Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- List of panels
- Preface
- Part I Elementary statistical analysis
- Part II Samples and inductive statistics
- Part III Multiple linear regression
- Chapter 8 Multiple relationships
- Chapter 9 The classical linear regression model
- Chapter 10 Dummy variables and lagged values
- Chapter 11 Violating the assumptions of the classical linear regression model
- Part IV Further topics in regression analysis
- Part V Specifying and interpreting models: four case studies
- Appendix A The four data sets
- Appendix B Index numbers
- Bibliography
- Index of subjects
- Index of names
Chapter 8 - Multiple relationships
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- List of figures
- List of tables
- List of panels
- Preface
- Part I Elementary statistical analysis
- Part II Samples and inductive statistics
- Part III Multiple linear regression
- Chapter 8 Multiple relationships
- Chapter 9 The classical linear regression model
- Chapter 10 Dummy variables and lagged values
- Chapter 11 Violating the assumptions of the classical linear regression model
- Part IV Further topics in regression analysis
- Part V Specifying and interpreting models: four case studies
- Appendix A The four data sets
- Appendix B Index numbers
- Bibliography
- Index of subjects
- Index of names
Summary
In this chapter we once again take up the subject of regression, first introduced in chapter 4, and this will now be our central theme for the remainder of this book. In chapter 4 we dealt only with simple regression, with one dependent and one explanatory variable. In the present chapter we will extend the model to see what happens when there is more than one explanatory variable. We introduce this idea in §8.1, and explain various aspects of the concept of multiple regression in §8.2. The related concepts of partial and multiple correlation are covered in §8.3.
In chapter 9 we will examine some of the underlying ideas in more depth, and will also deal with some of the issues arising from the fact that the data underlying our regressions are typically drawn from samples and so are subject to sampling error. Two further extensions of the basic linear regression model, the use of dummy variables and of lagged values, are then introduced in chapter 10.
The inclusion of additional explanatory variables
Extension of a regression to include several explanatory variables is frequently desirable, because it is usually appropriate to formulate and discuss relationships in which the behaviour of the dependent variable is explained by more than one factor.
The basic idea is very simple. In the initial treatment of the relationship between two variables (bivariate regression) in §4.2.1 there was one dependent variable (Y), which was influenced by one explanatory variable (X).
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- Making History CountA Primer in Quantitative Methods for Historians, pp. 231 - 257Publisher: Cambridge University PressPrint publication year: 2002