**This page was adapted from a page at the Stata Bookstore page. We thank Stata for
their permission to adapt and distribute this page via our web site. We are very
grateful to Professor Lawrence Hamilton for granting us permission to distribute the data
files for Regression with Graphics.**

Title: | Regression with Graphics | |

Author: | Lawrence C. Hamilton | |

Publisher: | Brooks/Cole | |

Copyright: | 1992 | |

ISBN: | 0-534-15900-1 | |

Pages: | 363; hardcover | |

Price: | $89.00 |

Regression with Graphics provides a unique treatment of regression by integrating graphical and regression methods for performing exploratory data analysis. More emphasis is given to practical issues and troubleshooting than statistical theory. Techniques are illustrated using real data with environmental themes from diverse disciplines, thus making it interesting and understandable to readers in any field. Stata graphs and output are used throughout the book.

Click here for descriptions and
optionally to download the datasets used in *Regression with Graphics*.

### Contents

- The Concord Water Study
- Mean, Variance, and Standard Deviation
- Normal Distributions
- Median and Interquartile Range
- Boxplots
- Symmetry Plots
- Quantile Plots
- Quantile-Quantile Plots
- Quantile-Normal Plots
- Power Transformations
- Selecting an Appropriate Power
- Conclusion
- Exercises
- Notes
- The Basic Linear Model
- Ordinary Least Squares
- Scatterplots and Regression
- Predicted Values and Residuals
*R*^{2}, Correlation, and Standardized Regression Coefficients- Reading Computer Output
- Hypothesis Tests for Regression Coefficients
- Confidence Intervals
- Regression Through the Origin
- Problems with Regression
- Residual Analysis
- Power Transformations in Regression
- Understanding Curvilinear Regression
- Conclusion
- Exercises
- Notes
- Multiple Regression Models
- A Three-Variable Example
- Partial Effects
- Variable Selection
- A Seven-Variable Example
- Standardized Regression Coefficients
*t*-Tests and Confidence Intervals for Individual Coefficients*F*-Tests for Sets of Coefficients- Multicollinearity
- Search Strategies
- Interaction Effects
- Intercept Dummy Variables
- Slope Dummy Variables
- Oneway Analysis of Variance
- Twoway Analysis of Variance
- Conclusion
- Exercises
- Notes
- Assumptions of Ordinary Least Squares
- Correlation and Scatterplot Matrices
- Residual Versus Predicted
*Y*Plots - Autocorrelation
- Nonnormality
- Influence Analysis
- More Case Statistics
- Symptoms of Multicollinearity
- Conclusion
- Exercises
- Notes
- Exploratory Band Regression
- Regression with Transformed Variables
- Curvilinear Regression Models
- Choosing Transformations
- Evaluating Consequences of Transformation
- Conditional Effect Plots
- Comparing Effects
- Nonlinear Models
- Estimating Nonlinear Models
- Interpretation
- Conclusion
- Exercises
- Notes
- A Two-Variable Example
- Goals of Robust Estimation
*M*-Estimation and Iteratively Reweighted Least Squares- Calculation by IRLS
- Standard Errors and Tests for
*M*-Estimates - Using Robust Estimation
- A Robust Multiple Regression
- Bounded-Influence Regression
- Conclusion
- Exercises
- Notes
- Limitations of Linear Regression
- The Logit Regression Model
- Estimation
- Hypothesis Tests and Confidence Intervals
- Interpretation
- Statistical Problems
- Influence Statistics for Logit Regression
- Diagnostic Graphs
- Conclusion
- Exercises
- Notes
- Introduction to Components and Factor Analysis
- A Principal Components Analysis
- How Many Components?
- Rotation
- Factor Scores
- Graphical Applications: Detecting Outliers and Clusters
- Principal Factor Analysis
- An Example of Principal Factor Analysis
- Maximum-Likelihood Factor Analysis
- Conclusion
- Exercises
- Notes
- Expected Values
- Covariance
- Variance
- Further Definitions
- Properties of Sampling Distributions
- Ordinary Least Squares
- Some Theoretical Distributions
- Exercises
- Notes
- Monte Carlo Simulation
- Bootstrap Methods
- Bootstrap Distributions
- Residual Versus Data Resampling
- Bootstrap Confidence Intervals
- Evaluating Confidence Intervals
- Computer-Intensive Methods in Research
- Exercises
- Notes
- Basic Ideas
- Matrix Addition and Multiplication
- Regression in Matrix Form
- An Example
- Regression from Correlation Matrices
- Further Definitions
- Exercises
- Notes
- A4.1: Critical Values for Student’s
*t*-Distribution - A4.2: Critical Values for the
*F*-Distribution - A4.3: Critical Values for the Chi-Square Distribution
- A4.4: Critical Values for the Durbin–Watson Test for Autocorrelation

**1 Variable Distributions**

**2 Bivariate Regression Analysis**

**3 Basics of Multiple Regression**

**4 Regression Criticism**

**5 Fitting curves**

**6 Robust regression**

**7 Logit regression**

**8 Principal Components and Factor Analysis**

**Appendix 1 Population and sampling distributions**

**Appendix 2 Computer-Intensive Methods**

**Appendix 3 Matrix Algebra**

**Appendix 4 Statistical tables**

**References**

**Index**

**This page was adapted from a page at the Stata Bookstore page. We thank Stata for
their permission to adapt and distribute this page via our web site. We are very
grateful to Professor Lawrence Hamilton for granting us permission to distribute the data
files for Regression with Graphics.**