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ProKit Exam 2 > Application of Statistical Tools and Methods > Flashcards

Application of Statistical Tools and Methods Flashcards

1
Q

In addition to waste there is another basic form of problem, what is it?

A

It is variation.

Variation is the deviation of single data points within a process.

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2
Q

What is a Six Sigma Black Belt?

A

A Six Sigma Black Belt is a certified Six Sigma Expert, similar to a certified Lean Consultant being an expert for Lean Consulting.

Overall there several Six Sigma qualification levels (highest on top):

  1. Six Sigma Champion
  2. Six Sigma Master Black Belt
  3. Six Sigma Black Belt
  4. Six Sigma Green Belt

Decisions are based on the maximum and minimum values

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3
Q

Why is the collection of data important?

A

The main tar­get of col­lect­ing data is to make “things” mea­sur­able.

  • Improvements can be shown based on a your data collection.
  • The data collection is done at the beginning on a project.
  • You need to show facts and fig­ures.
  • Facts and fig­ures you need to col­lect in your di­ag­no­sis phase.
  • You need to present your data (num­bers and key per­for­mance in­di­ca­tors, KPI) as well.
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4
Q

Why is data within a project important?

A
  • It is im­por­tant to be clear about the KPIs that can mea­sure the suc­cess of the project
  • The data with­in the project is im­por­tant for a good and ac­cept­ed base­line.
  • Im­prove­ments can be shown/proved based on col­lect­ing of data at the be­gin­ning of the project
  • Also quan­ti­ta­tive and not only qual­i­ta­tive re­sults are vis­i­ble!
  • The quan­ti­ta­tive re­sults most­ly con­vince.
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5
Q

With collected data you can?

A

You can verify the process

… con­firm your hypothe­ses as vaild
or
… re­ject them as in­valid

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6
Q

What is a UCL?

A

Upper control limit

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7
Q

What is a LCL?

A

Lower control limit

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8
Q

Why should we com­bine Lean and Six Sig­ma-Tools?

A

Lean Six Sig­ma con­nects the im­ple­men­ta­tion speed of Lean with the pre­ci­sion and high sustainability of Six Sig­ma.

“Lean Six Sig­ma“ com­bines the speed, the way of ob­ser­va­tion and the im­ple­men­ta­tion ori­en­ta­tion from Lean and the dis­ci­pline of this sys­tem­at­ic and the sta­tis­ti­cal analy­sis tools from Six Sig­ma.

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9
Q

What is “pure” lean?

A

Risk that complex problems and underestimated wrong measures are implemented

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10
Q

What is “pure” six sigma?

A

Risk that complex analysis are used for simple answers or that simple problems were left behind

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11
Q

What is Lean Six Sig­ma?

A

con­nects the im­ple­men­ta­tion speed of Lean with the pre­ci­sion and high sustainability of Six Sig­ma.

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12
Q

What is the main dif­fer­ence be­tween Lean & 6 Sig­ma?

A

The 4 Proofs

Proof of the Gage repeatability and reproducibility
Proof of main ef­fects
Proof of the im­prove­ment of the main KPI
Proof of sus­tain­abil­i­ty

Fast and sus­tain­able re­sults are en­sured through the 4 Proofs

Lean focuses on analyzing workflow to reduce cycle time and eliminate waste. Lean strives to maximize value to the customer while using a few resources as possible. Six Sigma strives for near perfect results that will reduce costs and achieve higher levels of customer satisfaction.

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13
Q

What are the 4 proofs?

A

Proof of the Gage R&R (gage repeatability and reproducibility) and of the base­line
-I have to en­sure that the mea­sure­ment is cor­rect, then I can see if there is a prob­lem or not

Proof of main ef­fects
-What is the biggest lever, were is the root cause

Proof of the im­prove­ment of the main KPI
-Is the im­ple­ment­ed ac­tion suc­cess­ful?

Proof of sus­tain­abil­i­ty
-Is it pos­si­ble to keep this good re­sult?

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14
Q

Lean Six Sig­ma tools are used to find what?

A

the strongest lever in our projects and to reach the tar­gets with less ef­forts and to sta­bi­lize the project re­sults.

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15
Q

What is the ba­sis to im­prove process­es?

A

Process-ori­ent­ed think­ing

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16
Q

What are the differences between Lean and Six Sigma?

A

Lean
• High implementation speed
• Sustainability needs to be improved

Six Sigma
• Implementation after lenghty analyses
• High sustainability

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17
Q

Six Sig­ma uses the pow­er of what to col­lect data in repet­i­tive process­es?

A

Statistics

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18
Q

What does the “cen­tral lim­it the­o­rem“ in math­e­mat­ics state?

A

If you col­lect enough data, the dis­tri­b­u­tion of it, tends to be a nor­mal dis­tri­b­u­tion (bell curve)

this the­o­rem is the foun­da­tion and the as­sump­tion that every process has the be­hav­ior of a bell curve

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19
Q

Six Sigma is generally not used in which cases?

A

Six Sig­ma is in gen­er­al not used in non-repet­i­tive process­es, since it is hard to col­lect the ad­e­quate amount of data.

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20
Q

Fo­cus­ing in the process in terms of Six Sig­ma means what?

A

look­ing at the process math­e­mat­i­cal­ly

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21
Q

What Is the De­f­i­n­i­tion of “Sig­ma“?

A

The stan­dard de­vi­a­tion sig­ma (sym­bol “σ“) is a mea­sure for the dis­tri­b­u­tion of mea­sured val­ues.

It de­scribes the dis­tance from the mean μ to the in­flec­tion point of the curve.

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22
Q

If σ is small, what does the bell curve look like?

A

The curve is nar­row, which means the process has low vari­a­tion and is per­form­ing good.

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23
Q

If σ is large, what does the bell curve look like?

A

The curve is wide spread, which means the process has high vari­a­tion and is per­form­ing bad.

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24
Q

Why are there additional signmas?

A

With the ad­di­tion of sev­er­al Sig­mas, we are able to de­scribe the dis­tri­b­u­tion of the data of a process.

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25
Q

What Six Sigma target did the founders set?

A

The founders of Six Sig­ma set the tar­get, that for an al­most per­fect process a range of 12 Sig­ma (± 6 Sig­ma – where the name comes from) should fit with­in the re­quire­ment lim­its.

That leaves over only 3.4 er­rors per mil­lion op­er­a­tions (de­fect rate), which in­di­cates, how dif­fi­cult it is to achieve that tar­get.

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26
Q

How do you ap­ply Six Sig­ma?

A

DMA­IC-cy­cle. That stands for Define, Mea­sure, Anal­yze, Improve and Con­trol

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27
Q

In the DMAIC cycle, what does DEFINE mean?

A 
What is the problem?
Detailing the projects
Step 1: Project Charter / potential
Step 2 : Customer Interview
Step 3: SIPOC (Sup­pli­er Input Pro­cess Out­put Cus­tomer)
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28
Q

In the DMAIC cycle, what does MEASURE mean?

A

How can we measure the processes?

Trustworthy measurement

  • Gage repeatability and reproducibility
  • Process capability analysis
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29
Q

In the DMAIC cycle, what does ANALYZE mean?

A 
What can we deduce from the measurement?
Determine the significant factors
Step 7: Determine the potential X‘s
Step 8: Analysis of the X
Step 9: Proof of relationship
Step 10: Functional relation Y = f (Xi)
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30
Q

In the DMAIC cycle, what does IMPROVE mean?

A 
How can we improve the processes?
Improve the main parameters
Step 11: Optimal setting of the important X
Step 12: Tolerancing X
Step 13: Process Capability Y and X
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31
Q

In the DMAIC cycle, what does CONTROL mean?

A

How can we monitor the process result?
Ensuring the improvements
Step 14: Control Plan
Step 15: Project conclusion

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32
Q

What are some of the main ideas to keep in mind about Six Sigma?

A
  • I can´t improve, what I don´t measure
  • Each process has variation
  • Mean and variation are needed to make a decision
  • Make customer requests measurable
  • Find out the main effect
  • Effective actions are the best actions (PDCA)
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33
Q

If you collect an infinite amount of data, what will the distribution look like?

A
  • like a normal distribution curve

* like a bell curve

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34
Q

If you achieve a world-class process according to Six Sigma understanding, what’s the defect rate then?

A

3.4

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35
Q

What different kinds of data types are there?

A 
Continuous
Binary
Quantity
Nominal 
Ordinal
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36
Q

Continuous data is?

A

Measurable on the basis of a scale, e.g. Diameter 1.25 mm

Continuous data is data that can take any value.

Height, weight, temperature and length are all examples of continuous data.

Some continuous data will change over time; the weight of a baby in its first year or the temperature in a room throughout the day.

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37
Q

Binary data is?

A

Ok/Not Okay

Binary data is data whose unit can take on only two possible states, traditionally labeled as 0 and 1 in accordance with the binary numeral system and Boolean algebra

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38
Q

Ordinary data is?

A

School grades

Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories is not known. The ordinal scale is distinguished from the nominal scale by having a ranking.

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39
Q

Quantity data is?

A

number of errors, number of scratches

Quantitative data is defined as the value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it

Quantitative data is data expressing a certain quantity, amount or range

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40
Q

Nominal (“named”) data is?

A

Colors

Type of data that is used to label variables without providing any quantitative value

It is the simplest form of a scale of measure

One of the most notable features of ordinal data is that, nominal data cannot be ordered and cannot be measured.

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41
Q

What different kinds of distribution are there?

A 
Normal distribution
Poisson distribution
Binomial distribution
Weibull distribution
Exponential distribution
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42
Q

What is normal distribution?

A

Normal distribution
• Continuous data with a symmetric distribution
• Characteristic “bell shape”
• Our standard distribution

Normal distribution describes continuous data which have a symmetric distribution, with a characteristic ‘bell’ shape.

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43
Q

What is poisson distribution?

A

Poisson distribution– DISCRETE
Describes the number of events occurring in a fixed time interval or region of opportunity

• Number of binary data from an infinite sample
• Distribution of rare independent events (e.g. errors)
• Average occurrence rate is known
(e.g. Roulette 1/37 per number = 2.70 %)

Poisson distribution describes the distribution of binary data from an infinite sample. Thus it gives the probability of getting r events in a population.

The Poisson distribution is used to describe discrete quantitative data such as counts in which the population size n is large, the probability of an individual event is small, but the expected number of events, n, is moderate (say five or more). Typical examples are the number of deaths in a town from a particular disease per day, or the number of admissions to a particular hospital.

44
Q

What is binomial distribution?

A

Binomial distribution
A binomial distribution can be thought of as simply the probability of a SUCCESS or FAILURE outcome in an experiment or survey that is repeated multiple times
(pass/fail; yes/no)

• Number of binary data from a finite (limited) sample
• Number of defective units (result OK/NOK)
• Can be estimated with the Poisson distribution with a high number
of runs

45
Q

What is weibull distribution?

A

The Weibull distribution is a family of distributions that can take on many shapes, depending on what parameters you choose.

It’s commonly used to assess product reliability, analyze life data and model failure times.

• Time to technical failures
• Reliability / service
“Bathtub Curve

46
Q

What is exponential distribution?

A

Exponential distribution
Probability distribution of the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate.

It is often used to model the time elapsed between events

  • Grows or falls very quick and then approaches very slow (endless)
  • Service life of electrical components
  • Radioactive decay
47
Q

What are three things to keep in mind about data?

A
  • Data can be discrete or continuous
  • Discrete data is subdivided in binary, quantity and category
  • Different distributions show different data

Discrete data is information that can only take certain values

Continuous data is data that can take any value

48
Q

What is a sample?

A

A sam­ple is a sub­set of a pop­u­la­tion.

With the help of this sam­ple we try to draw mean­ing­ful con­clu­sions with re­gard to the pop­u­la­tion.

49
Q

What is the target of a sample?

A

Tar­get: Rep­re­sen­ta­tive and mean­ing­ful quan­ti­ty over the dis­tri­b­u­tion of the ba­sic pop­u­la­tion and its rep­re­sen­ta­tives.

50
Q

What are the different types of samples?

A

Stratified Sample Selection
The selection is proportionate to the subgroups

Simple Sample Selection
Random

Systematic Sample Selection
Sample of every xth part

Sample subgroups
For non-normal distributions

51
Q

What is the rule of thumb for minimum sample size?

A

Average value 30

Frequency distribution 50

Pareto distribution 50

Scatter diagram/plot 30

Control charts 30

Proportions 200

52
Q

What are some things to keep in mind about samples?

A

A sample is a subset of a population

With the help of this sample we try to draw meaningful conclusions with regard to the population

Objective of a sample selection is to define a representative and meaningful quantity over the distribution of the basic population and its representatives

A rule of thumb gives you the minimum sample size according to the type of representation

53
Q

Mea­sure­ment Sys­tem Analy­sis (MSA) is?

A

A mea­sure­ment sys­tem analy­sis (MSA) is a thor­ough as­sess­ment of a mea­sure­ment process to iden­ti­fy the vari­a­tion in that mea­sure­ment process.

54
Q

What is the target of MSA?

A

The tar­get of an MSA is to en­sure a high mea­sure­ment qual­i­ty in or­der to avoid mis­in­ter­pre­ta­tions

Iden­ti­fy the share of er­ror gen­er­at­ed by the mea­sure­ment

55
Q

What are the different types of measuring errors?

A 
"Bias" or "offset"
 Drift
 “Stability" or “spam"
 Linearity
 Repeatability
 Reproducibility
56
Q

When should we perform a MSA?

A

When you need to eliminate variation in the measurement system; eliminate measurement system error

Whenever a measurement is being used to assess the quality or quantity of a product, a measurement system study is required.

For Y’s: al­ways
For X’s: for sig­nif­i­cant ef­fects

The more pre­cise we can mea­sure Y, the eas­i­er it will be to rec­og­nize the ef­fects of each X.

57
Q

The MSA for dis­crete data checks which of the fol­low­ing as­pects?

A

Re­peata­bil­i­ty (mea­sur­ing sys­tem spread be­tween a sin­gle op­er­a­tor)

Re­pro­ducibil­i­ty (mea­sur­ing sys­tem spread be­tween dif­fer­ent op­er­a­tors)

De­vi­a­tion from the stan­dard (stan­dard part = real val­ue)

58
Q

The Mea­sure­ment Sys­tem is con­sid­ered suit­able if there are what percentage of agree­ments?

A

The Mea­sure­ment Sys­tem is con­sid­ered suit­able if there are 80% of agree­ments (be­tween runs, em­ploy­ees and stan­dard).

59
Q

What kind of measurements do you use for continuous data?

A

Non-de­struc­tive Mea­sure­ment– testing and analysis technique used by industry to evaluate the properties of a material, component, structure or system for characteristic differences or welding defects and discontinuities without causing damage to the original part
Mea­sure­ment of di­am­e­ter (MSA Crossed)

De­struc­tive test ex. Tear­ing test–Destructive measurements are processes that completely destroy the system they are measuring, and they are primarily used when detecting light.
Part can not be mea­sured again (MSA nest­ed)

60
Q

What are 3 procedures to use for continuous data?

A
  1. Calibration: deviation from a standard value
    Measuring instruments without operator effect with standard
    part
  2. Gage R & R (repeatability and reproducibility)
    Measuring instruments with operator effect with real parts
  3. Repeatability (without operator effect)
    Measuring instruments without operator effect with real parts
61
Q

What are 3 evaluation criteria for continuous measuring system analysis (MSA)?

A
  1. Gage R&R
    What is the share of the Measurement system error(repeatability and reproducibility) compared with total variation?
  2. Process over tolerance
    How high is the share of the measuring system spread compared with part tolerance?
  3. Number of distinct categories
    Between how many categories can the measuring system distinguish? “Resolution” to recognize differences between parts
62
Q

What are some things to keep in mind for Measurement System Analysis (MSA) for discrete & continuous data?

A

-Measuring error could be a mean value error (location) or a spread error (variance)

-A lot errors could occur during measurements: “Bias” or “offset”, drift,
“stability” or “spam”, linearity, repeatability and reproducibility

  • Reproducibility error means different operators get different measurements
  • Repeatability error means the same operator gets different measurements
  • Different kind of data require different kind of measurements and MSA
  • Perform a MSA to find out the measuring error
63
Q

What are the best tools to use to figure out process variation?

A

His­togram and Box Plot are graph­i­cal meth­ods to vi­su­al­ize vari­ance with­in a process in dif­fer­ent ways to get a bet­ter un­der­stand­ing of what’s hap­pen­ing in the process.

Box plot is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending from the boxes (whiskers) indicating variability outside the upper and lower quartiles

In a histogram, each bar groups numbers into ranges. Taller bars show that more data falls in that range. A histogram displays the shape and spread of continuous sample data.

64
Q

What is a histogram?

A

A his­togram shows the dis­tri­b­u­tion of con­tin­u­ous data (lo­ca­tion & spread). His­tograms can be cre­at­ed be­fore or dur­ing an analy­sis to sup­port as­sump­tions and steer the fur­ther analy­sis in the right di­rec­tion.

65
Q

What is a box plot?

A

A box plot is a graph­i­cal sum­ma­ry of lo­ca­tion and spread of a process.

The box plot shows the median using 4 quartiles

66
Q

What are some things to keep in mind about process variation?

A

-A histogram shows the distribution of continuous data, it’s a graphical view of the location and spread of
continuous sample data

-Histograms can be created before or during an analysis to support assumptions and steer the further
analysis in the right direction

  • A box plot is a graphical summary of location and spread of a process
67
Q

What is the difference between location and spread & what should I optimize first?

A

Location: expected value of the output being measured. For a stable process, this is the value around which the process has stabilized.

Spread: expected amount of variation associated with the output. This tells us the range of possible values that we would expect to see.

68
Q

What’s the difference between a sample and and a population?

A
  • A population includes all of the elements from a set of data.
  • A sample consists one or more observations drawn from the population.

This method is used in the Measure Phase in order to find out, what is the current performance and deviation of a process.

It is used as well in the Improve Phase in order to show the improvement of the process by comparing it with the initial
analysis.

69
Q

What are some things to keep in mind when using sample and population?

A
  • If the spread is too high, you’ll have an un­sta­ble process
  • Very tight group­ing is worth noth­ing, if you don’t meet the tar­get
  • First work on the spread then work on the lo­ca­tion
70
Q

What do you have to do, when the spread of your process is too wide?

A

• understand the variation of the process in order to reduce it (smaller sigma)

71
Q

What do you have to do, when the location of your process is not centered?

A

• understand the parameters for the location and then move it to the center

72
Q

What is a Con­fi­dence In­ter­val?

A

as­sump­tions and then con­clu­sions about the pop­u­la­tion with a rel­a­tive small amount of data

We can de­rive as­sump­tions (sta­tis­ti­cal con­clu­sions) about the pop­u­la­tion with a rel­a­tive small amount of data (sam­ple)

A con­fi­dence in­ter­val tells you in which range (in­ter­val) the “true” lo­ca­tion and/or spread pa­ra­me­ters of the ba­sic pop­u­la­tion lie with a spe­cif­ic prob­a­bil­i­ty

73
Q

What is the Con­fi­dence In­ter­val for Means?

A

A confidence interval for the mean is a way of estimating the true population mean. Instead of a single number for the mean, a confidence interval gives you a lower estimate and an upper estimate.

As soon as the Con­fi­dence In­ter­val for mean over­laps, we can’t prove that there is a sta­tis­ti­cal dif­fer­ence be­tween the av­er­age mean of the pop­u­la­tions that the sam­ples are tak­en from. You will want to achieve this if the process­es need to be sta­ble.

As soon as the Con­fi­dence In­ter­vals for mean no longer over­lap, the dif­fer­ence is sig­nif­i­cant. You will want to achieve this if you want to change the process.

74
Q

The confidence interval for means is used in what phase?

A

IMPROVE

in or­der to graph­i­cal­ly show the im­prove­ment of the new process per­for­mance com­pared to the ini­tial process per­for­mance

75
Q

How does hypothesis testing work?

A

Hypothesis testing in statistics is a way for you to test the results of a survey or experiment to see if you have meaningful results.

Hypothesis testing ensures the statistical differences between 2 or more process outputs.

  • statistically determine if our improvements are significant
  • verify suspected causes
76
Q

What are the Procedures for Hypothesis Testing?

A

1️️. What do I want to find out?
• Lead time too long? Difference in paint thickness?
• Improvement is effective? Process is stable?

  1. Select the correct statistical test
  2. Set up the hypotheses and significance level
    • Formulate what is to be proved as Ha (Alternative hypothesis) and Ho (null hypothesis)

4️️. Take sample and read the p-value
Collect data, run test and read the p-value.
The P value or calculated probability is the estimated probability of rejecting the null hypothesis (H0) of a study question when that hypothesis is true
How do you interpret the result?
• Reject Ha assumption: if p > 0.05
• Accept Ha assumption: if p ≤ 0.05

77
Q

By using hypothesis testing, what are the two main goals?

A

Either we can test for deviation from a mean/median, that means location.

Or we can test for the standard deviation, that means spread.

78
Q

The idea of hypothesis testing is that every statistical test involves the formulation of which two complementary statements?

A
  • Null hypothesis (H 0): there is no difference (status quo)

* Alternative hypothesis (H a): there is a difference (change has occurred)

79
Q

What does a Hypothesis Testing Decision Tree help you do?

A

The decision tree helps you - depending of the kind of data - to choose easily the correct and required statistical test.

80
Q

What does hypothesis do?

A
  • ensures the statistical differences between 2 or more process outputs
  • statistically determines if our improvements are significant
  • verifies suspected causes
81
Q

What does the null hypothesis assume?

A

The null hypothesis (H 0) assumes there is no difference (status quo)

82
Q

What does the alternative hypothesis assume?

A

The alternative hypothesis (H a) assumes there is a difference (change has occurred)

83
Q

What are the two types of wrong decisions possible during hypothesis testing?

A

Two types of wrong decisions are possible during hypothesis testing, those are called α risk and β risk

84
Q

What are some things to keep in mind about hypothesis testing?

A
  • α risk– rejecting the null hypothesis even thought it is true, the measure/action is successful, but in reality it is not
  • In case of a β risk, the decision is that “x” is not the cause for this problem, although it actually is
  • p-value is a probability value
85
Q

In sta­tis­tics a re­la­tion be­tween dif­fer­ent vari­ables is called what?

A

a re­gres­sion

86
Q

What is graphical regression?

A

Tools for rec­og­niz­ing/rep­re­sent­ing (ob­vi­ous) con­nec­tions
Suit­able for steer­ing com­mit­tees

87
Q

What is statistical regression?

A

Tools for ver­i­fy­ing/prov­ing con­nec­tions

88
Q

What are different regressions?

A

Discreet

Continuous

89
Q

What is the best way to visualize data?

A

Scatter plot

90
Q

What is a matrix plot?

A

A Com­bi­na­tion of Scat­ter Plots

The ma­trix plot is a ma­trix of scat­ter plots with each X and the Y.

The ob­jec­tive of the ma­trix plot is to look at the in­flu­ence of each X and the Y and to also rec­og­nize any re­la­tions be­tween X to X.

91
Q

What are some things to keep in mind about visualizing data?

A
  • Cor­re­la­tion is the re­la­tion­ship be­tween an in­de­pen­dent vari­able and a de­pen­dent vari­able
  • The goal is to prove that there is a lin­ear re­la­tion­ship/cor­re­la­tion be­tween 2 (or more) vari­ables/fac­tors/pa­ra­me­ters
  • The KPI show­ing this is called “cor­re­la­tion co­ef­fi­cient”
92
Q

What happens during the IMPROVE phase of DMAIC?

A

Af­ter we have un­der­stood in the Analy­sis Phase what the root caus­es for vari­a­tions are, we have now to de­rive im­prove­ment ac­tions and then im­ple­ment them.

In the im­prove phase we use tools that you al­ready know from the mea­sure­ment, analyze and con­trol phase in or­der to de­vel­op and re­al­ize the im­prove­ment.

  • Spread and location
  • Confidence interval
  • Hypothesis testing
  • Control charts
93
Q

A process is sta­ble and pre­dictable if the vari­a­tion is?

A

nat­ur­al

We can only im­prove process­es when they are sta­ble and pre­dictable. If not: we need to sta­bi­lize the process.

94
Q

A process is stable when?

A

A process is sta­ble if all the data points are with­in two con­trol lim­its.

To mon­i­tor this, a con­trol chart is used for vi­su­al­iza­tion.

A control chart or process-behavior charts, are a statistical process control tool used to determine if a manufacturing or business process is in a state of control

95
Q

How do you monitor process stabilization?

A

A con­trol chart is an ex­ten­sion of the run chart and in­cludes spec­i­fi­ca­tion and con­trol lim­its.

A run chart, also known as a run-sequence plot is a graph that displays observed data in a time sequence.

A run chart is a line graph of data plotted over time. By collecting and charting data over time, you can find trends or patterns in the process. Because they do not use control limits, run charts cannot tell you if a process is stable. However, they can show you how the process is running.

96
Q

What is natural variation?

A

“nat­ur­al”: with­in the con­trol lim­its

The natural variation or “common cause” variation is the natural fluctuations in process flow introduced by individuals, slight differences in execution, or instrument performance fluctuations.

97
Q

What is exceptional variation?

A

“ex­cep­tion­al”: out­side the con­trol lim­its

Exceptional variation, also called special cause or assignable cause variation, does not follow a predictable pattern. Exceptional variation is a signal that the process is changing over time.

98
Q

What are some things to keep in mind about a control chart?

A

-UCL and LCL are set by the process

-USL and LSL are set by the cus­tomer re­quire­ments there­fore, the con­trol lim­its should al­ways be with­in the spec­i­fi­ca­tion lim­its
(upper specification limits, lower specification limits)

The Con­trol Chart can be used in each phase, since it is an uni­ver­sal method for vi­su­al­iz­ing the process per­for­mance

In the Con­trol Phase of DMAIC it is es­pe­cial­ly es­sen­tial be­cause here we want to prove the long-term sustainability of the im­prove­ment.

Be­sides that, the Con­trol Chart can eas­i­ly be im­ple­ment­ed as a stan­dard, e.g. in the Shopfloor Man­age­ment on a dai­ly ba­sis.

99
Q

What are some things to keep in mind with control charts?

A

A con­trol chart sets up con­trol lim­its to dif­fer­en­ti­ate be­tween com­mon and spe­cial caus­es

Up­per/Low­er Con­trol Lim­its (UCL/LCL) are set by the process

Com­mon caus­es lay in­side those con­trol lim­its - they are con­stant­ly present and act si­mul­ta­ne­ous­ly (back­ground noise/noise)

Spe­cial caus­es lay out of those con­trol lim­its - they act at a spe­cif­ic point in time in a spe­cif­ic lo­ca­tion (sys­tem­at­ic vari­a­tion/sig­nal)

Up­per/Low­er Spec­i­fi­ca­tion Lim­it (USL/LSL) are set by the cus­tomer re­quire­ments

100
Q

What three things help you to sustain improvements?

A
  1. Shopfloor Man­age­ment
  2. Stan­dards
  3. Sta­ble process­es
101
Q

What is a “Bias” or “offset” error?

A

is the deviation of the output, when the device is in its zero position, compared with the optimum value

102
Q

What is a Drift error?

A

Drift errors are caused by deviations in the performance of the measuring instrument (measurement system) that occur after calibration. Major causes are the thermal expansion of connecting cables and thermal drift of the frequency converter within the measuring instrument.

103
Q

What is a “Stability” or “spam” error?

A

Stability in numerical linear algebra Consider the problem to be solved by the numerical algorithm as a function f mapping the data x to the solution y.

The main causes of error are round-off error and truncation error.

104
Q

What is a Repeatability error?

A

Repeatability error is the maximum difference in output when approaching the same point twice from the same direction.

The difference between output readings for two or more consecutive pressure cycles to rated range under duplicate conditions, approached from the same (increasing or decreasing) direction.

105
Q

What is a Reproducibility error?

A

Refers to the inability to get the same answer from measurements taken by different people under identical conditions

Reproducibility refers to the variation in measurements made on a subject under changing conditions

ProKit Exam 2 (9 decks)

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