Statistical Averages : Course in Statistics for Data Science, Learn concept of Mean, Variance, Skewness, Kurtosis useful for Machine Learning, Neural Network and Business Analysis
- 5.0 (1 rating)
- Created by Shilank Singh
- English
- 4 hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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What you'll learn
- Measures of Central Tendency (Mean, Median, Mode, Geometric Mean and Harmonic Mean)
- Mathematical Expectations and Moments
- Measures of Dispersion (Quartile Deviation, Mean Deviation, Standard Deviation and Variance)
- Skewness
- Kurtosis
- Expected Values of Two-Dimensional Random Variables
Description
HOW STATISTICAL AVERAGES IS SET UP TO MAKE COMPLICATED STATISTICS EASY
In probability and statistics, a random variable is no doubt completely described by its probability mass function or probability distribution. Similarly a continuous random variable is completely described by its probability density function.
For many purposes, this description is often considered to consist too many details. It is sometimes simpler and more convenient to describe a random variable or to characterise its distribution by a few parameters, known as statistical measures, that are representative of the distribution.
The characteristics that are usually used to analyse the properties of a random variable or its probability distribution are the central tendency, dispersion, skewness and kurtosis. In this course, we shall discuss these characteristics and measures used to study them.
This 30+ lecture course includes video explanations of everything from Statistical Averages, and it includes more than 20+ examples (with detailed solutions) to help you test your understanding along the way. Statistical Averages is organized into the following sections:
Introduction
- Measures of Central Tendency
- Mathematical Expectations and Moments
- Measures Of Dispersion
- Skewness
- Kurtosis
- Statistical Averages - Solved Examples
- Expected Values of Two-Dimensional Random Variables
Who is the target audience?
- Current Probability and Statistics students
- Students of Machine Learning, Data Science, Computer Science, Electrical Engineering , as Statistics is the prerequisite course to Machine Learning, Data Science, Computer Science and Electrical Engineering
- Anyone who wants to study Statistical Averages for fun after being away from school for a while.
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