- Independence: This means that each record in the data must be a distinct and independent entity. This is met as each of the observation belongs to one group of the categorical variable only
- Normality: The responses for each factor level is normally distributed. i.e. average length of stay for each insurance group must be normally distributed.
- Homogeneity of variance: This means that the variance of the groups are equal
Linearity: there must be the existence of a linear relationship between the dependent and the independent variables.
No autocorrelation: Autocorrelation occurs when the residuals are not independent of each other. In other words when the value of y(x+1) is not independent of the value of y(x). For the linear regression model, we expect the residuals to be independent of one another.
Normality of residuals: We expect the residual from the model to be normally distributed.
No heteroscedasticity: We expect the residual variance to be constant. However, heteroscedasticity occurs if the variance of the residuals changes with the observation. Therefore, there should be no heteroscedasticity
No outliers: Outlier values may bias the estimate from the regression model. outliers are values that are too large or too small compared to other observations. We require that no outlier exists in the dataset.
There is little or no multicollinearity: multicollinearity exists if there is a very high correlation between the independent variables. Therefore, we expect there should be a not too high a correlation between the independent variables.
|Test||statistics||df||test statistics||p||multiple comparisons||p|