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
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
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
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
I show you some simple tricks in R to simulate large number games using three examples.
Lesson 29 – Large number games in R
In this lesson, we learn about the basics of standardizing the data.
If our interest lies in working simultaneously with data that are on different scales or units, we can standardize them, so they are placed on the same level or footing. In other words, we will move the distributions from their original scales to a new common scale. This transformation will enable an easy way to work with data that are related, but not strictly comparable.
Lesson 28 – Apples and Oranges
In this lesson we continue our study of variance operator and its properties.
Lesson 27 – More variety
In this new lesson we learn the basics of variance operator for a random variable. This is part I.
Lesson 26 – The variety in consumption
In part II of expected values, we go over the additive property.
Lesson 25 – More expectation
This is part I of the Expected Value lessons.
Lesson 24 – What else did you expect?