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| 1 | In the Measure phase of DMAIC, what are the items needed? |
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. |
| 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? |
| 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? |
| 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: |
| 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. |
| 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. |
| 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? |
| A) | 0 |
| B) | 20 |
| C) | 9 |
| D) | 180 |
| E) | None of the above |
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| 9 | Measurement system analysis MSA is used: |
| 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. |
| A) | True |
| B) | False |
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| 11 | Cp is a capability index with the units measured in: |
| 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? |
| 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? |
| 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. |
| A) | True |
| B) | False |
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| 17 | What is the layperson’s description of a hypothesis test? |
| 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? |
| 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? |
| A) | Yes |
| B) | No |
| C) | None of the above |
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| 20 | What would best describe a bimodal distribution? |
| 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? |
| 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? |
| 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? |
| 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? |
| 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: |
| 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? |
| 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. |
| 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? |
| 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? |
| A) | No |
| B) | Yes |
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| 39 | What does hypothesis testing fundamentally change? |
| 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. |
| 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? |
| 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? |
| 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?” |
| 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? |
| 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? |
| 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? |
| A) | So we can look for special causes |
| B) | To obtain a better estimate of the error and look at interactions |
| C) | To determine the factor levels |
| D) | So we can look at the same thing run again |
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| 53 | To use a center point in your experimental design, at least one factor must be able to be set at its midpoint coded value = 0. |
| A) | True |
| B) | False |
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| 54 | Why use center points in your experimental design? |
| A) | To check for linearity |
| B) | To check for interactions |
| C) | To detect curvature |
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| 55 | If a center point is significant, its p-value in the ANOVA table will be greater than 0.05. |
| A) | True |
| B) | False |
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| 56 | Fractional factorial designs require more runs than full factorial designs given the same number of factors. |
| A) | True |
| B) | False |
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| 57 | What is the main reason for using a fractional factorial design? |
| A) | Allows you to test and screen a large number of factors in fewer runs |
| B) | Gives you good estimates of low order interactions |
| C) | Gives you relative significance of the factors |
| D) | All of the above |
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| 58 | Given three factors, A, B, and C, the highest-order interaction would be ABC. |
| A) | True |
| B) | False |
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| 59 | In a four-factor 1/2 fractionated design, the AB interaction is confounded with the CD interaction. |
| A) | True |
| B) | False |
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| 60 | What does it mean when A is confounded with BC? |
| A) | A is contributing to the result. |
| B) | BC is contributing to the result. |
| C) | The computed coefficients are related to the sum of the two individual effects. |
| D) | The sums of squares are related to the sum of the two individual effects. |
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| 61 | In a resolution IV design, two-factor interactions are aliased with three-factor interactions. |
| A) | True |
| B) | False |
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| 62 | In a resolution III design, single factors are not aliased with any other factors. |
| A) | True |
| B) | False |
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| 63 | The identity expression I + ABCD is used to generate the confounding pattern. |
| A) | True |
| B) | False |
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| 64 | Why are resolution V designs preferred over resolution III and resolution IV designs? |
| A) | No main effect is confounded with any other main effect or second-order interactions. |
| B) | No second-order interactions are confounded with any other second-order interaction, and second-order interactions are confounded with third-order interactions. |
| C) | Allows for the differentiation of the effects down to the second order, assuming that the effects of third-order interactions are negligible. |
| D) | All of the above |
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| 65 | Why should you do a hypothesis test before running a DOE? |
| A) | Statistically test for the correct factors |
| B) | Find the trivial many |
| C) | To ensure that your measurement system is good |
| D) | To ensure that you have all the process steps identified |
| E) | To identify as many of the vital few factors prior to DOE |
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| 66 | What is the mission of the Improve phase? |
| A) | Find the relationships between X and Y |
| B) | Validate hypothesis tests |
| C) | Which inputs to control in the next phase |
| D) | Run a pilot to validate experiment |
| E) | All of the above |
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| 67 | Can you calculate epsilon-square percent contribution for the DOE given that the degree of freedom for each factor is different in the ANOVA? |
| A) | Yes |
| B) | No |
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| 68 | The WECO (Western Electric Company) rules are based on probability. We know that for a normal distribution, the probability of encountering a point outside ±2.5 is 0.3 percent. This is a rare event. Therefore, if we observe a point outside the control limits, we conclude that the process has shifted and is unstable. |
| A) | True |
| B) | False |
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| 69 | Outliers usually have a significant effect on an equation derived with regression analysis. |
| A) | True |
| B) | False |
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| 70 | Using Y = f(X), do we set tolerance limits for Y? |
| A) | Yes |
| B) | No |
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| 71 | What is the residual? |
| A) | It is the equation from the data. |
| B) | It is the standard error of the equation. |
| C) | It indicates how well the equation fits. |
| D) | It is a calculation of the expected value minus the observed value. |
| E) | c and d |
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| 72 | What are control charts? |
| A) | Design of experiment (DOE) |
| B) | A plot showing the Y over time |
| C) | Charts showing average control |
| D) | Charts used to routinely monitor quality |
| E) | None of the above |
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| 73 | What does a P-chart track? |
| A) | Process chart showing the main factors |
| B) | Sample size of the process over time |
| C) | Simple chart used to track the number of nonconforming units, percentage of defective parts, assuming that sample size is not necessarily constant |
| D) | None of the above |
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| 74 | A DOE is always needed to solve process issues. |
| A) | True |
| B) | False |
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| 75 | The purpose of performing a designed experiment is to determine what? |
| A) | The mathematical relationship Y = F(x1, x2, x3, . . .) |
| B) | Which X’s most impact Y and therefore need to be controlled |
| C) | The level of each X to achieve the desired mean Y |
| D) | The level of each X to minimize the variability of Y |
| E) | All of the above |
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| 76 | Within the DOE, the easiest way to test for curvature is to include center points. |
| A) | True |
| B) | False |
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| 77 | The most common response surface design is called the central composite design. |
| A) | True |
| B) | False |
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| 78 | Control plans provide a written description of the actions that are required at each phase of the process to ensure that all process inputs and outputs will be in a state of control. |
| A) | True |
| B) | False |
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| 79 | Control plans are only generated at the start of the life cycle of a product. |
| A) | True |
| B) | False |
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| 80 | SPC is a statistically based graphing technique that compares current process data to a set of stable control limits established from normal process variation. |
| A) | True |
| B) | False |
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| 81 | Control limits and specification limits are the same thing: |
| A) | True |
| B) | False |
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| 82 | Control limits are typically set at plus or minus 2 standard deviations from the target of the control chart. |
| A) | True |
| B) | False |
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| 83 | Regular residuals are the actual values of the residuals calculated by subtracting the expected value from the observed value. |
| A) | True |
| B) | False |
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| 84 | What is the best description of a data transform? |
| A) | A transformation of the data using a power table to treat linearity in the data |
| B) | A transformation of the data using a power table to treat nonlinearity in the data |
| C) | Improves the variance within the data |
| D) | A transformation of the data using a power table to treat the units of the data |
| E) | It transforms data into a more approximate normal distribution |
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| 85 | Outliers usually have a significant effect on an equation derived with regression analysis. |
| A) | True |
| B) | False |
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| 86 | When an “out of control” situation is signaled on a control chart, the person using the chart will know why the data are giving the signal. |
| A) | True |
| B) | False |
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| 87 | Once the special causes of variation in a process have been discovered and eliminated, the long-term goal of anyone managing a process will be to reduce common cause variation by improving the process or system itself. |
| A) | True |
| B) | False |
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| 88 | An "out of control" situation in a production process may be signaled by a sample f output that generates a data point outside the control limits on either the Xbar–R range chart. |
| A) | True |
| B) | False |
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| 89 | Mistakeproofing seeks to gain permanence by eliminating or rigidly controlling human intervention in a process. |
| A) | True |
| B) | False |
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| 90 | Six Sigma product and process design or process changes made to the product or process that eliminate the error condition from occurring are called what? |
| A) | TQM |
| B) | Mistakeproofing |
| C) | Mistake elimination |
| D) | Foolproofing |
| E) | All of the above |
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| 91 | Systems that monitor the process and automatically adjust critical X’s to correct settings are called what? |
| A) | Full automation |
| B) | Process interruption |
| C) | Mechanism |
| D) | SPC |
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| 92 | In order for mistakeproofing systems to operate effectively, the following rules must not be observed. |
| A) | The systems must be installed and properly adjusted before process startup. |
| B) | The systems must be periodically audited and maintained. |
| C) | Systems are periodically disabled. |
| D) | Inoperative or missing systems must be repaired or replaced before operating the process. |
| E) | System overrides must not be used except in an emergency. |
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| 93 | The EWMA or Exponentially Weighted Moving Average chart is a variable data control chart. |
| A) | True |
| B) | False |