Data Science and A.I. – Measures of Dispersion: Variance of Discrete Frequency Distribution In this post from PostNetwork Academy’s Data Science and A.I. Lecture Series, Bindeshwar Singh Kushwaha walks us through the process of calculating the variance of a discrete frequency distribution. This is an essential statistical measure for understanding the spread of data […]
How to Calculate Variance for a Given Distribution Introduction: In this post, we will explore the step-by-step process of calculating the variance and standard deviation for a given frequency distribution. Understanding these concepts is crucial for analyzing data variability and distribution spread. Example: Given the following frequency distribution, we will calculate both the variance and
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Understanding Variance: A Practical Example In our recent Data Science and AI lecture, we explored the concept of variance—a crucial measure in statistics that indicates how spread out a set of data points is around their mean. Today, we’ll dive into a practical example involving a batsman’s scores over ten matches. The Problem Let’s consider
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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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Before understanding Binomial distribution you have to understand Bernoulli trial. What is Bernoulli trial? A Bernoulli trial is a random experiment in which there are two possible outcomes failure and success getting head when tossing a coin is success and getting tail is failure getting 4 is success when rolling a dice and failure when
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Bernoulli Trials Many experiments have only two outcomes, for example if you are tossing a coin you will get head or tail. If you rolling a dice you will have number on the face which may be an even or an odd number. When a company manufactures items, so item may be defective or it
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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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Population- Population refers to group of individuals under study, you can understand it by an example. Suppose you have a pharmacy company and it has made a new drug and you want see the effect of the drug on the people of country. Here whole people of country are population for your study. Sample- Reality
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Covariance and Correlation- Covariance and correlation both measure linear relationship between two variables. However, they differ at some points. In this post I will explain covariance and correlation and how they differ from each other. Covariance between two variables is written as Cov(X,Y) and is defined as Calculation of Covariance If you look at the
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Variance of a Continuous Random Variable If X is a random variable having mean μ , then variance of random variable X is denoted as Var(X) and read as variance of X and defined as. Var(X)=E[(X-μ)²] Where Variance of a random variable measures dispersion, that means how for values spread out from mean or expected
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