# Checking the assumptions of the regression model

Most statistical methods have assumptions that should be true for the results to be
valid. In ordinary least squares linear regression the following assumptions must be
true:

- There is a linear relationship between the predictor and response variables.
- The error term has the same variance in each observation.
- The errors are uncorrelated between observations.
- The errors are normally distributed.

The residual plot and normality plot show that the assumptions do not seem to be seriously violated.

However the influence plot shows that McDonald's has a large influence on the fit.

Looking again at the scatter plot and fit shows there is a downturn in the fitted line, compared to the data, as the spend increases. It might be worth considering alternative models to better describe the relationship.

**Next topic:**Changing the regression fit

- Tutorials
- Distribution tutorial
- Correlation / PCA tutorial
- Compare groups means tutorial
- Association in 2-way contingency tables tutorial
- Simple linear regression tutorial
- Plotting bivariate data
- Fitting a simple regression model
- Checking the assumptions of the regression model
- Changing the regression fit
- Making predictions
- Bland-Altman method comparison tutorial
- Verifying the precision of a measurement procedure against a performance claim and estimating the bias (CLSI EP15-A3)
- Pareto charts tutorial
- Process control charts tutorial
- Process capability tutorial

Published 16-Nov-2017

Version 4.92