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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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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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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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. 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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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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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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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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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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How to Compute Quartiles from Given Data In this post, we will learn how to compute the quartiles \( Q_1 \), \( Q_2 \) (Median), and \( Q_3 \) using the provided data set. Quartiles help in dividing a data set into four equal parts, making it easier to understand the distribution of data.
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