Green Belt Final Exam
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Question 1 of 80
1. Question
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Question 2 of 80
2. Question
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Question 3 of 80
3. Question
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Question 4 of 80
4. Question
Given the process steps below and their data, what is the Rolled Throughput Yield of the entire process?
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Question 5 of 80
5. Question
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Question 6 of 80
6. Question
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Question 7 of 80
7. Question
The company Legendary Lights LLC. produces Christmas lights in strands with various bulb counts and colors. Their quality engineer has collected data on production defects and you need to generate a Pareto chart on “Defect Type” to determine where most of the light strand defects are occurring. Use the Minitab project file “GB_Exam.MPJ” and the “STRAND DEFECTS.MTW” worksheet to perform your Pareto chart. Based on your results, which defect type has the highest occurrence?
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Question 8 of 80
8. Question
Use the Minitab project file “GB_Exam.MPJ” and the “STRAND DEFECTS.MTW” worksheet to perform a Pareto chart. Based on your results, what is the cumulative percentage of the first two bars?
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Question 9 of 80
9. Question
Use the Minitab project file “GB_Exam.MPJ” and the “STRAND DEFECTS.MTW” worksheet to generate a Pareto chart on “Defect Type” by the variable “Shift”. Based on your results, is there a difference between the highest defect types by shift?
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Question 10 of 80
10. Question
Use the Minitab project file “GB_Exam.MPJ” and the “STRAND DEFECTS.MTW” worksheet to generate a Pareto chart on “Defect Type” by the variable “Shift”. Based on your results, what is the highest occurring defect for the 2nd shift?
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Question 11 of 80
11. Question
What is the difference between defects and defectives?
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Question 12 of 80
12. Question
Which of the following would be considered a “Direct” data collection method used to gather VOC?
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Question 13 of 80
13. Question
Y=f(x) refers to which of the following descriptions?
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Question 14 of 80
14. Question
The following is used to provide vital information about a project in a quick and easytocomprehend manner. It is a contract of sorts between project champions, sponsors, stakeholders, and the project team.
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Question 15 of 80
15. Question
Which word has the following meaning? “futility, uselessness, idleness, superfluity, waste, wastage, wastefulness.”
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Question 16 of 80
16. Question
Which of the following is the methodology prescribed to achieve Six Sigma. It is the systematic and rigorous method that can be applied to any process, in any industry.
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Question 17 of 80
17. Question
While working for a global media company you have been asked to look at the company’s web traffic of its numerous sites and site categories. Using the “GB_Exam.MPJ” Minitab project file and the “Web Traffic.mtw” worksheet. Perform a graphical summary of the Unique_Visits in column C4. Is this data normally distributed?
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Question 18 of 80
18. Question
Using the “GB_Exam.MPJ” Minitab project file and the “Web Traffic.mtw” worksheet. Perform a graphical summary of the Unique_Visits in column C4. Do the 95% confidence intervals of the Mean and Median overlap?
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Question 19 of 80
19. Question
Using the “GB_Exam.MPJ” Minitab project file and the “Web Traffic.mtw” worksheet. Perform a Scatterplot between Unique_Visits and Pageviews in columns C4 and C5. Based on the output, how would you characterize the relationship?
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Question 20 of 80
20. Question
Using the “GB_Exam.MPJ” Minitab project file and the “Web Traffic.mtw” worksheet. Perform a One Y Boxplot with Groups for Unique_Visits by Traffic_Source. Which Traffic_Source has the least variation?
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Question 21 of 80
21. Question
Using the “GB_Exam.MPJ” Minitab project file and the “Web Traffic.mtw” worksheet. Perform a One Y Boxplot with Groups for Unique_Visits by Traffic_Source. Which Traffic_Source has the highest median?
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Question 22 of 80
22. Question
Using the “GB_Exam.MPJ” Minitab project file and the “Web Traffic.mtw” worksheet. Perform a Histogram on Pageviews in column C5. How would you characterize the distribution of the output?
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Question 23 of 80
23. Question
Using the “GB_Exam.MPJ” Minitab project file and the “Web Traffic.mtw” worksheet. Perform a Run Chart on Views in column C9. Are there any obvious trends, cycles or seasonality?
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Question 24 of 80
24. Question
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Question 25 of 80
25. Question
You are an engineer tasked with performing a measurement systems analysis using a Variable Gage R&R. You have selected ten parts that represent the full range of parts produced by a production process. You have also selected 3 operators and asked them to measure the ten parts three times each in random sequence. Use the “Variable Gage R&R” worksheet found in the “GB_Exam.MPJ” project file and perform a crossed Variable Gage R&R on the data in the worksheet. What is the Total Gage R&R % Contribution?
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Question 26 of 80
26. Question
Use the “Variable Gage R&R” worksheet found in the “GB_Exam.MPJ” project file and perform a crossed Variable Gage R&R on the data in the worksheet. What is the Total Gage R&R % Study Variation?
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Question 27 of 80
27. Question
Use the “Variable Gage R&R” worksheet found in the “GB_Exam.MPJ” project file and perform a crossed Variable Gage R&R on the data in the worksheet. What is the value for number of distinct categories?
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Question 28 of 80
28. Question
Use the “Variable Gage R&R” worksheet found in the “GB_Exam.MPJ” project file and perform a crossed Variable Gage R&R on the data in the worksheet. Should you consider this a passing Gage R&R?
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Question 29 of 80
29. Question
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Question 30 of 80
30. Question
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Question 31 of 80
31. Question
You have been tasked to determine the sample size for a OneSample ttest with a power value of at least 90%. The tolerance that is applicable is +/0.25 inches. The historical data you have indicates a mean of 99.4 inches, a variance of 0.29 inches, and a standard deviation of 0.52 inches. Using this information determine the sample size necessary for a OneSample ttest.
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Question 32 of 80
32. Question
Which is not a characteristic of a successful project team?
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Question 33 of 80
33. Question
A company with steel mills fabricates 100 ft. long Ibeams for commercial building construction. Using the “IBeam_Length.mtw” worksheet located in the “GB_Exam.MPJ” Minitab project file, determine the 90% confidence interval of the mean of Beam_Length in column c1.
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Question 34 of 80
34. Question
A company with steel mills fabricates Ibeams for commercial building construction. The target length of each IBeam is 100 feet. Using the “IBeam_Length.mtw” worksheet located in the “GB_Exam.MPJ” Minitab project file, use a proper test to determine if the sample of 200 IBeams in column c1 are meeting the target of 100 ft.
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Question 35 of 80
35. Question
A company with steel mills fabricates Ibeams for commercial building construction. The target length of each IBeam is 100 feet. Using the “IBeam_Length.mtw” worksheet located in the “GB_Exam.MPJ” Minitab project file, perform a OneSample ttest to determine if the sample of 200 iBeams in column C1 are meeting the target of 100 ft. What is the Pvalue?
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Question 36 of 80
36. Question
A company with steel mills fabricates Ibeams for commercial building construction. The target length of each IBeam is 100 feet. Using the “IBeam_Length.mtw” worksheet located in the “GB_Exam.MPJ” Minitab project file, determine if the 200 samples found in column C1 are normally distributed?
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Question 37 of 80
37. Question
A company with steel mills fabricates Ibeams for commercial building construction. The target length of each IBeam is 100 feet. Using the “IBeam_Length.mtw” worksheet located in the “GB_Exam.MPJ” Minitab project file, determine which hypothesis test is the proper one to be used to compare Beam_Length to the target 100 ft.?
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Question 38 of 80
38. Question
Using the “IBeam_Length.mtw” worksheet located in the “GB_Exam.MPJ” Minitab project file, determine if both data sets within the 200 samples of Beam_Length “by” Steel_Mill are normally distributed?
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Question 39 of 80
39. Question
Using the “IBeam_Length.mtw” worksheet located in the “GB_Exam.MPJ” Minitab project file, determine if data sets of the 200 samples of Beam_Length “by” Steel_Mill have equal variances?
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Question 40 of 80
40. Question
Based on your assessment of the data located in column C1 of the “IBeam_Length.mtw” worksheet located in the “GB_Exam.MPJ” Minitab project file, which of the following hypothesis tests would be best to compare the two Steel_Mills to determine if they are different from one another?
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Question 41 of 80
41. Question
Using the “IBeam_Length.mtw” worksheet located in the file “GB_Exam.MPJ”, perform a TwoSample ttest on Steel_Mill_1_Shift_2 vs Steel_Mill_2_Shift_2 and select the best description of the test result.
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Question 42 of 80
42. Question
Based on the data found in the “IBeam_Length.mtw” worksheet located in the file “GB_Exam.MPJ”, we have assessed that Steel_Mill_2_Shift_1 and Steel_Mill_2_Shift_2 are both normally distributed and have equal variances. Which of the following hypothesis tests would be best to compare the two Steel_Mills for a statistically significant difference?
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Question 43 of 80
43. Question
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Question 44 of 80
44. Question
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Question 45 of 80
45. Question
A TwoSample ttest has several assumptions, which of the following is one of those key assumptions?
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Question 46 of 80
46. Question
A steel mill company has suppliers that provide finished cross beams for the mill to package and distribute with their 100 ft. IBeams. The suppliers have a 36 ft. length specification to meet. Using the data in the worksheet “IBeam_Length.mtw” located in the Minitab project file “GB_Exam.MPJ”. Assess the data in the two columns labeled Supplier_1 and Supplier_2 and based on your assessment of assumptions, perform the proper test necessary to compare each supplier to the target of 36 ft. Identify the correct test and which supplier is meeting the specification.
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Question 47 of 80
47. Question
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Question 48 of 80
48. Question
True or False, a correlation coefficient of 0.00 indicates that there is no linear relationship?
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Question 49 of 80
49. Question
In the “Cement Heat Loss.MTW” worksheet located in the Minitab file “GB_Exam.MPJ” determine the correlation coefficient between LHC_Heat_Loss and Water.
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Question 50 of 80
50. Question
This is a condition where the assumption of equal variance is violated and can lead us to believe a variable is a predictor when it is not.
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Question 51 of 80
51. Question
A situation when two or more independent variables in a multiple regression model are correlated with each other?
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Question 52 of 80
52. Question
Perform a simple linear regression using LHC_Heat_Loss as your response and Water are your predictor using the worksheet “Cement Heat Loss.MTW” in the project file “GB_Exam.MPJ”. What is the RSq(adj)?
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Question 53 of 80
53. Question
Perform a simple linear regression using LHC_Heat_Loss as your response and Water are your predictor using the worksheet “Cement Heat Loss.MTW” in the project file “GB_Exam.MPJ”. What is the model’s Pvalue?
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Question 54 of 80
54. Question
Perform a simple linear regression using LHC_Heat_Loss as your response and Water are your predictor using the worksheet “Cement Heat Loss.MTW” in the project file “GB_Exam.MPJ”. Are the residuals normally distributed with a mean equal to zero?
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Question 55 of 80
55. Question
Perform a simple linear regression using LHC_Heat_Loss as your response and Water are your predictor using the worksheet “Cement Heat Loss.MTW” in the project file “GB_Exam.MPJ”. Which is true about the residuals?
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Question 56 of 80
56. Question
A materials scientist studies the heat that is evolved from cement mixtures. The scientist varies the five ingredients in the mixtures to assess the impact on overall heat loss. Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide and Water. What is the RSq(adj)?
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Question 57 of 80
57. Question
Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide and Water. What is the Pvalue of the overall model?
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Question 58 of 80
58. Question
Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide, and Water. Does multicollinearity exist to an extent that we need to take action?
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Question 59 of 80
59. Question
Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide and Water. After running this model, remove the appropriate variable and rerun the model. What is the new RSqr(adj)?
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Question 60 of 80
60. Question
Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide and Water. After running this model, remove the appropriate variable and rerun the model. What is the new Pvalue for the overall model?
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Question 61 of 80
61. Question
Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide and Water. After running this model, remove the appropriate variable and rerun the model. Are the residuals normally distributed with a mean equal to zero?
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Question 62 of 80
62. Question
Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide and Water. After running this model, remove the appropriate variable and rerun the model. Are the residuals independent?
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Question 63 of 80
63. Question
Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide and Water. After running this model, remove the appropriate variable and rerun the model. Which factor has the greatest influence on heat loss?
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Question 64 of 80
64. Question
Using the “Cement Heat Loss.MTW” worksheet in the project file “GB_Exam.MPJ”, perform a multiple linear regression with OPC_Heat_Loss as the response and predictors as Aluminum_Oxide, Sulfate, Calcium_Oxide, Iron_Oxide and Water. After running this model, remove the appropriate variable(s) and rerun the model. Select the regression equation that represents this model?
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Question 65 of 80
65. Question
Using the worksheet “SPC.mtw” in the “GB_Exam.MPJ” project file, perform an IMR chart on the data in column C16. What tests for special cause if any, failed on the Individuals chart?
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Question 66 of 80
66. Question
Using the worksheet “SPC.mtw” in the “GB_Exam.MPJ” project file, perform an IMR chart on the data in column C16. What is the length of the moving range for this chart?
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Question 67 of 80
67. Question
Using the worksheet “SPC.mtw” in the “GB_Exam.MPJ” project file, perform an XbarR chart on the data in columns C1 and C2. Is this process in control?
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Question 68 of 80
68. Question
Using the worksheet “SPC.mtw” in the “GB_Exam.MPJ” project file, perform an XbarR chart on the data in columns C1 and C2. What does each data point on the X chart represent?
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Question 69 of 80
69. Question
Using the worksheet “SPC.mtw” in the “GB_Exam.MPJ” project file, perform an XbarS chart on the data in columns C4 and C5. What does each data point on the S chart represent?
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Question 70 of 80
70. Question
Using the worksheet “SPC.mtw” in the “GB_Exam.MPJ” project file, perform an XbarS chart on the data in columns C4 and C5. What is the value of the horizontal green line on the S chart and what does it represent?
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Question 71 of 80
71. Question
The _____ chart is used to monitor average defects per unit. It plots the count of defects per unit of a subgroup as a data point. It considers the situation when the subgroup size of inspected units for which the defects would be counted is not constant.
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Question 72 of 80
72. Question
The ___ chart is used to monitor the count of defectives. It plots the number of defectives in one subgroup as a data point. The subgroup size of this chart is constant.
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Question 73 of 80
73. Question
The ___ chart is used to monitor percent defective. This chart plots the percentage of defectives in one subgroup as a data point. It considers the situation when the subgroup size of inspected units is not constant.
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Question 74 of 80
74. Question
Continuous variables are measured, Discrete variables are counted?
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Question 75 of 80
75. Question
For the normal distribution, about _____% of the data fall within +/ 2 standard deviation from the mean?
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Question 76 of 80
76. Question
This is the risk of failing to reject the null when in fact you should have?
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Question 77 of 80
77. Question
_______ are the vertical differences between actual values and the predicted values or the “fitted line” created by a regression model?
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Question 78 of 80
78. Question
Which of the following is a way in which you might deal with multicollinearity?
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Question 79 of 80
79. Question
An FMEA ranks potential failures using values assigned to severity, occurrence, and detection?
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Question 80 of 80
80. Question
_____ evaluates whether the same appraiser can obtain the same value multiple times when measuring the same object using the same equipment under the same environment.
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