Regression Analysis by Example
The essentials of regression analysis through practical applications
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.
This new edition features the following enhancements:
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analyzed assumptions autocorrelation book’s Chapter classiﬁcation Coefﬁcient s.e. t-test collinearity conﬁdence interval Cor(Y correlation coefﬁcient corresponding data set deﬁned degrees of freedom deleted denote discussed distribution DOPROD Durbin-Watson statistic eigenvalues estimated regression coefﬁcients examine Figure ﬁrst ﬁt ﬁtted values full model given in Table graph Hadi heteroscedasticity index plot indicator variables inﬂuence ith observation least squares estimates linear model linear regression linear regression model linear relationship linearizable logistic regression mean square measures method multicollinearity multiple regression nonlinear null hypothesis obtained outliers Poisson regression predictor variables principal components problem reduced model regression analysis regression coefﬁcients regression equation regression output regression results residual plots response variable ridge regression ridge trace robust regression s.e. t-test p-value sample scatter plot signiﬁcant simple regression speciﬁc standard deviation standard error standardized residuals versus subset sum of squares t-test p-value Constant Variable Coefﬁcient s.e. variables X1 variance weight zero