Lesson 18: Negative Binomial Distribution - Part 1

In this lesson we give an introduction to Negative Binomial Distribution derive the probability mass function and show that the PMF sums to one.
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[@yusuffarah351]

Thanks for sharing these videos. i am studying exam p and very helpful.
Thanks again.

[@koyalkardevanshu5146]

watch at 2*x speed for best results....awesome videos

[@MADHURNAGPAL4]

pagal ha kya tu.

[@dydx3741]

mujhe toh ye chutiya lag rah hai

[@adwait9806]

thank you bro!! I got the best result (y)

[@sammerro]

Why on wikipedia and on some other sources (for ex MAple 14 documentation) EX = r(1-p)/p ?

[@StatCourses]

The parametrization could be different. We consider the number of trials until the first r successes as the negative binomial random variable. For example, some textbooks consider the number of trials until the first r failures as a negative binomial random variable. Others use the number of successes until the first r failures. The expected value, PMF, and variance also differ accordingly based on your parametrization.

[@martin48428]

Suppose im counting the #words in a sentence, so there would be no sentence with 0 words in it..suppose the max no of words in a sentence is 30 .. for eg there r 12 sentences having 4 words , 6 sentences having 5 words... what would 'p' be ...1/30 ? Coz clearly sentence with around 15 word tends to occur more than those with just 1 or 2 or 30

[@laugernberg4817]

no, but the sample space would be S={1,2,3, ... , 30} words, and the discrete random variable X could take the values [1,2,3, ... , 30}. That gives 30 different outcomes. Maybe the most frequent # of words is 12 and then p(X=12) could be something like .08. Only if the probabillty of a certain # of words is the exact same for all numbers, the probability of each outcome is 1/30. So P(X=3) could be .003 and P(X=11) could be .04 and P(X=15) could be .07... i dont know but if you made a statistic you could get that as a result :)

[@GladwinNewton]

Thanks mate