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
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Uniform Probability Distribution
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)
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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.
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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.
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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
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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.
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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.
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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.
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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.
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