3339:
2328:
3334:{\displaystyle {\begin{bmatrix}\sum _{i=1}^{n}x_{i}^{0}&\sum _{i=1}^{n}x_{i}^{1}&\sum _{i=1}^{n}x_{i}^{2}&\cdots &\sum _{i=1}^{n}x_{i}^{m}\\\sum _{i=1}^{n}x_{i}^{1}&\sum _{i=1}^{n}x_{i}^{2}&\sum _{i=1}^{n}x_{i}^{3}&\cdots &\sum _{i=1}^{n}x_{i}^{m+1}\\\sum _{i=1}^{n}x_{i}^{2}&\sum _{i=1}^{n}x_{i}^{3}&\sum _{i=1}^{n}x_{i}^{4}&\cdots &\sum _{i=1}^{n}x_{i}^{m+2}\\\vdots &\vdots &\vdots &\ddots &\vdots \\\sum _{i=1}^{n}x_{i}^{m}&\sum _{i=1}^{n}x_{i}^{m+1}&\sum _{i=1}^{n}x_{i}^{m+2}&\dots &\sum _{i=1}^{n}x_{i}^{2m}\\\end{bmatrix}}{\begin{bmatrix}\beta _{0}\\\beta _{1}\\\beta _{2}\\\cdots \\\beta _{m}\\\end{bmatrix}}={\begin{bmatrix}\sum _{i=1}^{n}y_{i}x_{i}^{0}\\\sum _{i=1}^{n}y_{i}x_{i}^{1}\\\sum _{i=1}^{n}y_{i}x_{i}^{2}\\\cdots \\\sum _{i=1}^{n}y_{i}x_{i}^{m}\\\end{bmatrix}}}
2056:
3686:
1539:
3394:
2051:{\displaystyle {\begin{bmatrix}y_{1}\\y_{2}\\y_{3}\\\vdots \\y_{n}\end{bmatrix}}={\begin{bmatrix}1&x_{1}&x_{1}^{2}&\dots &x_{1}^{m}\\1&x_{2}&x_{2}^{2}&\dots &x_{2}^{m}\\1&x_{3}&x_{3}^{2}&\dots &x_{3}^{m}\\\vdots &\vdots &\vdots &\ddots &\vdots \\1&x_{n}&x_{n}^{2}&\dots &x_{n}^{m}\end{bmatrix}}{\begin{bmatrix}\beta _{0}\\\beta _{1}\\\beta _{2}\\\vdots \\\beta _{m}\end{bmatrix}}+{\begin{bmatrix}\varepsilon _{1}\\\varepsilon _{2}\\\varepsilon _{3}\\\vdots \\\varepsilon _{n}\end{bmatrix}},}
3681:{\displaystyle {\begin{aligned}&\qquad {\widehat {y}}=\beta _{0}x^{0}+\beta _{1}x^{1}+\beta _{2}x^{2}+\cdots +\beta _{m}x^{m}\\&\qquad \\&\qquad {\text{Where:}}\\&\qquad n={\text{number of }}x_{i}y_{i}{\text{ variable pairs in the data}}\\&\qquad m={\text{order of the polynomial to be used for regression}}\\&\qquad \beta _{(0-m)}={\text{polynomial coefficient for each corresponding }}x^{(0-m)}\\&\qquad {\widehat {y}}={\text{estimated y variable based on the polynomial regression calculations.}}\end{aligned}}}
7311:
6704:
405:
6690:
609:
6728:
6716:
2322:
The above matrix equations explain the behavior of polynomial regression well. However, to physically implement polynomial regression for a set of xy point pairs, more detail is useful. The below matrix equations for polynomial coefficients are expanded from regression theory without derivation and
723:
In many settings, such a linear relationship may not hold. For example, if we are modeling the yield of a chemical synthesis in terms of the temperature at which the synthesis takes place, we may find that the yield improves by increasing amounts for each unit increase in temperature. In this case,
3695:
Although polynomial regression is technically a special case of multiple linear regression, the interpretation of a fitted polynomial regression model requires a somewhat different perspective. It is often difficult to interpret the individual coefficients in a polynomial regression fit, since the
3972:
can be useful alternatives to polynomial regression. Some of these methods make use of a localized form of classical polynomial regression. An advantage of traditional polynomial regression is that the inferential framework of multiple regression can be used (this also holds when using other
1348:
2248:
1124:
3963:
The goal of polynomial regression is to model a non-linear relationship between the independent and dependent variables (technically, between the independent variable and the conditional mean of the dependent variable). This is similar to the goal of
2130:
807:
4187:"On the Standard Deviations of Adjusted and Interpolated Values of an Observed Polynomial Function and its Constants and the Guidance They Give Towards a Proper Choice of the Distribution of the Observations"
3916:
1177:
6766:
3819:
2148:
692:
3387:
3399:
879:
3768:
964:
995:
1460:
6759:
1431:
537:
The explanatory (independent) variables resulting from the polynomial expansion of the "baseline" variables are known as higher-degree terms. Such variables are also used in
2281:
1486:
1373:
1515:
1402:
2312:
6828:
6752:
5825:
600:. More recently, the use of polynomial models has been complemented by other methods, with non-polynomial models having advantages for some classes of problems.
6330:
4370:. Classics in Applied Mathematics. Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104). p. 259.
6480:
6104:
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2067:
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5878:
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7275:
345:
730:
4375:
4348:
7170:
6810:
4740:
4440:
4102:
335:
5344:
4492:
6732:
1343:{\displaystyle y_{i}\,=\,\beta _{0}+\beta _{1}x_{i}+\beta _{2}x_{i}^{2}+\cdots +\beta _{m}x_{i}^{m}+\varepsilon _{i}\ (i=1,2,\dots ,n)}
7150:
6800:
3824:
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6127:
6019:
4279:
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1151:
analysis, the computational and inferential problems of polynomial regression can be completely addressed using the techniques of
6876:
6305:
6179:
2243:{\displaystyle {\widehat {\vec {\beta }}}=(\mathbf {X} ^{\mathsf {T}}\mathbf {X} )^{-1}\;\mathbf {X} ^{\mathsf {T}}{\vec {y}},\,}
299:
3773:
6363:
6024:
5769:
5140:
4730:
350:
288:
108:
83:
5354:
638:
7112:
6414:
5626:
5433:
5322:
5280:
210:
4519:
3350:
6657:
5616:
169:
5666:
7363:
7298:
7198:
7188:
7107:
7052:
6208:
6157:
6142:
6132:
6001:
5873:
5840:
5621:
5451:
4037:
428:
6277:
5578:
7325:
7140:
6552:
6353:
5332:
5001:
4465:
4022:
3712:, it is generally more informative to consider the fitted regression function as a whole. Point-wise or simultaneous
371:
6437:
6404:
3968:, which aims to capture non-linear regression relationships. Therefore, non-parametric regression approaches such as
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5797:
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538:
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340:
309:
236:
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4054:
Microsoft Excel makes use of polynomial regression when fitting a trendline to data points on an X Y scatter plot.
823:
7165:
6992:
6956:
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6169:
5937:
5658:
5512:
5441:
5361:
5219:
5200:
4908:
4629:
3981:
330:
319:
283:
190:
7145:
6282:
4071:
1119:{\displaystyle y=\beta _{0}+\beta _{1}x+\beta _{2}x^{2}+\beta _{3}x^{3}+\cdots +\beta _{n}x^{n}+\varepsilon .\,}
565:
391:
7023:
6987:
6915:
6805:
6787:
6652:
6419:
5967:
5932:
5896:
5681:
5123:
5032:
4991:
4903:
4594:
4433:
4032:
3965:
3918:. A drawback of polynomial bases is that the basis functions are "non-local", meaning that the fitted value of
3731:
262:
185:
78:
57:
5689:
5673:
920:
6886:
6561:
6174:
6114:
6051:
5411:
5273:
5263:
5113:
5027:
491:
421:
314:
6322:
6259:
1436:
7239:
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6920:
6871:
6599:
6529:
6014:
5901:
4898:
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4702:
4581:
4480:
4158:(November 1974). "Gergonne's 1815 paper on the design and analysis of polynomial regression experiments".
4000:
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3709:
2136:
278:
273:
215:
6720:
5598:
617:
7320:
7280:
7244:
7229:
7180:
7124:
6624:
6566:
6509:
6335:
6228:
6137:
5863:
5747:
5606:
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5295:
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5169:
5128:
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that are not constant (everywhere). Such "non-local" behavior has been widely discussed in statistics:
3953:
597:
593:
581:
569:
366:
7310:
6703:
5593:
404:
4124:(November 1974) . "The application of the method of least squares to the interpolation of sequences".
7285:
7224:
7211:
7160:
7060:
6982:
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6935:
6547:
6122:
6071:
6047:
6009:
5927:
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5339:
5312:
5268:
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1407:
698:
585:
558:
461:
386:
376:
257:
225:
180:
159:
67:
7303:
7234:
7119:
7086:
7038:
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7007:
7002:
6881:
6863:
6848:
6779:
6694:
6619:
6542:
6223:
5987:
5980:
5942:
5850:
5830:
5802:
5535:
5401:
5396:
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5378:
5196:
5157:
5047:
5037:
4946:
4725:
4681:
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3716:
can then be used to provide a sense of the uncertainty in the estimate of the regression function.
589:
457:
304:
205:
200:
154:
103:
93:
38:
6269:
2264:
1469:
1356:
588:. In the twentieth century, polynomial regression played an important role in the development of
7315:
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7033:
6708:
6519:
6373:
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6094:
5991:
5975:
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5174:
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5105:
5065:
5011:
4928:
4614:
4609:
4319:
4206:
4155:
4133:
2284:
468:
409:
138:
123:
1491:
1378:
4341:
Local
Polynomial Modelling and Its Applications: From linear regression to nonlinear regression
7270:
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5229:
5152:
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4918:
4697:
4571:
4398:
4371:
4365:
4344:
4295:
4275:
3985:
1152:
1130:
561:
511:
195:
98:
52:
6966:
6833:
6639:
6594:
6358:
6345:
6238:
6213:
6147:
6079:
5957:
5565:
5458:
5391:
5304:
5251:
5070:
4941:
4735:
4534:
4501:
4311:
4198:
4167:
4137:
4042:
3996:
910:
220:
149:
3960:. These families of basis functions offer a more parsimonious fit for many types of data.
2290:
7249:
7155:
7091:
6556:
6300:
6162:
6089:
5764:
5638:
5611:
5588:
5557:
5184:
5179:
5133:
4863:
4514:
4017:
3728:
to model a functional relationship between two quantities. More specifically, it replaces
3725:
3713:
613:
381:
88:
7096:
7343:
6744:
17:
7193:
6505:
6500:
4963:
4893:
4539:
4225:
3977:
3945:
624:
The goal of regression analysis is to model the expected value of a dependent variable
577:
133:
628:
in terms of the value of an independent variable (or vector of independent variables)
7357:
7265:
6795:
6775:
6662:
6629:
6492:
6453:
6264:
6233:
5697:
5651:
5256:
4958:
4785:
4549:
4544:
4171:
4141:
4097:
4096:
Yin-Wen Chang; Cho-Jui Hsieh; Kai-Wei Chang; Michael
Ringgaard; Chih-Jen Lin (2010).
4012:
1148:
898:
550:
252:
128:
4815:
6604:
6537:
6514:
6429:
5759:
5055:
4953:
4888:
4830:
4752:
4707:
1163:, ... as being distinct independent variables in a multiple regression model.
118:
4250:
6853:
6647:
6609:
6292:
6193:
6055:
5868:
5835:
5327:
5244:
5239:
4883:
4840:
4820:
4800:
4790:
4559:
530:. For this reason, polynomial regression is considered to be a special case of
164:
113:
3708:
on the interval (0, 1). Although the correlation can be reduced by using
2125:{\displaystyle {\vec {y}}=\mathbf {X} {\vec {\beta }}+{\vec {\varepsilon }}.\,}
697:
is used, where Îľ is an unobserved random error with mean zero conditioned on a
5493:
4973:
4673:
4604:
4554:
4529:
4449:
2139:
608:
479:
449:
1133:, since the regression function is linear in terms of the unknown parameters
978:
nonlinear even though the model is linear in the parameters to be estimated.
5646:
5498:
5118:
4913:
4825:
4810:
4805:
4770:
4343:. Monographs on Statistics and Applied Probability. Chapman & Hall/CRC.
3969:
523:
3944:. In modern statistics, polynomial basis-functions are used along with new
486:. Polynomial regression fits a nonlinear relationship between the value of
5162:
4780:
4657:
4652:
4647:
4619:
4098:"Training and testing low-degree polynomial data mappings via linear SVM"
554:
802:{\displaystyle y=\beta _{0}+\beta _{1}x+\beta _{2}x^{2}+\varepsilon .\,}
6667:
6368:
4323:
4210:
3957:
989:
th degree polynomial, yielding the general polynomial regression model
4274:(4th ed.). US: Brooks/Cole Publishing Company. pp. 539â542.
6589:
5570:
5544:
5524:
4775:
4566:
4302:
Magee, Lonnie (1998). "Nonlocal
Behavior in Polynomial Regressions".
3671:
estimated y variable based on the polynomial regression calculations.
4315:
4202:
4186:
1129:
Conveniently, these models are all linear from the point of view of
3724:
Polynomial regression is one example of regression analysis using
2135:
The vector of estimated polynomial regression coefficients (using
573:
514:
problem it is linear, in the sense that the regression function E(
549:
Polynomial regression models are usually fit using the method of
7347:
4509:
2314:
values are distinct. This is the unique least-squares solution.
527:
6748:
6478:
6045:
5792:
5091:
4861:
4478:
4422:
3911:{\displaystyle {\mathbin {\stackrel {\varphi }{\rightarrow }}}}
2287:, the invertibility condition is guaranteed to hold if all the
612:
A cubic polynomial regression fit to a simulated data set. The
2261:
which is required for the matrix to be invertible; then since
4418:
3696:
underlying monomials can be highly correlated. For example,
1353:
can be expressed in matrix form in terms of a design matrix
616:
is a 95% simultaneous confidence band constructed using the
4271:
Probability and
Statistics for Engineering and the Sciences
3389:, the regression polynomial may be constructed as follows:
7350:
Interactive simulations, University of
Colorado at Boulder
3814:{\displaystyle \varphi (x)\in \mathbb {R} ^{d_{\varphi }}}
705:. In this model, for each unit increase in the value of
687:{\displaystyle y=\beta _{0}+\beta _{1}x+\varepsilon ,\,}
584:
for polynomial regression appeared in an 1815 paper of
4367:
Elementary
Numerical Analysis: An Algorithmic Approach
3382:{\displaystyle \beta _{0}{\text{ through }}\beta _{m}}
3117:
3039:
2337:
1975:
1897:
1626:
1548:
812:
In this model, when the temperature is increased from
3827:
3776:
3734:
3397:
3353:
2331:
2293:
2267:
2151:
2070:
1542:
1494:
1472:
1439:
1410:
1381:
1359:
1180:
998:
923:
826:
733:
641:
6331:
Autoregressive conditional heteroskedasticity (ARCH)
2061:
which when using pure matrix notation is written as
1529:-th data sample. Then the model can be written as a
820: + 1 units, the expected yield changes by
568:. The least-squares method was published in 1805 by
7258:
7207:
7179:
7133:
7079:
7051:
7016:
6975:
6944:
6906:
6895:
6862:
6819:
6786:
6638:
6575:
6528:
6491:
6446:
6428:
6395:
6386:
6344:
6291:
6252:
6201:
6192:
6113:
6070:
6000:
5966:
5920:
5887:
5849:
5816:
5728:
5637:
5556:
5511:
5479:
5432:
5377:
5303:
5294:
5104:
5046:
5020:
4972:
4927:
4874:
4761:
4716:
4690:
4672:
4628:
4580:
4500:
4491:
4226:"Maths behind Polynomial regression, Muthukrishnan"
3623:
polynomial coefficient for each corresponding
3910:
3813:
3762:
3680:
3381:
3333:
2306:
2275:
2242:
2124:
2050:
1509:
1480:
1454:
1425:
1396:
1367:
1342:
1118:
958:
873:
801:
686:
3587:order of the polynomial to be used for regression
564:of the coefficients, under the conditions of the
5879:Multivariate adaptive regression splines (MARS)
981:In general, we can model the expected value of
724:we might propose a quadratic model of the form
3973:families of basis functions such as splines).
6760:
4434:
4255:Polynomial Regression, A PHP regression class
966:The fact that the change in yield depends on
429:
8:
874:{\displaystyle \beta _{1}+\beta _{2}(2x+1).}
4294:Such "non-local" behavior is a property of
4003:estimator may be used to account for that.
3770:in linear regression with polynomial basis
6903:
6767:
6753:
6745:
6488:
6475:
6392:
6198:
6067:
6042:
5813:
5789:
5517:
5300:
5101:
5088:
4871:
4858:
4497:
4488:
4475:
4441:
4427:
4419:
4399:"Tutorial: Polynomial Regression in Excel"
2209:
632:. In simple linear regression, the model
436:
422:
29:
4136:from the 1815 French ed.): 439â447.
4072:"Implementation of Polynomial Regression"
4028:Polynomial and rational function modeling
3899:
3880:
3852:
3847:
3845:
3844:
3843:
3826:
3803:
3798:
3794:
3793:
3775:
3763:{\displaystyle x\in \mathbb {R} ^{d_{x}}}
3752:
3747:
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3733:
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2010:
1996:
1982:
1970:
1953:
1932:
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997:
944:
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922:
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831:
825:
798:
783:
773:
757:
744:
732:
683:
665:
652:
640:
553:. The least-squares method minimizes the
510:fits a nonlinear model to the data, as a
1167:Matrix form and calculation of estimates
959:{\displaystyle \beta _{1}+2\beta _{2}x.}
607:
7276:Numerical smoothing and differentiation
4063:
3704:have correlation around 0.97 when x is
970:is what makes the relationship between
592:, with a greater emphasis on issues of
358:
244:
44:
37:
6405:KaplanâMeier estimator (product limit)
4251:"Mathematics of Polynomial Regression"
4132:(4) (Translated by Ralph St. John and
2218:
2185:
460:in which the relationship between the
3933:depends strongly on data values with
1455:{\displaystyle {\vec {\varepsilon }}}
7:
6811:Iteratively reweighted least squares
6715:
6415:Accelerated failure time (AFT) model
4224:Muthukrishnan, Gowri (17 Jun 2018).
4103:Journal of Machine Learning Research
6727:
6010:Analysis of variance (ANOVA, anova)
889:+1 and subtracting the equation in
6829:Pearson product-moment correlation
6105:CochranâMantelâHaenszel statistics
4731:Pearson product-moment correlation
4230:Maths behind Polynomial regression
25:
4364:Conte, S.D.; De Boor, C. (2018).
709:, the conditional expectation of
7309:
6726:
6714:
6702:
6689:
6688:
3570: variable pairs in the data
2269:
2212:
2192:
2179:
2087:
1474:
1361:
1171:The polynomial regression model
403:
6364:Least-squares spectral analysis
3653:
3595:
3578:
3536:
3525:
3519:
3403:
1426:{\displaystyle {\vec {\beta }}}
881:(This can be seen by replacing
351:Least-squares spectral analysis
289:Generalized estimating equation
109:Multinomial logistic regression
84:Vector generalized linear model
5345:Mean-unbiased minimum-variance
3976:A final alternative is to use
3905:
3861:
3848:
3840:
3828:
3786:
3780:
3643:
3631:
3613:
3601:
2230:
2197:
2174:
2160:
2112:
2097:
2077:
1501:
1446:
1417:
1388:
1337:
1307:
865:
850:
1:
6658:Geographic information system
5874:Simultaneous equations models
170:Nonlinear mixed-effects model
7299:Regression analysis category
7189:Response surface methodology
5841:Coefficient of determination
5452:Uniformly most powerful test
4172:10.1016/0315-0860(74)90033-0
4142:10.1016/0315-0860(74)90034-2
4038:Response surface methodology
2276:{\displaystyle \mathbf {X} }
1481:{\displaystyle \mathbf {X} }
1368:{\displaystyle \mathbf {X} }
1155:. This is done by treating
1147:, .... Therefore, for
526:that are estimated from the
7171:FrischâWaughâLovell theorem
7141:Mean and predicted response
6410:Proportional hazards models
6354:Spectral density estimation
6336:Vector autoregression (VAR)
5770:Maximum posterior estimator
5002:Randomized controlled trial
4023:Local polynomial regression
522:) is linear in the unknown
372:Mean and predicted response
7380:
6821:Correlation and dependence
6170:Multivariate distributions
4590:Average absolute deviation
3345:system of linear equations
1531:system of linear equations
1510:{\displaystyle {\vec {y}}}
1397:{\displaystyle {\vec {y}}}
566:Gauss–Markov theorem
532:multiple linear regression
165:Linear mixed-effects model
7294:
7166:Minimum mean-square error
7053:Decomposition of variance
6957:Growth curve (statistics)
6926:Generalized least squares
6684:
6487:
6474:
6158:Structural equation model
6066:
6041:
5812:
5788:
5520:
5494:Score/Lagrange multiplier
5100:
5087:
4909:Sample size determination
4870:
4857:
4487:
4474:
4456:
4403:facultystaff.richmond.edu
4304:The American Statistician
3982:support vector regression
331:Least absolute deviations
7024:Generalized linear model
6916:Simple linear regression
6806:Non-linear least squares
6788:Computational statistics
6653:Environmental statistics
6175:Elliptical distributions
5968:Generalized linear model
5897:Simple linear regression
5667:HodgesâLehmann estimator
5124:Probability distribution
5033:Stochastic approximation
4595:Coefficient of variation
4397:Stevenson, Christopher.
4185:Smith, Kirstine (1918).
4033:Polynomial interpolation
3966:nonparametric regression
3343:After solving the above
79:Generalized linear model
18:Polynomial least squares
6313:Cross-correlation (XCF)
5921:Non-standard predictors
5355:LehmannâScheffĂŠ theorem
5028:Adaptive clinical trial
4268:Devore, Jay L. (1995).
7316:Mathematics portal
7240:Orthogonal polynomials
7066:Analysis of covariance
6931:Weighted least squares
6921:Ordinary least squares
6872:Ordinary least squares
6709:Mathematics portal
6530:Engineering statistics
6438:NelsonâAalen estimator
6015:Analysis of covariance
5902:Ordinary least squares
5826:Pearson product-moment
5230:Statistical functional
5141:Empirical distribution
4974:Controlled experiments
4703:Frequency distribution
4481:Descriptive statistics
4339:Fan, Jianqing (1996).
4001:weighted least squares
3954:radial basis functions
3912:
3815:
3764:
3720:Alternative approaches
3710:orthogonal polynomials
3682:
3383:
3335:
3297:
3240:
3190:
3140:
3007:
2958:
2914:
2876:
2803:
2760:
2722:
2684:
2638:
2595:
2557:
2519:
2479:
2436:
2398:
2360:
2308:
2277:
2244:
2137:ordinary least squares
2126:
2052:
1511:
1482:
1462:of random errors. The
1456:
1427:
1398:
1369:
1344:
1120:
960:
885:in this equation with
875:
803:
688:
621:
604:Definition and example
512:statistical estimation
490:and the corresponding
410:Mathematics portal
336:Iteratively reweighted
7281:System identification
7245:Chebyshev polynomials
7230:Numerical integration
7181:Design of experiments
7125:Regression validation
6952:Polynomial regression
6877:Partial least squares
6625:Population statistics
6567:System identification
6301:Autocorrelation (ACF)
6229:Exponential smoothing
6143:Discriminant analysis
6138:Canonical correlation
6002:Partition of variance
5864:Regression validation
5708:(JonckheereâTerpstra)
5607:Likelihood-ratio test
5296:Frequentist inference
5208:Locationâscale family
5129:Sampling distribution
5094:Statistical inference
5061:Cross-sectional study
5048:Observational studies
5007:Randomized experiment
4836:Stem-and-leaf display
4638:Central limit theorem
3913:
3816:
3765:
3706:uniformly distributed
3683:
3384:
3336:
3277:
3220:
3170:
3120:
2987:
2938:
2894:
2856:
2783:
2740:
2702:
2664:
2618:
2575:
2537:
2499:
2459:
2416:
2378:
2340:
2309:
2307:{\displaystyle x_{i}}
2278:
2245:
2127:
2053:
1512:
1483:
1457:
1428:
1404:, a parameter vector
1399:
1370:
1345:
1121:
961:
893:from the equation in
876:
804:
689:
611:
508:polynomial regression
454:polynomial regression
367:Regression validation
346:Bayesian multivariate
63:Polynomial regression
7286:Moving least squares
7225:Approximation theory
7161:Studentized residual
7151:Errors and residuals
7146:GaussâMarkov theorem
7061:Analysis of variance
6983:Nonlinear regression
6962:Segmented regression
6936:General linear model
6854:Confounding variable
6801:Linear least squares
6548:Probabilistic design
6133:Principal components
5976:Exponential families
5928:Nonlinear regression
5907:General linear model
5869:Mixed effects models
5859:Errors and residuals
5836:Confounding variable
5738:Bayesian probability
5716:Van der Waerden test
5706:Ordered alternative
5471:Multiple comparisons
5350:RaoâBlackwellization
5313:Estimating equations
5269:Statistical distance
4987:Factorial experiment
4520:Arithmetic-Geometric
4160:Historia Mathematica
4126:Historia Mathematica
3825:
3774:
3732:
3395:
3351:
2329:
2323:easily implemented.
2291:
2265:
2149:
2068:
1540:
1492:
1470:
1437:
1408:
1379:
1375:, a response vector
1357:
1178:
996:
921:
824:
731:
639:
462:independent variable
392:GaussâMarkov theorem
387:Studentized residual
377:Errors and residuals
211:Principal components
181:Nonlinear regression
68:General linear model
7364:Regression analysis
7304:Statistics category
7235:Gaussian quadrature
7120:Model specification
7087:Stepwise regression
6945:Predictor structure
6882:Total least squares
6864:Regression analysis
6849:Partial correlation
6780:regression analysis
6620:Official statistics
6543:Methods engineering
6224:Seasonal adjustment
5992:Poisson regressions
5912:Bayesian regression
5851:Regression analysis
5831:Partial correlation
5803:Regression analysis
5402:Prediction interval
5397:Likelihood interval
5387:Confidence interval
5379:Interval estimation
5340:Unbiased estimators
5158:Model specification
5038:Up-and-down designs
4726:Partial correlation
4682:Index of dispersion
4600:Interquartile range
4156:Stigler, Stephen M.
3366: through
3322:
3265:
3215:
3165:
3025:
2979:
2935:
2891:
2824:
2775:
2737:
2699:
2659:
2610:
2572:
2534:
2494:
2451:
2413:
2375:
1883:
1861:
1798:
1776:
1740:
1718:
1682:
1660:
1290:
1256:
1153:multiple regression
590:regression analysis
458:regression analysis
237:Errors-in-variables
104:Logistic regression
94:Binomial regression
39:Regression analysis
33:Part of a series on
7321:Statistics outline
7220:Numerical analysis
6640:Spatial statistics
6520:Medical statistics
6420:First hitting time
6374:Whittle likelihood
6025:Degrees of freedom
6020:Multivariate ANOVA
5953:Heteroscedasticity
5765:Bayesian estimator
5730:Bayesian inference
5579:KolmogorovâSmirnov
5464:Randomization test
5434:Testing hypotheses
5407:Tolerance interval
5318:Maximum likelihood
5213:Exponential family
5146:Density estimation
5106:Statistical theory
5066:Natural experiment
5012:Scientific control
4929:Survey methodology
4615:Standard deviation
4296:analytic functions
3908:
3811:
3760:
3678:
3676:
3379:
3331:
3325:
3308:
3251:
3201:
3151:
3103:
3028:
3008:
2959:
2915:
2877:
2804:
2761:
2723:
2685:
2639:
2596:
2558:
2520:
2480:
2437:
2399:
2361:
2304:
2285:Vandermonde matrix
2273:
2240:
2122:
2048:
2039:
1961:
1886:
1869:
1847:
1784:
1762:
1726:
1704:
1668:
1646:
1612:
1507:
1478:
1452:
1423:
1394:
1365:
1340:
1276:
1242:
1116:
956:
871:
799:
684:
622:
469:dependent variable
124:Multinomial probit
27:Statistics concept
7334:
7333:
7326:Statistics topics
7271:Calibration curve
7080:Model exploration
7047:
7046:
7017:Non-normal errors
6908:Linear regression
6899:statistical model
6742:
6741:
6680:
6679:
6676:
6675:
6615:National accounts
6585:Actuarial science
6577:Social statistics
6470:
6469:
6466:
6465:
6462:
6461:
6397:Survival function
6382:
6381:
6244:Granger causality
6085:Contingency table
6060:Survival analysis
6037:
6036:
6033:
6032:
5889:Linear regression
5784:
5783:
5780:
5779:
5755:Credible interval
5724:
5723:
5507:
5506:
5323:Method of moments
5192:Parametric family
5153:Statistical model
5083:
5082:
5079:
5078:
4997:Random assignment
4919:Statistical power
4853:
4852:
4849:
4848:
4698:Contingency table
4668:
4667:
4535:Generalized/power
4377:978-1-61197-520-8
4350:978-0-412-98321-4
3986:polynomial kernel
3922:at a given value
3857:
3672:
3663:
3624:
3588:
3571:
3546:
3529:
3413:
3367:
2318:Expanded formulas
2233:
2168:
2163:
2115:
2100:
2080:
1517:will contain the
1504:
1449:
1420:
1391:
1306:
474:is modeled as an
446:
445:
99:Binary regression
58:Simple regression
53:Linear regression
16:(Redirected from
7371:
7314:
7313:
7071:Multivariate AOV
6967:Local regression
6904:
6896:Regression as a
6887:Ridge regression
6834:Rank correlation
6769:
6762:
6755:
6746:
6730:
6729:
6718:
6717:
6707:
6706:
6692:
6691:
6595:Crime statistics
6489:
6476:
6393:
6359:Fourier analysis
6346:Frequency domain
6326:
6273:
6239:Structural break
6199:
6148:Cluster analysis
6095:Log-linear model
6068:
6043:
5984:
5958:Homoscedasticity
5814:
5790:
5709:
5701:
5693:
5692:(KruskalâWallis)
5677:
5662:
5617:Cross validation
5602:
5584:AndersonâDarling
5531:
5518:
5489:Likelihood-ratio
5481:Parametric tests
5459:Permutation test
5442:1- & 2-tails
5333:Minimum distance
5305:Point estimation
5301:
5252:Optimal decision
5203:
5102:
5089:
5071:Quasi-experiment
5021:Adaptive designs
4872:
4859:
4736:Rank correlation
4498:
4489:
4476:
4443:
4436:
4429:
4420:
4414:
4413:
4411:
4409:
4394:
4388:
4387:
4385:
4384:
4361:
4355:
4354:
4336:
4330:
4327:
4292:
4286:
4285:
4265:
4259:
4258:
4247:
4241:
4240:
4238:
4236:
4221:
4215:
4214:
4182:
4176:
4175:
4152:
4146:
4145:
4118:
4112:
4111:
4093:
4087:
4086:
4084:
4083:
4068:
4043:Smoothing spline
3997:unequal variance
3917:
3915:
3914:
3909:
3904:
3903:
3885:
3884:
3860:
3859:
3858:
3856:
3851:
3846:
3820:
3818:
3817:
3812:
3810:
3809:
3808:
3807:
3797:
3769:
3767:
3766:
3761:
3759:
3758:
3757:
3756:
3746:
3714:confidence bands
3687:
3685:
3684:
3679:
3677:
3673:
3670:
3665:
3664:
3656:
3651:
3647:
3646:
3625:
3622:
3617:
3616:
3593:
3589:
3586:
3576:
3572:
3569:
3567:
3566:
3557:
3556:
3547:
3544:
3534:
3530:
3527:
3523:
3517:
3513:
3512:
3503:
3502:
3484:
3483:
3474:
3473:
3461:
3460:
3451:
3450:
3438:
3437:
3428:
3427:
3415:
3414:
3406:
3401:
3388:
3386:
3385:
3380:
3378:
3377:
3368:
3365:
3363:
3362:
3340:
3338:
3337:
3332:
3330:
3329:
3321:
3316:
3307:
3306:
3296:
3291:
3264:
3259:
3250:
3249:
3239:
3234:
3214:
3209:
3200:
3199:
3189:
3184:
3164:
3159:
3150:
3149:
3139:
3134:
3108:
3107:
3100:
3099:
3079:
3078:
3065:
3064:
3051:
3050:
3033:
3032:
3024:
3016:
3006:
3001:
2978:
2967:
2957:
2952:
2934:
2923:
2913:
2908:
2890:
2885:
2875:
2870:
2823:
2812:
2802:
2797:
2774:
2769:
2759:
2754:
2736:
2731:
2721:
2716:
2698:
2693:
2683:
2678:
2658:
2647:
2637:
2632:
2609:
2604:
2594:
2589:
2571:
2566:
2556:
2551:
2533:
2528:
2518:
2513:
2493:
2488:
2478:
2473:
2450:
2445:
2435:
2430:
2412:
2407:
2397:
2392:
2374:
2369:
2359:
2354:
2313:
2311:
2310:
2305:
2303:
2302:
2282:
2280:
2279:
2274:
2272:
2249:
2247:
2246:
2241:
2235:
2234:
2226:
2223:
2222:
2221:
2215:
2208:
2207:
2195:
2190:
2189:
2188:
2182:
2170:
2169:
2164:
2156:
2154:
2131:
2129:
2128:
2123:
2117:
2116:
2108:
2102:
2101:
2093:
2090:
2082:
2081:
2073:
2057:
2055:
2054:
2049:
2044:
2043:
2036:
2035:
2015:
2014:
2001:
2000:
1987:
1986:
1966:
1965:
1958:
1957:
1937:
1936:
1923:
1922:
1909:
1908:
1891:
1890:
1882:
1877:
1860:
1855:
1844:
1843:
1797:
1792:
1775:
1770:
1759:
1758:
1739:
1734:
1717:
1712:
1701:
1700:
1681:
1676:
1659:
1654:
1643:
1642:
1617:
1616:
1609:
1608:
1588:
1587:
1574:
1573:
1560:
1559:
1516:
1514:
1513:
1508:
1506:
1505:
1497:
1487:
1485:
1484:
1479:
1477:
1461:
1459:
1458:
1453:
1451:
1450:
1442:
1432:
1430:
1429:
1424:
1422:
1421:
1413:
1403:
1401:
1400:
1395:
1393:
1392:
1384:
1374:
1372:
1371:
1366:
1364:
1349:
1347:
1346:
1341:
1304:
1303:
1302:
1289:
1284:
1275:
1274:
1255:
1250:
1241:
1240:
1228:
1227:
1218:
1217:
1205:
1204:
1190:
1189:
1125:
1123:
1122:
1117:
1105:
1104:
1095:
1094:
1076:
1075:
1066:
1065:
1053:
1052:
1043:
1042:
1027:
1026:
1014:
1013:
965:
963:
962:
957:
949:
948:
933:
932:
913:with respect to
911:total derivative
909:is given by the
905:, the effect on
880:
878:
877:
872:
849:
848:
836:
835:
808:
806:
805:
800:
788:
787:
778:
777:
762:
761:
749:
748:
693:
691:
690:
685:
670:
669:
657:
656:
492:conditional mean
438:
431:
424:
408:
407:
315:Ridge regression
150:Multilevel model
30:
21:
7379:
7378:
7374:
7373:
7372:
7370:
7369:
7368:
7354:
7353:
7340:
7335:
7330:
7308:
7290:
7254:
7250:Chebyshev nodes
7203:
7199:Bayesian design
7175:
7156:Goodness of fit
7129:
7102:
7092:Model selection
7075:
7043:
7012:
6971:
6940:
6897:
6891:
6858:
6815:
6782:
6773:
6743:
6738:
6701:
6672:
6634:
6571:
6557:quality control
6524:
6506:Clinical trials
6483:
6458:
6442:
6430:Hazard function
6424:
6378:
6340:
6324:
6287:
6283:BreuschâGodfrey
6271:
6248:
6188:
6163:Factor analysis
6109:
6090:Graphical model
6062:
6029:
5996:
5982:
5962:
5916:
5883:
5845:
5808:
5807:
5776:
5720:
5707:
5699:
5691:
5675:
5660:
5639:Rank statistics
5633:
5612:Model selection
5600:
5558:Goodness of fit
5552:
5529:
5503:
5475:
5428:
5373:
5362:Median unbiased
5290:
5201:
5134:Order statistic
5096:
5075:
5042:
5016:
4968:
4923:
4866:
4864:Data collection
4845:
4757:
4712:
4686:
4664:
4624:
4576:
4493:Continuous data
4483:
4470:
4452:
4447:
4417:
4407:
4405:
4396:
4395:
4391:
4382:
4380:
4378:
4363:
4362:
4358:
4351:
4338:
4337:
4333:
4316:10.2307/2685560
4301:
4293:
4289:
4282:
4267:
4266:
4262:
4249:
4248:
4244:
4234:
4232:
4223:
4222:
4218:
4203:10.2307/2331929
4184:
4183:
4179:
4154:
4153:
4149:
4122:Gergonne, J. D.
4120:
4119:
4115:
4095:
4094:
4090:
4081:
4079:
4070:
4069:
4065:
4061:
4051:
4018:Line regression
4009:
3980:models such as
3946:basis functions
3943:
3932:
3895:
3876:
3823:
3822:
3799:
3792:
3772:
3771:
3748:
3741:
3730:
3729:
3726:basis functions
3722:
3693:
3675:
3674:
3649:
3648:
3626:
3596:
3591:
3590:
3574:
3573:
3558:
3548:
3545:number of
3532:
3531:
3521:
3520:
3515:
3514:
3504:
3494:
3475:
3465:
3452:
3442:
3429:
3419:
3393:
3392:
3369:
3354:
3349:
3348:
3324:
3323:
3298:
3274:
3273:
3267:
3266:
3241:
3217:
3216:
3191:
3167:
3166:
3141:
3113:
3102:
3101:
3091:
3088:
3087:
3081:
3080:
3070:
3067:
3066:
3056:
3053:
3052:
3042:
3035:
3027:
3026:
2985:
2980:
2936:
2892:
2853:
2852:
2847:
2842:
2837:
2832:
2826:
2825:
2781:
2776:
2738:
2700:
2661:
2660:
2616:
2611:
2573:
2535:
2496:
2495:
2457:
2452:
2414:
2376:
2333:
2327:
2326:
2320:
2294:
2289:
2288:
2263:
2262:
2210:
2196:
2177:
2147:
2146:
2066:
2065:
2038:
2037:
2027:
2024:
2023:
2017:
2016:
2006:
2003:
2002:
1992:
1989:
1988:
1978:
1971:
1960:
1959:
1949:
1946:
1945:
1939:
1938:
1928:
1925:
1924:
1914:
1911:
1910:
1900:
1893:
1885:
1884:
1867:
1862:
1845:
1835:
1833:
1827:
1826:
1821:
1816:
1811:
1806:
1800:
1799:
1782:
1777:
1760:
1750:
1748:
1742:
1741:
1724:
1719:
1702:
1692:
1690:
1684:
1683:
1666:
1661:
1644:
1634:
1632:
1622:
1611:
1610:
1600:
1597:
1596:
1590:
1589:
1579:
1576:
1575:
1565:
1562:
1561:
1551:
1544:
1538:
1537:
1490:
1489:
1468:
1467:
1435:
1434:
1433:, and a vector
1406:
1405:
1377:
1376:
1355:
1354:
1294:
1266:
1232:
1219:
1209:
1196:
1181:
1176:
1175:
1169:
1146:
1139:
1096:
1086:
1067:
1057:
1044:
1034:
1018:
1005:
994:
993:
940:
924:
919:
918:
840:
827:
822:
821:
779:
769:
753:
740:
729:
728:
719:
661:
648:
637:
636:
614:confidence band
606:
572:and in 1809 by
547:
442:
402:
382:Goodness of fit
89:Discrete choice
28:
23:
22:
15:
12:
11:
5:
7377:
7375:
7367:
7366:
7356:
7355:
7352:
7351:
7339:
7338:External links
7336:
7332:
7331:
7329:
7328:
7323:
7318:
7306:
7301:
7295:
7292:
7291:
7289:
7288:
7283:
7278:
7273:
7268:
7262:
7260:
7256:
7255:
7253:
7252:
7247:
7242:
7237:
7232:
7227:
7222:
7216:
7214:
7205:
7204:
7202:
7201:
7196:
7194:Optimal design
7191:
7185:
7183:
7177:
7176:
7174:
7173:
7168:
7163:
7158:
7153:
7148:
7143:
7137:
7135:
7131:
7130:
7128:
7127:
7122:
7117:
7116:
7115:
7110:
7105:
7100:
7089:
7083:
7081:
7077:
7076:
7074:
7073:
7068:
7063:
7057:
7055:
7049:
7048:
7045:
7044:
7042:
7041:
7036:
7031:
7026:
7020:
7018:
7014:
7013:
7011:
7010:
7005:
7000:
6995:
6993:Semiparametric
6990:
6985:
6979:
6977:
6973:
6972:
6970:
6969:
6964:
6959:
6954:
6948:
6946:
6942:
6941:
6939:
6938:
6933:
6928:
6923:
6918:
6912:
6910:
6901:
6893:
6892:
6890:
6889:
6884:
6879:
6874:
6868:
6866:
6860:
6859:
6857:
6856:
6851:
6846:
6840:
6838:Spearman's rho
6831:
6825:
6823:
6817:
6816:
6814:
6813:
6808:
6803:
6798:
6792:
6790:
6784:
6783:
6774:
6772:
6771:
6764:
6757:
6749:
6740:
6739:
6737:
6736:
6724:
6712:
6698:
6685:
6682:
6681:
6678:
6677:
6674:
6673:
6671:
6670:
6665:
6660:
6655:
6650:
6644:
6642:
6636:
6635:
6633:
6632:
6627:
6622:
6617:
6612:
6607:
6602:
6597:
6592:
6587:
6581:
6579:
6573:
6572:
6570:
6569:
6564:
6559:
6550:
6545:
6540:
6534:
6532:
6526:
6525:
6523:
6522:
6517:
6512:
6503:
6501:Bioinformatics
6497:
6495:
6485:
6484:
6479:
6472:
6471:
6468:
6467:
6464:
6463:
6460:
6459:
6457:
6456:
6450:
6448:
6444:
6443:
6441:
6440:
6434:
6432:
6426:
6425:
6423:
6422:
6417:
6412:
6407:
6401:
6399:
6390:
6384:
6383:
6380:
6379:
6377:
6376:
6371:
6366:
6361:
6356:
6350:
6348:
6342:
6341:
6339:
6338:
6333:
6328:
6320:
6315:
6310:
6309:
6308:
6306:partial (PACF)
6297:
6295:
6289:
6288:
6286:
6285:
6280:
6275:
6267:
6262:
6256:
6254:
6253:Specific tests
6250:
6249:
6247:
6246:
6241:
6236:
6231:
6226:
6221:
6216:
6211:
6205:
6203:
6196:
6190:
6189:
6187:
6186:
6185:
6184:
6183:
6182:
6167:
6166:
6165:
6155:
6153:Classification
6150:
6145:
6140:
6135:
6130:
6125:
6119:
6117:
6111:
6110:
6108:
6107:
6102:
6100:McNemar's test
6097:
6092:
6087:
6082:
6076:
6074:
6064:
6063:
6046:
6039:
6038:
6035:
6034:
6031:
6030:
6028:
6027:
6022:
6017:
6012:
6006:
6004:
5998:
5997:
5995:
5994:
5978:
5972:
5970:
5964:
5963:
5961:
5960:
5955:
5950:
5945:
5940:
5938:Semiparametric
5935:
5930:
5924:
5922:
5918:
5917:
5915:
5914:
5909:
5904:
5899:
5893:
5891:
5885:
5884:
5882:
5881:
5876:
5871:
5866:
5861:
5855:
5853:
5847:
5846:
5844:
5843:
5838:
5833:
5828:
5822:
5820:
5810:
5809:
5806:
5805:
5800:
5794:
5793:
5786:
5785:
5782:
5781:
5778:
5777:
5775:
5774:
5773:
5772:
5762:
5757:
5752:
5751:
5750:
5745:
5734:
5732:
5726:
5725:
5722:
5721:
5719:
5718:
5713:
5712:
5711:
5703:
5695:
5679:
5676:(MannâWhitney)
5671:
5670:
5669:
5656:
5655:
5654:
5643:
5641:
5635:
5634:
5632:
5631:
5630:
5629:
5624:
5619:
5609:
5604:
5601:(ShapiroâWilk)
5596:
5591:
5586:
5581:
5576:
5568:
5562:
5560:
5554:
5553:
5551:
5550:
5542:
5533:
5521:
5515:
5513:Specific tests
5509:
5508:
5505:
5504:
5502:
5501:
5496:
5491:
5485:
5483:
5477:
5476:
5474:
5473:
5468:
5467:
5466:
5456:
5455:
5454:
5444:
5438:
5436:
5430:
5429:
5427:
5426:
5425:
5424:
5419:
5409:
5404:
5399:
5394:
5389:
5383:
5381:
5375:
5374:
5372:
5371:
5366:
5365:
5364:
5359:
5358:
5357:
5352:
5337:
5336:
5335:
5330:
5325:
5320:
5309:
5307:
5298:
5292:
5291:
5289:
5288:
5283:
5278:
5277:
5276:
5266:
5261:
5260:
5259:
5249:
5248:
5247:
5242:
5237:
5227:
5222:
5217:
5216:
5215:
5210:
5205:
5189:
5188:
5187:
5182:
5177:
5167:
5166:
5165:
5160:
5150:
5149:
5148:
5138:
5137:
5136:
5126:
5121:
5116:
5110:
5108:
5098:
5097:
5092:
5085:
5084:
5081:
5080:
5077:
5076:
5074:
5073:
5068:
5063:
5058:
5052:
5050:
5044:
5043:
5041:
5040:
5035:
5030:
5024:
5022:
5018:
5017:
5015:
5014:
5009:
5004:
4999:
4994:
4989:
4984:
4978:
4976:
4970:
4969:
4967:
4966:
4964:Standard error
4961:
4956:
4951:
4950:
4949:
4944:
4933:
4931:
4925:
4924:
4922:
4921:
4916:
4911:
4906:
4901:
4896:
4894:Optimal design
4891:
4886:
4880:
4878:
4868:
4867:
4862:
4855:
4854:
4851:
4850:
4847:
4846:
4844:
4843:
4838:
4833:
4828:
4823:
4818:
4813:
4808:
4803:
4798:
4793:
4788:
4783:
4778:
4773:
4767:
4765:
4759:
4758:
4756:
4755:
4750:
4749:
4748:
4743:
4733:
4728:
4722:
4720:
4714:
4713:
4711:
4710:
4705:
4700:
4694:
4692:
4691:Summary tables
4688:
4687:
4685:
4684:
4678:
4676:
4670:
4669:
4666:
4665:
4663:
4662:
4661:
4660:
4655:
4650:
4640:
4634:
4632:
4626:
4625:
4623:
4622:
4617:
4612:
4607:
4602:
4597:
4592:
4586:
4584:
4578:
4577:
4575:
4574:
4569:
4564:
4563:
4562:
4557:
4552:
4547:
4542:
4537:
4532:
4527:
4525:Contraharmonic
4522:
4517:
4506:
4504:
4495:
4485:
4484:
4479:
4472:
4471:
4469:
4468:
4463:
4457:
4454:
4453:
4448:
4446:
4445:
4438:
4431:
4423:
4416:
4415:
4389:
4376:
4356:
4349:
4331:
4329:
4328:
4287:
4280:
4260:
4242:
4216:
4177:
4166:(4): 431â439.
4147:
4113:
4088:
4062:
4060:
4057:
4056:
4055:
4050:
4047:
4046:
4045:
4040:
4035:
4030:
4025:
4020:
4015:
4008:
4005:
3941:
3930:
3907:
3902:
3898:
3894:
3891:
3888:
3883:
3879:
3875:
3872:
3869:
3866:
3863:
3855:
3850:
3842:
3839:
3836:
3833:
3830:
3806:
3802:
3796:
3791:
3788:
3785:
3782:
3779:
3755:
3751:
3745:
3740:
3737:
3721:
3718:
3692:
3691:Interpretation
3689:
3668:
3662:
3659:
3652:
3650:
3645:
3642:
3639:
3636:
3633:
3629:
3620:
3615:
3612:
3609:
3606:
3603:
3599:
3594:
3592:
3584:
3581:
3577:
3575:
3565:
3561:
3555:
3551:
3542:
3539:
3535:
3533:
3524:
3522:
3518:
3516:
3511:
3507:
3501:
3497:
3493:
3490:
3487:
3482:
3478:
3472:
3468:
3464:
3459:
3455:
3449:
3445:
3441:
3436:
3432:
3426:
3422:
3418:
3412:
3409:
3402:
3400:
3376:
3372:
3361:
3357:
3328:
3320:
3315:
3311:
3305:
3301:
3295:
3290:
3287:
3284:
3280:
3276:
3275:
3272:
3269:
3268:
3263:
3258:
3254:
3248:
3244:
3238:
3233:
3230:
3227:
3223:
3219:
3218:
3213:
3208:
3204:
3198:
3194:
3188:
3183:
3180:
3177:
3173:
3169:
3168:
3163:
3158:
3154:
3148:
3144:
3138:
3133:
3130:
3127:
3123:
3119:
3118:
3116:
3111:
3106:
3098:
3094:
3090:
3089:
3086:
3083:
3082:
3077:
3073:
3069:
3068:
3063:
3059:
3055:
3054:
3049:
3045:
3041:
3040:
3038:
3031:
3023:
3020:
3015:
3011:
3005:
3000:
2997:
2994:
2990:
2986:
2984:
2981:
2977:
2974:
2971:
2966:
2962:
2956:
2951:
2948:
2945:
2941:
2937:
2933:
2930:
2927:
2922:
2918:
2912:
2907:
2904:
2901:
2897:
2893:
2889:
2884:
2880:
2874:
2869:
2866:
2863:
2859:
2855:
2854:
2851:
2848:
2846:
2843:
2841:
2838:
2836:
2833:
2831:
2828:
2827:
2822:
2819:
2816:
2811:
2807:
2801:
2796:
2793:
2790:
2786:
2782:
2780:
2777:
2773:
2768:
2764:
2758:
2753:
2750:
2747:
2743:
2739:
2735:
2730:
2726:
2720:
2715:
2712:
2709:
2705:
2701:
2697:
2692:
2688:
2682:
2677:
2674:
2671:
2667:
2663:
2662:
2657:
2654:
2651:
2646:
2642:
2636:
2631:
2628:
2625:
2621:
2617:
2615:
2612:
2608:
2603:
2599:
2593:
2588:
2585:
2582:
2578:
2574:
2570:
2565:
2561:
2555:
2550:
2547:
2544:
2540:
2536:
2532:
2527:
2523:
2517:
2512:
2509:
2506:
2502:
2498:
2497:
2492:
2487:
2483:
2477:
2472:
2469:
2466:
2462:
2458:
2456:
2453:
2449:
2444:
2440:
2434:
2429:
2426:
2423:
2419:
2415:
2411:
2406:
2402:
2396:
2391:
2388:
2385:
2381:
2377:
2373:
2368:
2364:
2358:
2353:
2350:
2347:
2343:
2339:
2338:
2336:
2319:
2316:
2301:
2297:
2271:
2251:
2250:
2238:
2232:
2229:
2220:
2214:
2206:
2203:
2199:
2194:
2187:
2181:
2176:
2173:
2167:
2162:
2159:
2133:
2132:
2120:
2114:
2111:
2105:
2099:
2096:
2089:
2085:
2079:
2076:
2059:
2058:
2047:
2042:
2034:
2030:
2026:
2025:
2022:
2019:
2018:
2013:
2009:
2005:
2004:
1999:
1995:
1991:
1990:
1985:
1981:
1977:
1976:
1974:
1969:
1964:
1956:
1952:
1948:
1947:
1944:
1941:
1940:
1935:
1931:
1927:
1926:
1921:
1917:
1913:
1912:
1907:
1903:
1899:
1898:
1896:
1889:
1881:
1876:
1872:
1868:
1866:
1863:
1859:
1854:
1850:
1846:
1842:
1838:
1834:
1832:
1829:
1828:
1825:
1822:
1820:
1817:
1815:
1812:
1810:
1807:
1805:
1802:
1801:
1796:
1791:
1787:
1783:
1781:
1778:
1774:
1769:
1765:
1761:
1757:
1753:
1749:
1747:
1744:
1743:
1738:
1733:
1729:
1725:
1723:
1720:
1716:
1711:
1707:
1703:
1699:
1695:
1691:
1689:
1686:
1685:
1680:
1675:
1671:
1667:
1665:
1662:
1658:
1653:
1649:
1645:
1641:
1637:
1633:
1631:
1628:
1627:
1625:
1620:
1615:
1607:
1603:
1599:
1598:
1595:
1592:
1591:
1586:
1582:
1578:
1577:
1572:
1568:
1564:
1563:
1558:
1554:
1550:
1549:
1547:
1525:value for the
1503:
1500:
1476:
1448:
1445:
1419:
1416:
1390:
1387:
1363:
1351:
1350:
1339:
1336:
1333:
1330:
1327:
1324:
1321:
1318:
1315:
1312:
1309:
1301:
1297:
1293:
1288:
1283:
1279:
1273:
1269:
1265:
1262:
1259:
1254:
1249:
1245:
1239:
1235:
1231:
1226:
1222:
1216:
1212:
1208:
1203:
1199:
1194:
1188:
1184:
1168:
1165:
1144:
1137:
1127:
1126:
1114:
1111:
1108:
1103:
1099:
1093:
1089:
1085:
1082:
1079:
1074:
1070:
1064:
1060:
1056:
1051:
1047:
1041:
1037:
1033:
1030:
1025:
1021:
1017:
1012:
1008:
1004:
1001:
955:
952:
947:
943:
939:
936:
931:
927:
870:
867:
864:
861:
858:
855:
852:
847:
843:
839:
834:
830:
810:
809:
797:
794:
791:
786:
782:
776:
772:
768:
765:
760:
756:
752:
747:
743:
739:
736:
717:
695:
694:
682:
679:
676:
673:
668:
664:
660:
655:
651:
647:
644:
605:
602:
546:
543:
539:classification
444:
443:
441:
440:
433:
426:
418:
415:
414:
413:
412:
397:
396:
395:
394:
389:
384:
379:
374:
369:
361:
360:
356:
355:
354:
353:
348:
343:
338:
333:
325:
324:
323:
322:
317:
312:
307:
302:
294:
293:
292:
291:
286:
281:
276:
268:
267:
266:
265:
260:
255:
247:
246:
242:
241:
240:
239:
231:
230:
229:
228:
223:
218:
213:
208:
203:
198:
193:
191:Semiparametric
188:
183:
175:
174:
173:
172:
167:
162:
160:Random effects
157:
152:
144:
143:
142:
141:
136:
134:Ordered probit
131:
126:
121:
116:
111:
106:
101:
96:
91:
86:
81:
73:
72:
71:
70:
65:
60:
55:
47:
46:
42:
41:
35:
34:
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
7376:
7365:
7362:
7361:
7359:
7349:
7345:
7344:Curve Fitting
7342:
7341:
7337:
7327:
7324:
7322:
7319:
7317:
7312:
7307:
7305:
7302:
7300:
7297:
7296:
7293:
7287:
7284:
7282:
7279:
7277:
7274:
7272:
7269:
7267:
7266:Curve fitting
7264:
7263:
7261:
7257:
7251:
7248:
7246:
7243:
7241:
7238:
7236:
7233:
7231:
7228:
7226:
7223:
7221:
7218:
7217:
7215:
7213:
7212:approximation
7210:
7206:
7200:
7197:
7195:
7192:
7190:
7187:
7186:
7184:
7182:
7178:
7172:
7169:
7167:
7164:
7162:
7159:
7157:
7154:
7152:
7149:
7147:
7144:
7142:
7139:
7138:
7136:
7132:
7126:
7123:
7121:
7118:
7114:
7111:
7109:
7106:
7104:
7103:
7095:
7094:
7093:
7090:
7088:
7085:
7084:
7082:
7078:
7072:
7069:
7067:
7064:
7062:
7059:
7058:
7056:
7054:
7050:
7040:
7037:
7035:
7032:
7030:
7027:
7025:
7022:
7021:
7019:
7015:
7009:
7006:
7004:
7001:
6999:
6996:
6994:
6991:
6989:
6988:Nonparametric
6986:
6984:
6981:
6980:
6978:
6974:
6968:
6965:
6963:
6960:
6958:
6955:
6953:
6950:
6949:
6947:
6943:
6937:
6934:
6932:
6929:
6927:
6924:
6922:
6919:
6917:
6914:
6913:
6911:
6909:
6905:
6902:
6900:
6894:
6888:
6885:
6883:
6880:
6878:
6875:
6873:
6870:
6869:
6867:
6865:
6861:
6855:
6852:
6850:
6847:
6844:
6843:Kendall's tau
6841:
6839:
6835:
6832:
6830:
6827:
6826:
6824:
6822:
6818:
6812:
6809:
6807:
6804:
6802:
6799:
6797:
6796:Least squares
6794:
6793:
6791:
6789:
6785:
6781:
6777:
6776:Least squares
6770:
6765:
6763:
6758:
6756:
6751:
6750:
6747:
6735:
6734:
6725:
6723:
6722:
6713:
6711:
6710:
6705:
6699:
6697:
6696:
6687:
6686:
6683:
6669:
6666:
6664:
6663:Geostatistics
6661:
6659:
6656:
6654:
6651:
6649:
6646:
6645:
6643:
6641:
6637:
6631:
6630:Psychometrics
6628:
6626:
6623:
6621:
6618:
6616:
6613:
6611:
6608:
6606:
6603:
6601:
6598:
6596:
6593:
6591:
6588:
6586:
6583:
6582:
6580:
6578:
6574:
6568:
6565:
6563:
6560:
6558:
6554:
6551:
6549:
6546:
6544:
6541:
6539:
6536:
6535:
6533:
6531:
6527:
6521:
6518:
6516:
6513:
6511:
6507:
6504:
6502:
6499:
6498:
6496:
6494:
6493:Biostatistics
6490:
6486:
6482:
6477:
6473:
6455:
6454:Log-rank test
6452:
6451:
6449:
6445:
6439:
6436:
6435:
6433:
6431:
6427:
6421:
6418:
6416:
6413:
6411:
6408:
6406:
6403:
6402:
6400:
6398:
6394:
6391:
6389:
6385:
6375:
6372:
6370:
6367:
6365:
6362:
6360:
6357:
6355:
6352:
6351:
6349:
6347:
6343:
6337:
6334:
6332:
6329:
6327:
6325:(BoxâJenkins)
6321:
6319:
6316:
6314:
6311:
6307:
6304:
6303:
6302:
6299:
6298:
6296:
6294:
6290:
6284:
6281:
6279:
6278:DurbinâWatson
6276:
6274:
6268:
6266:
6263:
6261:
6260:DickeyâFuller
6258:
6257:
6255:
6251:
6245:
6242:
6240:
6237:
6235:
6234:Cointegration
6232:
6230:
6227:
6225:
6222:
6220:
6217:
6215:
6212:
6210:
6209:Decomposition
6207:
6206:
6204:
6200:
6197:
6195:
6191:
6181:
6178:
6177:
6176:
6173:
6172:
6171:
6168:
6164:
6161:
6160:
6159:
6156:
6154:
6151:
6149:
6146:
6144:
6141:
6139:
6136:
6134:
6131:
6129:
6126:
6124:
6121:
6120:
6118:
6116:
6112:
6106:
6103:
6101:
6098:
6096:
6093:
6091:
6088:
6086:
6083:
6081:
6080:Cohen's kappa
6078:
6077:
6075:
6073:
6069:
6065:
6061:
6057:
6053:
6049:
6044:
6040:
6026:
6023:
6021:
6018:
6016:
6013:
6011:
6008:
6007:
6005:
6003:
5999:
5993:
5989:
5985:
5979:
5977:
5974:
5973:
5971:
5969:
5965:
5959:
5956:
5954:
5951:
5949:
5946:
5944:
5941:
5939:
5936:
5934:
5933:Nonparametric
5931:
5929:
5926:
5925:
5923:
5919:
5913:
5910:
5908:
5905:
5903:
5900:
5898:
5895:
5894:
5892:
5890:
5886:
5880:
5877:
5875:
5872:
5870:
5867:
5865:
5862:
5860:
5857:
5856:
5854:
5852:
5848:
5842:
5839:
5837:
5834:
5832:
5829:
5827:
5824:
5823:
5821:
5819:
5815:
5811:
5804:
5801:
5799:
5796:
5795:
5791:
5787:
5771:
5768:
5767:
5766:
5763:
5761:
5758:
5756:
5753:
5749:
5746:
5744:
5741:
5740:
5739:
5736:
5735:
5733:
5731:
5727:
5717:
5714:
5710:
5704:
5702:
5696:
5694:
5688:
5687:
5686:
5683:
5682:Nonparametric
5680:
5678:
5672:
5668:
5665:
5664:
5663:
5657:
5653:
5652:Sample median
5650:
5649:
5648:
5645:
5644:
5642:
5640:
5636:
5628:
5625:
5623:
5620:
5618:
5615:
5614:
5613:
5610:
5608:
5605:
5603:
5597:
5595:
5592:
5590:
5587:
5585:
5582:
5580:
5577:
5575:
5573:
5569:
5567:
5564:
5563:
5561:
5559:
5555:
5549:
5547:
5543:
5541:
5539:
5534:
5532:
5527:
5523:
5522:
5519:
5516:
5514:
5510:
5500:
5497:
5495:
5492:
5490:
5487:
5486:
5484:
5482:
5478:
5472:
5469:
5465:
5462:
5461:
5460:
5457:
5453:
5450:
5449:
5448:
5445:
5443:
5440:
5439:
5437:
5435:
5431:
5423:
5420:
5418:
5415:
5414:
5413:
5410:
5408:
5405:
5403:
5400:
5398:
5395:
5393:
5390:
5388:
5385:
5384:
5382:
5380:
5376:
5370:
5367:
5363:
5360:
5356:
5353:
5351:
5348:
5347:
5346:
5343:
5342:
5341:
5338:
5334:
5331:
5329:
5326:
5324:
5321:
5319:
5316:
5315:
5314:
5311:
5310:
5308:
5306:
5302:
5299:
5297:
5293:
5287:
5284:
5282:
5279:
5275:
5272:
5271:
5270:
5267:
5265:
5262:
5258:
5257:loss function
5255:
5254:
5253:
5250:
5246:
5243:
5241:
5238:
5236:
5233:
5232:
5231:
5228:
5226:
5223:
5221:
5218:
5214:
5211:
5209:
5206:
5204:
5198:
5195:
5194:
5193:
5190:
5186:
5183:
5181:
5178:
5176:
5173:
5172:
5171:
5168:
5164:
5161:
5159:
5156:
5155:
5154:
5151:
5147:
5144:
5143:
5142:
5139:
5135:
5132:
5131:
5130:
5127:
5125:
5122:
5120:
5117:
5115:
5112:
5111:
5109:
5107:
5103:
5099:
5095:
5090:
5086:
5072:
5069:
5067:
5064:
5062:
5059:
5057:
5054:
5053:
5051:
5049:
5045:
5039:
5036:
5034:
5031:
5029:
5026:
5025:
5023:
5019:
5013:
5010:
5008:
5005:
5003:
5000:
4998:
4995:
4993:
4990:
4988:
4985:
4983:
4980:
4979:
4977:
4975:
4971:
4965:
4962:
4960:
4959:Questionnaire
4957:
4955:
4952:
4948:
4945:
4943:
4940:
4939:
4938:
4935:
4934:
4932:
4930:
4926:
4920:
4917:
4915:
4912:
4910:
4907:
4905:
4902:
4900:
4897:
4895:
4892:
4890:
4887:
4885:
4882:
4881:
4879:
4877:
4873:
4869:
4865:
4860:
4856:
4842:
4839:
4837:
4834:
4832:
4829:
4827:
4824:
4822:
4819:
4817:
4814:
4812:
4809:
4807:
4804:
4802:
4799:
4797:
4794:
4792:
4789:
4787:
4786:Control chart
4784:
4782:
4779:
4777:
4774:
4772:
4769:
4768:
4766:
4764:
4760:
4754:
4751:
4747:
4744:
4742:
4739:
4738:
4737:
4734:
4732:
4729:
4727:
4724:
4723:
4721:
4719:
4715:
4709:
4706:
4704:
4701:
4699:
4696:
4695:
4693:
4689:
4683:
4680:
4679:
4677:
4675:
4671:
4659:
4656:
4654:
4651:
4649:
4646:
4645:
4644:
4641:
4639:
4636:
4635:
4633:
4631:
4627:
4621:
4618:
4616:
4613:
4611:
4608:
4606:
4603:
4601:
4598:
4596:
4593:
4591:
4588:
4587:
4585:
4583:
4579:
4573:
4570:
4568:
4565:
4561:
4558:
4556:
4553:
4551:
4548:
4546:
4543:
4541:
4538:
4536:
4533:
4531:
4528:
4526:
4523:
4521:
4518:
4516:
4513:
4512:
4511:
4508:
4507:
4505:
4503:
4499:
4496:
4494:
4490:
4486:
4482:
4477:
4473:
4467:
4464:
4462:
4459:
4458:
4455:
4451:
4444:
4439:
4437:
4432:
4430:
4425:
4424:
4421:
4404:
4400:
4393:
4390:
4379:
4373:
4369:
4368:
4360:
4357:
4352:
4346:
4342:
4335:
4332:
4325:
4321:
4317:
4313:
4309:
4305:
4300:
4299:
4297:
4291:
4288:
4283:
4281:0-534-24264-2
4277:
4273:
4272:
4264:
4261:
4256:
4252:
4246:
4243:
4231:
4227:
4220:
4217:
4212:
4208:
4204:
4200:
4197:(1/2): 1â85.
4196:
4192:
4188:
4181:
4178:
4173:
4169:
4165:
4161:
4157:
4151:
4148:
4143:
4139:
4135:
4134:S. M. Stigler
4131:
4127:
4123:
4117:
4114:
4109:
4105:
4104:
4099:
4092:
4089:
4077:
4076:GeeksforGeeks
4073:
4067:
4064:
4058:
4053:
4052:
4048:
4044:
4041:
4039:
4036:
4034:
4031:
4029:
4026:
4024:
4021:
4019:
4016:
4014:
4013:Curve fitting
4011:
4010:
4006:
4004:
4002:
3998:
3994:
3989:
3987:
3983:
3979:
3974:
3971:
3967:
3961:
3959:
3955:
3951:
3947:
3940:
3936:
3929:
3926: =
3925:
3921:
3900:
3896:
3892:
3889:
3886:
3881:
3877:
3873:
3870:
3867:
3864:
3853:
3837:
3834:
3831:
3804:
3800:
3789:
3783:
3777:
3753:
3749:
3738:
3735:
3727:
3719:
3717:
3715:
3711:
3707:
3703:
3699:
3690:
3688:
3666:
3660:
3657:
3640:
3637:
3634:
3627:
3618:
3610:
3607:
3604:
3597:
3582:
3579:
3563:
3559:
3553:
3549:
3540:
3537:
3509:
3505:
3499:
3495:
3491:
3488:
3485:
3480:
3476:
3470:
3466:
3462:
3457:
3453:
3447:
3443:
3439:
3434:
3430:
3424:
3420:
3416:
3410:
3407:
3390:
3374:
3370:
3359:
3355:
3346:
3341:
3326:
3318:
3313:
3309:
3303:
3299:
3293:
3288:
3285:
3282:
3278:
3270:
3261:
3256:
3252:
3246:
3242:
3236:
3231:
3228:
3225:
3221:
3211:
3206:
3202:
3196:
3192:
3186:
3181:
3178:
3175:
3171:
3161:
3156:
3152:
3146:
3142:
3136:
3131:
3128:
3125:
3121:
3114:
3109:
3104:
3096:
3092:
3084:
3075:
3071:
3061:
3057:
3047:
3043:
3036:
3029:
3021:
3018:
3013:
3009:
3003:
2998:
2995:
2992:
2988:
2982:
2975:
2972:
2969:
2964:
2960:
2954:
2949:
2946:
2943:
2939:
2931:
2928:
2925:
2920:
2916:
2910:
2905:
2902:
2899:
2895:
2887:
2882:
2878:
2872:
2867:
2864:
2861:
2857:
2849:
2844:
2839:
2834:
2829:
2820:
2817:
2814:
2809:
2805:
2799:
2794:
2791:
2788:
2784:
2778:
2771:
2766:
2762:
2756:
2751:
2748:
2745:
2741:
2733:
2728:
2724:
2718:
2713:
2710:
2707:
2703:
2695:
2690:
2686:
2680:
2675:
2672:
2669:
2665:
2655:
2652:
2649:
2644:
2640:
2634:
2629:
2626:
2623:
2619:
2613:
2606:
2601:
2597:
2591:
2586:
2583:
2580:
2576:
2568:
2563:
2559:
2553:
2548:
2545:
2542:
2538:
2530:
2525:
2521:
2515:
2510:
2507:
2504:
2500:
2490:
2485:
2481:
2475:
2470:
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2020:
2011:
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1950:
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899:infinitesimal
896:
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713:increases by
712:
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551:least squares
544:
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518: |
517:
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509:
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470:
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459:
456:is a form of
455:
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253:Least squares
251:
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219:
217:
214:
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209:
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199:
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189:
187:
186:Nonparametric
184:
182:
179:
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171:
168:
166:
163:
161:
158:
156:
155:Fixed effects
153:
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140:
137:
135:
132:
130:
129:Ordered logit
127:
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110:
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100:
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61:
59:
56:
54:
51:
50:
49:
48:
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40:
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32:
31:
19:
7259:Applications
7098:
6976:Non-standard
6951:
6731:
6719:
6700:
6693:
6605:Econometrics
6555: /
6538:Chemometrics
6515:Epidemiology
6508: /
6481:Applications
6323:ARIMA model
6270:Q-statistic
6219:Stationarity
6115:Multivariate
6058: /
6054: /
6052:Multivariate
6050: /
5990: /
5986: /
5760:Bayes factor
5659:Signed rank
5571:
5545:
5537:
5525:
5220:Completeness
5056:Cohort study
4954:Opinion poll
4889:Missing data
4876:Study design
4831:Scatter plot
4753:Scatter plot
4746:Spearman's Ď
4708:Grouped data
4406:. Retrieved
4402:
4392:
4381:. Retrieved
4366:
4359:
4340:
4334:
4310:(1): 20â22.
4307:
4303:
4290:
4270:
4263:
4254:
4245:
4233:. Retrieved
4229:
4219:
4194:
4190:
4180:
4163:
4159:
4150:
4129:
4125:
4116:
4110:: 1471â1490.
4107:
4101:
4091:
4080:. Retrieved
4078:. 2018-10-03
4075:
4066:
3990:
3975:
3962:
3938:
3934:
3927:
3923:
3919:
3723:
3701:
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2060:
1526:
1522:
1518:
1463:
1352:
1170:
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1141:
1134:
1128:
986:
982:
980:
975:
971:
967:
914:
906:
902:
894:
890:
886:
882:
817:
813:
811:
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714:
710:
706:
702:
696:
629:
625:
623:
576:. The first
548:
536:
519:
515:
507:
506:). Although
503:
499:
498:, denoted E(
495:
487:
483:
475:
471:
464:
453:
447:
310:Non-negative
62:
6733:WikiProject
6648:Cartography
6610:Jurimetrics
6562:Reliability
6293:Time domain
6272:(LjungâBox)
6194:Time-series
6072:Categorical
6056:Time-series
6048:Categorical
5983:(Bernoulli)
5818:Correlation
5798:Correlation
5594:JarqueâBera
5566:Chi-squared
5328:M-estimator
5281:Asymptotics
5225:Sufficiency
4992:Interaction
4904:Replication
4884:Effect size
4841:Violin plot
4821:Radar chart
4801:Forest plot
4791:Correlogram
4741:Kendall's Ď
1466:-th row of
901:changes in
320:Regularized
284:Generalized
216:Least angle
114:Mixed logit
7134:Background
7097:Mallows's
6600:Demography
6318:ARMA model
6123:Regression
5700:(Friedman)
5661:(Wilcoxon)
5599:Normality
5589:Lilliefors
5536:Student's
5412:Resampling
5286:Robustness
5274:divergence
5264:Efficiency
5202:(monotone)
5197:Likelihood
5114:Population
4947:Stratified
4899:Population
4718:Dependence
4674:Count data
4605:Percentile
4582:Dispersion
4515:Arithmetic
4450:Statistics
4408:22 January
4383:2020-08-28
4191:Biometrika
4082:2024-08-25
4059:References
3978:kernelized
3948:, such as
2140:estimation
1131:estimation
582:experiment
562:estimators
541:settings.
524:parameters
480:polynomial
478:th degree
450:statistics
359:Background
263:Non-linear
245:Estimation
7209:Numerical
5981:Logistic
5748:posterior
5674:Rank sum
5422:Jackknife
5417:Bootstrap
5235:Bootstrap
5170:Parameter
5119:Statistic
4914:Statistic
4826:Run chart
4811:Pie chart
4806:Histogram
4796:Fan chart
4771:Bar chart
4653:L-moments
4540:Geometric
3993:residuals
3970:smoothing
3937:far from
3890:…
3854:φ
3849:→
3805:φ
3790:∈
3778:φ
3739:∈
3661:^
3638:−
3608:−
3598:β
3496:β
3489:⋯
3467:β
3444:β
3421:β
3411:^
3371:β
3356:β
3279:∑
3271:⋯
3222:∑
3172:∑
3122:∑
3093:β
3085:⋯
3072:β
3058:β
3044:β
2989:∑
2983:…
2940:∑
2896:∑
2858:∑
2850:⋮
2845:⋱
2840:⋮
2835:⋮
2830:⋮
2785:∑
2779:⋯
2742:∑
2704:∑
2666:∑
2620:∑
2614:⋯
2577:∑
2539:∑
2501:∑
2461:∑
2455:⋯
2418:∑
2380:∑
2342:∑
2253:assuming
2231:→
2202:−
2166:^
2161:→
2158:β
2113:→
2110:ε
2098:→
2095:β
2078:→
2029:ε
2021:⋮
2008:ε
1994:ε
1980:ε
1951:β
1943:⋮
1930:β
1916:β
1902:β
1865:…
1824:⋮
1819:⋱
1814:⋮
1809:⋮
1804:⋮
1780:…
1722:…
1664:…
1594:⋮
1502:→
1447:→
1444:ε
1418:→
1415:β
1389:→
1329:…
1296:ε
1268:β
1261:⋯
1234:β
1211:β
1198:β
1110:ε
1088:β
1081:⋯
1059:β
1036:β
1020:β
1007:β
942:β
926:β
897:+1.) For
842:β
829:β
793:ε
771:β
755:β
742:β
701:variable
678:ε
663:β
650:β
620:approach.
598:inference
226:Segmented
7358:Category
7039:Logistic
7029:Binomial
7008:Isotonic
7003:Quantile
6695:Category
6388:Survival
6265:Johansen
5988:Binomial
5943:Isotonic
5530:(normal)
5175:location
4982:Blocking
4937:Sampling
4816:QâQ plot
4781:Box plot
4763:Graphics
4658:Skewness
4648:Kurtosis
4620:Variance
4550:Heronian
4545:Harmonic
4007:See also
3958:wavelets
586:Gergonne
570:Legendre
559:unbiased
555:variance
467:and the
341:Bayesian
279:Weighted
274:Ordinary
206:Isotonic
201:Quantile
7034:Poisson
6721:Commons
6668:Kriging
6553:Process
6510:studies
6369:Wavelet
6202:General
5369:Plug-in
5163:L space
4942:Cluster
4643:Moments
4461:Outline
4324:2685560
4211:2331929
3984:with a
3950:splines
3821:, e.g.
1159:,
720:units.
618:ScheffĂŠ
557:of the
545:History
502: |
300:Partial
139:Poisson
6998:Robust
6590:Census
6180:Normal
6128:Manova
5948:Robust
5698:2-way
5690:1-way
5528:-test
5199:
4776:Biplot
4567:Median
4560:Lehmer
4502:Center
4374:
4347:
4322:
4278:
4235:30 Jan
4209:
3956:, and
3528:Where:
1305:
985:as an
699:scalar
594:design
580:of an
578:design
258:Linear
196:Robust
119:Probit
45:Models
6214:Trend
5743:prior
5685:anova
5574:-test
5548:-test
5540:-test
5447:Power
5392:Pivot
5185:shape
5180:scale
4630:Shape
4610:Range
4555:Heinz
4530:Cubic
4466:Index
4320:JSTOR
4207:JSTOR
4049:Notes
3995:have
2283:is a
2257:<
2142:) is
574:Gauss
305:Total
221:Local
7348:PhET
6778:and
6447:Test
5647:Sign
5499:Wald
4572:Mode
4510:Mean
4410:2017
4372:ISBN
4345:ISBN
4276:ISBN
4237:2024
3999:, a
3700:and
3347:for
1521:and
1488:and
974:and
596:and
528:data
7113:BIC
7108:AIC
5627:BIC
5622:AIC
4312:doi
4199:doi
4168:doi
4138:doi
3991:If
816:to
494:of
482:in
448:In
7360::
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1050:2
1046:x
1040:2
1032:+
1029:x
1024:1
1016:+
1011:0
1003:=
1000:y
987:n
983:y
976:y
972:x
968:x
954:.
951:x
946:2
938:2
935:+
930:1
915:x
907:y
903:x
895:x
891:x
887:x
883:x
869:.
866:)
863:1
860:+
857:x
854:2
851:(
846:2
838:+
833:1
818:x
814:x
796:.
790:+
785:2
781:x
775:2
767:+
764:x
759:1
751:+
746:0
738:=
735:y
718:1
715:β
711:y
707:x
703:x
681:,
675:+
672:x
667:1
659:+
654:0
646:=
643:y
630:x
626:y
520:x
516:y
504:x
500:y
496:y
488:x
484:x
476:n
472:y
465:x
437:e
430:t
423:v
20:)
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