Question

Can you show how to answer this in excel and not Minitab? Refer to the Baseball 2016 data, which reports information on the 2016 Major League Baseball season. Let attendance be the dependent variable and total team salary be the independent variable. Determine the regression equation and answer the following questions. a. Draw a scatter diagram. From the diagram, does there seem to be a direct relationship between the two variables? b. What is the expected attendance for a team with a salary of $100.0 million? c. If the owners pay an additional $30 million, how many more people could they expect to attend? d. At the .05 significance level, can we conclude that the slope of the regression line is positive? Conduct the appropriate test of hypothesis. e. What percentage of the variation in attendance is accounted for by salary? f. Determine the correlation between attendance and team batting average and between attendance and team ERA. Which is stronger? Conduct an appropriate test of hypothesis for each set of variables.

Team Salary	Attendance
118.90	2281202
168.70	2880694
110.70	1755810
87.70	1388905
172.80	2726048
69.10	2153585
112.90	2708549
146.40	3012765
108.30	2220054
213.50	3193795
80.80	1768175
123.20	2193581
74.80	1287054
144.80	2491875
116.40	2794891
65.80	2080145
89.60	2001392
117.20	2959812
117.70	2419506
98.30	2506789
230.40	3764815
84.60	1752235
98.70	2542558
100.10	2569753
133.00	1831080
85.90	2498596
126.60	2459742
166.50	3375882
120.30	3520889
174.50	2619843

Answer 1

(a)

Scatterplot of Attendance vs Team Salary 4000000 3500000 3000000 2500000 2000000 1500000 1000000 200 50 100 150 250 Team Salary Attendance

From the scatter plot, it is observed that Team Salary and Attendance are positively correlated and there seems to be a direct relationship between these two variables.

(b)

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.706773855
R Square	0.499529282
Adjusted R Square	0.481655328
Standard Error	427738.1077
Observations	30
ANOVA
	df	SS	MS	F	Significance F
Regression	1	5.11324E+12	5.11324E+12	27.94733	1.26761E-05
Residual	28	5.12288E+12	1.8296E+11
Total	29	1.02361E+13
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	1196918.806	251124.0645	4.766244957	5.25E-05	682514.4788	1711323.133
Team Salary	10347.28988	1957.295783	5.286523357	1.27E-05	6337.951214	14356.62854

Let y Attendance, Team salary T= The regression equation 1196918.806 10347.2899 If 100 then expected attendance 1196918.80610347.2899*100 2231648 (c) If x 130 then erpected attendance 1196918.80610347.2899 130 - 2542066 additional $30 million, the expected no. of more peoples If the owners pay an 2542066 2231648 310418 (d) Let Null hypothesis, Ho B0 us. Alternative hypothesis, H > 0 Value of test statistic t5.2865 slope of true regression line P(t> 5.2865t~t2s)= 0.000006 < 0.05 reject Ho at 5% level of significance and we p - value conclude that the slope of Hence we the regression line is positive. e. Percentage of the variation in attendance is accounted for by salary 0.4995 100 49.95% f. Correlation coefficient r 0.7068 and it is a good positive correlation between these two variables