Probability Distributions in R
Probability Distributions in R
How to Apply the Central Limit Theorem in R (With Examples)
The central limit theorem states that the sampling distribution of a sample mean is approximately normal if the sample size is large enough, even...
Probability Distributions in R
How to Use the Triangular Distribution in R (With Examples)
The triangular distribution is a continuous probability distribution with a probability density function shaped like a triangle.
It is defined by three values:
The minimum value...
Probability Distributions in R
How to Use the Multinomial Distribution in R
The multinomial distribution describes the probability of obtaining a specific number of counts for k different outcomes, when each outcome has a fixed probability...
Probability Distributions in R
How to Use the Normal CDF in R (With Examples)
You can use the following methods to work with the normal CDF (cumulative distribution function) in R:
Method 1: Calculate Normal CDF Probabilities
#calculate probability that...
Probability Distributions in R
How to Apply the Empirical Rule in R
The Empirical Rule, sometimes called the 68-95-99.7 rule, states that for a given dataset with a normal distribution:
68% of data values fall within one standard deviation...
Probability Distributions in R
How to Use the Gamma Distribution in R (With Examples)
In statistics, the gamma distribution is often used to model probabilities related to waiting times.
We can use the following functions to work with the...
Probability Distributions in R
How to Calculate & Plot a CDF in R
You can use the following basic syntax to calculate and plot a cumulative distribution function (CDF) in R:
#calculate empirical CDF of data
p = ecdf(data)
#plot...
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