A Second Course in Statistics: Regression AnalysisAppropriate for either the second term of an introductory statistics course or for a course in applied regression analysis for graduate students in all disciplines. This text focuses on building linear statistical models and on developing skills for implementing regression analysis in real life situations. The fifth edition now includes applications for engineering, sociology, psychology, etc., as well as traditional business applications. The authors use material from news articles, magazines, professional journals, and actual consulting problems to illustrate real business problems and how to solve them by using the tools of regression analysis. |
Contents
Introduction to Regression Analysis | 90 |
Simple Linear Regression | 101 |
Model Building_273 | 273 |
Copyright | |
31 other sections not shown
Common terms and phrases
Analysis of Variance approximately assumptions average B₁ B₁x B₁x1 B₂ B₂x2 Calculate Chapter coefficient of determination confidence interval correlation data points data set example Exercise F test F Value first-order model fit the model forecast graph increase inferences interaction INTERCEP Interpret Journal least squares line least squares prediction linear model measured MINITAB MINITAB printout model E(y model for E(y multicollinearity normal distribution null hypothesis observed p-value Parameter Estimates Parameter=0 percentage plot predicted value prediction interval predictor Prob qualitative quantitative independent variables R-square random error random sample ratio regression analysis Rejection region relationship researchers residuals response Root MSE sample data sampling distribution SAS printout scattergram score shown in Figure squares prediction equation ẞ parameters ẞ₁ ẞo standard deviation straight-line model Sum of Squares Table test statistic Value Prob>F y-intercept y₁