Required model assumptions
Like the parametric tests we discussed in Chapter 4, Parametric Tests, linear regression is a parametric method and requires certain assumptions to be met for the results to be valid. For linear regression, there are four assumptions:
- A linear relationship between variables
- The normality of the residuals
- The homoscedasticity of the residuals
- Independent samples
Let’s discuss each of these assumptions individually.
A linear relationship between the variables
When thinking about fitting a linear model to data, our first consideration should be whether the model is appropriate for the data. When working with two variables, the relationship between the variables should be assessed with a scatter plot. Let’s look at an example. Three scatter plots are shown in Figure 6.6. The data is plotted, and the actual function used to generate the data is drawn over the data points. The leftmost plot shows data exhibiting a...
