A Second Course in Statistics: Regression Analysis
This book 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.
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APPENDIX V Statistical Tables CONTENTS Table 1 Normal Curve Areas
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Analysis of Variance ANOVA appraised approximately assumptions autocorrelation autoregressive Calculate coefficient coefficient of determination COMMAND complete model completely randomized design conducted confidence interval correlation data points data set degrees of freedom dummy variables evidence to indicate example Exercise F statistic F test factorial experiment factors first-order model fit the model forecast graph increase Interpret least squares line least squares prediction linear model main effects matrix Mean Square measured method MINITAB MINITAB printout model E(y multicollinearity null hypothesis observations obtained p-value Parameter Estimates population predicted values prediction interval predictor Prob procedure qualitative independent variables quantitative R-sq random error ratio reduced model regression analysis reject H0 Rejection region relationship researchers response sale price sample means SAS printout second-order model Section series model shown in Figure SOURCE DF standard deviation standard error sum of squares Table test statistic testing H0 treatment means