Model diagnostics and assumptions

Linear regression requires certain assumptions to be met to ensure accurate results:

• Linearity: The first assumption is linearity, which means there should be a straight-line relationship between the independent and dependent variables. For example, if you plot study hours against exam scores and find a straight line that fits well, then the linearity assumption is satisfied. However, if the data forms a curve, it indicates that the linearity assumption might be violated, which could affect the accuracy of the regression results. In other words, the relationship between independent and dependent variables should be linear.
• Assumption of independence: In this assumption, each data point should be independent of others. For example, in a clinical trial, each participant’s outcome should not be influenced by another’s. Violating this assumption can lead to incorrect results.
• Homoscedasticity: It means that the variance of...