## 字幕表 動画を再生する

• In statistics, when we use the term distribution, we usually mean a probability distribution.

• Good examples are the Normal distribution, the Binomial distribution, and the Uniform

• distribution.

• Alright.

• A distribution is a function that shows the possible values for a variable and how often

• they occur.

• Think about a die.

• It has six sides, numbered from 1 to 6.

• We roll the die.

• What is the probability of getting 1?

• It is one out of six, so one-sixth, right?

• What is the probability of getting 2?

• Once again - one-sixth.

• The same holds for 3, 4, 5 and 6.

• Now.

• What is the probability of getting a 7?

• It is impossible to get a 7 when rolling a die.

• Therefore, the probability is 0.

• The distribution of an event consists not only of the input values that can be observed,

• but is made up of all possible values.

• So, the distribution of the event - rolling a die - will be given by the following table.

• The probability of getting one is 0.17, the probability of getting 2 is 0.17, and so on...

• you are sure that you have exhausted all possible values when the sum of probabilities is equal

• to 1% or 100%.

• For all other values, the probability of occurrence is 0.

• Each probability distribution is associated with a graph describing the likelihood of

• occurrence of every event.

• Here’s the graph for our example.

• This type of distribution is called a uniform distribution.

• It is crucial to understand that the distribution is defined by the underlying probabilities

• and not the graph.

• The graph is just a visual representation.

• Alright.

• Now think about rolling two dice.

• What are the possibilities?

• One and one, two and one, one and two, and so on.

• Here’s a table with all the possible combinations.

• We are interested in the sum of the two dice.

• So, what’s the probability of getting a sum of 1?

• It’s 0, as this event is impossible.

• What’s the probability of getting a sum of 2?

• There is only one combination that would give us a sum of 2 – when both dice are equal

• to 1.

• So, 1 out of 36 total outcomes, or 0.03.

• Similarly, the probability of getting a sum of 3 is given by the number of combinations

• that give a sum of three divided by 36.

• Therefore, 2 divided by 36, or 0.06.

• We continue this way until we have the full probability distribution.

• Let’s see the graph associated with it.

• Alright.

• So, looking at it we understand that when rolling two dice, the probability of getting

• a 7 is the highest.

• We can also compare different outcomes such as: the probability of getting a 10 and the

• probability of getting a 5.

• It’s evident that it’s less likely that well get a 10.

• Great!

• The examples that we saw here were of discrete variables.

• Next, we will focus on continuous distributions, as they are more common in inferences.

• In the next few lessons, well examine some of the main types of continuous distributions,

• starting with the Normal distribution.

In statistics, when we use the term distribution, we usually mean a probability distribution.

B1 中級

# ディストリビューションとは？ (What is a distribution?)

• 0 0
林宜悉 に公開 2021 年 01 月 14 日