In a Uniform Distribution Probability Density Function (PDF) is same for all the possible X values. Sometimes this is called a Rectangular Distribution. There are two (2) parameters in this distribution, a minimum (A) and a maximum (B)
Uniform Probability Distribution Read More »
Normal Distribution is the most important probability distribution in Probability and Statistics. A normal probability distribution is a bell shaped curve. Many numerical populations have distributions that can be fit very closely by an appropriate normal curve.
Normal Distribution, Z Scores and Standardization Explained Read More »
Earlier we used Probability Mass Function to describe how the total probability of 1 is distributed among the possible values of the Discrete Random Variable X.
Probability Density Function Read More »
Estimation of model parameters is an essential part in regression analysis. We do that by using the Ordinary Least Squares method
Estimation of Best Fitting Line Read More »
A Random Variable is any rule that maps (links) a number with each outcome in sample space S. Mathematically, random variable is a function with Sample Space as the domain. It’s range is the set of Real Numbers.
Random Variables in Statistics Read More »
In the Negative Binomial Distribution, we are interested in the number of Failures in n number of trials. This is why the prefix “Negative” is there. When we are interested only in finding number of trials that is required for a single success, we called it a Geometric Distribution.
Negative Binomial Distribution Read More »
Binomial Distribution is used to find probabilities related to Dichotomous Population. It can be applied to a Binomial Experiment where it can result in only two outcomes. Success or Failure. In Binomial Experiments, we are interested in the number of Successes.
Binomial Probability Distribution Read More »
Probability Mass Function (PMF) of X says how the total probability of 1 is distributed (allocated to) among the various possible X values.
Probability Mass Function Read More »
Multiple Linear Regression Analysis with Categorical Predictors is done using Indicator Variables. We have to clearly analyze all possible models and select the best fitting model.
Multiple Linear Regression Analysis with Categorical Predictors Read More »
