Six Sigma Two

Front 1

In the measure phase of DMAIC, what are the items needed?

Back 1

A valid measurement system

Front 2

In the measure phase, we are going to establish a defect rate, but black belts typically see the defect rate go down

Back 2

false

Front 3

What is one of the first important milestones that indicates that a black belt is on track?

Back 3

The process map is complete

Front 4

How many data points do you need to have a short-term capability?

Back 4

Between 30 and 50 data points

Front 5

Process mapping

Back 5

is an ongoing living document used throughout the DMAIC process

Front 6

What are the two key questions to ask for each step?

Back 6

Why do we know this step and how do we know this step is good?

Front 7

An XY diagram is a statistical tool

Back 7

False

Front 8

A YX diagram helps point the black belt into a direction with factual evidence

Back 8

False

Front 9

A FMEA describes the following...

Back 9

What you want to know about a type of defect

Front 10

AN FMEA is complete during the measure phase

Back 10

False

Front 11

In an FMEA, what is the RPN if OCC is 5, DET is 4 and SEV is 9?

Back 11

OCC * SEV * DET
180

Front 12

Measurement system analysis MSA is used

Back 12

To validate the data used for analysis

Front 13

MSA is a tool that can be omitted from the DMAIC process

Back 13

False

Front 14

Cp is a capability index with the units measured in

Back 14

No units

Front 15

If Cp is 1.0 what is the sigma value

Back 15

3

Front 16

Can Cp be greater than Cpk

Back 16

yes

Front 17

What is the Cp and the Cpk index number when you have a six-sigma capability?

Back 17

Cp=2.0 and Cpk=1.5

Front 18

What is the purpose for gauge R&R?

Back 18

1. statistical analysis to evaluate measure error.
2. To understand repeatability and reproducibility of your MSA.
3. Validate what is/isn't a defect.
4. Look for variation within and between operators.

Front 19

If your data is nonnormal, you are stuck in the measure phase

Back 19

false

Front 20

What is the layperson's description of a hypothesis test?

Back 20

A tool to compare stuff

Front 21

What are the reasons for nonnormality?

Back 21

1. Bimodal conditions exist
2. Different normal distributions are within the data set.

Front 22

If you have a nonnormal data set does transforming the data fix the nonnormal causes of the problem?

Back 22

no

Front 23

What would best describe a bimodal distribution?

Back 23

A multivari chart and and an X factor that has two different Y-output distributions

Front 24

How does comparing factors help solve the problem

Back 24

breaks down the problem to the vital Xs. Contrasts the trivial few versus the the vital few. Helps answer the hypothesis question. Deals with data facts that can be proven, and focuses the team on data, not opinion.

Front 25

Tool wear can cause nonnormal distributions

Back 25

true

Front 26

What plot describes the many distributions in one graph in quartiles

Back 26

box plot

Front 27

Is it okay to remove outliers in a data set that causes an increase in standard deviation?

Back 27

no - yes, if you know the cause to stop it.

Front 28

What is the best way to show multimode distributions

Back 28

Interval plot, dot plot, one way ANOVA,

Front 29

Lowess analysis fits a robust line through the data to display a relationship between X and Y

Back 29

true

Front 30

In a multivari analysis, the X levels are randomly selected levels during the study

Back 30

true

Front 31

Different operators producing the same Y cannot cause nonsymmetrical distributions

Back 31

False

Front 32

In simple terms, what is meant by a p-value of less than 0.05?

Back 32

You're 95% confident that there is a statistical difference.

Front 33

The 95% confidence interval increases as the standard deviation increases

Back 33

true

Front 34

You do not need a capable measurement system for multivar analysis

Back 34

false

Front 35

Shift-to-shift variation can be measured on one shift

Back 35

false

Front 36

A hypothesis test can show the interactions of the factors

Back 36

false

Front 37

Sample size has no effect on the width of a distribution

Back 37

false

Front 38

If an X has been identified as statistically significant, do you disregard it owing to an expert telling you to ignore it?

Back 38

No - ask what data the expert has to show it is worth ignoring.

Front 39

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 show no statistical difference in productivity, do you purchase it?

Back 39

no

Front 40

What does hypothesis testing fundamentally change

Back 40

turns problem into a fact based process - destroys the emotions - departure from the I think/feel culture, and data is now used to drive the decision

Front 41

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

Back 41

No

Front 42

Using an Anderson-Darling normality test, normal data have a p-value of less than 0.5

Back 42

true

Front 43

How many runs does a 2/3 full factorial experiment consist of?

Back 43

8

Front 44

In an experiment, inputs are allowed to vary randomly throughout the specification range.

Back 44

false

Front 45

One-factor-at-a-time experiments generater more powerful data than a full factorial experiment

Back 45

false

Front 46

What is an experimental factor

Back 46

The input vairables for the experiment

Front 47

What does orthogonal mean

Back 47

A property that ensure that all experimental factors are independent of each other, no correlation exists between Xs.

Front 48

Standard order is the same as run order

Back 48

false

Front 49

Why use factorial plots

Back 49

1. see plots of the main effects
2. alls you to see the interaciton plots
3. allows you to see the cube plots
4. shows how to set each factor to max or min the response.

Front 50

What tools can be used to determine if factors have interaction

Back 50

1. balanced ANOVA
2. Standardized effects
3. Interaction plots
4. Fractional factorial fits.

Front 51

What does it mean when no p-values are presented in the ANOVA output?

Back 51

1. All factors were statistically significant.
2.Factors weren't different.
3.Only one repetition was run at each treatment combination.
4. Had no center points

Front 52

Why do we replicate our experimental runs?

Back 52

To obtain a better estimate of the error and look at interactions

Front 53

To use a center point in your experimental design, at least one factor must be able to be set at it’s midpoint coded value = 0.

Back 53

false

Front 54

Why use center points in your experimental design?

Back 54

to detect curvature

Front 55

If a center point is significant, its p-value in the ANOVA table will be greater than 0.05.

Back 55

false

Front 56

Fractional factorial designs require more runs than full factorial designs given the same number of factors.

Back 56

false

Front 57

What is the main reason for using a fractional factorial design?

Back 57

Allows you to test and screen a large number of factors in fewer runs
Gives you good estimates of low order interactions
Gives you relative significance of the factors

Front 58

Given three factors, A, B, and C, the highest-order interaction would be ABC.

Back 58

true

Front 59

In a four-factor 1/2 fractionated design, the AB interaction is confounded with the CD interaction.

Back 59

true

Front 60

What does it mean when A is confounded with BC?

Back 60

BC is contributing to the result.

Front 61

In a resolution IV design, two-factor interactions are aliased with three-factor interactions.

Back 61

true

Front 62

In a resolution III design, single factors are not aliased with any other factors.

Back 62

true

Front 63

The identity expression I + ABCD is used to generate the confounding pattern.

Back 63

true

Front 64

Why are resolution V designs preferred over resolution III and resolution IV designs?

Back 64

No main effect is confounded with any other main effect or second-order interactions.
No second-order interactions are confounded with any other second-order interaction, and second-order interactions are confounded with third-order interactions.
Allows for the differentiation of the effects down to the second order, assuming that the effects of third-=order interactions are negligible.

Front 65

Why should you do a hypothesis test before running a DOE?

Back 65

To identify as many the vital few factors prior to DOE

Front 66

What is the mission of the Improve phase?

Back 66

Find the relationships between X and Y
Validate hypothesis tests
Which inputs to control in the next phase
Run a pilot to validate experiment

Front 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?

Back 67

No

Front 68

The WECO 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.

Back 68

true

Front 69

Outliers usually have a significant effect on an equation derived with regression analysis

Back 69

true

Front 70

Using Y=f(x), do we set tolerance limits for Y?

Back 70

no

Front 71

What is the residual?

Back 71

It indicates how well the equation fits. And is a calculation of the expected value minus the observed value

Front 72

What are control charts?

Back 72

Charts used to routinely monitor quality.

Front 73

What does a P-chart track

Back 73

na

Front 74

A DOE is always needed to solve process issues

Back 74

true

Front 75

The purpose of performing a designed experiment is to determine what?

Back 75

all

Front 76

With the DOE, the easiest way to test for curvature is to include center points

Back 76

true

Front 77

The most common response surface design is called the central composite design

Back 77

true

Front 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.

Back 78

true

Front 79

Control plans are only generated at the start of the life cycle of a product.

Back 79

false

Front 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

Back 80

true

Front 81

Control limits and specification limits are the same thing

Back 81

false

Front 82

Regular residuals are the actual values of the residuals calculated by subtracting the expected value from teh observed value.

Back 82

false

Front 83

What is the best description of a data transform.

Back 83

It transforms data into a more approximate normal distribution

Front 84

Outliers usually have a significant effect on an equation derived with regression analysis

Back 84

true

Front 85

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.

Back 85

true

Front 86

Mistakeproofing seeks to gain permanence by eliminating or rigidly controlling human intervention in a process

Back 86

true

Front 87

Six Sigma product and process design made to the product/process that eliminates the error condition are called what

Back 87

mistake elimination

Front 88

Systems that monitor the process and automatically adjust critical Xs to correct settings are called what

Back 88

Full automation

Front 89

In order for mistakeproofing systems to operate effectively, the following rules must NOT be observed

Back 89

System overrides must not be used except in an emergency

Front 90

The EWMA is a variable data control chart

Back 90

True

Front 91

An “out of control” situation in a production process may be signaled by a sample f output, which generates a data point outside the control limits on either the Xbar–R range chart.

Back 91

true

Front 92

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.

Back 92

false