# Rupinder N

143

21

1. ## Correlation and Causation

The chosen best answer is that of Natwar Lal for listing multiple scenarios when causality exists but still the X and Y may not show strong correlation. Amlan Dutt's answer is must read to get good insight on the topic. Benchmark expert view is provided by Venugopal R.

3. ## Gage R&R

The chosen best answer is that of Amlan Dutt for the detailed explanation with data. Must read is Natwar's answer which is very detailed and accurate, too. To read the Benchmark expert view, refer to Venugopal's answer.
4. ## Kappa Value/ Kendall’s Coefficient

The chosen best answer is that of Amlan for providing a clear answer in an interesting way. Here is the crux of the answer and why Kappa may lead us to make erroneous inferences. Read through the complete answer!
5. ## Gage R&R

The chosen best answer is that of R Rajesh for clear definition and complete example. Read through Sreyash's answer to understand the paradox further.

8. ## Process Cycle Efficiency

The chosen best answer is that of Sreyash Sangam - PCE if looked at in isolation, may not give the complete picture about the process health. Please also read our expert view provided by Venugopal R.

10. ## Guide to Hypothesis Tests

Thank you Mohamed Asif and Sreyash for your suggestions. We value your inputs and will consider incorporating into the guide for selecting appropriate Hypothesis Test/s.
11. ## Net Present Value

Mohit Rawat's answer provides a clear and concise answer and, hence, is the chosen best. Read through Dr. Gautam.and Sreyash's answers for more examples. Benchmark expert view is provided by Venugopal.
12. ## Sigma level complexity with Attribute data

Steve C's answer is the chosen best answer as it explains the underlying concept of when to choose which calculation. Read through Ferdoz's answer to understand the calculation using an example.
13. ## Hypothesis Testing

The chosen best answer is that of R Rajesh, because of the structure and detail. Each one of the answers, however, outlines some unique challenges that keeps practicing GBs away from Hypothesis Testing. Read through to get a well rounded understanding before you devise a plan on how to overcome these obstacles. If availability of software is one of them, use our calculators at https://www.benchmarksixsigma.com/calculators/ Our expert view is provided by Venugopal R
14. ## Process FMEA and DMAIC

The chosen best answer is that of Sandra as she has outlined how FMEA can be used in each phase of a DMAIC project. In addition, read answers from Steve C, Kev N to complete a rounded view of how FMEA can be used in all phases of DMAIC. Charlie's answer highlights the different FMEAs that can be used in a project and Sujata's answer clearly outlines what FMEA is and why it should be done.

16. ## Root Cause

There are multiple high-quality responses to this question that have indicated deep understanding, experience and maturity in Root Cause Analysis. The chosen best answer is that of Sujata Dhawase - Three perspectives for root causes and a clear example explaining the perspectives. For readers, there is a wealth of knowledge on root cause in many responses, some of the notable ones are mentioned below. - "Interaction between causes" by Steve C - "Contributing vs. Root Cause" by Steve C - "Minimum and sufficient causes that eliminate the problem" view by Kevin - "Economic Elimination" view by Kevin - Crisp definition by Stephen J - Detailed definition by Leanne - "Multiple causal chains" view by Paul - "Scope of control" view by Chris- - "Influential factors" approach by R Rajesh - "Controlled Experiments" approach by Ransingh - "CNX Classification" by Prashanth Datta - "Primary root cause" concept by Chitra - "Actionability requirement" by Parsa - "Contributing cause" by Girish Vasan - "Practical Significance" view by Padmanabhan

18. ## Hypothesis Testing

You want output Y to improve and are looking for X that influences Y. Share examples when the outcome of a hypothesis test indicates statistical significance with respect to impact of X on Y but does not warrant a change in X
19. ## Attribute Agreement Analysis

The most common use of Attribute Agreement Analysis (AAA) is in evaluation of agreements in quality checks. In what other situations can you use AAA to your benefit?
20. ## Probability Calculation for Normal Distribution

Assuming that you have a data set that follows normal distribution, give examples of how you could use probability distribution to make predictions for the outcome of some business processes.

22. ## Central Limit Theorem

Please refer to the Benchmark Expert view to get a clear picture of usage of Central Limit Theorem.
23. ## Secondary Metric

It is necessary to have secondary metrics when we drive Six Sigma projects so that we are not solving one problem and creating another. In other words, we wouldn't be solving problems but they would just be changing shape. There could be several examples where the project fails despite improvement in the primary metric. Let's see a few here - 1. Customer Satisfaction increases but handling time increases 2. Order to ship time decreases but wrong deliveries increase 3. Order processing time of a certain component decreases but field failures increase 4. First Call Resolution increases but Handle Time also increases 5. A hair color that starts lasting longer but more users report damaged hair 6. Oil paints that dry faster but the pigments fade away faster. Please read the expert view given by Venugopal.