A manager of a large supermarket chain wants to predict the demand of the best-selling SKU of the Coke product family, namely the 16 oz 24-can case. The manager thinks that the demand of Coke may be related to its price, the national advertisement expenditure level of Coke, and the price of the 16 oz 24-can case Pepsi Cola. Based on a sample of 20 weeks of data of quantity sold (in cases) for the Coke Cola. price of Coke Cola (in dollars), Coke's advertisement expenditure (in million dollars), and the price of Pepsi Cola (in dollars), a multiple linear regression analysis was performed in Excel. A part of the output of the regression analysis is given below: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Price of Coke Ad Expenditure Price of Pepsi 0.70955 0.503461 0.410359 2.130054 20 Df 3 16 19 Coefficients 48.63081 -0.3035 0.342937 0.23406 SS 73.60593 72.59407 146.2 Standard Error 6.3247384 0.1711745 0.1655882 0.1393504 MS 24.53531 4.537129 t Stat 7.688984 -1.77307 2.071021 1.679653 F 5.40767 Significance F 0.0092117 P-value Lower 95% 9.2E-07 35.222968 0.09525 -0.6663779 0.05489 -0.0080947 0.11244 -0.0613493 a. Using a 10% significance level, determine if there exists a significant relationship between the independent variables and the dependent variable. State the hypotheses to be tested, the observed value of the test statistic, the corresponding p-value, and your decision.
A manager of a large supermarket chain wants to predict the demand of the best-selling SKU of the Coke product family, namely the 16 oz 24-can case. The manager thinks that the demand of Coke may be related to its price, the national advertisement expenditure level of Coke, and the price of the 16 oz 24-can case Pepsi Cola. Based on a sample of 20 weeks of data of quantity sold (in cases) for the Coke Cola. price of Coke Cola (in dollars), Coke's advertisement expenditure (in million dollars), and the price of Pepsi Cola (in dollars), a multiple linear regression analysis was performed in Excel. A part of the output of the regression analysis is given below: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Price of Coke Ad Expenditure Price of Pepsi 0.70955 0.503461 0.410359 2.130054 20 Df 3 16 19 Coefficients 48.63081 -0.3035 0.342937 0.23406 SS 73.60593 72.59407 146.2 Standard Error 6.3247384 0.1711745 0.1655882 0.1393504 MS 24.53531 4.537129 t Stat 7.688984 -1.77307 2.071021 1.679653 F 5.40767 Significance F 0.0092117 P-value Lower 95% 9.2E-07 35.222968 0.09525 -0.6663779 0.05489 -0.0080947 0.11244 -0.0613493 a. Using a 10% significance level, determine if there exists a significant relationship between the independent variables and the dependent variable. State the hypotheses to be tested, the observed value of the test statistic, the corresponding p-value, and your decision.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:& Find and interpret the value of coefficient of determination.
c. Using a 10% significance level, can we conclude that the demand of Coke is related to the price of Coke?
State the hypotheses to be tested, the observed value of the test statistic, the corresponding p-value, and
your decision.
d. Interpret the value of the coefficient of the price of Pepsi in the estimated regression equation.
e. Consider a week in which the Coke price is $23.5, the Pepsi price is $22, and the national advertising
expenditure is 2.6 million dollars. What is the predicted demand of Coke in this week?
Transcribed Image Text:A manager of a large supermarket chain wants to predict the demand of the best-selling SKU of the Coke
product family, namely the 16 oz 24-can case. The manager thinks that the demand of Coke may be
related to its price, the national advertisement expenditure level of Coke, and the price of the 16 oz 24-can
case Pepsi Cola. Based on a sample of 20 weeks of data of quantity sold (in cases) for the Coke Cola,
price of Coke Cola (in dollars), Coke's advertisement expenditure (in million dollars), and the price of
Pepsi Cola (in dollars), a multiple linear regression analysis was performed in Excel. A part of the output
of the regression analysis is given below:
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
Price of Coke
Ad Expenditure
Price of Pepsi
0.70955
0.503461
0.410359
2.130054
20
Df
3
16
19
Coefficients
48.63081
-0.3035
0.342937
0.23406
SS
73.60593
72.59407
146.2
Standard Error
6.3247384
0.1711745
0.1655882
0.1393504
MS
24.53531
4.537129
F
5.40767 0.0092117
Significance F
t Stat
P-value
Lower 95%
9.2E-07 35.222968
7.688984
-1.77307 0.09525 -0.6663779
2.071021
0.05489
-0.0080947
1.679653
0.11244
-0.0613493
a. Using a 10% significance level, determine if there exists a significant relationship between the
independent variables and the dependent variable. State the hypotheses to be tested, the observed value of
the test statistic, the corresponding p-value, and your decision.
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VIEWStep 3: Interpret the value of coefficient of determination.
VIEWStep 4: Determine if the demand of Coke is related to the price of Coke at a 10% significance level
VIEWStep 5: Interpret the value of the coefficient of the price of Pepsi in the estimated regression equation
VIEWStep 6: Predict the Demand for Coke
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