Excel 2013 Statistical Analysis #33: Binomial Probability Distributions: Tables, Charts, Functions

Download files (which file shown at begin of video): people.highlin...
Topics in this video:
1. (00:11) Discussion about Binominal Experiments and Probability Distributions
2. (11:00)Example 1: Experiment is attempting four sales calls. This example show how to create the Binomial Distribution in Table and Column Chart. This example show the 5 possibilities for comparative operators while using the BINOM.DIST function.
3. (24:10) Example 2: Experiment concerns late airline flights. This example show the 5 possibilities for comparative operators while using the BINOM.DIST function.
4. (29:47) Example 3: Experiment concerns West Seattle Bridge “Stuck in Traffic”.
5. (31:39)Example 4: Experiment concerns Student’s probability of withdrawing from Accounting 202. This example show how to use two BINOM.DIST function to calculate probabilities between two values.
6. (35:24) As number of trials get large, distribution approaches a Bell Shape. As the probability approaches 0.5, the distribution approaches a Bell Shape.

[@excelisfun]

Excel 2013 Statistical Analysis #33: Binomial Probability Distributions: Tables, Charts, Functions
Download files: people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:11) Discussion about Binominal Experiments and Probability Distributions
2. (11:00)Example 1: Experiment is attempting four sales calls. This example show how to create the Binomial Distribution in Table and Column Chart. This example show the 5 possibilities for comparative operators while using the BINOM.DIST function.
3. (24:10) Example 2: Experiment concerns late airline flights. This example show the 5 possibilities for comparative operators while using the BINOM.DIST function.
4. (29:47) Example 3: Experiment concerns West Seattle Bridge “Stuck in Traffic”.
5. (31:39)Example 4: Experiment concerns Student’s probability of withdrawing from Accounting 202. This example show how to use two BINOM.DIST function to calculate probabilities between two values.
6. (35:24) As number of trials get large, distribution approaches a Bell Shape. As the probability approaches 0.5, the distribution approaches a Bell Shape.

[@shahinmahmud4978]

Thank you sir

[@Strevoo]

In B4 example you could also mention =BINOM.DIST.RANGE function, it´s much easier :)

[@Strevoo]

But anyway, excellent video as allways! Thx!

[@excelisfun]

@@Strevoo Yah... I wish : ) But I made this video before that function came out.

[@kenham5324]

Hi Mike,
I tried the BINOM.DIST.RANGE and it works amazingly, but the issue is since it works as a array formula when there’s a trial missing in the array is gives an #value error message. Is there’s anything I could do to fix this issue? I tried using isblank with an IF formular but go no luck. Thanks for you help in advance

[@pphong]

Thanks!

[@pranshul67]

great video

[@kenham5324]

Fantastic video as always Mike!!! I would like to know created the formula in B(5) to update the number of trial (X) base on the n value you put in B1? I have tried using the same formula you using and mine doesn’t update. Thanks in advance for you help Mike.

[@vaniad25]

If you have 443 votes where each vote can be from -3 to 3 (7 points) and success is if the vote is 0, does that mean: 1) n=443 identical trials in an experiment; 2) 0=success, not 0=failure; 3)p=1/7 as the possibility of the vote to be any one of the 7 points on the scale is equal, and 4) trials are independent as the votes are personal, people do not know each other and are not even in the same room when voting? I am probably wrong. The real collected data shows relative frequency of 0.27 for a vote 0. Is this the number to be used for p? Or simply this data set should not be analysed by a binomial distribution?

[@AshishSahu25]

Thanks a lot, very well explained.
can you please put pdf and xls file link here, I'm unable to find it.

[@evandeguzman5148]

for example on 20:34 P(X

[@rupa2210]

How Binomial Distribution is different than Bernoulli distribution?
For example in a door to door sales problem,if 65% Female is opening the door and 35% Male is opening the door,which distribution to consider?

[@JoshuaDHarvey]

Is it the case that when “p remains the same for each trial” that also “All events are independent”?

[@Clifffffffffford]

Thanks !