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MIS 660 Topic 8 DQ 1
Suppose you were asked to investigate which
predictors explain the number of minutes that 10- to18-year-old students spend
on Twitter. To do so, you build a linear regression model with Twitter usage
(Y) measured as the number of minutes per week. The four predictors you include
in the model are Height, Weight, Grade Level, and Age of each student. You
build four simple linear
regression models with Y regressed separately on each predictor, and each
predictor is statistically significant. Then you build a multiple linear
regression model with Y regressed on all four predictors, but only one
predictor, Age, is statistically significant, and the others are not. What is
likely going on among the four predictors? If you include more than one of
these predictors in the model, what are some problems that can result?
MIS 660 Topic 8 DQ 2
After building a regression model and
performing residual diagnostics, you notice that the errors show severe
departures from normality and appear to have nonconstant variance. What steps
would you take in this case to resolve the errors? If the problems are not
corrected after all steps are taken, what does that imply about the modeling
approach you are taking? Explain in detail.
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