With milk sales sagging of late, The Milk Processor Education Program (MPEP) decided to move on from the famous "Got Milk" ad slogan in favor of a new one, "Milk Life." The new tagline emphasizes milk's nutritional benefits, including its protein content. MPEP began collecting data on the number of gallons of milk households consumed weekly (in millions), weekly price per gallon, and weekly expenditures on milk advertising (in hundreds of dollars) for the period following the launch of the new campaign. These data are available via the link below. Use these data to estimate a linear regression.  Suppose that the weekly price of milk is $3.40 per gallon and MPEP decides to ramp up weekly advertising by 35 percent to $150 (in hundreds). Use your regression model to estimate the weekly quantity of milk consumed after this advertising increase. Instructions: Round your intermediate calculations and enter your response rounded to three decimal places. ________ million gallons per week

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With milk sales sagging of late, The Milk Processor Education Program (MPEP) decided to move on from the famous "Got Milk" ad slogan in favor of a new one, "Milk Life." The new tagline emphasizes milk's nutritional benefits, including its protein content. MPEP began collecting data on the number of gallons of milk households consumed weekly (in millions), weekly price per gallon, and weekly expenditures on milk advertising (in hundreds of dollars) for the period following the launch of the new campaign. These data are available via the link below. Use these data to estimate a linear regression. 

Suppose that the weekly price of milk is $3.40 per gallon and MPEP decides to ramp up weekly advertising by 35 percent to $150 (in hundreds). Use your regression model to estimate the weekly quantity of milk consumed after this advertising increase.

Instructions: Round your intermediate calculations and enter your response rounded to three decimal places.

________ million gallons per week

 

Linear Model
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
0.73972379
0.54719129
0.53785503
1.06323599
Observations
100
ANOVA
df
MS
Significance F
F
Regression
2
132.5120801
66.25604004
58.60924693 2.05053E-17
Residual
97
109.6556639
1.130470762
Total
99
242.167744
Coefficients
Standard Error
t Stat
P-value
Lower 95% Upper 95% ower 95.0%/pper 95.0%
Intercept
6.51983904
0.823090822
7.921166011
3.94495E-12 4.886231606 8.153446 4.886232 8.153446
P
-1.6143824
0.151475863
-10.65768723
5.12726E-18
-1.91502003
-1.31374 -1.91502
-1.31374
A
0.00466438
0.001575003
2.961506969
0.003849344 0.001538437
0.00779 0.001538
0.00779
Log-Linear Model
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
0.63355944
0.40139756
0.38905524
0.58687607
Observations
100
ANOVA
df
MS
Significance F
Regression
22.40272212
11.20136106
32.52205574
1.55316E-11
Residual
97
33.40908188
0.344423524
Total
99
55.811804
Coefficients
Standard Error
t Stat
P-value
Lower 95% Upper 95% ower 95.0%/pper 95.0%
Intercept
-1.988675
2.243299214
-0.886495666 0.377543082
-6.44100299 2.463653
-6.441 2.463653
InP
-2.1695134
0.276091563
-7.857948763
5.37032E-12
-2.71747869
-1.62155 -2.71748 -1.62155
InA
0.91065837
0.370342406
2.458963249
0.015705726 0.175631206 1.645686 0.175631 1.645686
Transcribed Image Text:Linear Model SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.73972379 0.54719129 0.53785503 1.06323599 Observations 100 ANOVA df MS Significance F F Regression 2 132.5120801 66.25604004 58.60924693 2.05053E-17 Residual 97 109.6556639 1.130470762 Total 99 242.167744 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% ower 95.0%/pper 95.0% Intercept 6.51983904 0.823090822 7.921166011 3.94495E-12 4.886231606 8.153446 4.886232 8.153446 P -1.6143824 0.151475863 -10.65768723 5.12726E-18 -1.91502003 -1.31374 -1.91502 -1.31374 A 0.00466438 0.001575003 2.961506969 0.003849344 0.001538437 0.00779 0.001538 0.00779 Log-Linear Model SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.63355944 0.40139756 0.38905524 0.58687607 Observations 100 ANOVA df MS Significance F Regression 22.40272212 11.20136106 32.52205574 1.55316E-11 Residual 97 33.40908188 0.344423524 Total 99 55.811804 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% ower 95.0%/pper 95.0% Intercept -1.988675 2.243299214 -0.886495666 0.377543082 -6.44100299 2.463653 -6.441 2.463653 InP -2.1695134 0.276091563 -7.857948763 5.37032E-12 -2.71747869 -1.62155 -2.71748 -1.62155 InA 0.91065837 0.370342406 2.458963249 0.015705726 0.175631206 1.645686 0.175631 1.645686
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