Research and Development

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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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

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

  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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Skewness

Measures of Skewness – Data Science and AI Lecture Series

Artificial Intelligence, Data Handling, Research and Development, Statistics /

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

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

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

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

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

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

  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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Data Science and A.I. – Quartiles, Deciles, and Percentiles

Artificial Intelligence, Data Handling, Deep Learning, Generative A., Generative A. I., Machine Learning, Probability and Statistics, Research and Development, Statistics /

“`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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median

Median in Data Science and AI

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

Median in Data Science and AI In the realm of data science and AI, understanding measures of central tendency is crucial. In this post, we will explore the concept of median. What is Median? Median is a key measure of central tendency that divides a dataset into two equal halves. It represents the middle value

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