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Thus moderate departures from Normality are allowable. One could also use bootstrap methods described in Appendix 3. It is important to remember that the main purpose of the analysis is to assess a relationship, not test assumptions, so often we can come to a useful conclusion even when the assumptions are not perfectly satisfied. 7 Model sensitivity Model sensitivity refers to how estimates are affected by subgroups of the data. 2), and we were told that the estimates b0 and b1 altered dramatically if you deleted a subset of the data, or even a single individual.
When the dependent variable is 0 or 1 then the coefficients from a linear regression are proportional to what is known as the linear discriminant function. This can be useful for discriminating between groups, even if the assumption about Normality of the residuals is violated. However discrimination is normally carried out now using logistic regression (Chapter 3). Multiple choice questions 1. e. the proportion of total income accruing to the less well off 50% of households) in 282 USA metropolitan areas and 53 Canadian metropolitan areas.
However the model may be “good enough” for a valid analysis. If the model does not fit, is it valid to make inferences from the model? In general the answer is “yes”, but care is needed! A further check on the model is to look at the influential points and these are available in many packages now. In STATA an overall influential statistic, labelled Pregibon’s “dbeta” is available, but not influential statistics for each of the regression parameters as in multiple regression. 1 and indicates that there are about five observations that are influential on the coefficients of the model, and these could be explored in more detail.
