*The questions that I actually need answered are parts E through H, but you may
ID: 3053063 • Letter: #
Question
*The questions that I actually need answered are parts E through H, but you may need information from parts A through D to answer the question*
The 1991 accounting numbers for major league baseball are given in Table 2. All figures are in millions of dollars. The numerical variables are GtReceit (Gate Receipts), MediaRev (Media Revenue), StadRev (Stadium Revenue), TotRev (Total Revenue), PlayerCt (Player Costs), OpExpens (Operating Expenses), and FranValu (Franchise Value).
(a) Plot FranValu against each of the other variables in separate panels of the same graph. Discuss the graph.
(b) Report the correlation matrix for these variables. Can you determine a variable that is likely to be a good predictor of FranValu? And, what would you expect an issue of multicollinearity if the variables are used to predict FranValu? Explain why.
(c) Run a full multiple regression for predicting FranValu. Report the outputs. Discuss any issues you observe from the regression results.
(d) Interpret the meaning of the coefficient TotRev. Do you think the coefficient sign is correct?
QUESTIONS THAT NEED ANSWERING:
(e) Based on the correlation matrix in (b), which variable do you like to drop in order to try and resolve the issue in (c)? Explain why. Report the results after dropping the variable.
(f) Use stepwise regression to determine a final model with default significant level. Check the model adequacy and test if residuals are normal with 95% confidence intervals.
(g) Is the variable, TotRev, in the final model? If yes, interpret the coefficient. Does the coefficient sign make sense compared to the one from (c)?
(h) Player costs are likely to be a big component of operating expenses. Develop an equation for forecasting operating expenses from player costs. Comment on the strength of the relation. Using the residuals as a guide, identify teams that have unusually low or unusually high player costs as a component of operating expenses.
TABLE 2:
GtReceit MediaRev StadRev TotRev PlayerCt OpExpens FranValu Franchis 19.4 26.6 22.9 44.5 29.8 59.6 200 NyYank 18 79.3 180 LADodge 170 NYMets 160 TorBlJay 160 BosRdSox 140 BaltOrio 140 ChWhSox 132 StLCard 132 ChCubs 123 TxRanger 117 KCRoyals 32.5 35.2 29.7 35.4 70.4 62.4 70.8 39.5 12 88.7 5 50.6 25.7 27.5 19.9 22.8 12 59.1 23.2 46.4 27.5 25.5 20.5 23.2 12 64.5 20.7 47.6 16.9 15.2 25.7 14 53.6 30.4 60.8 7.6 48.2 115 PhilPhil 66.6 115 OakAthlt 103 CalAngls 99 SFGiants 98 CinnReds 96 SDPadres 95 HousAstr 39.2 27.9 5 54.1 15.5 17.1 15.6 10.6 16.2 15.6 15.4 18.2 15.5 53.3 48.8 48.8 26.2 5 48.9 33.3 27.1 23.1 7.5 48.4 25.2 8 5.5 45.8 49.8 87PittPirt 85 DetTiger 83 AtBraves 83 Minn Twin 79 SeatMar 77 MilBrews 77 Clevlndn 28.8 3.8 40.3 20.4 40.8 20.5 48.2 26.4 50.2 46.8 43.6 3 38.8 23.7 10.7 23.5 3 39.4 75 MonExposExplanation / Answer
The Correlation Matrix is as follows:
Multiple Regression output is as follows:
We can remove player cost in order to resolve the problem in (c)
The final model is as follows:
TotRev is present in the final model and the coefficient sign is negative.
Since the coefficient sign is unchanged, it does not affect the model much.
GtReceit MediaRev StadRev TotRev playerCt OpExpens FranValu GtReceit 1 MediaRev 0.303886 1 StadRev 0.587158 0.347697 1 TotRev 0.767571 0.794878 0.739676 1 playerCt 0.422641 0.44959 0.26869 0.505031 1 OpExpens 0.636053 0.55434 0.622914 0.774495 0.866861 1 FranValu 0.654737 0.780143 0.700911 0.915394 0.397333 0.634933 1Related Questions
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.