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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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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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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From the Following Data, Compute the Value of P27 Given Data xi 0 1 2 3 4 5 6 7 8 fi 1 9 26 59 72 52 29 7 1 Solution Percentile divides the dataset into 100 equal parts. To compute P27, we use the following formula: Formula: P27 = 27N/100 Given N
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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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Computing Deciles from Frequency Distribution From the following data, we compute the values of the deciles: Class Intervals and Frequencies Class Interval (C.I.) Frequency (fi) 0-10 3 10-20 10 20-30 17 30-40 7 40-50 6 50-60 4 60-70 2 70-80 1 Total N = 50 Cumulative Frequency Class Interval (C.I.) Frequency (fi) Cumulative Frequency
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How to Compute Deciles In this lesson, we are going to learn how to calculate the values of deciles, a using the given data. This guide will walk you through the computation process. Example Data The data provided is: Variable (xi) Frequency (fi) Cumulative Frequency (C.F.) 58 2 2 59 3 5 60
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“`html PostNetwork Academy guides us through three crucial statistical measures: Quartiles, Deciles, and Percentiles These measures are used to divide a data distribution into equal parts, making them essential tools in data analysis. Hello everyone! Welcome to another educational post from PostNetwork Academy. I’m Bindeshwar, and before we begin, make sure to follow us on
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