Bindeshwar S. Kushwaha

Zero, First, Second, Third and Fourth Central and Arbitrary Moments in Statistics

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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

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

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

  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

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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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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