Regression Analysis by ExampleJohn Wiley & Sons, 2006 M10 20 - 416 páginas 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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assumptions autocorrelation B₁ Chapter Coefficient s.e. t-test collinearity confidence interval considered Cor(Y correlation coefficient corresponding data set degrees of freedom deleted discussed distribution DOPROD Durbin-Watson statistic eigenvalues example Figure fitted model fitted values fitting the model full model given in Table graph Hadi heteroscedasticity index plot indicator variables least squares estimates linear model linear regression linear relationship linearizable logistic regression mean square measures method multicollinearity multiple regression nonlinear null hypothesis obtained outliers points 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 ẞ₁ ẞo standard error standardized residuals versus subset sum of squares t-test p-value Constant Variable Coefficient s.e. variables X1 variance weight X₁ zero