Probability and Statistics

Covariance

Covariance Made Simple: Unlocking the Secret of Relationships in Data

Artificial Intelligence, Data Handling, Machine Learning, Maths, Probability and Statistics, Statistics /

  Covariance Made Simple: Unlocking the Secret of Relationships in Data Welcome to Postnetwork Academy! In this post, Bindeshwar explains the concept of covariance, a fundamental tool in statistics and data science. Covariance helps us understand how two variables move together—whether they increase, decrease, or show no relationship at all. What You’ll Learn in This […]

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Covariance Explained: Change of Origin vs. Scale Made Simple!

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Covariance Explained: Change of Origin vs. Scale Made Simple! Welcome to PostNetwork Academy’s Data Science and AI Lecture Series! In this post, we’ll explore the mathematical concept of covariance and how it behaves under changes of origin and scale. Let’s break it down step by step. Theorem: Covariance Independence We aim to prove that: Covariance

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Covariance Simplified: Learn It Once, Understand It Forever! Video #|109 Data Science and A.I.

Covariance Simplified: Learn It Once, Understand It Forever

Artificial Intelligence, Data Handling, Machine Learning, Probability and Statistics, Research and Development, Statistics /

Covariance Simplified: Learn It Once, Understand It Forever! Covariance measures the relationship between two random variables \(X\) and \(Y\). The formula for covariance is: \[ \text{Cov}(X, Y) = \frac{1}{n} \sum_{i=1}^n (x_i – \bar{X})(y_i – \bar{Y}) \] Expanding the terms: \[ \text{Cov}(X, Y) = \frac{1}{n} \sum_{i=1}^n \textcolor{red}{x_i y_i} – \textcolor{green}{x_i \bar{Y}} – \textcolor{blue}{\bar{X} y_i} + \textcolor{red}{\bar{X}

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Bivariate Distribution

Bivariate Distribution Made Simple: From Definition to Covariance Calculation

Artificial Intelligence, Data Handling, Machine Learning, Probability and Statistics, Research and Development, Statistics /

  Introduction Welcome to the Data Science and AI Lecture Series! In this post, we will simplify the concept of Bivariate Distribution and demonstrate how to calculate Covariance. These are fundamental concepts in statistics for understanding the relationship between two variables. Let’s dive into it! Bivariate Distribution Made Simple: From Definition to Covariance Calculation Author:

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Moments and Pearson’s Coefficient Simplified | Data Science & A.I. Lecture Series

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  Introduction Welcome to the Data Science and A.I. Lecture Series presented by PostNetwork Academy. In this session, we’ll focus on key statistical concepts: moments about the mean, skewness, and kurtosis. These concepts are essential in understanding data distribution characteristics and play a significant role in data science, artificial intelligence, and statistical analysis. In this

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Pearson's Beta and Gamma Coefficients Karl Pearson defined the following coefficients based on the first four central moments:

Calculation of Skewness and Kurtosis using Pearson’s Beta and Gamma Coefficients

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  Calculation of Skewness and Kurtosis using Pearson’s Beta and Gamma Coefficients Subtitle: Data Science and A.I. Lecture Series Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Date: December 4, 2024 Contact PostNetwork Academy Website: www.postnetwork.co YouTube Channel: www.youtube.com/@postnetworkacademy Facebook Page: www.facebook.com/postnetworkacademy LinkedIn Page: www.linkedin.com/company/postnetworkacademy Pearson’s Beta and Gamma Coefficients Karl Pearson defined the following coefficients

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Understand Kurtosis |Data Science and A.I.

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  Understanding Kurtosis Data Science and AI Lecture Series   Introduction Welcome to our Data Science and AI lecture series at PostNetwork Academy! In this post, we’ll break down the concept of kurtosis, a crucial statistical measure that helps us understand the shape of data distributions. Whether you’re a beginner or looking to refresh your

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Understanding Quartile Deviation: Step-by-Step Calculation & Visualization

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  Understanding Quartile Deviation: Step-by-Step Calculation & Visualization Quartile deviation, also known as the semi-interquartile range, is a useful measure in statistics that helps to understand the spread or dispersion of the middle 50% of a dataset. This post explains how to calculate quartile deviation using a sample dataset, and how to visualize it on

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Data Science and A.I. : How to Calculate Percentiles of Grouped Data

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  Data Science and A.I. Lecture Series: Computing the 70th Percentile Welcome to our Data Science and A.I. lecture series! In this post, we’ll cover a fundamental concept in statistics – percentiles. Specifically, we’ll learn how to compute the 70th percentile, or P70, for a grouped data set. Understanding Percentiles Percentiles divide a data set

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Data Science and A.I. : How to Calculate Percentiles Step-by-Step Guide

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Numerical Example to Compute Percentile Given the data set: 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, calculate the 30th and 60th percentiles. Percentile Percentiles are those values of the variate which divide the distribution into 100 equal parts, therefore the number of

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