Statistics for Six Sigma Green Belts with Minitab and JMP

9.4. Assumptions

The discussion of hypothesis testing and the analysis of variance in Chapter 7, “Hypothesis Testing,” emphasized the importance of the assumptions to the validity of any conclusions reached. The assumptions necessary for regression are similar to those of the analysis of variance because both topics fall under the general heading of linear models [see Reference 4].

The four assumptions of regression (known by the acronym LINE) are as follows:

• Linearity between Y and X

• Independence of errors between the actual and predicted values of Y

• Normality of the distribution of error terms (differences between the actual and predicted values of Y)

• Equal variance (also called homoscedasticity) for the distribution of Y for each level of the ...