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Applied Regression Vol. 22 : An Introduction

by
Edition:
1st
ISBN13:

9780803914940

ISBN10:
0803914946
Format:
Paperback
Pub. Date:
8/1/1980
Publisher(s):
Sage Publications, Inc
List Price: $18.00

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Summary

Applied regression allows social scientists who are not specialists in quantitative techniques to arrive at clear verbal explanations of their numerical results. Provides a lucid discussion of more specialized subjects: analysis of residuals, interaction effects, specification error, multicollinearity, standardized coefficients, and dummy variables.

Table of Contents

Editor's Introduction 5(2)
Acknowldegments 7(2)
Bivariate Regression: Fitting a Straight Line
9(17)
Exact Versus Inexact Relationships
9(4)
The Least Squares Principle
13(2)
The Data
15(1)
The Scatterplot
15(2)
The Slope
17(2)
The Intercept
19(1)
Prediction
19(1)
Assessing Explanatory Power: The R2
20(5)
R2 Versus r
25(1)
Bivariate Regression: Assumptions and Inferences
26(21)
The Regression Assumptions
26(4)
Confidence Intervals and Significance Tests
30(3)
The One-Tailed Test
33(1)
Significance Testing: A Rule of Thumb
34(1)
Reasons Why a Parameter Estimate May Not Be Significant
35(2)
The Prediction Error for Y
37(1)
Analysis of Residuals
38(5)
The Effect of Safety Enforcement on Coal Mining Fatalities: A Bivariate Regression Example
43(4)
Multiple Regression
47(28)
The General Equation
48(1)
Interpreting the Parameter Estimates
49(2)
Confidence Intervals and Significance Tests
51(1)
The R2
52(1)
Predicting Y
53(1)
The Possibility of Interaction Effects
54(2)
A Four-Variable Model: Overcoming Specification Error
56(2)
The Multicollinearity Problem
58(4)
High Multicollinearity: An Example
62(1)
The Relative Importance of the Independent Variables
63(3)
Extending the Regression Model: Dummy Variables
66(5)
Determinants of Coal Mining Fatalities: A Multiple Regression Example
71(2)
What Next?
73(2)
Notes 75(2)
References 77


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