July 2012
Intermediate to advanced
352 pages
9h 30m
English
book
Common Errors in Statistics (and How to Avoid Them), 4th Edition
by Phillip I. Good, James W. Hardin
Content preview from Common Errors in Statistics (and How to Avoid Them), 4th Edition
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Chapter 11
Univariate Regression
Are the data adequate? Does your data set cover the entire range of interest? Will your model depend on one or two isolated datapoints?
THE SIMPLEST EXAMPLE OF A MODEL, THE RELATIONSHIP between exactly two variables, illustrates at least five of the many complications that can interfere with the task of model building:
1. Limited scope. The model we develop may be applicable for only a portion of the range of each variable.
2. Ambiguous form of the relationship. A variable may give rise to a statistically significant linear regression without the underlying relationship being a straight line.
3. Confounding. Undefined confounding variables may create the illusion of a relationship or may mask an existing one.
4. Assumptions. The assumptions underlying the statistical procedures we use may not be satisfied.
5. Inadequacy. Goodness of fit is not the same as prediction.
We consider each of these error sources in turn along with a series of preventive measures. Our discussion is divided into problems connected with model selection and difficulties that arise during the estimation of model coefficients.
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