Histogram (PART-2) : Different shapes of Histogram and its interpretation (Meaning)

Hello Friends,
Here are the links for online courses with practical examples to understand Lean Six Sigma Concepts in Excel and Minitab:
MSA (Measurement System Analysis): bit.ly/3pjtUjo
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7 Advance Tools (Management & Planning Tools): bit.ly/3sML370
Correlation and Regression: bit.ly/3pjnn84
Control Chart- Powerful tool in data analysis: bit.ly/3c7Pgwn
An analysis of the histogram is really tricky and explains many meaningful things from the collected data, and those things make this tool a very important tool.
Before drawing any conclusions from your histogram, satisfy yourself that the process was operating normally during the time period being studied.
If any unusual events affected the process during the time period of the histogram, your analysis of the histogram shape probably cannot be generalized to all time periods. Therefore, analyzing the meaning of the histogram’s shape is important, which I am going to explain in this video.
I am explaining all the distributions of histograms starting with Normal distribution, a Normality test to detect whether data is normal or not, and many other histogram shapes along with a description of “what they want to say?”
Everything is with practical examples and pictures which will be very easy to understand. I am sure you will enjoy it and it will make you a master in the interpretation of data.
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And finally, thank you for watching…
#Histogram #HistogramExample #HistogramInExcel #HistogramInMinitab #QualityControlTools #QCTools #QCToolsWithExample #7QCTools #7QualityControlTools #QualityControlToolsWithExample #LeanSixSigma #SixSigma #InterpretationOfHistogram #HistogramInterpretation #HistogramProcedure #DifferentShapesHistogram

[@tumilesisa7765]

Thank you for the explanation of Histograms. I am busy with my LSSBB certification and you explained the tool so nice. I would like to use your channel to learn more.

[@learnandapply]

Glad it was helpful! Thank you so much for your valuable comments and appreciation! 😊🙏

[@ranjan_v]

Very neatly explained, good job

[@learnandapply]

Thank you for your valuable comments. Please share it with your friends and colleagues.

[@vaibhavbarge9543]

Thanks sir....

[@direvosabostien3565]

Excellent video. Professional.

[@learnandapply]

+Direvo Sabostien Thank you very much for your valuable comments.

[@Sreejith_naturelover]

can you please, apply the statistical concept in shares and market index to forecast the market......i have already applied the concept of regression analysis in shares.....now im eager to apply other concepts.....so can consolidate all the advanced statistical concepts together and do a video.....you are very well explaining things....great..and thanks for showing a new way of learning

[@learnandapply]

You can visit the link for more details: vijaysabale.co/statistics

[@MrGamer-ql4ww]

Good job.... Keep it up

[@learnandapply]

Thank you so much for your valuable comments and appreciation 🙏☺

[@fr529]

can you please share link to part 1 of this video. thanks

[@learnandapply]

Just go to the playlist named 'Histogram'. You will get the 1st part of the video there.

[@RamKumar-ps7hp]

Can you post video related to errors in spc

[@learnandapply]

Ok, thank you for your valuable comments...

[@RamKumar-ps7hp]

Will explain MSA for attributes gauges

[@learnandapply]

+Ram Kumar Yes, but I am trying to cover everything in sequence I.e. With meaning and Define phase from next video onwards. I will cover this point in Measure phase. Again, thank you for your valuable comments.

[@madhusekhar17]

Put vedious on S.D ,moments,

[@gowthammurali1224]

First- Thank you so much ... Second - Can u explain how area under normal curve = 1 ....? If not possible to explain in statement please provide some link so that we can read for better understanding...

[@hafizasadurrehmanbajwa92]

sum of all probabilities(p1+p2+.....pN) = 1, In Normal distribution, as area under curve has been made by plotting bar graph for all possible probs of continuous data.