Question

[Part B: Module III - Regression Concert Nation Concert Nation, INC. is a nationwide promoter of rock concerts. The president of the company wants to develop a model to estimate the revenue of a major concert event at large venues (such as Ford Field, Madison Square Gardens) for planning marketing strategies. The company has collected revenue data of 41 recent large concert events. For each concert, they have also recorded the attendance, the number of concession stands in the venue, and the Billboard chart of the artist in the week of each event. This data is available in "Part B - Data Set for Assessment Assignment.xlsx". They have two potential models that could explain the revenue. The two competing models are: Model A: Revenue = B + B^ Attendance + En Model B: Revenue ue = BO+BBillboard + € Run regression on both models. Use only the regression outputs of the two models and the original data to answer questions 14 to 20 below.

Attendance	# of concessions	Billboard Charts	Concert Revenue
30650	8	56	1531762
80997	1	87	4047180
93686	8	24	5805972
44405	4	99	2516538
77767	4	39	4197208
95780	7	35	6226065
82701	7	86	4123048
50165	8	29	3465110
50619	5	93	2843474
36259	7	86	1866318
52013	5	35	2670798
97447	7	71	5756817
69982	7	97	3681670
31789	10	72	2072149
73159	5	41	5064323
51172	8	1	2901564
54187	9	17	3170058
56681	7	1	3316764
78466	7	86	3825369
65132	8	86	2983563
52866	4	8	3091641
39536	2	20	3068049
32541	1	53	1796727
36441	1	60	2011990
74987	6	58	4389931
33791	8	81	1545359
64961	6	94	3792136
61429	3	86	2695672
68178	4	50	4147528
85701	5	52	5335423
31471	11	73	1989263
51172	8	1	2901864
54187	9	17	3160058
63105	11	31	3704803
57498	5	72	3209550
56380	7	43	3250222
58536	6	40	4257077
54187	9	17	3170058
56681	7	1	3316764
78466	7	86	3825369
65132	8	86	2983563

Answer 1

for model A

using excel>data>data analysis>Regression

we have

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.922154
R Square	0.850368
Adjusted R Square	0.846531
Standard Error	443740.9
Observations	41
ANOVA
	df	SS	MS	F	Significance F
Regression	1	4.36E+13	4.36E+13	221.6387	1.12E-17
Residual	39	7.68E+12	1.97E+11
Total	40	5.13E+13
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	-6225.49	239546.4	-0.02599	0.979399	-490754	478302.8
Attendance	57.12112	3.836842	14.88754	1.12E-17	49.36037	64.88186

Confidence Interval Estimate
Data
X Value	35000
Confidence Level	95%
Intermediate Calculations
Sample Size	41
Degrees of Freedom	39
t Value	2.022690901
XBar, Sample Mean of X	59763.4878
Sum of Squared Differences from XBar	13375533566
Standard Error of the Estimate	443740.8766
h Statistic	0.070237411
Predicted Y (YHat)	1993013.574
For Average Y
Interval Half Width	237871.9357
Confidence Interval Lower Limit	1755141.6388
Confidence Interval Upper Limit	2230885.51
For Individual Response Y
Interval Half Width	928536.5894
Prediction Interval Lower Limit	1064476.9850
Prediction Interval Upper Limit	2921550.164

Model A : Revenue = -6225.49+57.12112 *Attendence

Ans 16) point estimate = -6225.49+57.12112 *35000 = 1993013.574

ANs 17) 95% predicted interval ( 1064476.985 ,2921550.164)

Ans 18) option c is true

using excel>data>data analysis>Regresison

we have

Simple Linear Regression Analysis
Regression Statistics
Multiple R	0.1329
R Square	0.0177
Adjusted R Square	-0.0075
Standard Error	1136967.2128
Observations	41
ANOVA
	df	SS	MS	F	Significance F
Regression	1	906233145951.5780	906233145951.5780	0.7010	0.4075
Residual	39	50415083280079.9000	1292694443078.9700
Total	40	51321316426031.5000
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	3661329.7132	351299.8575	10.4222	0.0000	2950758.6879	4371900.7385
Billboard Charts	-4795.2632	5727.1746	-0.8373	0.4075	-16379.5672	6789.0407

MOdel B :Revenue = 3661329.7132 -4795.2632*Billboard charts

Ans 19) sample correlation coefficient = -0.1329

Ans 20) option A is true