## Handling Missing Values Using R

Missing Value Imputation is a very critical part in any kind of Data Related Task. We can impute missing values using “mice” package in R

Skip to content
# Statistics Tutorials

## Handling Missing Values Using R

Missing Value Imputation is a very critical part in any kind of Data Related Task. We can impute missing values using “mice” package in R

Data Science, R Programming
## Uniform Probability Distribution

Probability, Probability Distributions
## Normal Distribution, Z Scores and Standardization Explained

Probability Distributions
## Probability Density Function

Probability Distributions
## Estimation of Best Fitting Line

Regression Analysis
## Random Variables in Statistics

Probability Distributions
## Negative Binomial Distribution

Probability, Probability Distributions
## Binomial Probability Distribution

Probability, Probability Distributions
## Probability Mass Function

Probability, Probability Distributions, Statistics

Statistics Tutorials

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)

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.

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

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.

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