With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The book covers such topics as the problem of collinearity in multiple regression, dealing with outlying and influential data, non-normality of errors, non-constant error variance and the problems and opportunities presented by discrete data. In addition, sophisticated diagnostics based on maximum-likelihood methods, scores tests, and constructed variables are introduced.

Non-Normally Distributed Errors

The assumption of normally distributed errors is almost always arbitrary. Nevertheless, the central-limit theorem assures that under very broad conditions inference based on the least-squares estimators is approximately valid in all but small samples. Why, then, should we be concerned about non-normal errors?

First, ...

locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles