When carrying out linear regression there are a number of assumptions that are made. Often these assumptions are not verified, whereas in reality they are very important to check if they are met (and do something about it, if they aren’t!).

Accounting for potential influential factors through randomisation and blocking is an essential part of good experimental design.

Heatmaps are a data visualisation technique used to graphically represent multivariate data in an intuitive and effective way

A special example of a blocked experimental design, Latin Squares allows for additional factors to be explored.

Assumptions of linear regression models are important to investigate; the Shapiro-Wilk test analyses normality.

Two-way ANOVA is a means to investigate the effect of multiple factors (and their interaction) on results.

A Plackett-Burman experimental design is an efficient method to assess the significance of a large number of main effects.

Factors sometimes influence other factors – interactions need to be analysed too.