/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

 glowingpython.blogspot.com
The Glowing Python: The Central Limit Theorem, a hands-on introduction
| | nelari.us
Using inverse transform sampling to generate random numbers in a given distribution // My thought repository
2.8 parsecs away

Travel
| | In inverse transform sampling, the inverse cumulative distribution function is used to generate random numbers in a given distribution. But why does this work? And how can you use it to generate random numbers in a given distribution by drawing random numbers from any arbitrary distribution?
| | blog.foletta.net
Greg Foletta - Bits and Blobs
3.5 parsecs away

Travel
| |
| | poissonisfish.com
Probability distributions in R - poissonisfish
4.1 parsecs away

Travel
| | Someof the most fundamental functions in R, in my opinion, are those that deal with probability distributions. Whenever you compute a P-value you relyon a probability distribution, and there are many types out there. In this exercise I will cover four: Bernoulli, Binomial, Poisson, and Normal distributions. Let me begin with some theory first: Bernoulli...
| | statsandr.com
One-proportion and chi-square goodness of fit test - Stats and R
29.8 parsecs away

Travel
| Learn how to perform the one proportion and goodness of fit test (useful to check if a distribution follows a specific known distribution) in R and by hand