## Regression with Graphics: A Second Course in Applied StatisticsThis text demonstrates how computing power has expanded the role of graphics in analyzing, exploring, and experimenting with raw data. It is primarily intended for students whose research requires more than an introductory statistics course, but who may not have an extensive background in rigorous mathematics. It's also suitable for courses with students of varying mathematical abilities. Hamilton provides students with a practical, realistic, and graphical approach to regression analysis so that they are better prepared to solve real, sometimes messy problems. For students and professors who prefer a heavier mathematical emphasis, the author has included optional sections throughout the text where the formal, mathematical development of the material is explained in greater detail. REGRESSION WITH GRAPHICS is appropriate for use with any (or no) statistical computer package. However, Hamilton used STAT A in the development of the text due to its ease of application and sophisticated graphics capabilities. (STATA is available in a student package from Duxbury including a tutorial by the same author: Hamilton, STATISTICS WITH STAT A, 5.0, 1998; ISBN: 0-534-31874-6.) |

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### Contents

Contents | 1 |

Basics of Multiple Regression | 65 |

Regression Criticism | 109 |

Copyright | |

8 other sections not shown

### Common terms and phrases

ANOVA assumptions autocorrelation bivariate bootstrap boxplots calculate Chapter confidence interval correlation cubic feet curves curvilinear degrees of freedom DFBETAS dummy variable effect equals estimated standard error example Exercise F-distribution F-statistic F-test factor analysis factor scores Figure graphs heteroscedasticity histogram household water hypothesis income interaction intercept Iteration least squares leverage plot Log Likelihood logarithms logit M-estimate matrix median methods multicollinearity negatively skewed nonlinear nonnormal normal distribution North Number of obs obtain outliers P-values parameters percentile pollution positively skewed postshortage water power transformations predicted values preshortage water principal components Prob problems Quantile-Normal Plot R-square radon Reading Prong regres regression coefficients regression equation relationship robust regression Root MSE scatterplot shows slope dummy standard deviation standard errors statistics sum of squares symmetry plot Table tails theoretical tion univariate Variable Coefficient Std variance variation versus water-use Y-intercept zero