The book 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.
* Examples and exercises contain real data and graphical illustration for ease of interpretation
* Outputs from SAS 7, SPSS 7, Excel, and Minitab are used for illustration, but any major statistical
software package will work equally well.
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Regression Analysis: Statistical Modeling of a Response Variable
Rudolf Jakob Freund,William J. Wilson,Ping Sa
No preview available - 2006
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 p-value Parameter Estimates Parameter Parameter Standard Variable population predicted values principal components procedure R-Sq R-Square 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