Problem #4 Some parts of this Problem must be done in Excel some in Minitab 19 The manager of a retailer has been experiencing quite a few online "order errors" (i.e. retrieving the wrong item). A...

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Extracted text: Problem #4 Some parts of this Problem must be done in Excel some in Minitab 19 The manager of a retailer has been experiencing quite a few online "order errors" (i.e. retrieving the wrong item). A training program then introduced as the manger believes the source of these errors are due to being as an inexperienced workers who have never attended an on-job training session. The 37 collected randomly selected sample data in the excel file are provided with number of errors, whether the worker attended a training sessions (train=1, otherwise=D0), years of experiences. 3 a) Estimate two linear regression models - one with experience and train as independent variables, and another one with an extra feature for the interaction between experience and training. Both models come with an intercept. You can use basic excel (Data Analysis adds-in) or Minitab 19 for this part. b) Comments on coefficient of determination, and the significance of the estimated coefficients of both models. c) Clearly explain- in a nontechnical language- the followings: d) Significance or insignificance of the interaction term estimated in part (a). e) Interpretation of the estimated coefficients of both models. f) Report which model your group will go with for predicting the errors, and why? g) Use the selected model in part (d), and predict the number of errors for an employee with 20 years of experience who attended the training session as well as for an employee with 20 years of experiences who did not attend the session. Any comments? [No need to use Minitab 19 for this part]. h) Discuss any potential influential observations for both of the estimated models. i) List some other potential features may have impact on the errors made by workers of this retailer, and explain how you would collect data for these potential extra features.


Extracted text: A C D 1 obs. Errors Exper Train 2 1 13 9. 3 2 3 27 4 3. 12 6. 5 1 6. 5 9. 1 7 6. 49 26 8. 7 39 4 9. 8. 8 5 1 10 6. 28 11 10 12 2 12 11 5 17 13 12 1 5 1 14 13 21 8 15 14 73 16 15 28 17 16 29 1 18 17 25 19 18 3 23 1 20 19 4 20 21 20 4 17 1 22 21 2 30 1 23 22 1 27 1 24 23 3 23 25 24 1 24 1 26 25 6 1 27 26 3 25 1 28 27 6 14 29 28 4 21 30 29 7 2 31 30 4 24 1 32 31 21 8. 33 32 73 2 34 33 28 35 34 39 1 36 35 17 37 36 1 23 1 38 37 20 20