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Linearity
The conditional expectation of Y is a linear function of the parameters and the regressors. Explanation:
Constant error variance
the conditional variance of the response is the same for all cases. Explanation:
Normality (not too important)
errors are normally distributed at each fixed combination of predictor values. Explanation:
Independence of observations (important)
each case provides an independent piece of information. Explanation:
Predictors independent of the errors
correlation between predictors and error term = 0. Explanation:
No perfect collinearity
no predictor is a perfect linear function of the others. Explanation: