Dixie Showtime Movie Theaters, Inc., owns· d operates chain f cinemas in several
ID: 3304864 • Letter: D
Question
Dixie Showtime Movie Theaters, Inc., owns· d operates chain f cinemas in several markets the southern U.S. The wners wsuld like touestimate weekgross reve ue as f ction of advertising ditures. Data for a sanm ple o marsets for a recenteek follow Weekly Gross Revene Television Advertising News aper Advertising $100s) 5-1 3-2 (S100s) C$100s) Market Mobile Shrevepart ackson Birmingham Litile Rock Biloxi NenOrleans Baton Ro uge 102.5 52.7 75.B 127.6 137.B 101.1 237.B 21 3.6 1-3 3.5 3-6 1.3 2.3 9-1 5.B 6-9 (a) se the data to develop an estim ted regressian níth,ermount or telev s *n dvertising as the independent variable Let x represent t he amount of tele isin a verti sing. required, round your answer "our cecimal pletes. For subtractive or negative numbers use a minus sign eefthere is a t ign before the bank-(Example:-30D> (b) Hvr much of the ariation in the s mple 'alues of weekly ros revenue dces the model in part a) explain? required, round your answer to tno decim al plates (c) use the data to deve! p an estim ted regressian eqation·with both televis n advertising and newspaper adwertising as the independent variables Let xi represent the amount of television advertising Let x2 represent the amount of newspaper advertising required, round your answer "our cecimal pletes. For subtractive or negative numbers use a minus sign even if there is a + ign belore the blank. CEsample:-300y 1 (d) tor much of the·ariation in the semple 'alues of weekly ros revenue dces the model in part c) explain? required, round your answer to tno decim al platesExplanation / Answer
Answer:
Regression Analysis
r²
0.5208
n
8
r
0.7217
k
1
Std. Error
49.088
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
15,714.4647
1
15,714.4647
6.52
.0433
Residual
14,457.7103
6
2,409.6184
Total
30,172.1750
7
Regression output
confidence interval
variables
coefficients
std. error
t (df=6)
p-value
95% lower
95% upper
Intercept
-43.2575
70.7599
-0.611
.5634
-216.4009
129.8858
x1
39.3669
15.4154
2.554
.0433
1.6467
77.0870
a).
y=-43.2575+39.3669*x
b).
variance explained = 52.08%
Regression Analysis
R²
0.9357
Adjusted R²
0.9100
n
8
R
0.967
k
2
Std. Error
19.698
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
28,232.1694
2
14,116.0847
36.38
.0010
Residual
1,940.0056
5
388.0011
Total
30,172.1750
7
Regression output
confidence interval
variables
coefficients
std. error
t (df=5)
p-value
95% lower
95% upper
Intercept
-43.2247
28.3942
-1.522
.1884
-116.2144
29.7649
x1
21.8592
6.9112
3.163
.0250
4.0933
39.6251
x2
20.1621
3.5497
5.680
.0024
11.0374
29.2869
c).
y = -43.2247+21.8592*x1+20.1621*x2
d).
variance explained = 93.57%
Regression Analysis
r²
0.5208
n
8
r
0.7217
k
1
Std. Error
49.088
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
15,714.4647
1
15,714.4647
6.52
.0433
Residual
14,457.7103
6
2,409.6184
Total
30,172.1750
7
Regression output
confidence interval
variables
coefficients
std. error
t (df=6)
p-value
95% lower
95% upper
Intercept
-43.2575
70.7599
-0.611
.5634
-216.4009
129.8858
x1
39.3669
15.4154
2.554
.0433
1.6467
77.0870