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Balaji Loganathan has provided the best answer to this question. Response from Oosman is also a must read.

The Shainin X methodology is described as a system for resolving issues created for medium- to high-volume processes where data are easily accessible, statistical techniques are frequently employed, and process intervention is challenging. It has mostly been used in facilities for part and assembly processes. The basic principality of the Red X technique is that there is always a dominant cause of variation. This claim is supported by the Pareto principle's application to the causes of the variance. Usually, changes in a number of inputs lead to changes in the output. These inputs (Xs) are divided into groups based on color, with the Red X serving as the primary cause. The GreenY state is how Shainin describes the desirable state of the output. Using Shainin tools has the benefit of requiring very low sample sets for problem analysis. Frequently, samples of just two or three are sufficient to draw statistically significant results. The data can be analyzed without the use of computerized statistical methods. Moreover underlying causes are identified through "interacting to the parts" as opposed to assumptions or preconceived notions. Due to the statistically robustness of the procedures, main effects and interaction effects were distinguished and quantified. A great variety of versatility is offered by the 12 various approaches. It is simpler to incorporate the entire workforce because the strategies are simple to implement and inexpensive to learn. The below four groups can be used to group the 12 techniques: Generation Clue: Until the fundamental cause can be isolated, quasi causes of variation are removed using the multi-vari analysis filtering technique. Pictograph: Used to indicate where a flaw is located on a component, in a design, or on a grid. Either a random pattern or a concentration in a specific location will result (s). Components Search: To identify the source of the issue, parts and sub-assemblies are switched between good and problematic products. -Comparing the greatest and worst product examples side by side will help you identify the traits or factors that set the best and worst goods apart. List and quantify the process variables in the product/process search -Search for Products/Processes: The process variables that affect a product's quality should be listed and measured. By contrasting data from a process that yields good parts with measurements from a process that yields faulty parts, you can identify which of these process factors is to blame for the problem. DOE optimization By displaying one variable against the other, a scatterplot (also known as a scatter diagram) can be used to visually depict the relationship between two variables. RSM, or Response Surface Methodology When we want to improve the settings of the essential elements in a process once they have been isolated, we apply a DOE technique. When we are aware of or believe that the response variable has curvature (i.e., non-linearity), we use RSM designs. DOE approach Variables Search: A grid search approach that distinguishes between significant and minor process variables through testing the optimal and limiting values for each variable Complete factor analyses: These tests cover all possible combinations of variables, and all of their interconnections, and work best when there are just a few variables that have a big effect on the answer variable. They take longer and cost more to execute than screening methods. B vs. C: B stands for the superior or improved method, whilst C stands for the existing process. Six samples—three B samples and three C samples—are used in the test. According to the Law of Combinations, there is simply one possibility in 20 that all three Bs will outrank all three Cs, providing us 95% certainty that this is not a coincidence. Positrol (or precontrol): Items are rated as red, yellow, or green depending on how closely they adhere to the standard or tolerance. Green represents the tolerance's middle portion; yellow represents its second half; and red represents the tolerance's upper limit. How frequently the process requires change determines the sampling frequency. Continue running if a specimen is green. Choose another sample if the first one is yellow. Stop the process and tweak or modify it if the second piece is yellow. If any of the samples are red, halt the procedure and make any necessary adjustments. The Process Certification (Process Control, and Management Plan) specifies the who, how, where, and when of controls that will guarantee that the significant variables or factors are kept under control.

The Shainin System is develop by Dorian Shainin. It is a tool for statistical engineering and generally used in Automobile sector. Shainin also called Red –X strategy. This is typically used to high volume processes where huge database exist and ease of data availability. This system is used in parts and assembly manufacturing processes. This work on below underlying principles 1. Assumption that there are large cause of variations 2. Assumptions there is diagnostic processes and remedial actions. Steps of Shainin system 1. define the project 2. Establish Measurement system 3. generate hints 4. list probable factors 5. DoE 6. found Red –X 7. Check interactions 8. Irreversible corrective actions 9. SPC 10. monitor outcome 11. Consumer satisfaction How it is different than Six sigma Six sigma is more statistical however this is based on Statistics and more mechanistic. Shainin is systems that are developed to achieve six sigma targets Shainin systems are evidence based and covers maximum source of variations. Shainin systems generally used FACTUAL path while Six sigma used DMAIC kind of methodology. FACTUAL: Focus>>Approach>> converge>>Test >> understand>> Apply >> leverage DMAIC: Define>> Measure>> Analyse>> Improve>> Control.

Shainin Red X Methodology is a statistical problem-solving approach used in industrial settings to quickly identify the root cause of complex and multifaceted issues. It is based on the idea that a small number of critical inputs (often referred to as the dominant "X's" based on pareto principle) are responsible for most of the variation in a system. The methodology involves a systematic process of testing, eliminating, and validating these inputs until the root cause of the issue is found. Compared to Six Sigma, Shainin Red X Methodology is considered to be a more efficient and quicker approach to problem-solving, particularly when dealing with complex, multivariate issues. Six Sigma, on the other hand, is a more comprehensive process improvement methodology that involves extensive data analysis, statistical process control, and a structured DMAIC (Define, Measure, Analyze, Improve, Control) process. Pros of Shainin Red X Methodology: · Quicker problem resolution time · Focuses on critical inputs for efficient problem-solving · Can be applied to a wide range of industrial settings · Can be used by individuals with limited statistical knowledge Cons of Shainin Red X Methodology: · May not be as comprehensive as other problem-solving approaches such as Six Sigma · May not be suitable for all types of problems, particularly those that are not complex or multivariate in nature · Can be less data-driven compared to other methodologies, relying more on intuition and experience of the problem-solver. Pros of Six Sigma: · Comprehensive approach to problem-solving and process improvement · Utilizes statistical tools and methodology to identify and eliminate causes of defects · Can be applied to a wide range of industries and processes Cons of Six Sigma: · Can be time-consuming and resource-intensive to implement · May not be as quick as Shainin Red X Methodology in solving specific problems. In summary, Shainin Red X Methodology is a fast and effective approach to solving complex problems, but it may not be as comprehensive as other methodologies like Six Sigma. The choice of methodology depends on the type and complexity of the problem, as well as the resources available.

Shainin Red X Methodology is a problem-solving technique used in the manufacturing and engineering industries to identify the root cause of a particular issue quickly and effectively. It's a data-driven approach that utilizes statistical analysis, hypothesis testing, and experimentation to isolate the key factor causing the problem. Compared to Six Sigma, Shainin Red X Methodology is a more streamlined and quicker approach to problem-solving. While Six Sigma is a comprehensive methodology that can take several weeks or months to complete, Shainin Red X can often find the root cause in a matter of days or even hours. Pros of Shainin Red X Methodology include: Faster problem-solving times Reduced number of trial and error tests Higher accuracy in identifying root cause Emphasis on simplicity, making it easy for non-experts to understand and participate in the problem-solving process. Cons of Shainin Red X Methodology include: Limited scope, as it's mainly focused on identifying root cause and not on process improvement or optimization. May not be suitable for complex problems or those requiring a deeper understanding of the underlying systems and processes. Overall, Shainin Red X Methodology can be an effective tool for solving problems quickly in specific cases, but it may not always be the best choice for all situations.

Shainin RED X projects are evidence-based; converging on the main source of variation, the emphasizing principle is DY = f(Dx)The largest value will result from a combination of a significant coefficient and a large change in X. What is the difference between Shainin and Six Sigma? The main difference between the Shainin Red X® approach (FACTUAL) and the Six Sigma methodology (DMAIC) is the phase Approach. The Red X develops a strategy based upon the physics of the problem and the comparison of the BOB (Best of Best) and WOW (Worst of Worst) parts Any problem-solving methodology involves two phases’ diagnostic and remedial phases. The diagnostic phase is concerned with measuring and analyzing the current process performance while the remedial phase involves of various corrective actions taken to improve the process and monitoring the new process to make it a culture. Tables show the comparison between the six sigma and Shainin methodological approaches. The basis for Comparison Six Sigma Red X Meaning Six sigma methodology attempts to improve the existing process The Shainin System™ (SS) is defined as a problem-solving system designed for medium- to high-volume processes where this methodology follows FACTUAL approach. Focuses on - Process Focused Red X statistical engineering identifies a set of tools first used to identify the Red X, and then to monitor the effectiveness of controlling the Red X. Shainin system focuses on understanding the machine or parts problem and assembly operations facilities Methodology Six Sigma uses DMAIC (Define, Measure, Analysis, Improve, control) Red X approach uses the following structure, called FACTUAL (Focus, Approach, Converge, Test, understand, Apply, Leverage Domain Knowledge No Deep understanding is required of the Y & the problem You must have a deep understanding of the Y and the problem. Tools used Descriptive statistics. Regression analysis, designed experiments, hypothesis tests, analysis of variance (ANOVA), and control charts. Shainin systems are such as Isoplot, Multi-Vari analysis, Concentration Chart, Component search, Paired comparison, Product/Process search, Variable search, Full factorial, B versus C, etc Skill Six sigma required strong statistical & analytical knowledge RED x requires good technical knowledge, engineering skills, common sense, and simple statistics to solve technical problems with statistical confidence

Fleiss’s Kappa and Cohen’s kappa both are used for checking agreements within and between appraisers. While Kappa value can be calculated for any number of appraiser and trial numbers, Cohen's kappa can only be calculated under some specific conditions (e.g. only 2 raters). Also the assumption with Cohen’s kappa is that the appraisers are deliberately chosen and fixed, while with Fleiss’ kappa, the appraisers are chosen at random from a larger pool. The best answer has been provided by Anupam Goswami.

Q 537. What is Shainin Red X Methodology? Compare it with Six Sigma and highlight its pros and cons. Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

Cohen Kappa Is an inter observer correlation measurement for a single factor with more than one observer evaluation. It is used to provide the user with calculable benchmark of the degree of agreements vis-à-vis all observers. As such, it is used to know how recurrent there is agreement in the observer’s interpretation. In a normal scenario where a yes/no answer is involved, the outcome is weak as it does account for chance. This is why Kappa which take into consideration the removal of chance to be much more preferable as a measurable statistical tool. Results of Kappa can range from -1 to 1. A Kappa 0 indicates that the agreed results are equal while expecting a chance. When Kappa value is 1, the agreed results are perfect. When Kappa is less than zero, the agreed result are less weighted with respect to chance. A good Kappa result can range from 0.75 to 0.90. Fleiss Kappa is a mathematically derived statistic to know how reliable is an agreement in relation to constant number of observers. It is relevant when observers label a rating when the items are classified or to the amount of ratings. Fleiss Kappa is a Fleiss Kappa for greater than 2 people who rates the agreements. However compared to Cohen Kappa, Fleiss Kappa are random people who are selected for rating an agreement while in Cohen Kappa, those who rate are known and fixed. A researcher will obviously look for Cohen Kappa when the values classified are of nominal order that is there is the results as no, bad, false, true, good, yes, crispy or nor crispy etc. however for ordinal values, a researcher will take Kendall coefficient into account.

Fleiss' Kappa Cohen's Kappa This is a way to measure agreement between 3 or more raters. Used for nominal data (e.g. likert scale). Therefore this measures agreement between 3 or more dependent categorical samples Similar to Fleiss’s Kappa This is a way to measure inter rater reliability but for below scenarios: - 2 raters rate same trial once each or - 1 rater rates 2 trials (measures agreement of new method with old or over time), Can be used for any number of raters Can be used for only 2 raters Allows for scenario where each rater is rating different items also Only works for scenario where raters are rating identical items Assumption includes that raters are chosen independently from larger set Assumption includes that raters are chosen deliberately and are fixed Scenarios for use: 5 raters randomly picked from a pool asked to give pass/fail by picking samples randomly from pool (e.g. destructive tests) Scenarios for use: 2 raters asked to give pass/fail for 20 interview candidates Have 2 machines for measuring pass/fail of an item’s attribute Condition of random sampling among raters means this is not suitable if all raters are reqd to rate all samples Conversely not suitable if all samples cant be rated because of cost of test or if its destructive in nature

Cohen's kappa is used for two raters considering same items will be rated by both raters while Fleiss Kappa used for multiple raters with a possibility of rating different items example: when in a study no of raters will be two and all the raters will rate for all the data points or observations like taste score (good , bad , neutral) then we can use Cohen's kappa

Cohen's Kappa and Fleiss Kappa are two different measures of agreement between two or more raters. Cohen's Kappa is used when there are two raters, while Fleiss Kappa is used when there are three or more raters. Cohen's Kappa is a measure of agreement between two raters that takes into account the possibility of agreement occurring by chance. It is calculated by subtracting the expected proportion of agreement from the observed proportion of agreement and dividing the result by one minus the expected proportion of agreement. Fleiss Kappa is a measure of agreement on multi-rater items. It is calculated by subtracting the average observed chance agreement from the observed agreement among the raters, and dividing the result by one minus the average observed chance agreement. Fleiss Kappa takes into account the number of raters involved and the number of levels or categories present. Both Cohen's Kappa and Fleiss Kappa are used to measure and quantify the amount of agreement between two or more ratings or observations of the same group of persons or things. They are both used to assess the reliability and accuracy of ratings given by different persons. While both measures provide a numeric score that indicates the level of agreement between raters, Fleiss Kappa tends to be more accurate when there are more than two raters and more than two categories.

Kappa, one of many coefficients used to evaluate inter-rater and similar types of reliability, was developed in 1960 by Jacob Cohen. Kappa is denoted k, an index used to measure the level of consistency between two raters. Kappa value used for? The kappa is frequently used to test inter-rater reliability. The significance of rater reliability lies in the fact that it characterizes the extent to which the data collected in the study are correct representations of the variables measured. What is a good kappa coefficient? Usually, a kappa of less than 0.4 is reflected as poor (a Kappa of 0 means there is no difference between the observers and chance alone). Kappa values of 0.4 to 0.75 are measured as moderate to good and a kappa of > 0.75 shows excellent agreement. What is Fleiss Kappa? Fleiss' kappa named after Joseph L. Fleiss is a way of measuring, for assessing the reliability of agreement between a fixed numbers of raters. This helps us to test to measure the inter-rater agreement between two or more raters. What is Cohen’s Kappa? Cohen’s kappa measures the agreement between two raters whom each classifies mutually exclusive categories. The best way to think about this is that Cohen’s Kappa is a quantitative measure of reliability for two raters that are rating the same thing, corrected for how often the raters may agree by chance. Cohen's kappa is a metric regularly used to assess the agreement between two raters. Cohen’s can also be used to measure the performance of a classification model. But before that, we need to understand the characteristics between reliability and validity Validity and Reliability - Validity means we are concerned with the degree to which a test measures what it claims to measure or in other words, how accurate the test is. On the other side, reliability is disturbed more by the degree to which a test produces similar results under consistent conditions or to put it another way, the precision of a test. Check this dartboard example of reliability and validity. For the results of a useful experiment, good reliability is important. But, reliability can be broken down into different types, Intra-rater reliability and Inter-rater reliability. · Intra-rater reliability is associated with the degree of agreement between different measurements done by the same person. · Inter-rater reliability is connected to the degree of agreement between two or more raters. Evaluating Cohen’s Kappa The value for kappa can be < 0 (negative). A score of 0 means that there is random agreement among raters, while a score of 1 means that there is the complete agreement between the raters. It’s essential that we acquaint ourselves with figure 2 to have a strong understanding. Figure 2: N x N grid used to interpret results of raters, Now let's break down each grid to our understanding A => The number of instances that both raters said were correct, and are in agreement. B => The total number of cases that Rater 2 said was incorrect, but Rater 1 said were correct. This case is a disagreement. C => The total number of occurrences that Rater 1 said was incorrect, but Rater 2 said were correct. This is also a disagreement. D => The total number of occasions that both Raters said were incorrect. And are in agreement. In order to work out the kappa value, we first need to know the probability of agreement (this explains why highlighted the agreement diagonal). This formula is derived by adding the number of tests to which the raters agree and then dividing it by the total number of tests. The formula for Cohen’s Kappa is the probability of agreement taken away from the probability of random agreement divided by 1 minus the probability of random agreement. Things to keep in mind when using Cohen’s 1. Cohen’s kappa is more useful than overall accuracy when working with unbalanced data. 2. The same simulation will give you lower values of Cohen’s kappa for unbalanced than for balanced test data. Lastly, When to Use Cohen’s over Fleiss? Fleiss' k works for any number of raters, however, Cohen's k only works for two raters; in addition, Fleiss' k permits each rater to be rating different items, while Cohen's k will only admit that both raters are rating identical items. However, Fleiss' k can lead to inconsistent results that, even with nominal categories, reordering the categories can change the results. But Cohen's version has its own problems and can lead to odd results when there are large differences in the occurrence of possible outcomes.

Below are the differences between Cohen's Kappa and Fleiss Kappa: - · Fleiss Kappa works for any number of raters whereas Cohen's Kappa only works for two raters · One of the important requirement of Fleiss Kappa is that each rater needs to rate different items, while Cohen's Kappa both raters need to rate identical items. · Fleiss Kappa can lead to paradoxical results namely that, even with nominal categories, reordering the categories can change the results. But Cohen's version lead to odd results when there are large differences in the occurrence of possible outcomes Example 1: - Let’s us take a response variable (categorical scale) with three values: yes, maybe, no and there are two raters. Both the raters are used to judge all observations. For Cohen’s Kappa there should be 2 raters and the same 2 raters judge all observations. So we in this scenario Cohens' kappa is suitable Example 2: - In the same example above let say there were three raters and different raters judge all observations. In Fleiss' kappa case, there should be 3 raters or more and the raters should be non-unique which is the above case so in this scenario Fleiss Kappa is suitable.

There were many answers to this question. However, not all were published as some of them were incorrect. The question was a tricky one as it dealt with 2 confusing MSA methods - Attribute Agreement Analysis and Attribute Gage Study. Attribute Agreement Analysis is used for discrete data to check for operator agreements on the attributes (there is no gauge being used here). Attribute Gage Study is used when attribute data (like Go/No Go etc.) is being generated using a gauge. The chosen best answer is from Balaji Loganathan. Do review the response from Mr Venugopal R, Benchmark Six Sigma's in-house expert.

Q 536. Minitab has the ability to report 2 different Kappa values for Attribute Agreement Analysis - Cohen's Kappa and Fleiss Kappa. What is the difference between the two? Using an example highlight the situation where a researcher will look at Cohen's Kappa instead of Fleiss Kappa. Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

Benchmark Six Sigma Expert View by Venugopal R ATTRIBUTE AGREEMENT ANALYSIS (AAA) is a method of performing MSA (Measurement Systems Analysis). MSA is done to assess reliability of Measurement Systems. When we have an attribute type of measurement; for example – visual inspection, manual validation of insurance claims, document classification etc., where the decisions are based on human (Appraiser) judgement, and no measuring instrument is involved – the outcome will be categorical (Example – Pass/Fail, Accept/Reject, Good/Bad etc.). For such measurement systems, we can use the Attribute Agreement Analysis to evaluate the acceptability of the system. The AAA helps to evaluate the extent of agreement 'within appraisers', 'between appraisers', 'the accuracy of appraisers' and the 'overall measurement system’s accuracy'. The Attribute Agreement Analysis is capable of giving 4 outputs, viz. 1. Within Appraiser variation (Repeatability) 2. Between Appraiser variation (Reproducibility) 3. Appraiser vs Standard variation (Appraiser Accuracy) 4. Overall accuracy (Team Accuracy) Point nos. 3 and 4 as above will be possible if we use a ‘Master Standard’ for comparing the decisions made by the Appraisers. For example, if Medical Insurance Claims are processed and the acceptability of each claim is judged by few auditors, the AAA would be good method to assess the reliability of the Audit Quality ATTRIBUTE GAGE STUDY is also an MSA methodology, and used to evaluate an attribute measurement system that uses an attribute gauge, which only screens the acceptable and non-acceptable parts, though the parts will have a value for the measured parameter. Attribute Gage Study examines the bias and repeatability of the system. For example, if an air plug gauge is used as a ‘go – no go’ measurement for diameter, the Attribute GageSstudy can be performed to assess the bias and repeatability of the system. For performing the Attribute Gage Study, a few parts are selected such that the range of their dimensions represent the normal operating range. Each part should have a reference value, which is the known and correct value for that part. Each part must be measured repeatedly for multiple times (recommended 20 times) and the number of ‘Accepts’ and ‘Rejects’ need to be recorded. Detailed requirements for the parts are given by AIAG’s MSA reference manual. The Attribute Gage Study report will contain: 1. Fitted line for ‘% acceptance vs Ref value of measured part.’ 2. Bias (acceptability of bias is based on p-value) 3. Repeatability 4. Graph for Probability of acceptance vs Ref value of measured part’.

An investigation into the bias and consistency of an attribute measuring system is known as an attribute gauge study. A 100% end-of-line inspection, for instance, might be carried out by an automatic inspection gauge. This gauge needs to be reliable and reproducible. It is more common to refer to the study as Attribute Agreement Analysis when the performance of the gauge or technique under consideration is utilized to generate judgements on a non-continuous scale, such as Pass/Fail or a rating Nowadays, a lot of businesses conduct studies on their measuring devices using gauge R&R. Less well known are the resources that are currently available for conducting comparable studies for non-measurement type instruments, such as Go/No-Go gauges. The study of instruments that measure properties on a continuous scale, such as force, length, viscosity, pH, etc., falls under the purview of gauge research and development. It is more common to refer to the study as Attribute Agreement Analysis when the performance of the gauge or technique under consideration is utilized to generate judgements on a non-continuous scale, such as Pass/Fail or a rating. An Attribute Agreement Analysis study is created like a regular Gage R&R study. A series of parts are selected from the process and evaluated by two or more operators. From this research, it will be possible to determine how consistent operators are in their own ratings and the degree of agreement between operators. If you can set the evaluation criteria for each part, you can also compare the performance of each operator to the criteria. Attribute agreement analysis studies are not only applicable to pass/fail ratings used in pass/fail gauges, but can also be used to test operator consistency when making ratings on rating scales. increase. Collect survey data and perform analysis using modern statistical software such as Minitab. Graphical output and statistical kappa values can be used to examine operator effectiveness and accuracy in conducting assessments.

An Attribute Gage study is a study that studies the bias and repeatability of an attribute measurement system. It is useful to decide which sources are responsible for the variation of the measurement data. The best one of these is a go/no go gage. This gage basically tells you if the part passes or it fails. There are only two likely outcomes. Other attribute measurement systems can have many categories such as very good, good, poor and very poor What is an attribute agreement analysis? Attribute Agreement Analysis (or Attribute MSA) is one of the tools within MSA, used to estimate your measurement system when attribute (qualitative) measurements are involved. With this we can confirm that measurement error is at an acceptable level before conducting data analysis Attribute Agreement Analysis is the type of Measurement System Analysis (MSA) that is used to measure how well an attribute (discrete) measurement system is working. Gage R&R is the type of Measurement System Analysis (MSA) that is used to measure how well a variable (continuous) measurement system is working. The Attribute Agreement Analysis study can be set up in a similar way as a regular Gauge R&R study. A number of parts are selected from the process, and are gage by two or more operators. This doesn’t just relate to Pass/Fail type assessments, such as with Go/No-Go type gauges, but it can also be used to test the reliability of operators where they make assessment on a rating scale. When to Work with Attribute Agreement Analysis ? Work with Attribute Agreement Analysis if you aim at doing a Gage R&R (MSA) for a test/measurement that does NOT lead to results you could gage with a measurement instrument. we want it if the results are qualitative. Examples: Visual quality control of parts. Examination of whether the cleaning was done well. Check whether a document is accessible. To Run the Analysis What is our objective to find out? Accuracy or Precision? Reproducibility or Repeatability? To Calculate Accuracy Understand how well the measurement system comes to the same results as per the “standard”. Calculate % R & R Shows how exactly the different appraisers agree about results and how precisely one and the same appraiser comes to the same results when measuring multiple times. Total % R&R defines the level of agreement within/ between appraisers and a standard Total %R&R should be 100% (when the study conditions are different from reality every result below 100% will lead to a measurement system not worth trusting) If the Total %R&R is less than 100% examine the results to see if this is a problem of repeatability, reproducibility or both

There are 2 answers that stand out - Dheeraj Bhardwaj - for explaining the two underlying plots i.e. box plot and kernel density plot Anupam Goswami - for providing insights into where Violin Plot can be used along with examples. Both the answers have been selected as winners. Congratulations to the joint winners!

A Violin plot is a amalgam of a box plot and a kernel density plot that depicts peaks in the data. It is more informative than a plain box plot. A box plot only shows summary statics such as mean/median and interquartile ranges while violin plot presents the full distribution of data including density and statistics of each variable. This is very useful in cases where the data distribution is multimodal. Violin plot are useful while making a comparison of distributions between multiple groups and one can compare the peaks, valleys, and tails of each group's density curve. It intuitively shows the distribution of data in a data set. The violin plot helps in easy visualization with a viewer to understand the probability of things and therefore it is imperative for one to use a violin plot if you have a large enough dataset to capture the underlying probabilities of your dataset. But in situations where the data set is not big enough with unimodal distribution and additional information within do not add much information to show the audience which might not understand the essence of densities like in violin plot, then simple Box plot will be better suited.

Q 535. What is Attribute Gage Study? How is it different from Attribute Agreement Analysis? Provide an example to highlight when will you use one over the other. Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

A violin plot is a fusion of a box plot and a kernel density plot, which displays peaks in the data. It helps us to visualize the distribution of numerical data. Box plots can only show summary statistics, but violin plots can depict summary statistics and the density of each variable. The width of the Probability Density Function defines how often that value occurs in the data set. The wider areas of the density plot display values that occur more frequently. Violin plots are also useful in multiple distributions at once for comparison. A narrower density plot indicates values that occur less frequently. The picture below shows the shape of a data set, a violin plot can summarize a data set using five values by the following: 1. The minimum 2. First quartile 3. Median 4. Third quartile 5. Maximum How do you read a violin plot? A violin plot shows how a data set varies along one variable by combining a boxplot with a Probability Density Function (PDF) the boxplot summarizes the center and spread: The white spot in the center of the box denotes the median. The length of the box indicates the (IQR) interquartile range

Violin Plot This is a way of plotting numerical data which is a combination of box plot and kernel density plot. Like a box plot, this too shows the median (indicated by a white dot), the interquartile range (indicated by the broad black bar running along the plot), the minimum/ maximum (indicated by the thin black line running along the plot) and the outliers. However, on top of the above summary statistics, the violin plot also shows the data distribution which is especially preferred if the data has multiple modes (reference to the shape of violin). This allows us to see the distribution of the data and especially useful if we want to compare multiple groups. In the above diagram, the violin plot has 2 wide sections showing that majority data points are grouped around that value. So for example if we want to study the grades obtained by students where there are generally multiple groups or modes (say Grades A and C), the violin plot is better to visualize and compare the data. Another example is if we want to compare heights of people across countries, then again, the violin plot is better. For plot of each country, we would typically observe 2 peaks (for males and females)