# 15.8: Assumptions of Regression


The linear regression model that I’ve been discussing relies on several assumptions. In Section 15.9 we’ll talk a lot more about how to check that these assumptions are being met, but first, let’s have a look at each of them.

• Normality. Like half the models in statistics, standard linear regression relies on an assumption of normality. Specifically, it assumes that the residuals are normally distributed. It’s actually okay if the predictors X and the outcome Y are non-normal, so long as the residuals ϵ are normal. See Section 15.9.3.
• Linearity. A pretty fundamental assumption of the linear regression model is that relationship between X and Y actually be linear! Regardless of whether it’s a simple regression or a multiple regression, we assume that the relatiships involved are linear. See Section 15.9.4.
• Homogeneity of variance. Strictly speaking, the regression model assumes that each residual ϵi is generated from a normal distribution with mean 0, and (more importantly for the current purposes) with a standard deviation σ that is the same for every single residual. In practice, it’s impossible to test the assumption that every residual is identically distributed. Instead, what we care about is that the standard deviation of the residual is the same for all values of $$\ \hat{Y}$$, and (if we’re being especially paranoid) all values of every predictor X in the model. See Section 15.9.5.
• Uncorrelated predictors. The idea here is that, is a multiple regression model, you don’t want your predictors to be too strongly correlated with each other. This isn’t “technically” an assumption of the regression model, but in practice it’s required. Predictors that are too strongly correlated with each other (referred to as “collinearity”) can cause problems when evaluating the model. See Section 15.9.6
• Residuals are independent of each other. This is really just a “catch all” assumption, to the effect that “there’s nothing else funny going on in the residuals”. If there is something weird (e.g., the residuals all depend heavily on some other unmeasured variable) going on, it might screw things up.
• No “bad” outliers. Again, not actually a technical assumption of the model (or rather, it’s sort of implied by all the others), but there is an implicit assumption that your regression model isn’t being too strongly influenced by one or two anomalous data points; since this raises questions about the adequacy of the model, and the trustworthiness of the data in some cases. See Section 15.9.2.

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