9780803914940

Applied Regression Vol. 22 : An Introduction

by
  • ISBN13:

    9780803914940

  • ISBN10:

    0803914946

  • Format: Paperback
  • Copyright: 8/1/1980
  • Publisher: Sage Publications, Inc

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  • The Used and Rental copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

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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