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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Pandas Series Series is like one dimensional array or a column in a table and can be hold any type data. Furthermore, pandas series can be created in two ways. 1- Create a Pandas Series with Default Indices- In this method, you can create a series by passing list inside Series() method. As a result,
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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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A random variable X is a function from a sample S to real numbers R, i.e X: S——>R. Suppose you are tossing three coins then the sample space would be. S={HHH,TTH, THT, THH, HTT, HTH, HHT, HHH} Then X maps sample space to real numbers. Where you can see that R={0,1,2, 3} You can also
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