10. Simple Linear Regression

  1. Paolo Brandimarte

Published Online: 24 MAY 2011

DOI: 10.1002/9781118023525.ch10

Quantitative Methods: An Introduction for Business Management

Quantitative Methods: An Introduction for Business Management

How to Cite

Brandimarte, P. (2011) Simple Linear Regression, in Quantitative Methods: An Introduction for Business Management, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118023525.ch10

Publication History

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

ISBN Information

Print ISBN: 9780470496343

Online ISBN: 9781118023525

SEARCH

Keywords:

  • heteroskedastic error;
  • linear algebra;
  • linear regression;
  • nonstochastic regressor;
  • statistical framework;
  • stochastic regressor;
  • weighted least-squares method

Summary

This chapter describes the least-squares method. Least squares provide a reader with estimators of the parameters in the data-generating processes. In order to investigate the properties of the least-squares estimators, he needs a precise statement of conditions on regressor variables and errors. The chapter explains in detail the case of nonstochastic regressors. It also outlines the case of stochastic regressors. The chapter turns out that the fundamental results for the case of stochastic regressors are not that different, but the underlying assumptions must be expressed in a more complicated way. Further, the chapter outlines extensions such as the weighted least-squares method, and takes a look at the links between linear algebra and linear regression.

Controlled Vocabulary Terms

linear regression; stochastic processes; weighted least squares