What statistic should I use when? (January 4, 2008).

Someone was asking about a multiple choice question on a test that reads something like this: A group of researchers investigating in patients with diabetes on the basis of demographic characteristics and the level of diabetic control. Select the most appropriate statistical method to use in analyzing the data:

1. a t-test,
2. ANOVA,
3. multiple linear regression, or
4. a chi-square test.

This is one of the more vexing things that people face--what statistic should I use when.

I don't like answering questions with as little context as this, but I made the assumption that level of diabetic control is a continuous variable (HbA1c levels would be an example of a continuous variable). When you are trying to predict a continuous outcome on the basis of one or more continuous predictor variables, the approach that is most commonly used is multiple linear regression. If some of your predictor variables are categorical, you will find that some people continue to describe this as a multiple linear regression model, but some will use different terms like ANCOVA. It turns out that ANCOVA is just special cases of multiple linear regression. If all of your predictor variables are categorical, then most people will describe the method as ANOVA, even though any ANOVA model can be considered as a multiple regression model.

If I get a chance, I will try to delineate further what statistic should be used in what situation.

This webpage was written on 2008-01-04 and was last modified on 2008-07-08. Category: Analysis of variance, Category: Linear regression