1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute...
-
Upload
stephen-houston -
Category
Documents
-
view
227 -
download
0
Transcript of 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute...
![Page 1: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/1.jpg)
1
Chapter 3: Attribute Measurement Systems Analysis (Optional)
3.1 Introduction to Attribute Measurement Systems Analysis
3.2 Conducting an Attribute MSA
![Page 2: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/2.jpg)
2
Chapter 3: Attribute Measurement Systems Analysis (Optional)
3.1 Introduction to Attribute Measurement Systems 3.1 Introduction to Attribute Measurement Systems AnalysisAnalysis
3.2 Conducting an Attribute MSA
![Page 3: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/3.jpg)
Objectives Introduce the basic concepts of an attribute
measurement systems analysis (MSA). Understand operational definitions for inspection
and evaluation. Define attribute MSA terms.
3
![Page 4: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/4.jpg)
What Is an MSA? A measurement systems analysis is an evaluation of
the efficacy of a measurement system. It is applicable to both continuous and attribute data. An attribute MSA evaluates whether a classification
system correctly sorts items. Companies make decisions each day based on
classifications; it is necessary to evaluate the efficacy of such classifications.
4
![Page 5: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/5.jpg)
Operational DefinitionsIn order for a rater to decide if a product is defective or not, he must have a clear description, or an operational definition, of what constitutes a defect. Such a definition might include the following: photographs physical specimens descriptions specifications.
5
![Page 6: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/6.jpg)
EffectivenessThe effectiveness of an inspection process is the percentage of time that a rater, or other
measurement tool, is correct in its classification of quality
is often significantly low before any attempts at improvement are instigated
should be at least 95%.
Effectiveness = number of correct evaluations
number of total opportunities
6
![Page 7: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/7.jpg)
![Page 8: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/8.jpg)
3.01 Multiple Choice PollSuppose 100 windshields are inspected, and 10 are defective and 90 are non-defective. If an inspector decides that 6 non-defectives are defective, and 1 defective is non-defective, what is his effectiveness?
a. .67
b. .067
c. .1
d. .93
e. None of the above
8
![Page 9: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/9.jpg)
3.01 Multiple Choice Poll – Correct AnswerSuppose 100 windshields are inspected, and 10 are defective and 90 are non-defective. If an inspector decides that 6 non-defectives are defective, and 1 defective is non-defective, what is his effectiveness?
a. .67
b. .067
c. .1
d. .93
e. None of the above
9
![Page 10: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/10.jpg)
False AlarmsA false alarm is a non-defective item that is classified as defective.
The probability of a false alarm, also known as Type I error or producer’s risk, is given by:
P(False Alarm) = number of false alarms
number of non-defective items
10
![Page 11: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/11.jpg)
![Page 12: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/12.jpg)
3.02 Multiple Choice PollSuppose 100 windshields are inspected, and 10 are defective and 90 are non-defective. If an inspector decides that 6 non-defectives are defective, and 1 defective is non-defective, what is the probability of a false alarm?
a. .67
b. .067
c. .1
d. .93
e. None of the above
12
![Page 13: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/13.jpg)
3.02 Multiple Choice Poll – Correct AnswerSuppose 100 windshields are inspected, and 10 are defective and 90 are non-defective. If an inspector decides that 6 non-defectives are defective, and 1 defective is non-defective, what is the probability of a false alarm?
a. .67
b. .067
c. .1
d. .93
e. None of the above
13
![Page 14: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/14.jpg)
MissesA miss is a defective item that is classified as non-defective.
The probability of a miss, also known as Type II error or consumer’s risk, is given by:
P(Miss) = number of misses
number of defective items
14
![Page 15: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/15.jpg)
![Page 16: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/16.jpg)
3.03 Multiple Choice PollSuppose 100 windshields are inspected, and 10 are defective and 90 are non-defective. If an inspector decides that 6 non-defectives are defective, and 1 defective is non-defective, what is the probability of a miss?
a. .67
b. .067
c. .1
d. .93
e. None of the above
16
![Page 17: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/17.jpg)
3.03 Multiple Choice Poll – Correct AnswerSuppose 100 windshields are inspected, and 10 are defective and 90 are non-defective. If an inspector decides that 6 non-defectives are defective, and 1 defective is non-defective, what is the probability of a miss?
a. .67
b. .067
c. .1
d. .93
e. None of the above
17
![Page 18: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/18.jpg)
Escape RateAn escape rate gives the percentage of time a customer is likely to see a defective item.
Escape Rate = P(Miss) × P(Defect)
where P(Defect) = number of defects
number of items inspected.
18
![Page 19: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/19.jpg)
Bias Bias is the tendency of an inspector to classify items
either as defective or as non-defective. Bias is defined as P(False Alarm)/P(Miss). Bias =1 implies there is no bias. Bias < 1 implies a bias towards accepting bad items. Bias > 1 implies a bias towards rejecting good items.
19
![Page 20: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/20.jpg)
![Page 21: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/21.jpg)
3.04 Multiple Choice PollThe bias is given by the probability of a false alarm divided by the probability of a miss. In the windshield example, the bias is given by .067/.1 = .67. What is the interpretation of this value?
a. There is no bias.
b. There is a bias towards accepting bad items.
c. There is a bias towards rejecting good items.
21
![Page 22: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/22.jpg)
3.04 Multiple Choice Poll – Correct AnswerThe bias is given by the probability of a false alarm divided by the probability of a miss. In the windshield example, the bias is given by .067/.1 = .67. What is the interpretation of this value?
a. There is no bias.
b. There is a bias towards accepting bad items.
c. There is a bias towards rejecting good items.
22
![Page 23: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/23.jpg)
Rater AgreementRater agreement is a measure of how well raters agree with each other is not an indication of correctness
23
![Page 24: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/24.jpg)
Kappa StatisticThe Kappa statistic is used to measure between-rater variability, or how
often two or more raters agree in their interpretations is a measure and not a test is given by:
kappa = po – pe
1 – pe
where po is the sum of observed proportions in diagonal cells of the contingency table and pe is the sum of expected proportions in diagonal cells of the contingency table.
24
![Page 25: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/25.jpg)
25
![Page 26: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/26.jpg)
26
Chapter 3: Attribute Measurement Systems Analysis (Optional)
3.1 Introduction to Attribute Measurement Systems Analysis
3.2 Conducting an Attribute MSA3.2 Conducting an Attribute MSA
![Page 27: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/27.jpg)
Objectives Examine the requirements for an attribute MSA. Perform an attribute MSA in JMP.
27
![Page 28: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/28.jpg)
Sample SizeTo conduct an attribute MSA, the minimum recommended sample sizes are given as follows:
28
Number of
Raters
Minimum Number of Parts
Number of Evaluations
1 40 3
2 30 3
3+ 25 3
![Page 29: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/29.jpg)
Attribute MSA Example Suppose three inspectors, Henry, Matt, and Tom, are
independently going to classify each of 30 parts as defective or non-defective in a random order. They will evaluate each part three different times. Of the 30 parts, 13 are defective and 17 are non-defective.
The classification will be based on a predetermined operational definition of defective and non-defective.
29
![Page 30: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/30.jpg)
30
This demonstration illustrates the concepts discussed previously.
Attribute MSA
![Page 31: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/31.jpg)
31
![Page 32: 1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.](https://reader034.fdocuments.in/reader034/viewer/2022051001/56649f2e5503460f94c486ee/html5/thumbnails/32.jpg)
32
Exercise
This exercise reinforces the concepts discussed previously.