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Probability Distribution
XG
by
Xuan Geng
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Updated Nov 29, 2021
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Chapters
00:00
Chapter 1
00:06
Chapter 2
00:39
Chapter 3
01:20
Chapter 4
Audio Transcript
Auto-generated
00:01 - 00:05
the second type of distribution that we're going to talk about is
00:06 - 00:07
the nominal distribution.
00:10 - 00:13
His definition is the probability distribution
00:13 - 00:17
associated with the discrete random bearable.
00:17 - 00:21
It's called a phenomenal probability distribution.
00:21 - 00:26
And we will introduce this kind of distribution to you by two perspectives.
00:27 - 00:32
The first one is his properties and then we will introduce the property
00:32 - 00:39
function and use one marketing related example to give it a practice.
00:40 - 00:43
And there are four points about the problem
00:43 - 00:46
practice of phenomenal distribution.
00:47 - 00:49
The first one there should be an identical trials.
00:50 - 00:55
Then the two outcomes are possible on a trail. For example, success or failure,
00:55 - 00:59
the property of a success or failure
01:00 - 01:01
does not change
01:01 - 01:04
the final is the trials are independent.
01:05 - 01:11
Um If you need to remember this for private properties of phenomenal distribution,
01:11 - 01:14
the simplest example is coin tossing
01:22 - 01:27
when they come see the phenomenal privacy function which is shown below
01:27 - 01:35
um where PX echoes to the probability of x successes and trials
01:36 - 01:38
and and pursue the number of trials
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and pie. It can sue. The probability of a success on anyone trial,
01:45 - 01:49
then y minus pi. It comes to the probability of failure
01:50 - 01:51
of anyone trial.
01:52 - 01:59
Then let's use the example of marine closing store to um practice this function.
02:01 - 02:04
Let's consider the purchase decisions of the next
02:04 - 02:08
three customers who entered the marine clothing store.
02:08 - 02:12
On the basis of past experience.
02:12 - 02:16
The store manager estimates the probability that any
02:16 - 02:20
one customer will make a purchase is 3.3.
02:20 - 02:24
So the pie should be equal to 3.3 other situation.
02:25 - 02:29
So the problem is what is the property that two
02:29 - 02:33
of the next three customers will make a purchase?
02:33 - 02:38
So obviously in this situation, the total trails through the three
02:39 - 02:41
and the success trials tribute to
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And uh hi echoes to 2.3. So put this number into the function.
02:48 - 02:50
We can calculate
02:51 - 02:56
the probability that two of the next three customers making the purchase
02:57 - 02:59
Is .189.
02:59 - 03:00
Similarly,
03:00 - 03:06
we can calculate um zero or one or three of the next three cosmos making the purchase
03:07 - 03:12
and adding this for province. Together we get the total property.
03:12 - 03:13
It goes to one.
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