A random variable X is said to follow exponential distribution if it follows the following probability mass function. Exponential probability distribution is a continuous distribution. Probability Distribution Function of Exponentially Distributed Variable X   It is heavily used in the Internet traffic modelling and of study queuing models. Numerical Example- Problem- If a computer receives

A random variable X is said to follow Poisson distribution, if it follows the following probability distribution function. Poisson distribution is a discrete probability distribution. It models the following types of problems Find out number  of new born babies in a city in certain time duration. Find out defective materials in manufactured by a company.

Let X be a random variable, it is said to follow binomial distribution if it follows the following probability mass function. And it can have only non-negative values. The binomial distribution is a discrete probability distribution. Binomial distribution is used to model problems, for instance,  getting  number of success after certain  number of random experiments

Uniform Distribution or Rectangular Distribution Uniform distribution or rectangular distribution is a continuous probability distribution. Definition- A random variable X is said to follow uniform or rectangular distribution it follows the following probability distribution function. And is denoted by X~U(a,b) Python Code to Plot Uniform Distribution import numpy import matplotlib.pyplot as plt a=20 b=50 x=

  Why is normal distribution is important? To understand the question you have to go through the Central Limit Theorem. Central Limit Theorem According to central limit theorem if X1, X2, X3,……Xn are random variables drawn from any probability distribution function with mean  Σμi  and standard deviation Σσi where (i=1,2,3,……n). The sum of random variables

Correlation Correlation measures  the relation between two variables  that how they are related.  And is denoted by r and  ρ moreover, the correlation quantifies the level of relationship between -1 to +1. If the value of correlation  r is -1 then there is perfect negative relationship. If value of  correlation is  +1  then there is

Standard Deviation Variance and Covariance Standard deviation, variance and covariance have very important applications in machine learning and data science. Further, they are closely related to each other. In feature reduction techniques, such as PCA ( Principle Component Analysis) features are selected based on  high variance.  In this post I will explain standard deviation, variance

Skewness and Kurtosis- Introduction- Skewness and Kurtosis are very important  concepts in statistics and have several applications.  In addition, they characterize the nature of data distribution which make data analysis easier. Moreover, I will separately discuss skewness and kurtosis in further sections. Skewness- Skewness  refers the measurement of lack of symmetry in data distribution. Measures

Measures of central tendency in statistics  refers to a value which characterizes all the the data.  Mean, median and mode are very important measures of central tendency. Measures of central tendency is very fundamental in data science,  machine learning and data analytics areas. Moreover, if you want to make a carrier in data analytics you

Frequency distribution  in statistics refers to a graph or table which depicts or illustrate the occurrences of values. Frequency distribution is very important in study of statistics, machine learning and data science area.  To  understand frequency distribution you have to understand organization of data. Basically, there are three types of data organization techniques. 1- Individual