- Shopping Bag ( 0 items )
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.
Linear Least-Squares Regression
Outlying and Influential Data
Non-Normally Distributed Errors
Non-Constant Error Variance
Maximum-Likelihood Methods, Score Tests, and Constructed Variables