Statistics Using Python Tutorial Part 9 | Probability Mass Function | Data Science Tutorial #9
Statistics Using Python Tutorial Part 9 | Probability Mass Function | Data Science Tutorial #9
https://acadgild.com/big-data/data-sc...
Hello and welcome back to another session of statistics tutorial using Python Powered by Acadgild. In the previous video, you have learned the Central Limit Theorem and sample distribution in statistics.
In this video, you will be able to learn, probability mass function, Probability density function, Cumulative distribution function. Before that, if you have missed the previous, please check the links as follows.
Central Tendency, Sample, and Population - • Statistics Using Python Tutorial Part 1 | ...
Measure of Spread - • Statistics Using Python Tutorial Part 2 | ...
Descriptive and Inferential Statistics - • Statistics Using Python Tutorial Part 3|De...
Types of Data in Statistics - • Statistics Using Python Tutorial Part 4 | ...
Data Visualization Using Matplotlib - • Statistics Using Python Part 5 | Data Visu...
Seaborn Python Tutorial - • Statistics Using Python Tutorial Part 6 | ...
Types of Distribution in Statistics - • Statistics Using Python Tutorial Part 7 | ...
Central Limit Theorem - • Statistics Using Python Tutorial Part 8 | ...
What is the probability mass function?
A probability mass function gives us the frequency function which gives us the probability for a discrete random variable. When it is said random variables from an experiment like rolling a dice, choosing the number of hats, or getting a high score in a test. The discrete part of this means, that there is a set of numbers of outcomes.
What is the probability density function?
This nothing but a statistical expression, that defines the probability distribution for continuous random variable supposed to a discrete random variable.
What is the cumulative distribution function?
The cumulative distribution function of a random variable is another method to describe the distribution of random variables. The advantage of the cumulative distribution function is that it can be defined for any kind of random variable that is discrete, continuous or even mixed.
Cumulative distribution function gives you a cumulative probability associated with the function. It is a similar concept to a cumulative frequency table.
Kindly, go through the complete video and know the examples associated with the above topics. Please like, share and subscribe to the channel for more tutorials.
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