1. Attendance at sports events depends on various factors. Teams typically do not change ticket prices from game to game to attract more spectators to less attractive games. However, there are other marketing tools used, such as fireworks, free hats, etc., for this purpose. Suppose you work as a consultant for the Athletics. You help them forecast attendance, so that they can potentially devise strategies for pricing. After collecting data over two years for 162 home games, you run the following regression: = 15,005 + 201 \times Temperat + 465 \times ANetWin + 82 \times OppNetWin + (8,770) (121) (169) (26) 9647 \times DFSaSu + 1328 \times Drain + 1609 \times D150m + 271 \times DDiv – 978 \times D2001 (1505) (3355) (1819) (1,184) (1,143) R2 = 0.416, SER = 6983 where Attend is announced stadium attendance, Temperat it the average temperature on game day, ANetWin are the net wins of the A’s before the game (wins-losses), OppNetWin is the opposing team's net wins at the end of the previous season, and DFSaSu, Drain, D150m, Ddiv, and D2001 are binary variables, taking a value of 1 if the game was played on a weekend, it rained during that day, the opposing team was within a 150 mile radius, the opposing team plays in the same division as the Athletics, and the game was played during 2001, respectively. Numbers in parentheses are heteroskedasticity- robust standard errors. a. Which of the individual slope coefficients are statistically significant? b. To test whether the effect of the last four binary variables is significant, you have your regression program calculate the relevant F-statistic, which is 0.295. What is the critical value? What is your decision about excluding these variables?