While regression models are easy to run given their short, simple syntax, this accessibility also makes it easy to use regression inappropriately. These models have several key assumptions that need to be met in order for their output to be valid, but your code will typically run whether or not these assumptions have been met.

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For linear regression (used with a continuous outcome), these assumptions are as follows:

1. Independence: All observations are independent of each other, residuals are uncorrelated
2. Linearity: The relationship between X and Y is linear
3. Homoscedasticity: Constant variance of residuals at different values of X
4. Normality: Data should be normally distributed around the regression line

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