Regression AnalysisRegression Analysis provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design.

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Contents
1  
Problems and Remedies  117 
Additional Uses of Regression  267 
Statistical Tables  413 
A Brief Introduction Tomatrices  433 
Estimation Procedures  439 
445  
449  
Other editions  View all
Regression Analysis: Statistical Modeling of a Response Variable Rudolf Jakob Freund,William J. Wilson,Ping Sa No preview available  2006 
Regression Analysis: Statistical Modeling of a Response Variable Rudolf Jakob Freund,William J. Wilson No preview available  1998 
Common terms and phrases
Analysis of Variance Chapter Coeff coefficient of determination confidence interval Corrected Total correlation curve degrees of freedom Dependent Mean DF Estimate Error DF Squares Square DFFITS effect equation error mean square error sum Error t Value error terms Estimates Parameter Standard example F Model F Value Pr factor levels Figure function independent variables inferences Intercept linear model logistic regression matrix Mean Source DF methods multicollinearity nonlinear normally distributed null hypothesis obtained OFGAT outliers output pvalue Parameter Estimates Parameter Parameter Standard Variable population predicted values principal components procedure RSq RSquare regression analysis relationship response variable Root MSE sampling distribution SAS System shown in Table simple linear regression Source DF Squares Square F Value Squares Square F SSErestricted standard deviation standard errors Standard Variable DF statistic Sum of Mean sum of squares transformation unrestricted model Variable DF Estimate variable selection Variance Sum zero