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! 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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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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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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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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Relation Between Moments About Mean and Arbitrary Point By Bindeshwar Singh Kushwaha Data Science and A.I. Lecture Series – PostNetwork Academy Reach PostNetwork Academy Website: PostNetwork Academy YouTube Channel: PostNetwork Academy Facebook Page: PostNetwork Academy LinkedIn Page: PostNetwork Academy Relation Between Moments About Mean and Arbitrary Point The \(r\)th moment about the mean is given
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Zero, First, Second, Third and Fourth Central and Arbitrary Moments in Statistics Data Science and A.I. Lecture Series Author: Bindeshwar Singh Kushwaha Institute: PostNetwork Academy Reach 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 Moments About an Arbitrary Point (Ungrouped Data) Definition: The general formula is: μr’ = Σ(xi –
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Moments in Statistics Moments in Statistics is an essential concept in understanding the characteristics of a distribution. This post explains the different types of moments and provides formulas for individual data and frequency distributions. Reach PostNetwork Academy Website: PostNetwork Academy YouTube Channel: PostNetwork Academy YouTube Facebook Page: PostNetwork Academy Facebook LinkedIn Page: PostNetwork Academy
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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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Measures of Skewness – Data Science and AI Lecture Series In this post, Bindeshwar Singh Kushwaha from PostNetwork Academy explains the concept of Measures of Skewness. Skewness refers to the lack of symmetry in a data distribution. Understanding skewness is essential in data science and AI, as it helps to interpret the distribution of
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