1. Which model will you choose to use to explain and predict X222. How accurate is the regression mo

  

1. Which model will you choose to use to explain and predict X222. How accurate is the regression model you chose?3. List the variables that have a significant impact on X22 based on the chosen model4. What region (e.g. country) provides the most sales for the company?5. What distribution is the most effective?6. If the company can improve only 3 strategic variables (from X6 to X18), which three should it improve in order to maximize X22?
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Regression output #1
Model
1
R
.854a
Model Summaryb
Adjusted R
Std. Error of
R Square
Square
the Estimate
.729
.716
4.7765
DurbinWatson
2.076
ANOVAa
Model
1
Regression
Residual
Total
Sum of
Squares
11663.109
4334.891
15998.000
df
Mean Square
9
1295.901
190
22.815
199
F
56.800
Sig.
.000b
Coefficientsa
Unstandardized Coefficients
Model
B
Std. Error
1
(Constant)
-.835
3.212
X2 – Industry Type
-2.089
.709
X3 – Firm Size
.238
.781
X5 – Distribution System
-3.207
.904
X6 – Product Quality
3.189
.343
X7 – E-Commerce
2.078
.555
X8 – Technical Support
-.179
.210
X9 – Complaint
3.634
.381
Resolution
X10 – Advertising
.105
.361
X19 – Satisfaction
1.357
.550
a. Dependent Variable: X23 – Purchase Level
Standardized
Coefficients
Beta
-.117
.013
-.179
.492
.178
-.033
.490
t
-.260
-2.945
.305
-3.546
9.302
3.748
-.851
9.545
Sig.
.795
.004
.761
.000
.000
.000
.396
.000
.013
.188
.291
2.466
.772
.015
Regression output #2
Model
1
R
.889a
Model Summaryb
Adjusted R
Std. Error of
R Square
Square
the Estimate
.791
.779
4.2171
DurbinWatson
2.047
ANOVAa
Model
1
Regression
Residual
Total
Sum of
Squares
12654.624
3343.376
15998.000
df
11
188
199
Mean Square
1150.420
17.784
F
64.689
Sig.
.000b
Coefficientsa
Unstandardized Coefficients
Model
B
Std. Error
1
(Constant)
-18.818
4.166
X2 – Industry Type
-1.506
.623
X3 – Firm Size
-.160
.743
X4 – Region
1.995
1.032
X5 – Distribution System
-3.028
.770
X6 – Product Quality
3.864
.292
X11 – Product Line
4.728
.315
X12 – Salesforce Image
1.108
.332
X13 – Competitive
.308
.255
Pricing
X14 – Warranty &
-.367
.363
Claims
X15 – New Products
-.206
.208
X17 – Price Flexibility
3.448
.329
a. Dependent Variable: X23 – Purchase Level
Standardized
Coefficients
Beta
-.084
-.009
.110
-.169
.596
.695
.139
.054
t
-4.517
-2.417
-.215
1.933
-3.934
13.236
14.998
3.338
1.207
Sig.
.000
.017
.830
.055
.000
.000
.000
.001
.229
-.036
-1.013
.313
-.034
.459
-.990
10.475
.324
.000
Please answer the following questions:
1. Which model will you choose to use to explain and predict X22
2. How accurate is the regression model you chose?
3. List the variables that have a significant impact on X22 based on the chosen model
4. What region (e.g. country) provides the most sales for the company?
5. What distribution is the most effective?
6. If the company can improve only 3 strategic variables (from X6 to X18), which three should it
improve in order to maximize X22?

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