Data Handling

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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Understand Kurtosis |Data Science and A.I.

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

  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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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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Skewness

Understanding Skewness: An Essential Concept in Data Science

Artificial Intelligence, Data Handling, Machine Learning, Statistics /

Understanding Skewness: An Essential Concept in Data Science Welcome to the Data Science and AI Lecture Series by PostNetwork Academy! In this blog post, we’ll dive deep into the concept of skewness, a critical aspect of understanding data distribution in statistics. Whether you’re a beginner or an advanced learner, this post will help clarify the

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

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

  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

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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Deciles

Deciles Calculation and Visualization

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

Hello everyone! Welcome back to Postnetwork Academy. I’m Bindeshwar Singh, and today we’re going to uncover a key concept in statistics that helps you understand data more deeply—deciles! Have you ever wondered how to break down a data set into smaller, meaningful parts? Deciles allow us to split data into 10 equal sections, giving us

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Data Science and A.I. : Computing Quartiles from Grouped Data: Step-by-Step Guide

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

Quartiles help to divide a dataset into four equal parts. In this post, we will compute the values of the lower quartile (Q₁), median (Q₂), and upper quartile (Q₃) from a given set of grouped data. Data: | C.I. | fᵢ | |——-|—–| | 5-10 | 5 | | 10-15 | 6 | | 15-20

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Data Science and A.I. : Numerical Related to Quartile

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

Numerical Example to Compute Quartile Problem Statement: Given the data set: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, calculate the first quartile, second quartile, and third quartile using the quartile formulas. Understanding Quartiles: Quartiles divide the data set into four equal parts. There are three quartiles, denoted by \( Q_1 \), \(

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