📗 WORK IN PROGRESS
Welcome to Statify, a personal project aimed at providing a comprehensive set of statistical functions and tools for Stat 101. This repository is currently a work in progress, and I plan to develop it further into a pip module in the future. Here, you will find a collection of functions that I have learned in my Statistics class and found useful.
- Normal CDF: Compute the cumulative distribution function (CDF) of a normal distribution.
- Normal PDF: Calculate the probability density function (PDF) of a normal distribution.
- Binomial CDF: Calculate the cumulative distribution function (CDF) of a binomial distribution.
- Binomial PDF: Compute the probability mass function (PMF) of a binomial distribution.
- t CDF: Compute the cumulative distribution function (CDF) of a t-distribution.
- t PDF: Calculate the probability density function (PDF) of a t-distribution.
- Inv Norm: Calculate the inverse of the normal distribution.
- Inv T: Compute the inverse of the t-distribution.
- Linear Regression: Perform linear regression analysis on a given dataset.
- Quartiles: Calculate the quartiles of a dataset.
- Five Value Summary: Compute the minimum, first quartile, median, third quartile, and maximum values of a dataset.
- Histogram: Generate a histogram to visualize the distribution of a dataset.
- Scatter Plot: Create a scatter plot to explore the relationship between two variables.
- Dot Plot: Generate a dot plot to visualize the distribution of a dataset.
- Box-Whisker Plot: Generate a box-whisker plot to visualize the distribution of a dataset.
- Anova Table: Perform an analysis of variance (ANOVA) and generate an ANOVA table.
In addition to the current functions, I have exciting plans for future expansions of Statify. Here are some features I am considering:
- One-sided and Two-sided Tests: Include functions for conducting one-sided and two-sided hypothesis tests.
- Additional Probability Distributions: Add support for Poisson, Hypergeometric, and other probability distributions.
- Archive Dumping: Implement a feature to allow dumping and archiving of statistical data for future reference.
- Rejection Region and p-value Tests: Develop functions for calculating rejection regions and p-values for hypothesis testing.
- Expected Value and Variance: Include functions to calculate the expected value (E(x)) and variance (V(x)) for various probability distributions.
- Sampling: Introduce functions for sampling from different distributions to facilitate simulation and inference.