Metadata-Version: 2.4
Name: mystats-helper-adamdockstader
Version: 0.0.1
Summary: A lightweight statistics helper library for learning
Author: Adam Dockstader
License: MIT
Project-URL: Homepage, https://github.com/adamdockstader/mystats-helper
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# mystats-helper

A lightweight, pure-Python statistics helper library built for learning.

## Installation

```bash
pip install mystats-helper-adamdockstader
```

### In futurecoder / Pyodide

```python
import micropip
await micropip.install("mystats-helper-adamdockstader")
```

## Quick Start

```python
import mystats_helper

data = [4, 8, 15, 16, 23, 42]

# Descriptive statistics
print(mystats_helper.mean(data))       # 18.0
print(mystats_helper.median(data))     # 15.5
print(mystats_helper.stdev(data))      # 13.28...
print(mystats_helper.describe(data))   # full summary dict

# Z-scores
print(mystats_helper.z_scores(data))

# Correlation & regression
x = [1, 2, 3, 4, 5]
y = [2, 4, 5, 4, 5]
print(mystats_helper.correlation(x, y))
slope, intercept = mystats_helper.linear_regression(x, y)
print(f"y = {slope:.2f}x + {intercept:.2f}")
```

## Available Functions

| Function | Description |
|---|---|
| `mean(data)` | Arithmetic mean |
| `median(data)` | Median |
| `mode(data)` | Mode (most frequent value) |
| `variance(data, population=False)` | Sample or population variance |
| `stdev(data, population=False)` | Standard deviation |
| `z_score(x, mu, sigma)` | Single z-score |
| `z_scores(data)` | Z-scores for all values |
| `percentile(data, p)` | p-th percentile (0–100) |
| `iqr(data)` | Interquartile range |
| `correlation(x, y)` | Pearson's r |
| `linear_regression(x, y)` | Returns (slope, intercept) |
| `describe(data)` | Summary dict (count, mean, stdev, min, Q1, median, Q3, max) |

## License

MIT
