Rupinder N

Q. 174 Kano Model helps identify the attributes in a product that a customers think of as "Threshold, Performance and Excitement". The attributes shift over a period of time i.e. what is Performance today may become a Threshold requirement and what is Excitement today may become Performance. How does Kano Model help an organization? What kind of decisions can one take based on Kano Analysis? Explain with examples. 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 as per Indian Standard Time. 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.

Thank you for a phenomenal response to the question. All the respondents have brought forth important aspects about the Boxplot. There are 3 winners today: Amlan - for explaining where the 1.5 IQR length concept came from and also mentioning situations where a Boxplot fails, besides using pictures to explain, where needed. Mohammed Asif - for well structured answer with pictures and an example of how Boxplot can be used for subgroup comparison Natwar Lal - for adding a twist to the answer and building up the Boxplot concept from a Histogram and sharing examples. (In no particular order) Rachit has shared a simple practical example on how Boxplot can be used to drive improvements, Nilesh and Vastu's exaplanations are simple to read and understand for complete beginners. For Benchmark Expert view, read through Venugopal R's answer.

Q. 173 What are the key differences in OFAT (One Factor at A Time) testing and DOE (Design of Experiments)? Share examples to explain. What are the advantages of 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 as per Indian Standard Time. 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.

The best answer is that of Nilesh Akre for providing a well structured answer, mentioning all properties and clarifying with an example. For Benchmark Expert View, please go through Venugopal's answer.

Natwar Lal has provided the best answer - covering all aspects of the question. Mohamed Asif's answer should be read to get more insights, too. For Benchmark expert view, refer to Venugopal's answer.

Q. 171 The most common distribution for Defectives type of data is Binomial Distribution. What are the key features of Binomial Distribution? Explain with examples. 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 as per Indian Standard Time. 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.

The chosen best answer is that of Natwar Lal. For a practical approach, read through Sandra's answer. For Benchmark expert view, refer to Venugopal's answer.

Explain the difference between Prediction Interval and Confidence Interval with application based examples.

What are the key differences between Multiple Regression using historical data and Multiple Regression based on Experimental Data (DOE)? What are the advantages of one over the other, if at all?

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.

Q. 169 How can you check if you have taken enough samples for carrying out a Regression Analysis? 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 as per Indian Standard Time. 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.

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.

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!

Q. 167 While analyzing the XBar R Chart in Gage R&R Output, a sound measurement system will have more than 50% of the data points falling outside of the control limits. It is intuitively the reverse of what we want to see (data points to be inside the control limits for a well controlled process). Explain this seeming aberration. 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 as per Indian Standard Time. 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.

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.

Q. 166 Kappa value is used to make inference about soundness of a Measurement System when Attribute Data is used. Kendall's coefficient is used to infer the soundness of Measurement System when ordinal Attribute Data is used. If one used Kappa value to make inferences for an Ordinal Attribute Data set, what errors are likely to happen?  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 as per Indian Standard Time. 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.

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.

Q. 165 In statistics, causal relationships need to be examined carefully before making any inferences. Simpson's paradox is one of the phenomenon that is observed if such causal relationships are ignored. What is Simpson's Paradox? Explain with the help of examples.  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 as per Indian Standard Time. 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.

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.

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.

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.

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

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.

Q. 160 Hypothesis Testing - Business Success for any company depends on Continuous Improvement(CI) . CI requires creating hypothesis and testing them frequently. Most organizations create and test hypothesis (do trials for new ideas or carry on small experiments). For some reason, most professionals do not carry out statistical testing of hypothesis on sample data and tend to rely on gut feeling. It is seen that most Green Belt professionals do not use it while they have been trained on it. What are the key reasons for lack of use of this powerful family of statistical tests?  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 as per Indian Standard Time. 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.

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