Q. What are the assumptions for a linear regression model?
There are five assumptions: independence, constant variance, normality, linearity, and unusual observations. The first three assumptions all describe the error term in the model. Linearity means that a linear model is the best fit for the data (i.e. the structure of the model matches the trend in the data). Unusual observations are records that are considered “extreme” in some way.