Dixie Showtime Movie Theaters, Inc., owns· d operates chain f cinemas in several

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 plates

Explanation / 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

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