Classical and Modern Regression with Applications |
Contents
Regression Analysis 135 | 6 |
The Multiple Linear Regression Model | 51 |
72 | 63 |
Copyright | |
10 other sections not shown
Common terms and phrases
analyst assumption b₁ b₂ candidate models centered and scaled Chapter collinearity Consider Cook's correlation matrix data point 23 data set degrees of freedom DFFITS diagonal discussed eigenvalues error of prediction error variance example F PROB>F F-tests Figure fitted value function Gauss-Newton procedure given by Equation H₁ homogeneous variance hospital data illustration involves iteration ith observation least squares estimator leverage linear model model errors model of Equation multicollinearity nonlinear model nonlinear regression normal outlier plot prediction interval PRESS residuals principal components produce properties quadratic form R-Student values reader regression analysis regression coefficients regression sum regressor variables residual mean square residual sum response result ridge regression robust regression role sample Section simple linear regression SSRes standard error starting values statistic studentized residuals subset sum of squares tion transformation variance-covariance matrix vector weighted x₁ Y₁ zero σ²