## Normal Distribution, Z Scores and Standardization Explained

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.

## Probability Density Function

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.

## Estimation of Best Fitting Line

Estimation of model parameters is an essential part in regression analysis. We do that by using the Ordinary Least Squares method

## Random Variables in Statistics

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.

## Negative Binomial Distribution

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.

## Binomial Probability Distribution

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.

## Probability Mass Function

Probability Mass Function (PMF) of X says how the total probability of 1 is distributed (allocated to) among the various possible X values.

## Multiple Linear Regression Analysis with Categorical Predictors

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.

## What are dummy variables in regression?

We use indicator variables when we have categorical variables in the Regression Equation. They are also known as Dummy Variables.