Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.

Transcribing Speech with Multinomial Diffusion, training code and models.

:rocket: A library designed to facilitate work with probability, statistics and stochastic calculus

PHP implementation of statistical probability distributions: normal distribution, beta distribution, gamma distribution and more.

10 Days of Statistics Challenges at HackerRank

A comprehensive collection of R scripts focusing on Probability and Statistics. Includes foundational data manipulation, Exploratory Data Analysis (EDA), statistical visualization (histograms, boxplots), and implementation of various probability distributions like Binomial, Normal, and Poisson. Ideal for showcasing core statistical computing skills

PyPi package for modelling Probability distributions

Exercises solved for the Practical Statistics Module of Udacity's DAND: assignments and practice problems

The binomial tree model is a commonly used approach for pricing derivatives, such as options. The basic idea behind the model is to create a tree of possible stock prices over time, based on a set of input parameters

A Binomial Distribution Calculator with useful charts

Option pricing models

Confidence intervals for binomial proportions.

This model can be used to find how many genes are involved in psychiatric disease. To verify the model we have taken the example of schizophrenia.

Simples calculadora de probabilidade desenvolvida em Java para distribuição binomial e poisson.

An Jupyter notebook experimental discrete binomial distribution grapher

This repository contains the solutions to HackerRank's 10 Days of Statistics.

A python package for easy dealing with Binomial and Gaussian distribution

The most common discrete and continuous distributions, showing how they find use in decision and estimation problems, and constructs computer algorithms for generating observations from the various distributions. and applications, and lastly the most important concept is covered is entropy

Generation of random samples using R

This repository is a resource for learning and applying statistics in data science. It contains code examples and explanations for many common statistical concepts, from descriptive statistics through regression and time series analysis.

Use random sampling to calculate probabililties of winning tennis matches based on constant and alternating probabilities of winning a (service) game.

Meta-regression of diagnostic accuracy studies in Stata

PyPi package for implementation of Gaussian and Binomial probability distributions.

R package binomial

A package that can calculate Gaussian as well as Binomial distributions.

Solutions for the basic statictics problems

Assignment Basic Stats

A PyPi package for Gaussian Distribution and Binomial Distribution

Saratov State University, CSIT. Discipline "pseudo-random numbers generators" (псевдослучайные генераторы, rus)

Simulating a Pandemic in Python from scratch & implemntation of famous SIR method for pandemic simualtion