Question

Please explain this question in detail.

Problem 3: Question 2 in Section 6.7 (pg. 215) in the textbook A Modern Approach to Regression with R. Chapter 5-2 of the award-winning book on baseball (Keri, 2006) makes extensive use of multiple linear regression. For example, since the "30 Major League Baseball teams play eighty-one home games during the regular season and receive the largest share of their income from the ticket sales associated with these games", the author develops a linear regression model to predict Y, yearly income (in 2005 US dollars) from ticket sales for each team from home games each year. Ticket sales data for each team for each of the years from 1997 to 2004 are used to develop the model. Thus, there are 30 x 8 240 rows of data. Twelve potential predictor variables are identified as follows: Six predictor variables measure team quality, namely: x1 - Number of games won in current season x2Number of games won in previous season x3 Dummy variable for playoff appearance in current season » . x4- Dummy variable for playoff appearance in previous season » x5- Number of winning seasons in the past 10 years . x6 Number of playoff appearances in the past 10 years Three predictors measure stadium of quality, namely: . x7 Seating capacity . x8-Stadium quality rating » x9 -Honeymoon effect (i.e. excitement about a new stadium) Two predictors measure market quality, namely: x10 Market size x11 Per-capita income Finally, x12-Year is included to allow for inflation. The author found that "seven of these (predictor) variables had a statistically significant impact on attendance revenue" (ie., had a t-statistic significant at least at the 10% level). Describe in detail two major concerns that potentially threaten the validity of the model. Hint: you do not need to load any data for this problem. Base your answer on the type of variables, how they relate to one another, and potential issues you think might occur when fitting a multiple linear regression model.

Answer 1

Answer of previous question on regression analysis
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