# What is Hypergeometric Distribution?

If there are R Pepsi cans in a total of N cans (N-R Cokes) and we are asked to identify them correctly, in our choice selection of R Pepsi, we can get k = 0, 1, 2, … R Pepsi. The number of correct guesses and the probability of correctly selecting k Pepsi cans is Hypergeometric distribution.

Hypergeometric distribution is typically used in quality control analysis for estimating the probability of defective items out of a selected lot.

The Pepsi-Coke marketing analysis is another example application. Companies can analyze the preferences of one product to other among a subset of customers in their region.

Learn more about Hypergeometric distribution and how to derive the probability from the ground up in lesson 38 of our data analysis classroom.

Lesson 38 – Correct guesses: The language of Hypergeometric distribution

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# Poisson distribution Part 2

Does Poisson distribution arise from the binomial distribution? You can find out in Lesson 37.

Lesson 37 – Still counting – Poisson distribution

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# Poisson distribution

If we assume events are independent (the occurrence of one event does not affect the probability that a second event will occur), then the counts per unit interval can be assumed a random variable that follows a probability distribution. Counts, i.e., the number of times an event occurs in an interval follows a Poisson distribution.

Poisson distribution has one control parameter. It is the rate of occurrence; the average number of events per unit interval.

In lesson 36, we learn the fundamentals of Poisson distribution. You will also meet Able and Mumble two of my friends.

Lesson 36 – Counts: The language of Poisson distribution

# What is Return Period

Your recent vocabulary may include “100-year event” (happening more often), (drainage system designed for) “10-year storm,” and so on, courtesy mainstream media and news outlets.

Does a 10-year return period event occur diligently every ten years? Can a 100-year event occur three times in a row?

If we define T as a random variable that measures the time between the events (wait time or time to the next event or time to the first event since the previous event), the return period of the event is the expected value of T, i.e., E[T], its average measured over a large number of such occurrences.

In lesson 34, we learn about return period through Bob and his reappearance. Bob’s time of occurrence also relates to Geometric distribution.

Lesson 34 – I’ll be back: The language of Return Period

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# Geometric distribution and its basics

Try, try and try again till you succeed. That is Geometric distribution.

If we consider independent Bernoulli trials of 0s and 1s with some probability of occurrence p and assume X to be a random variable that measures the number of trials it takes to see the first success, then, X is said to be Geometrically distributed.

In lesson 33, we learn the basics of Geometric distribution.

Lesson 33 – Trials to first success: The language of Geometric distribution

# Binomial Distribution Explained

In lesson 31, we learned the idea of Bernoulli sequence. In lesson 32, we take this idea as the basis to understand Binomial distribution. When we are interested in the random variable that is the number of successes in so many trials, it follows a Binomial distribution. “Exactly k successes” is the language of Binomial distribution.

Full lesson here.

Lesson 32 – Exactly k successes: The language of Binomial distribution

# Bernoulli trials: the essential things to know

There are two possibilities, a hit (event occurred — success) or miss (event did not occur — failure). A yes or a no. These events can be represented as a sequence of 0’s and 1’s called Bernoulli trials with a probability of occurrence of p. This probability is constant over all the trials, and the trials itself are assumed to be independent, i.e., the occurrence of one event does not influence the occurrence of the subsequent event.

In lesson 31, we discuss the fundamentals of Bernoulli trials. They form the basis for deriving several discrete probability distributions that we will learn over the next several weeks.

Lesson 31 – Yes or No: The language of Bernoulli trials

# Recap

Why I created data analysis classroom? What did we learn so far? Where do we go from here? Find the answers in lesson 30.

Lesson 30 – Pause and rewind