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 Brisbane Broncos, 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 its home games of the 2017 and 2018 seasons, you run the following regression: Attend = 15005 + 201 x Temperat + 465 x BroncNetWin
+82 x OppNetWin +9647 x DFSaSu
+1328 x Drain + 1609 x DQld-978 x D2018, R2 = 0.416, SER = 6983,
where Attend is the announced stadium attendance, Temperat it the average temperature on game day, BronNetWin are the net wins of the Broncos before the game (wins – losses), OppNetWin is the opposing team's net wins before the game, and DFSasu, Drain, DQld, and D2018 are binary variables, taking a value of 1 if the game was played on a weekend, it rained during that day, the opposing team is from Queensland, and the game was played during 2018, respectively.
a) What was the average attendance in 2017, on a sunny weekday with a temperature of 30C, when the Broncos had a Win-Loss record of 15-5 and facing an opponent from NSW with a record of 11-9? What would be the difference in attendance if the exact same game (e. g. under identical conditions) took place in 2018?
b) Interpret the R2. Do the coefficients on BroncNetWin, DFSaSu, and Drain have the expected signs?
c) Excluding the last three binary variables yields the following regression result: Attend = 14838 + 202 x Temperat + 435 x BroncNetWin +90 OppNetWin + 10472 x DFSasu, R2 = 0.410, SER = 6925, Why did the R2 decrease in this regression? Is it justified to remove these three explanatory variables from the regression?

d? maybe

step-by-step explanation:

if you are saying purchasing 3 social media ads and one newspaper ad counts as one purchase then there are:

6 purchases

if you are asking how many of each then:

18 social media ads

6 newspaper ads

the results from a pre-test for students for the year 2000 and the year 2010 are illustrated in the box plot. what do these results tell us about how students performed on the 29 question pre-test for the two years?

if we compare only the lowest and highest scores between the two years, we might conclude that the students in 2010 did better than the students in 2010. this conclusion seems to follow since the lowest score of 8 in 2010 is greater in value than the lowest score of 6 in 2000. also, the highest score of 28 in 2010 is greater in value than the highest score of 27 in 2000.

but the box portion of the illustration gives us more detailed information. the middle bar in each box shows us that the median score of 20 in 2000 is greater in value than the median score of 17 in 2010. further, we note that the box and whiskers divide the illustration into four pieces. each of these four pieces represents the same portion of students. so, the upper half of the students in 2000 scored in the same score range as the upper one-fourth of the students in 2010, see the illustration at a score of 20.

by considering the upper one-fourth, upper half, and upper three-fourths instead of just the lowest and highest scores, we would conclude that the students as a whole did much better in 2000 than in 2010. we would conclude that as a whole the students in 2010 are less prepared than the students in 2000.

in this section, we discuss box-and-whisker plots and the five key values used in constructing a box-and-whisker plot. the key values are called a five-number summary, which consists of the minimum, first quartile, median, third quartile, and maximum.

step-by-step explanation:

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