## 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

1 variable Distributions | 1 |

Bivariate Regression Andlysis | 29 |

Bosics of Multiple Regression | 65 |

Copyright | |

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### Common terms and phrases

ANOVA assumptions autocorrelation bivariate biweight bootstrap Bounded-influence boxplots calculate Chapter confidence intervals covariance cubic feet curve curvilinear degrees of freedom deleting DFBETAS diagonal downweights dummy variable effect equals Equation estimated standard error example Exercise F-statistic F-test factor analysis factor scores Figure graphs heteroscedasticity income influential intercept Iteration leverage plot linear loadings Log Likelihood logarithms logit regression M-estimation mean median methods Monte Carlo multicollinearity negative nonlinear nonnormal normal distribution null hypothesis Number of obs obtain outliers P-values parameters pattern percentile population positively skewed postshortage water predicted values predictors preshortage water principal components Prob problems quantile radon Reading Prong regression coefficients relationship resampling residuals robust regression rotation sampling distributions scatterplot slope ſº standard deviation standard errors statistics sum of squares symmetrical t-statistics Table tails theoretical Toble transformations variance versus weights Y-intercept zero