Sir Your videos are different and completely practical please make more videos on applications of all the probability distribution in solving business problems
Your application of the concepts is amazing. I enjoy watching your videos. My question goes: With your example for Binomial distribution where the empirical evidence showed that just 20% of the tickets generated require swift response and are thus high severity ones, how does this 20% relate to our answer using the Binomial distribution function i.e: 4/10=40% of high severity tickets? It is because our observation of 20% is based on just a couple of trials, while the distribution takes a whole lot of data, and is therefore more reliable? Can you pls elaborate a little more. Thanks!
Thanks Mohsin. It feels encouraging to know that the information shared is useful. To answer your question, 20% relates to incidence of high severity tickets in the entire lot of tickets received till that point (irrespective of weekend rush). This is the value of 'p' in the eqn. The case is related to specific response/action/planning required over the weekend. Again, historical data suggests roughly 10 tickets (no classification being considered) are received every weekend. We calculate the probability of getting 1/2/3 .. (x) number of high sev tickets expected from the (n) 10 tickets (over the weekend). Further, it is not just 4/10 tickets but also the probability related to these 4 tickets (i.e. 86%) that should be considered as well. You may have accidentally skipped that part of the explanation in the video. Trust this clarifies.
Hello I am trying to make a school project so can u just directly tell me real life applications of probability distribution or discrete random variable instead of a detailed explanation
That's exactly what the tutorial is about ... Not sure which grade you are studying in ... In absence of the objective of what your project is about, you could be better off picking up textbook examples to cite them. However, if you need to understand the application of the concept to solve a real-life problem, then you will need to know what and why you are doing it ... Incidentally, there is no easier way out ... Best wishes for your project.
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WoW !!
Thanks for that Life saving example :) :) Interesting explanations of theory. Thank You.
Appreciate your support. Glad you liked it!
The explanation about the application are amazing. Keep up the good work
Thanks for your encouraging words!
Amazing sir...love from Bangladesh 🇧🇩
Thank you
You are good, Sir! Continue posting this kind of video. Very informative!
Thanks
Good job and really like the way you skip "a" between 2b's and pronounce it as "Probbility". Keep it up.
Thanks.
Sir Your videos are different and completely practical please make more videos on applications of all the probability distribution in solving business problems
Thanks. Yes that is planned ahead.
Your application of the concepts is amazing. I enjoy watching your videos. My question goes:
With your example for Binomial distribution where the empirical evidence showed that just 20% of the tickets generated require swift response and are thus high severity ones, how does this 20% relate to our answer using the Binomial distribution function i.e: 4/10=40% of high severity tickets?
It is because our observation of 20% is based on just a couple of trials, while the distribution takes a whole lot of data, and is therefore more reliable?
Can you pls elaborate a little more. Thanks!
Thanks Mohsin. It feels encouraging to know that the information shared is useful. To answer your question, 20% relates to incidence of high severity tickets in the entire lot of tickets received till that point (irrespective of weekend rush). This is the value of 'p' in the eqn. The case is related to specific response/action/planning required over the weekend. Again, historical data suggests roughly 10 tickets (no classification being considered) are received every weekend. We calculate the probability of getting 1/2/3 .. (x) number of high sev tickets expected from the (n) 10 tickets (over the weekend). Further, it is not just 4/10 tickets but also the probability related to these 4 tickets (i.e. 86%) that should be considered as well. You may have accidentally skipped that part of the explanation in the video. Trust this clarifies.
Amazing .
Very structured
Thankyouuuu
Keeep that smile & zest sir ..
Its infectious
Thank you. Most welcome 😊
Thankyou, its very help a lot of people especially students, keep it up👍❤️
Happy you found it useful
Keep doing the great work thanks again.
Glad you liked it!
Literally, great explanation..... Can you explain how probability is used in Machine Learning by the same way taking real life applications
Thanks. The concept is the same. Will address it in tutorials on machine learning.
Great. Where I can find more list of Binomial probability applications in real life ,Kindly Help sir .
There are no specific websites/books. The idea is to understand the concept based on the example and apply it to your situation.
pls also tell about comulative probability
Sure. Will touch upon it in future videos.
Yes, Sir please make it for cumulative too
You help me alot!!😊
Happy to know that !!
NIce video !! Thank you
Glad you liked it!
greatness is not measured with exams
👍
Hello I am trying to make a school project so can u just directly tell me real life applications of probability distribution or discrete random variable instead of a detailed explanation
That's exactly what the tutorial is about ... Not sure which grade you are studying in ... In absence of the objective of what your project is about, you could be better off picking up textbook examples to cite them. However, if you need to understand the application of the concept to solve a real-life problem, then you will need to know what and why you are doing it ... Incidentally, there is no easier way out ... Best wishes for your project.
Not yet.
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