2. PYTHON: Identifying Good System Administrators. A management consultant is studying the roles played by experience and training in a system administrators ability to complete a set of tasks in a specified amount of time. In particular, she is interested in discriminating between administrators who are able to complete given tasks within a specified time and those who are not. Data are collected on the performance of 75 randomly selected administrators. They are stored in the file SystemAdministrators.csv. (data below) The variable Experience measures months of full-time system administrator experience, while Training measures the number of relevant training credits. The outcome variable Completed is either Yes or No, according to whether or not the administrator completed the tasks.

A. Create a scatter plot of Experience vs. Training using color or symbol to distinguish programmers who completed the task from those who did not complete it. Which predictor(s) appear(s) potentially useful for classifying task completion? B. Run a logistic regression model with both predictors using the entire dataset as training data. Among those who completed the task, what is the percentage of programmers incorrectly classified as failing to complete the task? C. To decrease the percentage in part (b), should the cutoff probability be increased or decreased?

D. How much experience must be accumulated by a programmer with 4 years of training before his or her estimated probability of completing the task exceeds 0.5?

Experience Training Completed task

10.9 4 Y

9.9 4 Y

10.4 6 Y

13.7 6 Y

9.4 8 Y

12.4 4 Y

7.9 6 Y

8.9 4 Y

10.2 6 Y

11.4 4 Y

8.6 4 Y

9.2 4 Y

11.7 8 Y

7.6 4 Y

7 4 Y

4.9 4 N

7.1 6 N

5 4 N

4.8 4 N

4.4 4 N

4.2 4 N

6.3 4 N

4.7 4 N

5.2 4 N

8.9 4 N

5.5 6 N

5.4 4 N

5.7 4 N

7.7 4 N

2.7 4 N

6.7 4 N

6.2 6 N

6.6 6 N

3.6 4 N

5 4 N

5.4 4 N

7.3 6 N

7.6 4 N

8.6 6 N

12.2 4 N

5.8 4 N

6.1 4 N

5.5 6 N

4.1 4 N

4.5 8 N

7.8 4 N

7 4 N

4.3 4 N

6.1 4 N

4.9 4 N

6.9 4 N

5.2 4 N

6.8 4 N

5.8 4 N

4.6 4 N

7.2 4 N

5.2 4 N

5.1 4 N

9.2 4 N

4.7 4 N

5.9 6 N

8 4 N

4 4 N

6.3 4 N

6.6 4 N

6.5 4 N

7.4 8 N

6.5 4 N

8.2 4 N

5.9 4 N

5.6 4 N

5.9 8 N

6.4 6 N

3.8 4 N

5.3 4 N