The assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
Important assumptions in regressionmodel analysis are
There should be a linear and additive relationship between dependent (response) variable and independent (predictor) variable(s).
There should be no correlation between the residual (error) terms. Absence of this phenomenon is known as auto correlation.
The independent variables should not be correlated. Absence of this phenomenon is known as multi col-linearity.
The error terms must have constant variance. This phenomenon is known as homoskedasticity. The presence of non-constant variance is referred to heteroskedasticity.
The error terms must be normally distributed.
Hence we can conclude that the assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
