Classical and Modern Regression with ApplicationsFor seniors or graduate students with backgrounds in calculus and linear algebra; concepts are emphasized by using a blend of real data sets and mathematical development. |
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assumption B₁ b₂ centered and scaled Chapter collinearity computed confidence intervals Consider correlation matrix data of Table data point data set degrees of freedom detection DFBETAS DFFITS diagonal discussed eigenvalues equation error variance F PROB>F F-test Figure Gauss-Newton H₁ hypothesis illustration intercept involves iteration least squares estimator leverage linear model linear regression model logistic regression maximum likelihood estimator model errors model of Eq multicollinearity multiple linear regression nonlinear regression normal observation outlier parameter estimates Poisson Poisson regression PRESS residuals principal components procedure produce quadratic R-student values reader regression analysis regression coefficients regressor variables residual mean square residual plots residual sum response result ridge regression robust regression Section simple linear regression ẞo SSRes standard error starting values statistic studentized residuals subset sum of squares Technometrics transformation variance-covariance matrix vector x₁ Xẞ y₁ zero