16. Advanced Regression Models

  1. Paolo Brandimarte

Published Online: 24 MAY 2011

DOI: 10.1002/9781118023525.ch16

Quantitative Methods: An Introduction for Business Management

Quantitative Methods: An Introduction for Business Management

How to Cite

Brandimarte, P. (2011) Advanced Regression Models, in Quantitative Methods: An Introduction for Business Management, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118023525.ch16

Publication History

  1. Published Online: 24 MAY 2011
  2. Published Print: 4 APR 2011

ISBN Information

Print ISBN: 9780470496343

Online ISBN: 9781118023525

SEARCH

Keywords:

  • forecasting uncertainty;
  • linear regression;
  • logistic regression;
  • nonlinear regression;
  • nonstochastic regressors;
  • ordinary least squares;
  • polynomial regression;
  • regressor variables

Summary

This chapter describes the simple linear regression concepts. The first quite natural idea is building a linear regression model involving more than one regressor. Finding the parameters by ordinary least squares (OLS) is a rather straightforward exercise. What is much less straightforward is the statistical side of the coin, since the presence of multiple variables introduces some new issues. The chapter discusses the problems of testing a multiple regression model, selecting regressor variables, and assessing forecasting uncertainty. The chapter does so for the simpler case of nonstochastic regressors and under restrictive assumptions about the errors, that is, independence, homoskedasticity, and normality. The chapter also describes logistic regression, a possible approach to cope with a categorical regressed variable, based on a nonlinear transformation of the output of a linear regression model. There are many settings in which nonlinearity in data must be explicitly recognized, leading to nonlinear regression.

Controlled Vocabulary Terms

forecasting; linear regression; logistic regression; nonlinear regression; ordinary least squares; polynomial regression