The linear regression model assumes that there is a linear, or "straight line," relationship between the dependent variable and each predictor. This relationship is described in the following formula.

The model is linear because increasing the value of the jth predictor by 1 unit increases the value of the dependent by bj units. Note that b0 is the intercept, the model-predicted value of the dependent variable when the value of every predictor is equal to 0.

For the purpose of testing hypotheses about the values of model parameters, the linear regression model also assumes the following:

- The error term has a normal distribution with a mean of 0.
- The variance of the error term is constant across cases and independent of the variables in the model. An error term with non-constant variance is said to be heteroscedastic.
- The value of the error term for a given case is independent of the values of the variables in the model and of the values of the error term for other cases.