Introduction

Statistical models summarise relationships between variables. Regression models focus on one variable (response) and how it can be modelled by other explanatory variables. In general, response = function of explanatory variables + random scatter. We need to describe function and scatter. Balance between simplicity and accuracy.

Our approach:

• explore data using graphics and summary statistics
• construct useful model (iterative)
• use model to gain knowledge about system
• communicate findings

Role of graphics

• data cleaning: locate outliers, gross errors, special codings etc.
• exploration: what type of model could be useful?
• diagnostics: what is wrong with our model?

Data cleaning

All real data contain errors. Data should be carefully checked prior to analysis, but suitable plots are likely to reveal gross errors.