Reference no: EM132630235
IE-332 Engineering Statistics - King Abdulaziz University
Case studies
1. An experiment was conducted in order to evaluate the effectiveness of two devices for improving the efficiency of car engine systems. Engine efficacy was measured after one of the two devices was fixed. The two devices were an electric vent-200F (200F) and electric vent-149S (149S). The Engine efficacy data (BTU.In) are stacked in one column with a grouping column (Vent-type) containing identifiers or subscripts to denote the population. See attached excel file on tab (car_engine)
• Suppose that you performed a variance test and found no evidence for variances being unequal -Test the normality assumption (by graph and goodness of fit) and Show your work using confidence interval and hypothesis testing procedures to test the variances equality- (Solve manually and Minitab)
• Now you want to compare the effectiveness of these two devices by determining whether or not there is any evidence that the difference between the devices is different from zero. - (Solve manually and Minitab)
2. Suppose that you work in a shoe company want to compare two materials, A and B, for use on the soles of boys' shoes. In this problem, each of ten boys in a study wore a special pair of shoes with the sole of one shoe made from Material A in column (Mat-A) and the sole on the other shoe made from Material B in column (Mat-B). The sole types were randomly assigned to account for systematic differences in wear between the left and right foot. After three months, the shoes are measured for wear. See attached excel file on tab (shoe)
• Weight measurements were made on nine boys in column (weight lb). You know that the distribution of measurements has historically been close to normal with σ = 0.2. Test if the population mean is 50 and obtain a 90% confidence interval for the mean. - (Solve manually and Minitab)
• You want to see if these is difference between the two materials. Justify your answers by using hypothesis testing and confidence interval procedures. - (Solve manually and Minitab)
• Compare the results from the paired procedure with those from an unpaired- (Solve manually and Minitab)
3. You are hired by a ministry of health and you work as IE in the research and development (R&D) department. You are conducting a new research to analyze the number of COVID-19 in Jeddah. You use a U chart to monitor the number of COVID-19 cases per month, however, a U chart assumes that data follow a Poisson distribution. Therefore, you as a professional IE, want to assess whether the number of COVID-19 cases follow a Poisson distribution. Records of the daily number of cases at this infection are available for 50 days. See attached excel file on tab (COVID-19_Jeddah)
How can you make sure that the number of COVID-19 cases follows a Poisson distribution? (Solve by Minitab)
Forecasting Ticket Revenue for A western Football Team Games
at King Abdullah Sport City
Discussion Questions * (Solve using Minitab/other statistical software)
1. Use the data in Table 1 to build a regression model with three independent variables mentioned above.
2. Check the model adequacy
3. Which variables are significant? Justify your answer using ANOVA table
4. Use the data to build a model with the temperature of the day as the sole independent variable.
5. Using the multiple-regression model from question 1, what would be the additional sales potential of a 42 °C , 528.36 Km, and 79% ?
6. What additional independent variables might you suggest to include in Nawaf's model?
Table1. Data for Last Year's Western Team Ticket Sales Pricing Model
|
Temp (°C)
|
Distance (KM)
|
Humidity%
|
Additional Sales Potential (S.R.)
|
|
43.36
|
1050.15
|
73.712
|
46,241.25
|
|
41.18
|
1108.37
|
70.006
|
108,765.00
|
|
37.27
|
899.25
|
63.359
|
410,295.00
|
|
42.53
|
52.36
|
72.301
|
284,208.75
|
|
43.15
|
1011.59
|
73.355
|
159,588.75
|
|
38.24
|
900.25
|
65.008
|
450,795.00
|
|
29.58
|
1086
|
50.286
|
76,721.25
|
|
34.11
|
3.15
|
57.987
|
866,325.00
|
|
37.45
|
795.25
|
63.665
|
106,706.25
|
|
43.49
|
75.25
|
73.933
|
414,603.75
|
|
39.22
|
958.25
|
66.674
|
168,641.25
|
|
32.14
|
952.88
|
54.638
|
113,463.75
|
|
30.27
|
1025.66
|
51.459
|
91,983.75
|
|
42.49
|
400.25
|
72.233
|
372,967.50
|
|
28.43
|
582.32
|
48.331
|
217,327.50
|
Attachment:- Engineering Statistics.rar