Attendance 2013, revisited in the previous chapter, you looked at the relationship between the number of wins by american league baseball teams and the average attendance at their home games for the 2013 season. here are the scatterplot, the residuals plot, and part of the regression analysis: avg attendance 40,000 35,000 30,000 25,000 20,000 50 60 70 80 90 wins 24,000 210,000 25,000 0 5,000 10,000 27,000 30,000 fitted values residuals 33,000 dependent variable is home attendance r-squared = 6.45% variable coefficient intercept 16484.0 wins 147.4 a) do you think a linear model is appropriate here? explain. b) interpret the meaning of r2 in this context. c) do the residuals show any pattern worth remarking on? d) the point at the top of the plots is the new york yankees. what can you say about the residual for the

b i think

step-by-step explanation:

not really sure

uhhh idk u yolo?

the answer is 1/4

step-by-step explanation:

step-by-step explanation:

part a. the differences are decreasing, so we know the sequence is not arithmetic. the ratios are constant (=3/4), so we know the sequence is geometric.

part b. from part a, we know the common ratio is 3/4, so each term is 3/4 of the one before. the initial term is the number of gallons at h=1.

g[h] = (3/4)·g[h-1]

g[1] = 384 . . . . initial value required for a recursive definition

part c. the general formula for a geometric sequence is

a[n] = a[1]·r^(n-1)

we have a[1] = 384, r = 3/4, and the term number given by h. the formula that follows is

g[h] = 384·(3/4)^(h-1)

part d. filling in the value and doing the arithmetic, we have

g[12] = 384·(3/4)^11 ≈ 384·0.042235136 ≈ 16.2183 . . gallons

part e. each gallon is 4 quarts, so

16.2183 gal × 4 qt/gal = 64.8732 qt