1. Bivariate Regression: Fitting a Straight Line
Exact Versus Inexact Relationships
The Least Squares Principle
The Data
The Scatterplot
The Slope
The Intercept
Prediction
Assessing Explanatory Power: The R 2
R 2 Versus r
2. Bivariate Regression: Assumptions and Inferences
The Regression Assumptions
Confidence Intervals and Significance Tests
The One-Sided Test
Significance Testing: The Rule of Thumb
Reasons Why a Parameter Estimate May Not Be Significant
The Prediction Error for y
Analysis of Residuals
The Effect of School Size on Educational Performance: A Bivariate Regression Example
3. Multiple Regression: The Basics
The General Equation
Interpreting Intervals and Significance Tests
The R 2
Predicting y
Dummy Variables
The Possibility of Interaction Effects
The Four-Variable Model: Overcoming Specification Error
4. Multiple Regression: Special Topics
The Multicollinearity Problem
High Multicollinearity: An Example
The Relative Importance of the Independent Variables
Flexing the Regression Model: Nonlinearity
Determinants of Presidential Popularity: A Multiple Regression Example
Presentation of Regression Results in a Research Paper
What Next?
OBNB doesn't sell books, but you may be able to find a copy at one of these websites:
Applied regression : an introduction by Colin Lewis-Beck. ISBN 9781483381497. Published by SAGE in 2016. Publication and catalogue information, links to buy online and reader comments.
