What is Regression?
Regression analysis comprises several related analytical tools that are useful to researchers in a variety of disciplines, including biological anthropology. Different styles of regression are available for different types of dependent variables (see Table 1 for a non-exhaustive list) and can accommodate a wide range of contexts that are challenging for many standard statistical methods, including controlling for any number of potential confounding variables, adjusting for non-independent sampling units, and using cases that have missing data.
Table 1. Some types of regression.
Most types of regression analysis can be used to: (a) test hypotheses about the association between variables, (b) model the relationship between variables and provide measures of the strength of model fit and proportion of variation in a dependent variable explained by the independent variables, and (c) build equations that can be used for prediction. Regression analysis can accommodate any number of independent variables, ranging from the simplest cases where you are interested in the effect of a single independent variable on the dependent variable, to the case of multiple regression where you are interested in two or more independent variables. The simplest case is where the independent variables are continuous, but other types of independent variables can be accommodated. Table 2 introduces some resources for issues in the application of regression analysis.
Table 2. Resources for issues in the application of regression analysis.
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Dr Geoff Kushnick (firstname.lastname@example.org)
The Australian National University, Canberra
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