Normal Probability Plotting with Case Study
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- Опубликовано: 6 июл 2024
- Dear friends, we are pleased to released our 88th technical video on a very important topic in Statistical Quality Control and Six Sigma: Normal Probability Plotting! In this video, Hemant Urdhwareshe explains the concepts and basic procedure of Normal Probability Plotting using probability paper with a simple real life case study!
Hemant is a Fellow of ASQ and is certified by ASQ as Six Sigma Master Black Belt (CMBB), Black Belt (CSSBB), Quality Manager (CMQ/OE), Quality Engineer (CQE) and Reliability Engineer (CRE)!
Here are links to our other related videos:
(1) The Normal Distribution and Z-Score: • Normal Distribution an...
(2) Weibull Probability Plotting • Weibull Probability Pl...
Very clear .. most helpful!
Thank you for your feedback!🙏
Wonderfully explained. Thanks.
Welcome! I am glad you liked it!
Glad it was helpful!
Clear explanation, thank you!
Glad to know your feedback!
Amazing lecture prof
Hope to see more videos from you
My pleasure! Welcome!
Sir in 3:30 you said, Cumulative Probability = j-0.5/10. Sir in normal distribution, CPF comes in non-integrable exponential form!?
Please tell the reasoning behind the formula used?? You can share any video/blog link for such reading.
And thank you for the video sir.
If we take cumulative frequency value as claculated, it will mean that no tube will fail below 26.8 and all tubes will fail by 34.9! It is not appropriate to draw this conclusion based on a sample of 10! Thus this formula, knows as Modified Kapaln Meier Method, makes a correction based on sample size n. As na becomes larger, the correction becomes smaller! A more populalar method of such a correction is called median rank and is given by (j-0.3)/(n+0.4). Applicability of median rank is wider as many distributions, such as Weibull, are skewed.
You can watch my video on Weibull probability plotting:
ruclips.net/video/dsuLVS2yQ4U/видео.html.
Hope this helps.
@@instituteofqualityandrelia7902 that's super detailed response.
Got it sir. Thank you for sharing the resource... and thank you for taking out your time to reply.
Welcome! Keep watching my technical videos!
What is the purpose of normality test
The purpose is exactly the same. To verify whether data fits normal distribution.
@@uhemant1 thank you sir for answer, I have last 2 question
1. How do we know the plotted data is normal or not( what are the criteria in scattered diagram)
2. How do we check probability in scattered diagram
3 what is the role of P value in normality plott, in video you did not explain about probability. So I wanted to under. Please help me
3. What is correlations in between normality and probability
Please sir provide your feedback on above doubts
Sir please batiye na P value kaise calculate krte hai normality test me P value ka kya role hai.
The p-value is calculated using the goodness of the fit test such as a chi-square test. Most software calculates this with the Anderson Darling test which calculates the AD value and a p-value. The p-value indicates closeness to the normal distribution. A low p-value indicates that the distribution is not a good fit. Based on the confidence level, you can make a decision. For example, if the confidence level is 90%, and if the p-value is less than 0.1, we conclude that the data does not fit normal distribution.
Also watch my video on Goodness of fit test. Here is the link: ruclips.net/video/Ddvqf22Cv6c/видео.htmlsi=oUwNVh5pLmeW507V
Hope this helps.
@@instituteofqualityandrelia7902 than so much sir, really appreciate.