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. You work as a consultant for a sports team, the Los Angeles Dodgers, to help them forecast attendance, so that they can potentially devise strategies for price discrimination. After collecting data over two years for every one of the 162 home games of the 2000 and 2001 season, you run the following regression: img = 15,005 + 201 × Temperat + 465 × DodgNetWin + 82 × OppNetWin (8,770)(121)(169)(26) + 9647 × DFSaSu + 1328 × Drain + 1609 × D150m + 271 × DDiv - 978 × 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, DodgNetWin are the net wins of the Dodgers 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 Dodgers, and the game was played during 2001, respectively. Numbers in parentheses are heteroskedasticity- robust standard errors. (a)Are the slope coefficients 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?