Real Simple Stats Documentation
Real Simple Stats is a comprehensive Python library for statistical analysis and education. It provides easy-to-use functions for descriptive statistics, probability calculations, hypothesis testing, and data visualization.
Key Features
Descriptive Statistics: Mean, median, mode, variance, standard deviation, and more
Probability Utilities: Simple, joint, conditional probability calculations
Hypothesis Testing: t-tests, F-tests, chi-square tests with p-values
Probability Distributions: Normal, binomial, Poisson distributions
Linear Regression: Simple and multiple regression analysis
Time Series Analysis: Moving averages, autocorrelation, seasonal decomposition
Bayesian Statistics: Conjugate priors, credible intervals, Bayes factors
Resampling Methods: Bootstrap, permutation tests, cross-validation
Effect Sizes: Cohen’s d, eta-squared, Cramér’s V, odds ratios
Power Analysis: Sample size calculations, power for various tests
Multivariate Analysis: PCA, multiple regression, factor analysis
Data Visualization: Statistical plots and charts
Command Line Interface: Easy-to-use CLI for quick calculations
Educational Focus: Clear explanations and examples for learning
Quick Start
Installation:
pip install real-simple-stats
Basic usage:
from real_simple_stats import descriptive_statistics as desc
data = [1, 2, 3, 4, 5]
mean = desc.mean(data)
std_dev = desc.standard_deviation(data)
print(f"Mean: {mean}")
print(f"Standard Deviation: {std_dev}")
Command line usage:
rss-calc stats --data "1,2,3,4,5" --stat mean
rss-calc probability --type binomial --n 10 --k 3 --p 0.5
Documentation Contents
Comprehensive Guides
API Reference
- Descriptive Statistics
coefficient_of_variation()detect_fake_statistics()draw_cumulative_frequency_table()draw_frequency_table()five_number_summary()interquartile_range()is_continuous()is_discrete()mean()median()sample_std_dev()sample_variance()- Functions Overview
- Usage Examples
- Mathematical Background
- See Also
- real_simple_stats package