stats211 0.1.0

Comprehensive statistical functions for hypothesis testing, confidence intervals, regression, and more

Stats Package

A comprehensive Python package for statistical analysis, providing functions for hypothesis testing, confidence intervals, regression analysis, and more.

Features

• Descriptive Statistics: Basic statistics, z-score conversions, and percentile calculations
• Standard Error Calculations: For means, proportions, and their differences
• Critical Values: Calculate critical values for hypothesis testing
• Confidence Intervals: For means and proportions
• Sample Size Calculations: Determine required sample sizes for studies
• Hypothesis Testing: Single-sample and two-sample tests for means and proportions
• Regression Analysis: Linear regression with confidence intervals and predictions
• Probability Calculations: Central Limit Theorem applications
• Inverse Calculations: Solve for various statistical parameters

Installation

pip install stats-package

Quick Start

from stats import *

# Hypothesis testing
test_mean(0, 3.1, 1.3, 18)

# Confidence intervals
ci_mean(3.1, 1.3, 18, 0.95)

# Two-sample tests
test_two_means(1610, 129.8, 18.906, 1929, 127.07, 21.975, equal_var=True, alpha=0.13)

# Sample size calculations
sample_size_mean(0.9, 11.71, 0.84)

# Get help
stats_help()

Module Organization

The package is organized into the following modules:

• utils: Color printing and file I/O utilities
• descriptive: Basic statistics and z-score conversions
• standard_error: Standard error calculations
• critical_values: Critical value functions
• confidence_intervals: Confidence interval calculations
• sample_size: Sample size calculations
• hypothesis_testing: Single-sample hypothesis tests
• two_sample: Two-sample hypothesis tests
• regression: Linear regression functions
• probability: Probability calculations (CLT)
• inverse: Inverse calculations

Usage Examples

Hypothesis Testing

from stats import test_mean, test_proportion

# Test a mean
test_mean(0, 3.1, 1.3, 18)

# Test a proportion
test_proportion(0.03, 0.04, 500, tail='right')

Confidence Intervals

from stats import ci_mean, ci_proportion

# Confidence interval for mean
ci_mean(3.1, 1.3, 18, 0.95)

# Confidence interval for proportion
ci_proportion(0.5, 100, 0.95)

Two-Sample Tests

from stats import test_two_means, test_two_proportions

# Two-sample t-test
test_two_means(1610, 129.8, 18.906, 1929, 127.07, 21.975, equal_var=True, alpha=0.13)

# Two-sample proportion test
test_two_proportions(314, 59, 319, 101)

Regression Analysis

from stats import slope_test, linregress_ci

# Test slope
slope_test(1.57, 0.606, 170, 0.05, 'two')

# Confidence interval for regression
linregress_ci(slope, se_slope, n, confidence=0.95)

Requirements

• Python >= 3.8
• numpy >= 1.20.0
• pandas >= 1.3.0
• scipy >= 1.7.0

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Author

Isaac Lagoy