A Second Course in Statistics: Regression AnalysisThis reader-friendly book focuses on building linear statistical models and developing skills for implementing regression analysis in real-life situations. It includes applications for a range of fields including engineering, sociology, and psychology, as well as traditional business applications.The authors use the latest material available from news articles, magazines, professional journals, the Internet, and actual consulting problems to illustrate real business situations and how to solve them using the tools of regression analysis. In addition, this book emphasizes model building and multiple regression models and pays special attention to model validation and spline regression.For professionals in any number of fields, including engineering, sociology, and psychology, who would benefit from learning how to use regression analysis to solve problems. |
Other editions - View all
A Second Course in Statistics: Regression Analysis William Mendenhall,Terry Sincich No preview available - 2003 |
A Second Course in Statistics: Regression Analysis Mark Dummeldinger,William Mendenhall,Terry Sincich No preview available - 2003 |
Common terms and phrases
Analysis of Variance assumptions B₁ B₁x Bo+B1x1 calculated Chapter coefficient coefficient of determination complete second-order model conducted confidence interval correlation data points data set example F test F Value Pr first-order model fit the model forecast fuel type graph increase inference interaction terms least squares line main effects Mean Square measured method MINITAB printout model E(y model for E(y multicollinearity multiple regression model null hypothesis observations output for Exercise p-value Parameter Estimates plot population mean predicted value prediction interval predictor qualitative variable quantitative independent variables R-Sq random error regression analysis Rejection region relationship researchers residuals response sale price Sample Statistics sampling distribution SAS printout scatterplot score shown in Figure slope SPSS Square F Value ẞ parameters standard deviation Standard Error stepwise regression straight-line model Sum of Squares Table test statistic y-intercept