# 2.1 Overview

Regression analysis refers to a set of techniques for predicting or explaining an outcome variable using one or more explanatory variables. It is essentially about creating a model for estimating one variable based on the values of others. Simple linear regression is regression analysis in its most basic form - it is used to predict a continuous (scale) outcome variable from Correlation is excellent for showing association between two variables. Simple Linear regression takes correlation's ability to show the strength and direction of an association a step further by allowing the researcher to use the pattern of previously collected data to build a predictive model. Here are some examples of how this can be applied: - Does time spent revising influence the likelihood of obtaining a good exam score?
- Are some schools more effective than others?
- Does changing school have an impact on a pupil's educational progress?
It is important to point out that there are limitations to regression. We can't always use it to analyse association. We'll start this module by looking at association more generally.
We're keen on training you with real world data so that you may be able to apply regression analysis to your own research. For this reason all of the examples we use come from the LSYPE and we provide an adapted version of the LYSPE dataset for you to practice with and to test your new found skills from. We recommend that you run through the examples we provide so that you can get a feel for the techniques and for SPSS/PASW in preparation for tackling the exercises. |