Price($) Promotional exp(K) Quality City:1/Suburban:0 Sales(K) 949 5 100 1 168 9
ID: 3222625 • Letter: P
Question
Price($)
Promotional exp(K)
Quality
City:1/Suburban:0
Sales(K)
949
5
100
1
168
941
4.3
94
0
150
934
3
89
1
168
921
2
85
0
148
915
0.75
79
1
152
909
4.8
75
0
162
904
3.6
70
1
160
1014
3
63
1
123
1006
1.5
60
0
130
990
0.7
55
0
116
978
4.7
51
1
142
962
3.5
47
0
145
955
2.8
42
1
134
953
1.3
35
0
128
1050
0.25
30
1
117
1040
4.5
26
1
118
1038
3.2
22
0
107
1022
2.4
17
0
124
1021
1.2
12
1
104
1018
0
6
0
106
Please consider the data presented above for the monthly sales of Ever-cool brand of refrigerators in 1,000s of dollars and answer the following questions:
Independent variables are
Price (in dollars); Promotional Expenditure (in 1,000s of dollars); Quality of service (scale of 1-10); location (categorical variable: city area: 1; suburban area: 0).
a. Based on the relevant residual plots, do you see any evidence of violation of assumptions (Linearity, Normality, Equal variance)?
b. State the multiple regression equation and interpret the meaning of the slopes, b1, b2, b3, and b4.
c. At the 0.05 level of significance, determine whether each independent variable makes a significant contribution to the regression model. On the basis of these results, indicate the independent variables to include in this model. (Based on t - test results)
d. Construct a 95% confidence interval estimate of the population slope between Quality and the monthly sales () (please note that Minitab can’t do this directly, however you may use the relevant information from Minitab output and then construct the confidence interval manually)
e. Perform the overall F- test and comment on the significance of the model.
Please follow the following instructions:
Use Excel/ or Minitab to run the analysis.
Price($)
Promotional exp(K)
Quality
City:1/Suburban:0
Sales(K)
949
5
100
1
168
941
4.3
94
0
150
934
3
89
1
168
921
2
85
0
148
915
0.75
79
1
152
909
4.8
75
0
162
904
3.6
70
1
160
1014
3
63
1
123
1006
1.5
60
0
130
990
0.7
55
0
116
978
4.7
51
1
142
962
3.5
47
0
145
955
2.8
42
1
134
953
1.3
35
0
128
1050
0.25
30
1
117
1040
4.5
26
1
118
1038
3.2
22
0
107
1022
2.4
17
0
124
1021
1.2
12
1
104
1018
0
6
0
106
Explanation / Answer
SUMMARY OUTPUT Regression Statistics Multiple R 0.94863 R Square 0.899899 Adjusted R Square 0.873205 Standard Error 7.426158 Observations 20 ANOVA df SS MS F Significance F Regression 4 7436.583 1859.146 33.71204 2.47E-07 Residual 15 827.2173 55.14782 Total 19 8263.8 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 327.9547 57.66385 5.687353 4.31E-05 205.0471 450.8623 205.0471 450.8623 Price($) -0.22204 0.055369 -4.0102 0.001136 -0.34006 -0.10403 -0.34006 -0.10403 Promotional exp(K) 2.841239 1.185589 2.39648 0.030031 0.314217 5.368261 0.314217 5.368261 Quality 0.274701 0.09661 2.843389 0.01233 0.068781 0.480621 0.068781 0.480621 City:1/Suburban:0 3.737943 3.402595 1.098557 0.289282 -3.51452 10.9904 -3.51452 10.9904
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