SUMMARY OUTPUT Regression Statistics Multiple R 0.816023 R Square 0.665894 Adjusted R Squa 0.63862 Standard Error 757.7088 Observations 54 ANOVA df SS MS F Significance F Regression 4 56068898.12 14017224.53 24.41504 3.75E-11 Residual 49 28132005.81 574122.5676 Total 53 84200903.93 Coefficients Standard Error P-value Intercept Price-H-oil Price-C-oil income Advertise 5.64E-10 t Stat 4006.582 1993.549899 2.009772688 -70.7673 34.71936034 -2.038267429 59.84518 30.45208347 1.965224555 -0.16841 0.14856184 -1.133610149 8.291498 1.077744533 7.693379807 Lower 95% 0.393628888 0.049978 0.046939 -140.5385081 0.05507 -1.350570674 0.262474 -0.466957404 6.1256893 Upper 95% 8012.770652 -0.996174565 0.393628888 -140.5385081 Lower 95.0% Upper 95.0% 8012.770652 -0.996174565 121.040935 -1.350570674 121.040935 0.130134985 6.1256893 10.45730675 0.130134985 -0.466957404 10.45730675

ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN:9780190931919
Author:NEWNAN
Publisher:NEWNAN
Chapter1: Making Economics Decisions
Section: Chapter Questions
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Hide student question Time Left : 01:58:26 Student question   Analyze the model significance using the regression statistics: R-square, t-statistics, and use the standard error to determine the range within which demand for the product will fall with a 95% confidence interval. Calculate and interpret the following elasticities: Own price, cross price, income, and advertising and explain what these results suggest about the effects of changes in each of these variables on the demand for Maa mustard oil? Based on the elasticities you have estimated in question 8, what will be the impact of a price increase by the company on the total revenue of Maa mustard oil (assuming other variables remain constant)? What is the revenue maximizing price for Maa oil if the competitors price does not increase in October 2015? Solve pls
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.816023
R Square
0.665894
Adjusted R Squa
0.63862
Standard Error
757.7088
Observations
54
ANOVA
df
SS
MS
F
Significance F
Regression
4
56068898.12
14017224.53
24.41504
3.75E-11
Residual
49
28132005.81 574122.5676
Total
53 84200903.93
Coefficients
Standard Error
P-value
Intercept
Price-H-oil
Price-C-oil
income
Advertise
5.64E-10
t Stat
4006.582 1993.549899 2.009772688
-70.7673 34.71936034 -2.038267429
59.84518 30.45208347 1.965224555
-0.16841 0.14856184 -1.133610149
8.291498 1.077744533 7.693379807
Lower 95%
0.393628888
0.049978
0.046939 -140.5385081
0.05507 -1.350570674
0.262474 -0.466957404
6.1256893
Upper 95%
8012.770652
-0.996174565
0.393628888
-140.5385081
Lower 95.0%
Upper 95.0%
8012.770652
-0.996174565
121.040935 -1.350570674
121.040935
0.130134985
6.1256893
10.45730675
0.130134985 -0.466957404
10.45730675
Transcribed Image Text:SUMMARY OUTPUT Regression Statistics Multiple R 0.816023 R Square 0.665894 Adjusted R Squa 0.63862 Standard Error 757.7088 Observations 54 ANOVA df SS MS F Significance F Regression 4 56068898.12 14017224.53 24.41504 3.75E-11 Residual 49 28132005.81 574122.5676 Total 53 84200903.93 Coefficients Standard Error P-value Intercept Price-H-oil Price-C-oil income Advertise 5.64E-10 t Stat 4006.582 1993.549899 2.009772688 -70.7673 34.71936034 -2.038267429 59.84518 30.45208347 1.965224555 -0.16841 0.14856184 -1.133610149 8.291498 1.077744533 7.693379807 Lower 95% 0.393628888 0.049978 0.046939 -140.5385081 0.05507 -1.350570674 0.262474 -0.466957404 6.1256893 Upper 95% 8012.770652 -0.996174565 0.393628888 -140.5385081 Lower 95.0% Upper 95.0% 8012.770652 -0.996174565 121.040935 -1.350570674 121.040935 0.130134985 6.1256893 10.45730675 0.130134985 -0.466957404 10.45730675
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