Linear regression with a single continuous predictor assumes independence of the observations, a straight line relationship between the predictor and the outcome, and the deviations from the line are normally distributed with equal spread across the line. If any of these conditions are not met, linear regression may not be appropriate for your analysis.

If the outcome variable is highly skewed or not a continuous variable (i.e. ordinal or dichotomous), linear regression may not be appropriate for your analysis. If the relationship between the outcome and predictor is not a straight line, linear regression may also not be appropriate. Finally, linear regression is inappropriate if the observations are not independent. For example, repeated measurements from the same subject are not independent. Therefore, if subjects contribute more than one observation, then linear regression is inappropriate. If linear regression is inappropriate for your data, you may schedule a Catalyst consultation for further assistance (link).