Data
Employee Starting Salary On Road Pct
Chris Al
ight: Percentage of time on the road with a client
State Univ
Chris Al
ight: Whether the employee graduated from State University
CIS Degree
Chris Al
ight: Whether the employee got his/her degree in the computer information systems area (or a similar area)
Stayed 3 Years
Chris Al
ight: Whether the employee stayed with the company for at least 3 years
Tenure
Chris Al
ight: Number of months employee stayed with the company (for those who left before 3 years)
1 38900 57% Yes Yes No 11
2 42300 68% Yes Yes No 5
3 39800 75% No Yes No 21
4 35700 29% No Yes Yes
5 40400 71% No Yes No 19
6 36400 62% No Yes No 18
7 37300 41% Yes No Yes
8 36600 37% No Yes No 17
9 34600 65% No Yes No 20
10 42300 53% Yes Yes No 16
11 37800 73% No Yes Yes
12 39700 60% No Yes No 12
13 36100 56% Yes Yes Yes
14 36100 42% Yes Yes Yes
15 33800 35% No Yes Yes
16 38900 36% No Yes Yes
17 35500 50% No Yes No 15
18 37600 58% No Yes Yes
19 38100 78% Yes Yes Yes
20 38300 16% No No Yes
21 34600 29% No Yes Yes
22 36300 26% No Yes Yes
23 37200 59% Yes Yes No 14
24 37700 55% Yes Yes Yes
25 36600 45% No Yes No 13
26 41000 81% Yes Yes No 9
27 40800 111% Yes Yes No 7
28 36700 56% Yes Yes Yes
29 37700 56% No Yes No 22
30 37700 42% No Yes No 11
31 39300 69% Yes Yes No 16
32 37500 50% No Yes No 18
33 40000 69% Yes Yes No 9
34 41500 64% Yes No No 5
35 39600 72% No Yes No 18
36 36900 42% No Yes Yes
37 39500 62% No Yes No 5
38 37800 20% No Yes No 23
39 38600 57% No Yes No 8
40 40600 70% Yes Yes Yes
41 41600 63% Yes Yes Yes
42 40800 44% No No Yes
43 36200 68% No Yes No 31
44 39600 59% No Yes No 10
45 36100 56% Yes Yes Yes
46 36100 42% No Yes Yes
47 39500 53% Yes Yes No 17
48 39100 38% No Yes Yes
49 40300 53% Yes Yes Yes
50 38800 78% Yes Yes Yes
51 39400 34% Yes Yes No 27
52 38400 23% No Yes Yes
53 35200 33% No Yes Yes
54 36100 24% No No Yes
55 37100 44% No Yes Yes
56 37200 64% Yes Yes No 23
57 36800 46% Yes No Yes
58 36500 27% No No Yes
59 35900 29% No No Yes
60 37900 76% Yes Yes No 14
61 37900 58% No Yes No 26
62 37900 28% No Yes No 14
63 36700 37% No Yes Yes
64 37400 49% No Yes No 19
65 38200 62% Yes Yes Yes
66 36600 51% Yes Yes No 7
This is fictitious data.
5. This problem illustrates an interesting variation of simple random sampling.
a. Open a blank spreadsheet and use the RAND() 
function to create a column of 1000 random numbers. Don’t freeze them. This is actually
a simple random sample from the uniform distribution between 0 and 1. Use the COUNTIF function to count the number of values between
0 and 0.1, between 0.1 and 0.2, and so on. Each such interval should contain about 1/10 of all values. Do they? (Keep pressing the F9 key to see how the results change.) 

. Repeat part a, generating a second column of random numbers, but now generate the first 100
as uniform between 0 and 0.1, the next 100 as uniform between 0.1 and 0.2, and so on, up to
0.9 to 1. (Hint: For example, to create a random number uniformly distributed between 0.5 and 0.6, use the formula =0.5+0.1*RAND(). Do you see why?) Again, use COUNTIF to find the number of the 1000 values in each of the intervals, although there shouldn’t be any surprises this time. Why might this type of random sampling be preferable to the random sampling in part a? (Note: The sampling in part a is called Monte Carlo sampling, whereas the sampling in part b is basically Latin Hypercube sampling, the form of sampling we advocate in Chapters 15 and 16 on simulation.) 

4. The file P08_06.xlsx contains data on repetitive task times for each of two workers. John has been doing this task for months, whereas Fred has just started. Each time listed is the time (in seconds) to perform a routine task on an assembly line. The times shown are in chronological order.
a. Calculate a 95% confidence interval for the mean time it takes John to perform the task. Do the same for Fred. 

. Do you believe both of the confidence intervals in part a are valid and/or useful? Why or why not? Which of the two workers would you rather have, assuming that task time is the only issue? 

Data
Observation John Fred
1 66.4 75.6
2 63.8 75.1
3 69.3 74.6
4 64.2 76.1
5 55.7 71.6
6 72.5 73.7
7 66.2 75.8
8 64.0 81.8
9 68.3 73.3
10 66.1 73
11 64.3 77.8
12 67.1 74.8
13 70.1 61.3
14 65.7 69.7
15 63.0 68.7
16 59.0 76.1
17 59.1 77.7
18 65.9 71
19 67.8 71.6
20 77.0 62.3
21 65.4 66.4
22 70.5 78.3
23 65.2 70.9
24 73.3 70
25 63.2 70.9
26 65.6 70
27 63.2 66.5
28 62.7 71.9
29 61.1 67.8
30 65.2 70.1
31 64.6 72.1
32 70.0 70.7
33 65.8 60.3
34 68.2 65.7
35 70.2 68.6
36 68.8 70.4
37 58.5 61.4
38 63.8 62.7
39 64.0 64
40 68.5 69.5
41 69.0 62.3
42 56.8 66.1
43 64.8 62.1
44 60.1 68
45 66.8 60.6
46 73.0 64.7
47 66.0 60.6
48 62.5 62.3
49 69.7 59.1
50 65.8 60.8
51 65.2 68
52 69.6 61.6
53 64.8 58.3
54 70.5 55.8
55 61.3 67.8
56 65.8 64.1
57 67.9 63.9
58 68.8 60.2
59 64.1 62.3
60 60.5 61.1
61 63.6 63.4
62 67.6 64.5
63 66.4 58.5
64 64.1 60.1
65 72.1 62.9
66 66.6 57.2
67 66.0 63.5
68 71.9 65.7
69 65.6 64.7
70 58.8 57.4
This is fictitious data.
Demand for systems analysts in the consulting
industry is greater than ever. Graduates with a combination of business and computer knowledge— some even from liberal arts programs—are getting great offers from consulting companies. Once these people are hired, they frequently switch from one company to another as competing companies lure them away with even better offers. One consulting company, D&Y, has collected data on a sample of systems analysts with undergraduate degrees they hired several years ago. The data are in the file C08_02.xlsx. The variables are as follows:
· Starting Salary: employee’s starting salary at D&Y 

· On Road Pct: percentage of time employee has 
spent on the road with clients 

· State Univ: whether the employee graduated from State University (D&Y’s principal source of recruits) 

· CIS Degree: whether the employee majored in Computer Information Systems (CIS) or a similar computer-related area 

· Stayed 3 Years: whether the employee stayed at least three years with D&Y 

· Tenure: tenure of employee at D&Y (months) if he or she moved before three years
· 
D&Y is trying to learn everything it can about retention of these valuable employees. You can help by solving the following problems and then, based on your analysis, presenting a report to D&Y. 

· 1. Although starting salaries are in a fairly na
ow band, D&Y wonders whether they have anything to do with retention.
· B. Calculate a 95% confidence interval for the mean starting salary of all employees who stay at least three years with D&Y. Do the same for those who leave before three years. Then calculate a 95% confidence interval for the difference between these means.
c. Among all employees whose starting salary is below the median ($37,750), calculate a 95% confidence interval for the proportion that stay with D&Y for at least three years. Do the same for the employees with starting salaries above the median. Then calculate a 95% confidence interval for the difference between these proportions.
2. D&Y wonders whether the percentage of time on the road might influence who stays and who leaves. Repeat the previous problem, but now do the analysis in terms of percentage of time on the road rather than starting salary. (The median percentage of time on the road is 54%.)
3. Find a 95% confidence interval for the mean tenure (in months) of all employees who leave D&Y within three years of being hired. Why is
 it not possible with the given data to calculate a confidence interval for the mean tenure at D&Y among all systems analysts hired by D&Y?
4. State University’s students, particularly those in its nationally acclaimed CIS area, have traditionally been among the best of D&Y’s recruits. But are they relatively hard to retain? Calculate one or more relevant confidence intervals to help you make an argument one-way or the other