1. Less Variation = Higher Quality What is Variation?
Major Point Quality improvement relies on reducing variation rather than inspect-rework-scrap based on spec limits. This theme forms the basis for the remainder of the module.
Discussion Open the class for any discussion, or question and answer that has not taken place yet. 32

2. What is Variation? How the Loss Function Affects Product
Both are within spec
Which is more desirable?
LSL
USL
LSL
USL
Major Point Any deviation from the target degrades performance to some extent
Discuss Assuming cost is the same, which nuts would you rather get?
Why?
There is an optimum size for the hole. Any deviation from that size means a loss in quality and/or functionality.
How will a larger deviation from the target impact performance?
Record responses on a flipchart
Beware of the possible response that “in spec” is okay. Counter with the question about whether “just out of spec” will not work. For example, nut #1 = spec (in spec) and nut #2 = spec (out of spec). Is there a difference? 24

3. Examining Variation
Definition
A Stable Process has the same normal distribution at all times.
A stable process is In Control
A stable process still has variation
Major Point To establish the definition of a stable process
Explain Discuss each element of the definition
A stable process has the same distribution at all times.
Emphasize that a stable process has the same shape, same spread, and same center at all times with no time trends.
Comment: This definition is arbitrary. There are other definitions of stability. This one is chosen because it is more useful than others.
Ask Does this definition say anything about how we want the process to behave?
This definition of stability is descriptive, not prescriptive: What we’ve got vs. what we want.
Discuss Introduce the concept of normal distribution: for this class, and frequently in the factory, a stable process has the same normal distribution at all times. 41

4. Normal distribution at all times
Examining Variation
Stable Process
Normal distribution at all times
Prediction
Major Point A stable process has the same center and spread at all times
Explain For a stable process to have a normal distribution it must have the same bell-shaped curve (i.e., the same spread, and same center) at all times.
Discuss The general definition of stability just says the same distribution at all times. Some distributions, e.g., defects, will NOT be normal.
For simplicity, in this class we will assume a stable process has the same normal distribution at all times.
Poster Keep the poster of the definition of stability in front of the class throughout the remainder of this session.
Time 43

5. Examining Variation
Common Causes
The cause of variations in a stable process is called a Common Cause.
A common cause is a natural cause of variation in the system.
Major Point To introduce the definition of common causes
Example Make sure the funnel has been reset and the funnel is centered. Drop a couple of balls through the funnel.
Ask What causes the variation in this process?
The pins.
Do the pins always cause variation?
Yes
Explain The cause of variation that always acts the same way in a process is called a common cause variation.
Variations in a stable process are always caused by common causes.
Define Common causes of variation are ever-present sources of variation in a stable process.
Exercise Use the following idea to get people thinking about common cause variation
Process = Driving from home to work
Measurement = time to get from home to work 44

6. Examining Variation Common Cause Examples Machine vibration
Temperature fluctuations
Slight variation in raw materials
Human variation in setting control dials
Exercise (cont’d) What are the causes of variability? (ANY cause)
Record Record the causes the students name on a flipchart
Make sure some special causes are included, e.g., accident blocks intersection, car breaks down, you have an accident, etc.
Ask Which of these are common causes?
Make sure that one-of-a-kind occurrences are omitted from the common cause list.
Summarize Common causes of variation are always present in the process, with essentially random effects over time. (Sometimes you hit all the lights “green”, sometimes you hit some red lights, and sometimes you don’t get through any lights without having to stop for red.)
Discuss Point out the examples on this slide. Ask students what other common causes of variation exist in the factory.
Write down causes on flipchart. Leave any debate for later. When several causes have been written down, analyze the list and identify common causes. 45

7. Examining Variation Tools for Examining Stability
Trend Chart: A plot showing the behavior of a process over time.
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Major Point A trend chart is a stretched out histogram.
Ask How can a stable process be recognized on a trend chart?
The chart doesn’t contain any dramatic changes in the plot pattern (or trend).
Discuss A trend chart is a measurement plotted against time. 46

8. Examining Variation Tools for Examining Stability
Histogram: A barchart showing the distribution of the process. 35 30 25 20
Percentage 15 10 5
Major Point A histogram is a collapsed trend chart.
Ask How can a stable process be recognized on a histogram?
It has the same normal distribution all the time. Refer to flipchart definition.
What is the relationship between a trend chart and a histogram?
A histogram results from tipping a trend chart on its side and letting all the plots fall to the bottom.
Summarize Both graphs are useful ways to assess process behavior. We will always look at both as a trend chart-histogram pair. 10 20 30 40 50 60 70 80 90
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9. Examining Variation Activity: Comparing stable processes
Which process has better quality?
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Major Point The less the variation from the target, the better the quality
Ask For each A-B pair which is better?
Why?
What is meant by on-target?
For pair #1 which is on target?
Trick question. Both are on target
For pair #2 which has less variation from its mean?
Vocabulary On target refers to the average, long term behavior 50
Sequence 5 10 15 20 25
Sequence 5 10 15 20 25 B A 49

10. Examining Variation Activity: Comparing stable processes (cont’d)
Which process has better quality?
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Sequence 5 10 15 20 25 B A 50

11. Examining Variation Unstable Process
Any process that is not stable is called an unstable or out-of-control process. ? ?
Prediction
Major Point To introduce the definition of an unstable process.
Ask What is an unstable process?
this one is just stupid enough to be fun
Any process that is NOT stable is unstable!
Time 51

12. Kinds of Instability: Excursions
Examining Variation
Kinds of Instability: Excursions
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Major Point To introduce new vocabulary describing different types of instability
Vocabulary Discuss characteristics of the following:
Excursions/Outliers - occasional spikes appear in trend chart. Histogram contains data that is separate from the main grouping.
Cycles - Trend chart contains microtrends that gradually vary from the target and then gradually shift in the other direction.
Shifts - Trend chart contains sudden shifts in trend. More dramatic than cycles.
Chaos - Trend chart jumps all over the place. Unpredictable.
Trends - Trend chart shows gradual shifting away from target. 20
Time 53

13. Kinds of Instability: Shifts
Examining Variation
Kinds of Instability: Shifts
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14. Kinds of Instability: Drifts
Examining Variation
Kinds of Instability: Drifts
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15. Kinds of Instability: Cycles
Examining Variation
Kinds of Instability: Cycles
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16. Kinds of Instability: Chaos
Examining Variation
Kinds of Instability: Chaos
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17. Examining Variation Special Causes
Anything that causes variations that are not part of the stable process is called a special cause, assignable cause, or unnatural cause.
Major Point To introduce the definition of special cause.
Do Display the results of the driving to work example on the flipchart.
Ask What are the special causes of variation?
accidents, breakdown, road repair, etc. 58

18. Examining Variation Examples of Special Causes
Batch of defective raw material
Faulty set-up
Human error
Incorrect recipe
Blown gasket
Earthquake
Major Point Special causes also occur in factory processes.
Discuss Point out examples that affect the packaging process. Ask the class for others; write them on flipchart.
Summarize Special causes result in non-random patterns in trend charts or histograms; they are always associated with unstable processes. 59

19. Reducing Variation Improving a Stable Process
Two strategies for improving a stable process
Centering at Target
Reducing Common Cause Variation
Major Point There are two strategies for improving a stable process: centering at target, reducing common cause variation.
Ask How can you tell which is most appropriate?
Examine trend charts 79

20. Reducing Variation Centering at Target Time Thickness 200 200 180 180
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Major Point If the process is off-target and stable, then you must make the necessary adjustments to move toward the target. 60 40 40 20 20
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21. Reducing Common Cause Variation
Reducing Variation
Reducing Common Cause Variation
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Major Point If the process is on-target and stable, but has large variation, then you must identify and work to reduce common cause variation. 20 20
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22. Reducing Variation Reducing Variation in a Stable Process
Make Permanent Changes
Changes are based on the scientific approach
Structured problem solving
Planned experiments
Major Point To improve a stable process, the process must be permanently changed
Discuss There are two key concepts on this slide
It is important that changes be permanent rather than temporary “let’s see if this will work” changes.
Changes must be scientific rather than “seat-of-the-pants” or intuitive
Examples New Equipment
Upgraded Equipment
New Procedure
New Machine Settings
Higher Quality Materials
Examples: new equipment, equipment upgrade, new procedure, new machine settings, better raw material 82

23. Reducing Variation in an Unstable Process
Do not ignore special causes.
Do quickly detect special cause variations.
Do stop production until the process is fixed. (Reactive)
Do identify and permanently eliminate special causes. (Preventive)
Major Point Since unstable processes are characterized by special causes of variation, they can be improved by removing these special causes.
Ask How can we avoid tampering?
Never make adjustments
Discuss Tampering is adjusting a stable process, reacting to a common cause as if it were a special cause.
The opposite of this is ignoring special cause variation in unstable processes. This is equally bad.
There are two modes of dealing with special cause variation:
Reactive (local): Quick detection and response to special cause variation.
Preventive (systemic): Identification and permanent elimination of special cause. 85

24. Improving an Unstable Process
Reducing Variation
Improving an Unstable Process
Four Step Process
Detect the special cause variation.
Identify the special cause.
Fix the process
Remove the special cause, or
Compensate for the special cause.
Prevent the special cause from occurring again
Major Point We must react carefully and appropriately to special cause variation. The four step model is one way to apply a systematic approach.
Discuss Point out that fixing is reactive, while preventing is preventive. Both are appropriate.
Note the options under fix
Remove
Compensate
Two ways to deal with idiots: idiot proof the process, or fire the idiots. 86

25. Improving an Unstable Process
Reducing Variation
Improving an Unstable Process
Reactive
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Not Here
Detect Here
Detect Here
Major Point Both local and system efforts can improve an unstable process.
Discuss The term reactive is not intended to carry a negative connotation. Rather, it denotes reaction (response) to special causes that occur locally.
A trend chart makes timely response possible. 87

26. Detecting Variation
How can we decide if variation is the result of common or special cause?
Major Point To motivate the need for a control chart
Ask How can we avoid tampering?
Never adjust
What is wrong with this strategy?
It ignores special cause variation.
What is the key to taking correct action (not tampering vs. corrective action)
Recognize the type of variation
If stable, do not adjust; work on reducing common cause variation
If unstable, respond to special cause variation
How can we tell if a process is stable or unstable?
There is subjective judgment involved 95

27. Benefit: Prevents tampering or ignoring
Detecting Variation
Tool: Control Chart
Benefit: Prevents tampering or ignoring
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Major Point To introduce the control chart as a tool to help distinguish common vs. special causes
Discuss A control chart is an augmented trend chart that eliminates the subjective judgment about stable vs. unstable processes.
Helps prevent tampering with a stable process
Helps avoid ignoring an unstable process 96

28. Control Chart for Detecting Variation
Observe
Variation
Common
Cause
Detect
Special Cause
Control
Chart
Don’t
Tamper
Identify
Major Point Control charts make the reduction of variation flowchart operational, providing a tool to guide us down the correct branch.
Discuss Review the reduction of variation flow. Point out that the critical juncture is at the top: distinguishing common from special cause.
A control chart is the tool for making this distinction
Fix
Reduce Overall
Variation
Prevent 97

29. Control Chart for Detecting Variation
Trend Chart +
Center Line +
Control Limits
Major Point The structure of a control chart = trend chart + center line + control limits
Discuss Very briefly go over the structure of a control chart. Emphasize that it is just a trend chart with some extra horizontal lines superimposed.
Upper Control Limit
Center Line
Lower Control Limit 98

30. Detecting Variation Control Limits
Control limits tell us where the measurements in a stable process should fall
Lower
Control
Limit
Upper
Major Point Control limits are based on the distribution of a stable process.
Ask What is a stable process?
A process that has the same distribution all the time
Discuss For a stable process we can make a strong prediction about where we expect the next observation to fall.
Control limits simply describe this prediction
They define where we expect most (>99%) of the observations from the stable process to fall. 99

31. Detecting Variation Creating a Control Chart
Turn the distribution on its side
Upper Control Limit
Center Line
Major Point Recalling the relationship between histograms and trend charts, control limits can be obtained by superimposing the histogram (turned on its side) on a trend chart.
Discuss Briefly review the relationship of a histogram to a trend chart. Discuss the control limits and center line and their relationship to the histogram superimposed on the trend chart.
Lower Control Limit
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32. Process mean, based on historical data
Detecting Variation
Creating a Control Chart
What is the Center Line?
Process mean, based on historical data or
Process Target
Major Point To get people thinking about two options for a center line.
Discuss Main points on slide
Center line = process mean
Center line = target
Ask Is this choice completely arbitrary?
No. In a stable off-target process the center line represents the process mean
What are the implications of this choice?
What factors are relevant in making this choice?
The aim here is simply to get the class thinking about the meaning of the center line. Issues include:
Description vs. prescription
Ability to control the target
Effectiveness of a control chart to drive improvement.
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33. Detecting Variation Creating a Control Chart Selecting the Center Line
The center line should be the target, unless we are unable or unwilling to control the process to target.
Measurements:
Major Point For measurements, first choice should be the center line = the target.
For defects, the center line = process mean
Discuss Emphasize that choosing the center line = target for measurements directs us to improve the process by moving it to the target.
This choice is particularly useful when multiple machines are tracked on one chart; the implication is to move all of them to the target. (Avoid this if at all possible.)
Only if moving the process to target is not possible or undesirable should the center line = process mean.
For defects, since target = 0, always take the center line = process mean.
Ask What about cases where the process has never been “near” target?
What about process in which the mean is not significantly different from target?
Note Expect this slide to be very controversial
Defects:
Since the target is zero defects, the center line is the process mean.
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34. Control Limits vs. Spec Limits
Detecting Variation
Control Limits vs. Spec Limits
Control Limits
Based on performance of the process.
Tell us when to take action on the process.
Spec Limits
Based on performance of the product.
Tell us when to disposition the product.
Major Point To introduce the difference between the control limits and spec limits as process performance (CLs) vs. product performance (SLs).
Discuss Each point on the slide
Highlight the distinction between process-based control limits and product-based spec limits.
Control limits describe behavior of a stable process. They tell us when action is needed to remove special-cause variation.
Spec limits prescribe performance of the product. They tell us when to disposition the product.
Try to get the class involved in discussion. Make sure the distinction between process-based control limits and product-based spec limits is clear.
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35. Detecting Variation Control Limits vs. Spec Limits Focus On
Improve Process Quality
Spec Limits
Improve Product Quality
Major Point Modern quality improvement efforts focus on control limits to improve the process rather than spec limits to improve the product. Improving the process quality results in product quality improvements. You cannot inspect quality into a process.
Ask Where should your attention be focused for process improvement?
Control limits focus our attention on continuously reducing variability from the target.
Spec limits do not tell you how to make the process better.
You cannot inspect quality into the process.
Are spec limits useful for anything?
DO NOT use spec limits to manage the process.
DO use spec limits to disposition the product.
Discuss Recall the loss function
Traditional loss function uses spec limits to inspect-rework-scrap without helping to fix the process.
Modern loss function focuses on reducing variability in the process.
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36. Measurement Capability
Have you ever been bitten by a measurement system?
Observed
Data
True
Data
METROLOGY
SYSTEM
Major Point We often assume that a measurement system is valid. Measurement is a process and has variation just like any other process
Ask What kinds of problems can measurement systems cause?
An off-target measurement system might make you think the process is off-target when in reality it is on-target. Time/money is wasted trying to fix a nonexistent problem.
An unstable measurement system might make you think a process is on target when it really isn’t.
Others?
black box
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37. Measurement Capability
A Measurement Process
Measurement tools themselves
hardware
software
All the procedures for using the tools
which operators
set-up/handling procedures
off-line calculations and data entry
calibration frequency and technique
Major Point Measurement system consists of tools and procedures.
Ask What is the definition of measurement?
Not just equipment
Not just units
Process = equipment + procedure
Not a production process
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38. Measurement Capability
Why Do Measurements Vary?
Work Methods
ease of data entry
operator training
calibration frequency
operator technique
maintenance of standards
standard procedure
sufficient time for work
Measurement
Variation
Environment
line voltage
variation
temperature
fluctuation
vibration
humidity
cleanliness
Tool
mechanical
instability
electrical
wear
algorithm
Major Point Variation in measurement is caused by many things. Some are common causes and some are special causes.
Discuss Fishbone diagram helps to identify causal relationships. Understanding these relationships helps to eliminate causes of variation.
Discuss each entry in diagram
Ask Can you think of other causes of measurement variation?
NOTE: Not all of these will necessarily be significant sources of variation for every measurement system.
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39. Measurement Capability
Assumptions We Often Make
Metrology tools are perfectly accurate
No day-to-day variation in performance
No operator-to-operator variation
Major Point We often make certain invalid assumptions about the process.
Ask Are these assumptions beneficial?
Yes, to a degree. At a superficial level we must assume that everything is well in order to get work done. However, at a lower level we must be aware that variation exists and we must continually work to reduce variation.
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40. Measurement Capability
MCA Tells Us:
How big is the measurement error?
What are the sources of measurement error?
Is the tool stable over time?
Is the tool capable of making the measurements for this project?
Is the tool capable of making the measurements for this process?
What needs to be done to improve the measurement process?
Major Point A measurement capability analysis helps to answer several questions about the measurement process.
Discuss Remind the class that this section is about measurement capability analysis. We are analyzing the capability of metrology systems. This analysis will provide a lot of information.
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41. Measurement Capability
Capability vs. Calibration
Calibration
Procedure to compare readings from a tool with a standard and then correct for any deviations.
Statistically: centering the mean of the distribution of readings on the “true value” (obtained from a standard).
Major Point Calibration is one way to improve the measurement process.
Ask What are the two ways to improve a process?
Move process toward the target
Reduce variability
Ask What is the target of a measurement process?
To give a true measurement
Can a measurement process be off-target?
How can an off-target measurement process be made on-target?
Calibrate the measurement tools
Discuss One way to improve a measurement process is by calibrating the tools to ensure that the process is on-target.
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42. Measurement Capability
Capability vs. Calibration (cont’d)
Capability
Procedure to identify and quantify sources of variation in readings and then eliminate them.
Statistically: fitting the model to the readings so that the components of variance can be estimated.
Both work together to keep measurement tool performing optimally.
Major Point Assessing measurement capability is another way to improve the measurement process.
Ask Can a measurement process have variation?
How can we reduce measurement process variation?
Detect variation
Identify variation
Fix or prevent variation
Discuss Analyzing measurement capability is a way to detect and identify variation in the measurement system.
Both capability analysis and calibration are useful for improving a measurement process.
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43. Concepts and Vocabulary
Sources Of Variation
Process Variation +
Measurement Variation
Major Point Total variation is a combination of process variation together with measurement variation.
Ask If you have variation in your measurement process, would you expect this to impact the variation in the process?
Discuss Explain that the total variation is a sum of all variation anywhere in the system. =
Total Variation
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44. Introduction Total Variation Product Measurement System Accuracy
Major Point Total variation is product variation combined with measurement system variation. Measurement system variation is dependent upon accuracy and precision
Purpose To establish a framework for how accuracy and precision relate to variation.
Accuracy
Precision
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45. Concepts and Vocabulary
Accuracy
The degree to which a process mean is on target
Related Terms
True Value
Bias
Major Point Accuracy describes how on-target a process is.
Ask Is it possible for a process to be on-target but have a lot of variability?
Discuss The degree to which a process is on-target is called accuracy.
Ask What is the target of a measurment process?
To measure the true value of a parameter.
Discuss The actual value of a parameter is called the true value.
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46. Concepts and Vocabulary
Precision
The degree of variability in a process
Related Terms
Repeatability
Reproducibility
Major Point Precision describes how much variability a process has regardless of its accuracy.
Ask Is it possible for a process to have very low variability without being on target?
Discuss A process can be precise but not accurate, or accurate but not precise.
Goal Accurate and precise process
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47. Concepts and Vocabulary
Bias
Distance between the average value of all the measurements and the true value. Can be positive or negative.
Bias = m - True Value
Measures the amount by which a tool is consistently off target from the truth.
Bias is the numerical value we use to measure accuracy.
Synonyms: systematic error, offset.
Major Point Bias is the measure of accuracy.
Discuss The concepts in each bullet
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48. Concepts and Vocabulary
Bias
bias
Major Point To show a graphical depiction of bias
Observed
Average
True
Value
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49. Concepts and Vocabulary
Precision Says Nothing About How Close The Measurements Are To The Truth.
Accuracy Says Nothing About How Close Measurements Are To Each Other.
Major Point Comparison of precision and accuracy. Demonstrates the importance of both.
Discuss The differences between precision and accuracy. Both are important.
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50. Concepts and Vocabulary
Precision
Can be separated into repeatability and reproducibility
Total Variation
Product
Measurement System
Accuracy
Precision
Major Point Precision (measurement variability) is determined by repeatibility and reproducibility
Discuss The overall variation components. Especially focus on repeatabilty and reproducibility.
Repeatability
Reproducibility
These characteristics have the relationship:
s2ms = s2rpt + s2rpd
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51. Concepts and Vocabulary
Repeatability
Variation that results when repeated measurements are made of the same parameter under absolutely identical conditions.
Same operator
Same set-up procedure
Same part
Same environmental conditions
Repeatability (s2rpt) is usually much smaller (better) than the precision of the system.
Major Point Define repeatability
Ask Suppose you take several measurements under identical circumstances. Would you expect to see variation?
Yes. There is variation in everything.
Ask Would you expect that variation to be very large?
No. s2rpt is usually very small.
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52. Concepts and Vocabulary
Reproducibility
The variation that results when different conditions are used to make the measurement.
Different Operators
Different Set-Up Procedures
Different Measurement Tools
Different Environmental Conditions
Different Days
Reproducibility (srpd), is approximately the standard deviation of the averages of measurements from different measurement conditions.
Major Point Define reproducibility
Ask Suppose you took several measurements under different circumstances. Would you expect to see variation?
Yes. There is variation in everything.
Ask Would you expect this variation to be large?
Maybe. Certainly larger than srpt
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