Use The Following Data For Questions 1-3

12 Jul Use The Following Data For Questions 1-3

Use the following data for questions 1-3.

 

A delivery company checked delivery time for 40 randomly-selected packages for 8 consecutive days. The chart below shows the number of packages that were delivered late on each of those days. Develop a p-chart and answer questions 1, 2 and 3 from this data.

 

 

 

Day	late packages
1	2
2	0
3	1
4	5
5	2
6	1
7	0
8	0

 

 

 

1. What is the UCL on the p-chart?
   1. 0.0344
   2. 0.0288
   3. 0.1407
   4. 0.1208

 

 

 

 

 

 

 

1. What is the LCL on the p-chart?
   1. -0.052
   2. 0, does not exist
   3. 0.0344
   4. 0.052

 

 

 

 

 

 

 

 

 

1. What can you determine about the process?
   1. There are special causes of variation.
   2. Only common cause variations are present
   3. The process is in statistical control
   4. It cannot be determined if the process is in statistical control.

 

 

 

1. A hotel property logged the number of complaints they received in each of 10 days. The table below reports the findings. Determine the UCL of the control chart.

 

 

 

day	Complaints
1	0
2	3
3	2
4	4
5	2
6	5
7	4
8	8
9	2
10	0

 

 

 

1. 1. 1.73
   2. 5.20
   3. 8.20
   4. 9.24

 

 

 

Use the following data to answer questions 5 and 6

 

 

 

A soda company pulled 7 soda cans off the line each hour for 5 hours. The chart below shows the mean and range of the fluid content of the cans in each sample.

 

 

 

Mean	Range
12.08	0.25
11.94	0.18
11.88	0.16
12.05	0.20
12.00	0.16

 

 

 

1. What is the LCL of the range chart?
   1. 0 (does not exist)
   2. 0.080
   3. 0.014
   4. 0.060

 

 

 

1. What is the UCL of the mean chart?
   1. 12.07
   2. 12.18
   3. 12.01
   4. 12.09

 

 

 

Data for Q 7 – 13 is in worksheet named Problems 7-13 in the Excel file, on page 1.

 

1. Management is considering opening an additional dealership with 14 sales people. Using linear regression, what is the expected weekly totalprofit for the new dealership?
   1. $41592.41
   2. $43,484.83
   3. $48,231.74
   4. $23,471.25

 

 

 

1. Management asks whether using number of sales people to predict total profit is a reasonable approach. Which of the following is the best answer to that question?
   1. Yes, it is intuitively logical that the more sales people the company has, the better it can serve the customers.
   2. No, a correlation coefficient value of 0.02 indicates a bad linear fit to the data
   3. Yes, a correlation coefficient value of 0.02 being positive indicates a positive relationship so the more sales people the better
   4. No, a coefficient of determination value of 0.02 indicates a bad fit to the data.

 

 

 

1. An old saying in car business is, “the more cars you sell, the higher your total profits will be”. Is this statement accurate for this dealership?
   1. Yes, because the number of new cars sold each week and used cars sold each week both have positive correlation values with total profit.
   2. Yes, because the number of new cars sold each week and used cars sold each week both have positive coefficient of determination values with total profit.
   3. No, number of new cars sold each week has a positive correlation value with total profit, and used cars sold each week has a negative correlation value with total profit.
   4. Yes, number of new cars sold each week and used cars sold each week both have positive intercept values from linear regression with total profit.

 

 

 

1. Management believes that a new dealership can sell 12 new cars each week. What is the 90% confidence interval for expected total profit?
   1. $49,072.61 to $53,662.37
   2. $36,907.97 to $65,827.01
   3. $45,812.31 to $52,127.96
   4. $12,0148.35 to $58,147.38

 

 

 

1. Management is admitting that number of new cars sold each week is the key to predicting total profit and asks you to confirm their belief. Which of the following statements would you use to answer management?
   1. Yes, everyone knows the more new cars you sell the higher the profit
   2. Yes, number of new cars sold each week is a good predictor of profit because the coefficient of determination is 0.66 and is the population coefficient of determination is statistically different from zero at the 0.05 level.
   3. Yes, number of new cars sold each week is a good predictor of profit because the coefficient of determination is 0.81 and is the population coefficient of determination is statistically different from zero at the 0.05 level.
   4. No, number of new cars sold each week is not a good predictor of profit because the coefficient of determination is 0.05 and it can’t be said that the population coefficient of determination is statistically different from zero at the 0.05 level.
2. Much to your surprise, the company CEO (who everyone thought was losing his mind) is actually a world renowned mathematician and he asks you if the assumptions regarding linear regression between total profit and number of new cars sold are met. You say yes, and quickly show him which of the following graphics to prove your point?

 

 

1. 1.  

 

 

 

 

1. 1. Both A and B are used
2. Jill, an aggressive member of the management team, has suggested the company should close all their used-car lots. She proposes to simply sell every used car taken in on trade, to the wholesale market. You are asked to analyze the data and worthiness of this suggestion, and offer an opinion based on your analysis. Which of the following is the best response?
   1. Jill is incorrect because every used car sold results in an additional profit of $3,298 with a correlation coefficient value of 0.81.
   2. While Jill appears to be correct since a used-car sale results in an average loss of ~$1,684, you cannot say, at the 0.05 significance level, that the population slope is different than zero.
   3. Jill appears to be correct since a used-car sale results in an average loss of ~$1,684, and the correlation coefficient value of 0.22 is considered good.
   4. You suggest that analysis of only one week of data (which is contained in the Excel file) is not sufficient information to draw a solid conclusion.
   5. Both B and D are correct.

 

 

 

Use the following time series datato answer Questions 14 – 17.

 

Quarter	Year 1	Year 2	Year 3	Year 4	Year 5
1	24,850	22,130	21,705	16,039	19,236
2	21,300	18,711	17,012	18,526	16,778
3	19,500	19,602	18,326	20,087
4	24,422	23,005	22,639	23,236

 

 

 

1. Compute a 4-Quarter moving average forecast for Qtr. 3 of Year 5 (round to the nearest unit)
   1. 23,326
   2. 19,834
   3. 19,379
   4. 19,472
2. Compute a 3-Quarter weighted moving average forecast for Quarter 3 of Year 5. Use these weights: 7 on the prior quarter, and then 4 and 1 (respectively) on quarters prior to that (rounded to the nearest unit).
   1. 17,890
   2. 18,136
   3. 19,834
   4. 19,750

 

 

 

1. Use exponential smoothing method with α = 0.33 to forecast demand in Quarter 3 of Year 5. Assume Forecast for Q1 of Year 1= 24,850. Round off the final answer to nearest integer.
   1. 19,472
   2. 23,218
   3. 17,890
   4. 19,030

 

 

 

1. Examine the 3 methods you used in the previous 3 problems. Which is best to use for this time series?

 

1. 4-Quarter moving average
2. 3-Quarter weighted moving average
3. Exponential smoothing
4. They are all equally good

 

 

 

1. Use the following sales data to compute a trend- and seasonally-adjusted forecast for quarter 3 of year 7. Round off seasonal indexes to two decimals.

 

 

 

	Year
Quarter	2008	2009	2010	2011	2012	2013
1	86.4	95.3	93.5	99.1	103.4	113.4
2	93	102.7	100.9	103	108.6	113.5
3	91.9	98.6	99.4	101.2	109.5	113.2
4	107.3	107.9	113.7	115.6	122.3	127.1

 

 

 

1. 109.4
2. 125.8
3. 114.6
4. 115.7

 

 

 

1. In the Excel data file on page 1, use worksheet for Problem 19.
   City Hospital wants to build a multiple regression equation to estimate a patient’s cost of stay using age of the patient and the number of days of stay. The manager has collected sample data which is found in the Excel file embedded below. Compute the multiple regression equation and use it to estimate Cost of Stay for a patient who is 22 years old and stays in the hospital 7 days.

 

 

 

1. 1. $14,781
   2. $12,189
   3. $13,060
   4. $12,812

 

 

 

 

 

Use the following information about a project to answer Q20 – Q22.
Times are in weeks.

 

 

1. How many weeks are required to finish this project?
   1. 48 weeks
   2. 22 weeks
   3. 35 weeks
   4. 23 weeks

 

 

 

1. What is the slack for activity B?
   1. 3 weeks
   2. 5 weeks
   3. 0 weeks
   4. 7 weeks

 

 

 

1. What is the latest start time for activity D?
   1. 21 weeks
   2. 24 weeks
   3. 29 weeks
   4. 10 weeks

 

 

 

Use the following for Questions 23 – 24.
There is only one critical path composed of activities 1,3,4,5 in a PERT project.
We also know the time estimates listed below (in days) for those activities.

 

Activity	Optimistic	Most Likely	Pessimistic
1	6	8	10
2	1	3	5
3	3	4	5
4	2	2	2
5	1	10	13

 

 

 

1. What is the expected completion time for this project?
   1. 13 days
   2. 17 days
   3. 23 days
   4. 68 days

 

 

 

1. The project was promised to be delivered in 22 days. Calculate the probability that we will miss that delivery date (i.e., the project will take longer than 22 days to finish).
   1. 0.9535
   2. 0.6802
   3. 0.4808
   4. 0.3191

 

 

 

Use the following tables (related to crashing a project) to answer question 25.

 

Times are in weeks.

 

Activity	Normal time	Crash time	Normal Cost	Crash Cost	Predecessor 1	Predecessor 2
A	2	1	2000	2300
B	3	1	3000	3400
C	2	1	2600	2700	A
D	4	2	4800	4900	B
E	4	2	5600	5800	C
F	3	2	3000	3050	C
G	5	2	8000	8600	D	E
H	2	1	1600	1900	F	G

 

 

 

1. What will it take to finish this project in 8 weeks?
   1. Total project cost is $31,900.
   2. Total project cost is $1300.
   3. Total project cost is $30,600.
   4. Not enough information.