Previous Chapter Chapter 5: Regression Analysis: Assumptions and Diagnostics Next Chapter

Bart Meuleman, Geert Loosveldt & Viktor Emonds

In: The SAGE Handbook of Regression Analysis and Causal Inference

Chapter 5: Regression Analysis: Assumptions and Diagnostics

  • Citations
  • Add to My List
  • Text Size

Regression Analysis: Assumptions and Diagnostics
Regression analysis: Assumptions and diagnostics
BartMeuleman, GeertLoosveldt and ViktorEmonds
Introduction

As shown in the previous chapter, ordinary least squares (OLS) regression links the values of dependent variable Yi(i = 1, 2,…, n) to the values of a set of independent variables Xik by means of a linear function and an error term εi:

where k ranges from 0 to p-1. This model thus contains p regression parameters (namely effects of p −1 predictors and one intercept: X' equals 1 for all cases). The linear function is called the linear predictor or the structural part of the model, while the error term is the random or stochastic component of the model. In general, regression analysis can be used for two purposes: (1) to describe the ...

Looks like you do not have access to this content.

Login

Don’t know how to login?

Click here for free trial login.

Back to Top

Copy and paste the following HTML into your website