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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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 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. 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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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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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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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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“`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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