(1)A sports enthusiast created an equation to predict Victories (the team’s number of victories in the National Basketball Association regular season play) using predictors FGP (team field goal percentage), FTP (team free throw percentage), Points = (team average points per game), Fouls (team average number of fouls per game), TrnOvr (team average number of turnovers per game), and Rbnds (team average number of rebounds per game). The fitted regression was Victories=−281 + 523 FGP + 3.12 FTP + 0.781 Points − 2.90 Fouls + 1.60 TrnOvr + 0.649 Rbnds (R2 = .802, F = 10.80, SE = 6.87). The strongest predictors were FGP (t = 4.35) and Fouls (t=−2.146). The other predictors were only marginally significant and FTP and Rbnds were not significant. The matrix of correlations is shown below. At the time of this analysis, there were 23 NBA teams. (a) Do the regression coefficients make sense? (b) Is the intercept meaningful? Explain. (c) Is the sample size a problem (using Evans’s Rule or Doane’s Rule)? (c) Why might collinearity account for the lack of significance of some predictors? (Data are from a research project by MBA student Michael S. Malloy.)” (Doane & Seward, 2007, p 600).
(2)Plot the voter participation rate. (b) Describe the trend (if any) and discuss possible causes. (c) Fit both a linear and an exponential trend to the data. (d) Which model is preferred? Why? (e) Make a forecast for 2004, using a trend model of your choice (or a judgment forecast). (f) Check the Web for the actual 2004 voter participation rate. How close was your forecast? Note: Time is in 4-year increments, so use t = 19 for the 2004 forecast.” (Doane & Seward, 2007, p 642).