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‘The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.’

- John Fox, Professor, Department of Sociology, McMaster University

‘The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.’

- Ben Jann, Executive Director, Institute of Sociology, University of Bern

‘Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for ...

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 ...

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