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 […]
Data Science and A.I. : How to Calculate Percentiles Step-by-Step Guide Read More »
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
Data Science and A.I. : Computing Quartiles from Grouped Data: Step-by-Step Guide Read More »
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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In this post, we dive into one of the core statistical concepts used in data science and artificial intelligence—calculating the variance of a continuous frequency distribution. Breaking down the steps If you have data distributed across class intervals with frequencies, the goal is to find the variance. The video explains the columns in the table:
Exponential Distribution Formula A random variable X is said to follow Exponential distribution if it has the following probability density function the value f(x) is zero when x < 0 otherwise it is equal to f(x). Moment Generating Function The moment generating function (MGF) is a powerful tool in probability theory and statistics used to
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Poisson Distribution- The Poisson distribution is defined by a single parameter, usually denoted by λ (lambda), which represents the average rate of occurrence of the events. The probability mass function (PMF) of the Poisson distribution is given in the image above. Poisson Distribution is Limiting Case of Binomial Distribution It’s worth noting that the Poisson
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Random variable is a very important concept in probability, statistics, data science and machine learning, one must learn concept of random variable and related concepts. In this post, I have explained random variable and probability mass function that will help you to have basic understanding of random variable and probability mass function. To understand random
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Panda’s Series- In the last post, I explained how to create a panda’s series. Further, a pandas series has a lot of you often need to analyze, visualize and clean data. In this post, I will be explaining min(), max(), mean(), median(), and mode functions. min() Function- import pandas as pd lst=[2, 4, 6, 8,
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