Question

# We wish to determine the impact of Specification Buying, X11, on Satisfaction Level, X10. To do...

We wish to determine the impact of Specification Buying, X11, on Satisfaction Level, X10. To do so we will split the Hatco data file into two separate data sets based on the Specification Buying, X11. This variable has two categories:

1=employs total value analysis approach, evaluating each purchase separately;

0 = use of specification buying.

Sort the entire Hatco data set based on Specification Buying. This will create two separate groups of records. Those records with X11 = 0 and those records with X11 = 1.Treat these as two distinct data sets.

For the 2 data sets, X11 = 0 and X11 = 1, perform regression analysis on Satisfaction Level X10 as a function of the first seven variables (Delivery Speed, Price Level, Price Flexibility, Manufacturer Image, Service, Salesforce Image, Product Quality). Use Alpha = .01 to determine variables to remove from model.

For X11 = 0 and X11 = 1:

Compare the two models and explain the differences between the two models. From a business perspective, why do they differ?

 Delivery Speed Price Level Price Flexibility Manufacturer Image Service Salesforce Image Product Quality Firm Size Usage Level Satisfaction Level Specification Buying Structure of Procurement Type of Industry (SIC) Type of Buying Situation 1.0 4.1 0.6 6.9 4.7 2.4 2.3 5.2 0 32.0 4.2 1 0 1 1 2.0 1.8 3.0 6.3 6.6 2.5 4.0 8.4 1 43.0 4.3 0 1 0 1 3.0 3.4 5.2 5.7 6.0 4.3 2.7 8.2 1 48.0 5.2 0 1 1 2 4.0 2.7 1.0 7.1 5.9 1.8 2.3 7.8 1 32.0 3.9 0 1 1 1 5.0 6.0 0.9 9.6 7.8 3.4 4.6 4.5 0 58.0 6.8 1 0 1 3 6.0 1.9 3.3 7.9 4.8 2.6 1.9 9.7 1 45.0 4.4 0 1 1 2 7.0 4.6 2.4 9.5 6.6 3.5 4.5 7.6 0 46.0 5.8 1 0 1 1 8.0 1.3 4.2 6.2 5.1 2.8 2.2 6.9 1 44.0 4.3 0 1 0 2 9.0 5.5 1.6 9.4 4.7 3.5 3.0 7.6 0 63.0 5.4 1 0 1 3 10.0 4.0 3.5 6.5 6.0 3.7 3.2 8.7 1 54.0 5.4 0 1 0 2 11.0 2.4 1.6 8.8 4.8 2.0 2.8 5.8 0 32.0 4.3 1 0 0 1 12.0 3.9 2.2 9.1 4.6 3.0 2.5 8.3 0 47.0 5.0 1 0 1 2 13.0 2.8 1.4 8.1 3.8 2.1 1.4 6.6 1 39.0 4.4 0 1 0 1 14.0 3.7 1.5 8.6 5.7 2.7 3.7 6.7 0 38.0 5.0 1 0 1 1 15.0 4.7 1.3 9.9 6.7 3.0 2.6 6.8 0 54.0 5.9 1 0 0 3 16.0 3.4 2.0 9.7 4.7 2.7 1.7 4.8 0 49.0 4.7 1 0 0 3 17.0 3.2 4.1 5.7 5.1 3.6 2.9 6.2 0 38.0 4.4 1 1 1 2 18.0 4.9 1.8 7.7 4.3 3.4 1.5 5.9 0 40.0 5.6 1 0 0 2 19.0 5.3 1.4 9.7 6.1 3.3 3.9 6.8 0 54.0 5.9 1 0 1 3 20.0 4.7 1.3 9.9 6.7 3.0 2.6 6.8 0 55.0 6.0 1 0 0 3 21.0 3.3 0.9 8.6 4.0 2.1 1.8 6.3 0 41.0 4.5 1 0 0 2 22.0 3.4 0.4 8.3 2.5 1.2 1.7 5.2 0 35.0 3.3 1 0 0 1 23.0 3.0 4.0 9.1 7.1 3.5 3.4 8.4 0 55.0 5.2 1 1 0 3 24.0 2.4 1.5 6.7 4.8 1.9 2.5 7.2 1 36.0 3.7 0 1 0 1 25.0 5.1 1.4 8.7 4.8 3.3 2.6 3.8 0 49.0 4.9 1 0 0 2 26.0 4.6 2.1 7.9 5.8 3.4 2.8 4.7 0 49.0 5.9 1 0 1 3 27.0 2.4 1.5 6.6 4.8 1.9 2.5 7.2 1 36.0 3.7 0 1 0 1 28.0 5.2 1.3 9.7 6.1 3.2 3.9 6.7 0 54.0 5.8 1 0 1 3 29.0 3.5 2.8 9.9 3.5 3.1 1.7 5.4 0 49.0 5.4 1 0 1 3 30.0 4.1 3.7 5.9 5.5 3.9 3.0 8.4 1 46.0 5.1 0 1 0 2 31.0 3.0 3.2 6.0 5.3 3.1 3.0 8.0 1 43.0 3.3 0 1 0 1 32.0 2.8 3.8 8.9 6.9 3.3 3.2 8.2 0 53.0 5.0 1 1 0 3 33.0 5.2 2.0 9.3 5.9 3.7 2.4 4.6 0 60.0 6.1 1 0 0 3 34.0 3.4 3.7 6.4 5.7 3.5 3.4 8.4 1 47.0 3.8 0 1 0 1 35.0 2.4 1.0 7.7 3.4 1.7 1.1 6.2 1 35.0 4.1 0 1 0 1 36.0 1.8 3.3 7.5 4.5 2.5 2.4 7.6 1 39.0 3.6 0 1 1 1 37.0 3.6 4.0 5.8 5.8 3.7 2.5 9.3 1 44.0 4.8 0 1 1 2 38.0 4.0 0.9 9.1 5.4 2.4 2.6 7.3 0 46.0 5.1 1 0 1 3 39.0 0.0 2.1 6.9 5.4 1.1 2.6 8.9 1 29.0 3.9 0 1 1 1 40.0 2.4 2.0 6.4 4.5 2.1 2.2 8.8 1 28.0 3.3 0 1 1 1 41.0 1.9 3.4 7.6 4.6 2.6 2.5 7.7 1 40.0 3.7 0 1 1 1 42.0 5.9 0.9 9.6 7.8 3.4 4.6 4.5 0 58.0 6.7 1 0 1 3 43.0 4.9 2.3 9.3 4.5 3.6 1.3 6.2 0 53.0 5.9 1 0 0 3 44.0 5.0 1.3 8.6 4.7 3.1 2.5 3.7 0 48.0 4.8 1 0 0 2 45.0 2.0 2.6 6.5 3.7 2.4 1.7 8.5 1 38.0 3.2 0 1 1 1 46.0 5.0 2.5 9.4 4.6 3.7 1.4 6.3 0 54.0 6.0 1 0 0 3 47.0 3.1 1.9 10.0 4.5 2.6 3.2 3.8 0 55.0 4.9 1 0 1 3 48.0 3.4 3.9 5.6 5.6 3.6 2.3 9.1 1 43.0 4.7 0 1 1 2 49.0 5.8 0.2 8.8 4.5 3.0 2.4 6.7 0 57.0 4.9 1 0 1 3 50.0 5.4 2.1 8.0 3.0 3.8 1.4 5.2 0 53.0 3.8 1 0 1 3 51.0 3.7 0.7 8.2 6.0 2.1 2.5 5.2 0 41.0 5.0 1 0 0 2 52.0 2.6 4.8 8.2 5.0 3.6 2.5 9.0 1 53.0 5.2 0 1 1 2 53.0 4.5 4.1 6.3 5.9 4.3 3.4 8.8 1 50.0 5.5 0 1 0 2 54.0 2.8 2.4 6.7 4.9 2.5 2.6 9.2 1 32.0 3.7 0 1 1 1 55.0 3.8 0.8 8.7 2.9 1.6 2.1 5.6 0 39.0 3.7 1 0 0 1 56.0 2.9 2.6 7.7 7.0 2.8 3.6 7.7 0 47.0 4.2 1 1 1 2 57.0 4.9 4.4 7.4 6.9 4.6 4.0 9.6 1 62.0 6.2 0 1 0 2 58.0 5.4 2.5 9.6 5.5 4.0 3.0 7.7 0 65.0 6.0 1 0 0 3 59.0 4.3 1.8 7.6 5.4 3.1 2.5 4.4 0 46.0 5.6 1 0 1 3 60.0 2.3 4.5 8.0 4.7 3.3 2.2 8.7 1 50.0 5.0 0 1 1 2 61.0 3.1 1.9 9.9 4.5 2.6 3.1 3.8 0 54.0 4.8 1 0 1 3 62.0 5.1 1.9 9.2 5.8 3.6 2.3 4.5 0 60.0 6.1 1 0 0 3 63.0 4.1 1.1 9.3 5.5 2.5 2.7 7.4 0 47.0 5.3 1 0 1 3 64.0 3.0 3.8 5.5 4.9 3.4 2.6 6.0 0 36.0 4.2 1 1 1 2 65.0 1.1 2.0 7.2 4.7 1.6 3.2 10.0 1 40.0 3.4 0 1 1 1 66.0 3.7 1.4 9.0 4.5 2.6 2.3 6.8 0 45.0 4.9 1 0 0 2 67.0 4.2 2.5 9.2 6.2 3.3 3.9 7.3 0 59.0 6.0 1 0 0 3 68.0 1.6 4.5 6.4 5.3 3.0 2.5 7.1 1 46.0 4.5 0 1 0 2 69.0 5.3 1.7 8.5 3.7 3.5 1.9 4.8 0 58.0 4.3 1 0 0 3 70.0 2.3 3.7 8.3 5.2 3.0 2.3 9.1 1 49.0 4.8 0 1 1 2 71.0 3.6 5.4 5.9 6.2 4.5 2.9 8.4 1 50.0 5.4 0 1 1 2 72.0 5.6 2.2 8.2 3.1 4.0 1.6 5.3 0 55.0 3.9 1 0 1 3 73.0 3.6 2.2 9.9 4.8 2.9 1.9 4.9 0 51.0 4.9 1 0 0 3 74.0 5.2 1.3 9.1 4.5 3.3 2.7 7.3 0 60.0 5.1 1 0 1 3 75.0 3.0 2.0 6.6 6.6 2.4 2.7 8.2 1 41.0 4.1 0 1 0 1 76.0 4.2 2.4 9.4 4.9 3.2 2.7 8.5 0 49.0 5.2 1 0 1 2 77.0 3.8 0.8 8.3 6.1 2.2 2.6 5.3 0 42.0 5.1 1 0 0 2 78.0 3.3 2.6 9.7 3.3 2.9 1.5 5.2 0 47.0 5.1 1 0 1 3 79.0 1.0 1.9 7.1 4.5 1.5 3.1 9.9 1 39.0 3.3 0 1 1 1 80.0 4.5 1.6 8.7 4.6 3.1 2.1 6.8 0 56.0 5.1 1 0 0 3 81.0 5.5 1.8 8.7 3.8 3.6 2.1 4.9 0 59.0 4.5 1 0 0 3 82.0 3.4 4.6 5.5 8.2 4.0 4.4 6.3 0 47.0 5.6 1 1 1 2 83.0 1.6 2.8 6.1 6.4 2.3 3.8 8.2 1 41.0 4.1 0 1 0 1 84.0 2.3 3.7 7.6 5.0 3.0 2.5 7.4 0 37.0 4.4 1 1 0 1 85.0 2.6 3.0 8.5 6.0 2.8 2.8 6.8 1 53.0 5.6 0 1 0 2 86.0 2.5 3.1 7.0 4.2 2.8 2.2 9.0 1 43.0 3.7 0 1 1 1 87.0 2.4 2.9 8.4 5.9 2.7 2.7 6.7 1 51.0 5.5 0 1 0 2 88.0 2.1 3.5 7.4 4.8 2.8 2.3 7.2 0 36.0 4.3 1 1 0 1 89.0 2.9 1.2 7.3 6.1 2.0 2.5 8.0 1 34.0 4.0 0 1 1 1 90.0 4.3 2.5 9.3 6.3 3.4 4.0 7.4 0 60.0 6.1 1 0 0 3 91.0 3.0 2.8 7.8 7.1 3.0 3.8 7.9 0 49.0 4.4 1 1 1 2 92.0 4.8 1.7 7.6 4.2 3.3 1.4 5.8 0 39.0 5.5 1 0 0 2 93.0 3.1 4.2 5.1 7.8 3.6 4.0 5.9 0 43.0 5.2 1 1 1 2 94.0 1.9 2.7 5.0 4.9 2.2 2.5 8.2 1 36.0 3.6 0 1 0 1 95.0 4.0 0.5 6.7 4.5 2.2 2.1 5.0 0 31.0 4.0 1 0 1 1 96.0 0.6 1.6 6.4 5.0 0.7 2.1 8.4 1 25.0 3.4 0 1 1 1 97.0 6.1 0.5 9.2 4.8 3.3 2.8 7.1 0 60.0 5.2 1 0 1 3 98.0 2.0 2.8 5.2 5.0 2.4 2.7 8.4 1 38.0 3.7 0 1 0 1 99.0 3.1 2.2 6.7 6.8 2.6 2.9 8.4 1 42.0 4.3 0 1 0 1 100.0 2.5 1.8 9.0 5.0 2.2 3.0 6.0 0 33.0 4.4 1 0 0 1

Using excel, first filter the data and copy the data with

Specification Buying, X11 with categories 1(employs total value analysis approach, evaluating each purchase separately) and 0 (use of specification buying) on 2 separate sheets.

The regression output for the model with specification buying = 1:

The regression output for the model with specification buying = 0:

Comparing the R square, for the two models, we find that in the model where, specification buying is employed, the predictors explain the variation in satisfaction level better than the other.

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