ti fi Consider the SPSS output provided below The rst three ques ons will refer to this model. Q1: From the SPSS output, which of the coe cients are sta s cally signi cant? Which are not? Why? Q2: If the model that was being es mated was: Runs Allowed/9 = α β1 * Strikeouts/9 β2 * Walks /9 β3 * HomeRuns/9 ε, write out the model. Q3: What percent of the unexplained varia on (when you predict the ERA for a pitcher is equal to the average ERA across the league for all pitchers) does this model explain? Q 4-6: Suppose you determined the following model for the number of touchdowns that a football quarterback would throw this season (values on the right-hand side are last year’s numbers): Touchdownsthis_year = 8 0.5 * Touchdownslast_year 0.002*Yards – 0.45 * Intercep ons Q4: If a player had 25 touchdowns, 4,250 yards, and 20 intercep ons last year, what would his predicted number of touchdowns this year be? Q5: If this player had thrown 5 more touchdowns, by how much would the predic ons of touchdowns this year change by? Q6: How much would the yards last year need to change by for the predic on to increase by 1 touchdown? ti ti ti ti ti ti fi ti ti ti ti ffi ti ti ti tt ti ti ti Q7: If we are trying to determine if a player is a good passer/playmaker one sta s c that we may examine is assists (in Basketball, Hockey, Volleyball, and Soccer, among poten ally other sports). Discuss the usefulness of this sta s c in evalua ng the player’s passing skill. Are there things it captures well? Are there things it leaves out? Are there any other problems with the sta s c (for instance, if you see one person has more assists than another over the course of a season, can we conclude that one is a be er playmaker than the other?)