Article Text

This article has a correction. Please see:

Download PDFPDF
ANOVA, t tests, and linear regression
  1. Robert W Platt
  1. McGill University/Montreal Children's Hospital Research Institute, 2300 Tupper, Montreal, PQ H3H 1P3, Canada
  1. Correspondence to: Dr Platt.

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

In the last issue, I discussed logistic regression and the structure of linear models when the response or outcome is binary. Binary outcomes can take on only two values, like dead/alive or boy/girl, as compared with continuous outcomes which can take on any value on a numeric scale, like blood pressure or weight. Now, let's take a step back and consider the various models and tests for continuous outcomes. The common theme in these methods is explaining variability in the response variable, and dividing the total variance of a statistic into variation that can be explained and random variation that cannot be explained.

The t test is probably the simplest commonly used statistical procedure. To compare the mean of a continuous variable in two different populations, the difference between the two means divided by its standard deviation has a special distribution, known in this case as the “t distribution”. This relationship also allows construction of confidence intervals for the difference in means, and these provide information about the mean difference and its variability. When the difference …

View Full Text