.g.,
1411:outcome
1293:NeurIPS
1110:(ECRAM)
1064:AlexNet
706:Bagging
312:Partial
151:Poisson
13727:India
13702:China
13574:Safety
13255:Worker
12521:Census
12111:Normal
12059:Manova
11879:Robust
11629:2-way
11621:1-way
11459:-test
11130:
10707:Biplot
10498:Median
10491:Lehmer
10433:Center
10011:Robust
9613:Huawei
9593:OpenAI
9495:People
9465:MuZero
9327:Gemini
9322:Claude
9257:DALL-E
9169:Theano
8782:
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7356:. For
7344:. The
7338:probit
7302:F-test
7298:t-test
7287:F-test
7111:, and
6865:, the
6672:
6215:where
5357:where
4525:
4005:, and
3743:is an
1633:vector
1629:scalar
1086:Vision
942:RANSAC
820:OPTICS
815:DBSCAN
799:-means
606:AutoML
270:Linear
208:Robust
131:Probit
57:Models
13752:U.S.
13596:HACCP
13545:Food
13437:-test
13429:-test
13015:Light
13000:Water
12145:Trend
11674:prior
11616:anova
11505:-test
11479:-test
11471:-test
11378:Power
11323:Pivot
11116:shape
11111:scale
10561:Shape
10541:Range
10486:Heinz
10461:Cubic
10397:Index
9679:Mamba
9450:SARSA
9414:LLaMA
9409:BLOOM
9394:GPT-J
9384:GPT-4
9379:GPT-3
9374:GPT-2
9369:GPT-1
9332:LaMDA
9164:Keras
8559:(PDF)
8548:(PDF)
8429:(PDF)
8388:S2CID
8315:JSTOR
8293:(PDF)
8225:JSTOR
8186:JSTOR
8142:JSTOR
8073:JSTOR
4159:is a
4076:, or
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1822:is a
1531:Gauss
1419:label
1308:IJCAI
1134:SARSA
1093:Mamba
1059:LeNet
1054:U-Net
880:t-SNE
804:Fuzzy
781:BIRCH
317:Total
233:Local
13528:WASH
13484:List
13472:List
13005:Soil
12378:Test
11578:Sign
11430:Wald
10503:Mode
10441:Mean
9791:and
9603:Mila
9404:PaLM
9337:Bard
9317:BERT
9300:Text
9279:Sora
8780:PMID
8718:ISBN
8676:ISBN
8667:Sage
8651:ISBN
8622:ISBN
8578:and
8526:SSRN
8475:ISBN
8447:ISBN
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1298:ICML
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1000:LSTM
786:CURE
472:and
12995:Air
11558:BIC
11553:AIC
10126:BIC
10121:AIC
9344:NMT
9227:OCR
9222:HWR
9174:JAX
9128:VPU
9123:TPU
9118:IPU
8942:SGD
8770:PMC
8760:doi
8695:doi
8634:,"
8516:hdl
8508:doi
8380:doi
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