Rock n' Roll Heaven is an outdoor pavilion that presents musical performers throughout a six-month season, from late spring to early fall. Rock n' Roll presents a diverse venue of artists in a set of approximately 40 events each season. In order to better project its costs and expected attendance, Rock n' Roll uses regression analysis to project expected ticket sales for upcoming events for each performer. The regression results shown below are derived from the three most recent seasons. The dependent variable for Rock n' Roll is the number of paying tickets holders for each event, and the independent variables are

1. Whether or not this particular performer appeared at Rock n' Roll previously (a dummy variable, 0 if no and 1 if yes).

2. The spending on advertising targeted to the performer's appearance.

3. The performer's local sales of CDs in the most recent year prior to their appearance.

4. The number of television appearances for the performer in the most recent year.

5. The number of public performances in the United States by the performer in the recent year.

Independent Variables Results

Regression intercept ...........1,224

Attendance at prior concert

Coefficient ..............3,445

t -value ................. 4.11

Spending on advertising

Coefficient ..............0.113

t -value ................. 1.88

Performer’s CD sales

Coefficient .............. 0.00044

t -value ................. 1.22

Television appearances

Coefficient .............. 898

t -value ................. 2.4

Other public performances

Coefficient ..............1,233

t-value .................. 3.7

R -squared ............... 0.88

Standard error of the estimate ........2,447

Required

1. Using the above regression, what attendance would be predicted for a performer who had appeared at Rock n’ Roll previously, had six other public performances but no TV appearances, had local CD sales of $10 million, and Rock n’ Roll planned to spend $35,000 on advertising?

2. Evaluate the precision and reliability of the regression results shown above. What changes, if any, do you propose for the regression? Which variables should be deleted, and which do you think should be added, and why?