Normal view
MARC view
Regression diagnostics / John Fox.
Series: Sage university papers series. Quantitative applications in the social sciences ; ; no. 07-079Publication details: Newbury Park, Calif. : Sage Publications, c1991.Description: 92 p. : ill. ; 22 cmISBN:- 080393971X :
- QA278.2 .F63 1991
Contents:
Summary: With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The book covers such topics as the problem of collinearity in multiple regression, dealing with outlying and influential data, non-normality of errors, non-constant error variance and the problems and opportunities presented by discrete data. In addition, sophisticated diagnostics based on maximum-likelihood methods, scores tests, and constructed variables are introduced.
Linear Least-Squares Regression -- The Regression Model -- Least-Squares Estimation -- Statistical Inference for Regression Coefficients -- The General Linear Model -- Collinearity -- Collinearity and Variance Inflation -- Coping with Collinearity: No Quick Fix -- Outlying and Influential Data -- Measuring Leverage: Hat-Values -- Detecting Outliers: Studentized Residuals -- Measuring Influence: Cook's Distance and Other Diagnostics -- Numerical Cutoffs for Diagnostic Statistics -- Jointly Influential Subsets of Observations: Partial-Regression Plots -- Should Unusual Data Be Discarded? -- Non-Normally Distributed Errors -- Normal Quantile-Comparison Plot of Residuals -- Histograms of Residuals -- Correcting Asymmetry by Transformation -- Nonconstant Error Variance -- Detecting Nonconstant Error Variance -- Correcting Nonconstant Error Variance -- Nonlinearity -- Residual and Partial-Residual Plots -- Transformations for Linearity -- Discrete Data -- Testing for Nonlinearity -- Testing for Nonconstant Error Variance -- Maximum-Likelihood Methods, Score Tests, and Constructed Variables -- Box-Cox Transformation of y -- Box-Tidwell Transformation of the xs -- Nonconstant Error Variance Revisited -- Recommendations -- Computing Diagnostics -- Least-Squares Fit, Joint Confidence Regions, and Tests -- Ridge Regression -- Hat-Values and the Hat Matrix -- The Distribution of the Least-Squares Residuals -- Deletion Diagnostics -- The Partial-Regression Plot -- Smoothing Scatterplots by Lowess -- Weighted-Least-Squares Estimation -- Correcting Least-Squares Standard Errors for Heteroscedasticity -- The Efficiency and Validity of Least-Squares Estimation When Error Variances Are Not Constant.
| Item type | Current library | Home library | Shelving location | Call number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|---|
Books
|
American University in Dubai | American University in Dubai | Main Collection | QA 278.2 .F63 1991 (Browse shelf(Opens below)) | Available | 631960 |
Browsing American University in Dubai shelves, Shelving location: Main Collection Close shelf browser (Hides shelf browser)
| QA 278.2 .B47 1985 Multiple regression in practice / | QA 278.2 .D7 1981 Applied regression analysis / | QA 278.2 .F62 2000 Nonparametric simple regression : smoothing scatterplots / | QA 278.2 .F63 1991 Regression diagnostics / | QA 278.65 .R482 Analysis of nominal data / | QA 278.8 .S52 2018 Data analysis with small samples and non-normal data : nonparametrics and other strategies / | QA 279 .B35 2008 Design of comparative experiments / |
Includes bibliographical references (p. 89-92).
Linear Least-Squares Regression -- The Regression Model -- Least-Squares Estimation -- Statistical Inference for Regression Coefficients -- The General Linear Model -- Collinearity -- Collinearity and Variance Inflation -- Coping with Collinearity: No Quick Fix -- Outlying and Influential Data -- Measuring Leverage: Hat-Values -- Detecting Outliers: Studentized Residuals -- Measuring Influence: Cook's Distance and Other Diagnostics -- Numerical Cutoffs for Diagnostic Statistics -- Jointly Influential Subsets of Observations: Partial-Regression Plots -- Should Unusual Data Be Discarded? -- Non-Normally Distributed Errors -- Normal Quantile-Comparison Plot of Residuals -- Histograms of Residuals -- Correcting Asymmetry by Transformation -- Nonconstant Error Variance -- Detecting Nonconstant Error Variance -- Correcting Nonconstant Error Variance -- Nonlinearity -- Residual and Partial-Residual Plots -- Transformations for Linearity -- Discrete Data -- Testing for Nonlinearity -- Testing for Nonconstant Error Variance -- Maximum-Likelihood Methods, Score Tests, and Constructed Variables -- Box-Cox Transformation of y -- Box-Tidwell Transformation of the xs -- Nonconstant Error Variance Revisited -- Recommendations -- Computing Diagnostics -- Least-Squares Fit, Joint Confidence Regions, and Tests -- Ridge Regression -- Hat-Values and the Hat Matrix -- The Distribution of the Least-Squares Residuals -- Deletion Diagnostics -- The Partial-Regression Plot -- Smoothing Scatterplots by Lowess -- Weighted-Least-Squares Estimation -- Correcting Least-Squares Standard Errors for Heteroscedasticity -- The Efficiency and Validity of Least-Squares Estimation When Error Variances Are Not Constant.
With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The book covers such topics as the problem of collinearity in multiple regression, dealing with outlying and influential data, non-normality of errors, non-constant error variance and the problems and opportunities presented by discrete data. In addition, sophisticated diagnostics based on maximum-likelihood methods, scores tests, and constructed variables are introduced.
There are no comments on this title.
Log in to your account to post a comment.