Applied Regression Modeling

✍ Scribed by Iain Pardoe

• Publisher: Wiley
• Year: 2020
• Tongue: English
• Leaves: 339
• Edition: 3
• Category: Library

✦ Synopsis

Master the fundamentals of regression without learning calculus with this one-stop resource

The newly and thoroughly revised 3rd Edition of Applied Regression Modeling delivers a concise but comprehensive treatment of the application of statistical regression analysis for those with little or no background in calculus. Accomplished instructor and author Dr. Iain Pardoe has reworked many of the more challenging topics, included learning outcomes and additional end-of-chapter exercises, and added coverage of several brand-new topics including multiple linear regression using matrices.

The methods described in the text are clearly illustrated with multi-format datasets available on the book's supplementary website. In addition to a fulsome explanation of foundational regression techniques, the book introduces modeling extensions that illustrate advanced regression strategies, including model building, logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series forecasting. Illustrations, graphs, and computer software output appear throughout the book to assist readers in understanding and retaining the more complex content. Applied Regression Modeling covers a wide variety of topics, like:

• Simple linear regression models, including the least squares criterion, how to evaluate model fit, and estimation/prediction
• Multiple linear regression, including testing regression parameters, checking model assumptions graphically, and testing model assumptions numerically
• Regression model building, including predictor and response variable transformations, qualitative predictors, and regression pitfalls
• Three fully described case studies, including one each on home prices, vehicle fuel efficiency, and pharmaceutical patches

Perfect for students of any undergraduate statistics course in which regression analysis is a main focus, Applied Regression Modeling also belongs on the bookshelves of non-statistics graduate students, including MBAs, and for students of vocational, professional, and applied courses like data science and machine learning.

✦ Table of Contents

Cover
Title Page
Copyright
Contents
Preface
Acknowledgments
INTRODUCTION
I.1 Statistics in Practice
I.2 Learning Statistics
About the Companion Website
Chapter 1 Foundations
1.1 Identifying and Summarizing Data
1.2 Population Distributions
1.3 Selecting Individuals at Random—Probability
1.4 Random Sampling
1.4.1 Central limit theorem—normal version
1.4.2 Central limit theorem—t‐version
1.5 Interval Estimation
1.6 Hypothesis Testing
1.6.1 The rejection region method
1.6.2 The p‐value method
1.6.3 Hypothesis test errors
1.7 Random Errors and Prediction
1.8 Chapter Summary
Chapter 2 Simple Linear Regression
2.1 PROBABILITY MODEL FOR X and Y
2.2 Least Squares Criterion
2.3 Model Evaluation
2.3.1 Regression standard error
2.3.2 Coefficient of determination—R2
2.3.3 Slope parameter
2.4 Model Assumptions
2.4.1 Checking the model assumptions
2.4.2 Testing the model assumptions
2.5 Model Interpretation
2.6 Estimation and Prediction
2.6.1 Confidence interval for the population mean, E(Y)
2.6.2 Prediction interval for an individual Y‐value
2.7 Chapter Summary
2.7.1 Review example
Chapter 3 Multiple Linear Regression
3.1 Probability Model for (X1, X2, …) and Y
3.2 Least Squares Criterion
3.3 Model Evaluation
3.3.1 Regression standard error
3.3.2 Coefficient of determination—R2
3.3.3 Regression parameters—global usefulness test
3.3.4 Regression parameters—nested model test
3.3.5 Regression parameters—individual tests
3.4 Model Assumptions
3.4.1 Checking the model assumptions
3.4.2 Testing the model assumptions
3.5 Model Interpretation
3.6 Estimation and Prediction
3.6.1 Confidence interval for the population mean, E(Y)
3.6.2 Prediction interval for an individual Y‐value
3.7 Chapter Summary
Chapter 4 Regression Model Building I
4.1 Transformations
4.1.1 Natural logarithm transformation for predictors
4.1.2 Polynomial transformation for predictors
4.1.3 Reciprocal transformation for predictors
4.1.4 Natural logarithm transformation for the response
4.1.5 Transformations for the response and predictors
4.2 Interactions
4.3 Qualitative Predictors
4.3.1 Qualitative predictors with two levels
4.3.2 Qualitative predictors with three or more levels
4.4 Chapter Summary
Chapter 5 Regression Model Building II
5.1 Influential Points
5.1.1 Outliers
5.1.2 Leverage
5.1.3 Cook's distance
5.2 Regression Pitfalls
5.2.1 Nonconstant variance
5.2.2 Autocorrelation
5.2.3 Multicollinearity
5.2.4 Excluding important predictor variables
5.2.5 Overfitting
5.2.6 Extrapolation
5.2.7 Missing data
5.2.8 Power and sample size
5.3 Model Building Guidelines
5.4 Model Selection
5.5 Model Interpretation Using Graphics
5.6 Chapter Summary
NOTATION AND FORMULAS
UNIVARIATE DATA
SIMPLE LINEAR REGRESSION
MULTIPLE LINEAR REGRESSION
Bibliography
Glossary
Index
EULA