# Welcome to Python for Statistics & Quantitative Methods

This book is designed for the **Quantitative Methods and Statistics** course at the University of Winchester. Whether you're completely new to programming or brushing up on your skills, this book will guide you step-by-step through Python fundamentals, data analysis, and statistical inference.

**Author:** Sakib Anwar

## What You'll Learn

By the end of this book, you will be able to:

- Write Python programs using variables, functions, and control flow
- Work with data structures like lists and dictionaries
- Load, clean, and manipulate data using pandas
- Create visualisations with seaborn and matplotlib
- Understand the normal distribution, sampling, and the Central Limit Theorem
- Construct and interpret confidence intervals
- Perform hypothesis testing (z-tests and t-tests)
- Run and interpret simple and multiple regression models
- Diagnose regression model assumptions
- Build interactive dashboards with Streamlit

## How to Use This Book

### Reading
Each chapter builds on the previous one. We recommend going through them in order, especially if you're new to programming.

### Running Code
The code examples in this book show real output, but the cells are not interactive. To run the code yourself, copy and paste it into [Google Colab](https://colab.research.google.com/) — a free, browser-based Python environment that requires no installation.

### Exercises
At the end of each chapter, you'll find exercises to test your understanding. Give each exercise a genuine try before clicking the dropdown to reveal the solution.

## Prerequisites

No prior programming experience is required. However, you should be comfortable with:
- Basic mathematics (arithmetic, percentages)
- Using a web browser
- A willingness to experiment and make mistakes!

## Textbook Reference

The lecture materials for this course are based on:

> Békés, G., & Kézdi, G. (2021). *Data analysis for business, economics, and policy*. Cambridge University Press.
>
> Website: [gabors-data-analysis.com](https://gabors-data-analysis.com/)

> Diez, D. M., Çetinkaya-Rundel, M., & Barr, C. D. (2019). *OpenIntro Statistics* (4th ed.). OpenIntro, Inc.
>
> Website: [openintro.org](https://www.openintro.org/)

## Let's Get Started!

Ready to write your first Python program? Head to [Chapter 1: Getting Started with Python](chapters/01_getting_started.md).

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```{admonition} Other Versions
:class: tip

Looking for the **Data Analysis** version of this book? <a href="../intro.html">Click here</a>.
```

```{note}
If you find any errors or have suggestions for improvement, please open an issue on [GitHub](https://github.com/sakibanwar/python-notes/issues).
```