Applied Regression Modeling

3rd edition

Iain Pardoe

A concise, comprehensive treatment of the application of statistical regression analysis suitable for undergraduate and graduate students. Covers simple linear regression, multiple linear regression, model building, and advanced regression topics.

Table of Contents

Introduction

  • Statistics in Practice
  • Learning Statistics

1. Foundations

  • Identifying and Summarizing Data
  • Population Distributions
  • Selecting Individuals at Random – Probability
  • Random Sampling
  • Interval Estimation
  • Hypothesis Testing
  • Random Errors and Prediction

2. Simple Linear Regression

  • Probability Model for X and Y
  • Least Squares Criterion
  • Model Evaluation
  • Model Assumptions
  • Model Interpretation
  • Estimation and Prediction

3. Multiple Linear Regression

  • Probability Model for (X1, X2, …) and Y
  • Least Squares Criterion
  • Model Evaluation
  • Model Assumptions
  • Model Interpretation
  • Estimation and Prediction

4. Regression Model Building I

  • Transformations
  • Interactions
  • Qualitative Predictors

5. Regression Model Building II

  • Influential Points
  • Regression Pitfalls
  • Model Building Guidelines
  • Model Selection
  • Model Interpretation Using Graphics

6. Case Studies [online]

  • Home Prices
  • Vehicle Fuel Efficiency
  • Pharmaceutical Patches

7. Extensions [online]

  • Generalized Linear Models
  • Discrete Choice Models
  • Multilevel Models
  • Bayesian Modeling

Appendices

  • Computer Software Help [online]
  • Critical Values for t-distributions [online]
  • Notation and formulas
  • Mathematics Refresher [online]
  • Multiple Linear Regression Using Matrices [online]
  • Brief Answers to Selected Problems [online]