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1 | |
In the Measure phase of DMAIC, what are the items needed? |
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| | A) | A solution to the problem. |
| | B) | Data for doing a Design of Experiments. |
| | C) | Data to help break down the problem. |
| | D) | A problem, a process, a financial benefit, a metric and a goal, and a customer metric. |
| | E) | A valid measurement system. |
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2 | |
In the Measure phase, we are going to establish a defect rate, but black belts typically see the defect rate go down. |
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| | A) | True |
| | B) | False |
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3 | |
What is one of the first important milestones that indicates that a black belt is on track? |
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| | A) | Lack of buy-in from the team members. |
| | B) | No data is available. |
| | C) | The champion does not know the project benefit. |
| | D) | The process map is complete. |
| | E) | When the team adopts a desire to constantly learn |
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4 | |
How many data points do you need to have a short-term capability? |
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| | A) | Two data points. |
| | B) | Over 100 data points. |
| | C) | Fewer than five data points. |
| | D) | Between 30 and 50 data points. |
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5 | |
Process mapping is a: |
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| | A) | A one-time event. |
| | B) | A tool used for statistical validation. |
| | C) | A tool used at the end of the DMAIC process. |
| | D) | An ongoing living document used throughout the DMAIC process. |
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6 | |
A failure modes and effects analysis FMEA describes the following. |
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| | A) | Potential defects |
| | B) | The risk of the problem |
| | C) | Capability of the process |
| | D) | Root cause |
| | E) | What you want to know about a type of defect |
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7 | |
An FMEA is complete during the Measure phase. |
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| | A) | True |
| | B) | False |
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8 | |
In an FMEA, what is the RPN if POCC is 5, PDET is 4, and the PSEV is 9? |
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| | A) | 0 |
| | B) | 20 |
| | C) | 9 |
| | D) | 180 |
| | E) | None of the above |
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9 | |
Measurement system analysis MSA is used: |
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| | A) | To assess capability |
| | B) | To validate the data used for analysis |
| | C) | As an optional tool during the DMAIC process |
| | D) | A nonstatistical assessment of the process |
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10 | |
MSA is a tool that can be omitted in the DMAIC model. |
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| | A) | True |
| | B) | False |
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11 | |
Cp is a capability index with the units measured in: |
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| | A) | Meters |
| | B) | Gallons |
| | C) | Yards |
| | D) | Productivity |
| | E) | Defect rate or yield |
| | F) | No units |
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12 | |
If the Cp is 1.0, what is the sigma value? |
|
| | A) | 1 |
| | B) | 2 |
| | C) | 3 |
| | D) | 6 |
| | E) | None of above |
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13 | |
Can Cp be greater than Cpk? |
|
| | A) | Yes |
| | B) | No |
| | C) | Sometimes |
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14 | |
What is the Cp and Cpk index number when you have a six-sigma capability? |
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| | A) | Cp = 1.0 and Cpk = 0.5 |
| | B) | Cp = 1.5 and Cpk = 2.0 |
| | C) | Cp = 3.0 and Cpk = 6.0 |
| | D) | Cp = 2.0, and Cpk = 1.5 |
| | E) | None of the above |
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15 | |
What is the purpose for gauge R&R? |
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| | A) | Statistical analysis to evaluate measure error |
| | B) | To understand repeatability and reproducibility of your MSA |
| | C) | To help validate what is a defect and what is not
d. Look for variation within operator and between operators |
| | D) | To gauge the rest and relaxation needed for a black belt |
| | E) | a through d |
| | F) | None of the above |
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16 | |
If your data are nonnormal, you are stuck in the Measure phase. |
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| | A) | True |
| | B) | False |
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17 | |
What is the layperson’s description of a hypothesis test? |
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| | A) | Helps to solve problems |
| | B) | Breaking the problem up |
| | C) | Dissecting the data |
| | D) | There is no way to make it simple |
| | E) | A tool to compare stuff |
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18 | |
What are the reasons for nonnormality? |
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| | A) | a. All data have that pattern |
| | B) | Due to abnormal conditions |
| | C) | Bimodal conditions exist |
| | D) | Different normal distributions are within the data set |
| | E) | a and d |
| | F) | None of the above |
| | G) | c and d |
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19 | |
If you have a nonnormal data set, does transforming the data fix the nonnormal causes of the problem? |
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| | A) | Yes |
| | B) | No |
| | C) | None of the above |
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20 | |
What would best describe a bimodal distribution? |
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| | A) | A manufacturing process |
| | B) | Material variance |
| | C) | Transactional defects |
| | D) | Mutlivari chart |
| | E) | Within-part variation |
| | F) | An X-factor that has two different Y-output distributions |
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21 | |
How does comparing factors help solve the problem? |
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| | A) | It breaks down the problem into the vital X’s. |
| | B) | It contrasts the trivial many versus the vital few. |
| | C) | It helps answer the hypothesis question. |
| | D) | It deals with data facts that can be proven. |
| | E) | It focuses the team on data, not opinion. |
| | F) | All of the above |
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22 | |
Tool wear can cause nonnormal distributions. |
|
| | A) | True |
| | B) | False |
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23 | |
What plot describes the many distributions in one graph in quartiles? |
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| | A) | Interval plot |
| | B) | Capability plot |
| | C) | Probability plot |
| | D) | Median plot |
| | E) | Box plot |
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24 | |
Is it okay to remove outliers in a data set that cause an increase in standard deviation? |
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| | A) | Yes |
| | B) | No |
| | C) | Yes, only if you know the cause of stopping it |
| | D) | b and c |
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25 | |
What is the best way to show multimode distributions? |
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| | A) | a. Bimodal graph |
| | B) | b. Interval plot |
| | C) | Dot plot |
| | D) | One-way ANOVA |
| | E) | Box plot |
| | F) | a and b |
| | G) | b through d |
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26 | |
Lowess analysis fits a robust line through the data to display a relationship between X and Y. |
|
| | A) | True |
| | B) | False |
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27 | |
In a multivari analysis, the X levels are randomly selected levels during the study. |
|
| | A) | True |
| | B) | False |
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28 | |
Different operators producing the same Y cannot cause nonsymmetrical distributions: |
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| | A) | True |
| | B) | False |
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29 | |
Two-way interaction cannot cause asymmetrical distributions. |
|
| | A) | True |
| | B) | False |
| | C) | Sometimes |
| | D) | None of the above |
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30 | |
In simple terms, what is meant by a p-value of less than 0.05? |
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| | A) | That there are no significant difference |
| | B) | The variance terms are equal |
| | C) | The mean has a shift of 1.5 sigma |
| | D) | You’re 95 percent confident that there is a statistical difference |
| | E) | All the above |
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31 | |
The 95 percent confidence interval increases as the standard deviation decreases. |
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| | A) | True |
| | B) | False |
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32 | |
A multvari analysis is an active form of comparing within- and between-part variation over time. |
|
| | A) | True |
| | B) | False |
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33 | |
You do not need a capable measurement system for multivari analysis. |
|
| | A) | True |
| | B) | False |
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34 | |
Shift-to-shift variation can be measured on one shift. |
|
| | A) | True |
| | B) | False |
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35 | |
A hypothesis test can show the interaction of the factors. |
|
| | A) | True |
| | B) | False |
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36 | |
Sample size has no effect on the width of a distribution. |
|
| | A) | True |
| | B) | False |
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37 | |
If an X has been identified as statistically significant, do you disregard it owing to an expert telling you to ignore it? |
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| | A) | No |
| | B) | Yes |
| | C) | Ask what data does the expert have to show to ignore it |
| | D) | None of the above |
| | E) | a and c |
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38 | |
If you were told to purchase new technology for over $2 million to make the business more productive, but the hypothesis of the new technology shows no statistical difference in productivity, do you purchase it? |
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| | A) | No |
| | B) | Yes |
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39 | |
What does hypothesis testing fundamentally change? |
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| | A) | It’s a departure from the “I think” and “I feel” culture |
| | B) | Destroys the emotions of the problem |
| | C) | Turns the problem into a fact-based process |
| | D) | Data are now used to drive decisions |
| | E) | All of the above |
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40 | |
If you changed an X that was proven to be statistically significant and the Y was given to you with 3 months prior to the change and 1 month after, could you show a before and after hypothesis to validate the change? |
|
| | A) | No |
| | B) | Yes |
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41 | |
Using an Anderson-Darling normality test, normal data have a p-value of less than 0.5. |
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| | A) | True |
| | B) | False |
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42 | |
How many runs does a 23 full factorial experiment consist of ? |
|
| | A) | 6 |
| | B) | 5 |
| | C) | 8 |
| | D) | 12 |
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43 | |
In an experiment, inputs are allowed to vary randomly throughout the specification range. |
|
| | A) | True |
| | B) | False |
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44 | |
One-factor-at-a-time experiments generate more powerful data than a full factorial experiment. |
|
| | A) | True |
| | B) | False |
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45 | |
What is an experimental factor? |
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| | A) | The input variables for the experiment |
| | B) | The metrics of the process |
| | C) | A covariant |
| | D) | The largest standard deviation |
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46 | |
What does orthogonal mean? |
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| | A) | One or more effects that cannot unambiguously be attributed to a single factor or factor interaction |
| | B) | Involves running the experimental runs in random order |
| | C) | A property that ensures that all experimental factors are independent of each other; no correlation exists between X’s. |
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47 | |
What is a “Balanced Design?” |
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| | A) | A design in which each of the variables has a different number of runs at the high and low levels |
| | B) | A design in which each of the variables or factors has the same number of runs at the high and low levels |
| | C) | A design in which two of the variables has a different number of runs at the high and low levels |
| | D) | All of the above |
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48 | |
Standard order is the same as run order. |
|
| | A) | True |
| | B) | False |
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49 | |
Why use factorial plots? |
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| | A) | Allow you to see the plots of the main effects |
| | B) | Allow you to see the interaction plots |
| | C) | Allow you to see the cube plots |
| | D) | Show how to set each factor to either maximize or minimize the response |
| | E) | All of the above |
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50 | |
What tools can be used to determine if factors have interaction? |
|
| | A) | Balanced ANOVA |
| | B) | Standardized effects |
| | C) | Interaction plots |
| | D) | Fractional factorial fits |
| | E) | All of the above |
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51 | |
What does it mean when no p-values are presented in the ANOVA output? |
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| | A) | Means the factors are statistically significant |
| | B) | Means the factors are not different |
| | C) | Only one repetition was run at each treatment combination |
| | D) | Had no center points |
| | E) | All of the above |
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52 | |
Why do we replicate our experimental runs? |
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