SOC350H5 Lecture Notes - Lecture 9: Pearson Product-Moment Correlation Coefficient, Multicollinearity, Standard Error
Document Summary
Left out variables that are negatively affecting outcomes. It"s a problem with x highly related to each other. Because cannot decide what variable has the effect cuz you can t separate the two. Parameters that you decide are boundaries of outcome. Part of understanding outcome is first am i putting in irrelevant things. Not affecting outcome but also screwing up outcome. When understanding multivariate model saying stat sig are interpreted in relation to all x. Can make things be non sig when they may be sig but fact that model is on shaky grounds because of variables that you select. This happens when you add irrelevant x or miss important ones. Solution add variable that you think are important. You cannot substitute something else in there if data doesn"t contain it. People rely on comparing r2 to adj r2 to see is variable bias.
