Applied Statistics Course

📚 Course Materials for Students

Welcome to Applied Statistics! This repository contains all the materials you need for this course.

🎯 Course Overview

An introduction to applied statistics with materials released progressively throughout the semester.

Currently available:

• Statistical modeling and exploratory data analysis
• Estimation methods (Maximum Likelihood, Method of Moments)
• Estimator properties (bias, variance, confidence intervals, bootstrap)
• Final project brief (Birds Biodiversity case study)

Coming soon:

• Hypothesis testing and applications

Prerequisites

• Basic probability theory (random variables, distributions)
• Python programming fundamentals
• Linear algebra basics

📁 What's in This Repository

📖 Lessons (lessons/)

Each lesson folder contains:

• material.md or PDF - Lesson content and theory
• exercises/ - Practice problems (when applicable)
• data/ - Datasets for the lesson

Lesson	Topic	Materials
00	Welcome & Introduction	Introduction to the course
01	Statistical Modeling	Random variables, distributions, EDA
02	Statistical Learning	MLE, Method of Moments, Fisher information
03	Estimator Properties	Bias, variance, MSE, confidence intervals, bootstrap

Note: Additional lessons (04-06) will be released throughout the semester.

🧪 Lab Sessions (labs/)

Interactive Jupyter notebooks for hands-on practice. Completed labs include both the assignment and a worked solution; in-progress topics ship the assignment only.

1. 01-random-variables/
   • assignment.ipynb
   • solution.ipynb
2. 02-maximum-likelihood-estimation/
   • assignment.ipynb
   • solution.ipynb
3. 03-inference-estimators/
   • assignment.ipynb
   • solution.ipynb
4. 04-gaussian-confidence-intervals/
   • assignment.ipynb
   • dataset-recommendations.md
5. 05-non-parametric-estimation/
   • assignment.ipynb
6. 06-model-fitting/
   • assignment.ipynb

Labs 07+ (hypothesis testing, project integration) will be posted after they are covered in class.

🧾 Final Project (projects/birds-biodiversity/)

• data/raw/Observations 2012-2025.xlsx – primary dataset for the capstone.
• final_project_assignment.pdf – detailed brief outlining objectives, deliverables, and submission rules.
• Use this folder as the starting point for your analysis; create your own notebooks/scripts to keep the workflow reproducible.

📊 Data (shared/data/)

Sample datasets for exercises and projects:

• heights_weights_sample.csv - Anthropometric data
• ab_test_clicks.csv - A/B testing data
• manufacturing_defects.csv - Quality control data
• Additional datasets in lesson-specific folders

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/stephane-rivaud/Applied-Statistics.git
cd Applied-Statistics

2. Set Up Python Environment

Create a virtual environment (recommended):

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install required packages:

pip install -r requirements.txt

This installs:

• NumPy - Numerical computing
• Pandas - Data manipulation
• Matplotlib & Seaborn - Visualization
• SciPy - Statistical functions
• Scikit-learn - Machine learning
• Jupyter - Interactive notebooks

3. Launch Jupyter

jupyter lab

Navigate to labs/ and open the notebook for the current lab.

4. Alternative: Google Colab

You can also run notebooks in Google Colab:

1. Upload the notebook to Google Drive
2. Open with Google Colaboratory
3. Install packages: !pip install numpy pandas matplotlib scipy scikit-learn

📚 How to Use This Repository

Weekly Workflow

1. Before class: Read the lesson material in lessons/XX-topic/
2. During class: Follow along with slides and examples
3. After class: Complete the lab assignment in the corresponding labs/XX-topic/ folder
4. Practice: Work through exercises in lessons/XX-topic/exercises/

Completing Lab Assignments

1. Open the notebook in the numbered labs/XX-topic/ directory for that session
2. Read instructions carefully
3. Write your code in the designated cells
4. Test your code thoroughly
5. Save your work regularly
6. Submit according to instructor guidelines

Working with Data

# Example: Loading a dataset
import pandas as pd

# From shared data folder
df = pd.read_csv('shared/data/heights_weights_sample.csv')

# From lesson-specific folder (when available)
# df = pd.read_csv('lessons/03-estimator-properties/data/dataset.csv')

🔄 Getting Updates

New materials are released throughout the semester. To get updates:

# Make sure you've committed or saved your work first!
git pull origin public-main

Important: If you've modified any files, save your changes first or they may be overwritten.

📖 Course Schedule & Syllabus

See syllabus.md for:

• Detailed lesson schedule
• Learning outcomes
• Grading criteria
• Course policies
• Important dates

Assessment

• Lab Assignments: Hands-on exercises throughout the course
• Final Project: Data analysis project with report and presentation

See syllabus.md for detailed grading breakdown and deadlines.

📚 Suggested Textbooks

Primary References

1. All of Statistics by Larry Wasserman

   • Comprehensive coverage of statistical theory
   • PDF Link

2. A Modern Introduction to Probability and Statistics by Dekking et al.

   • Gentle introduction with applications
   • PDF Link

Additional Resources

• Think Stats by Allen Downey - Python-based statistics
• Statistical Learning MOOC - Stanford University
• OpenIntro Statistics - Free online textbook

💻 Software & Tools

Required

• Python 3.9 or later
• Jupyter Notebook/Lab (included in requirements.txt)

Python Libraries (all in requirements.txt)

Library	Purpose
NumPy	Numerical arrays and operations
Pandas	Data manipulation and analysis
Matplotlib	Basic plotting
Seaborn	Statistical visualizations
SciPy	Statistical functions and tests
Scikit-learn	Machine learning tools

Optional Tools

• Git - Version control (for getting updates)
• VS Code - Code editor with Jupyter support
• Google Colab - Cloud-based Jupyter environment

Statistical Analysis Tips

• Always start with exploratory data analysis (EDA)
• Check assumptions before applying methods
• Visualize your results
• Interpret p-values carefully (they're not everything!)
• Report confidence intervals, not just point estimates
• Consider practical significance, not just statistical significance

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Repository: https://github.com/stephane-rivaud/Applied-Statistics Last Updated: October 11, 2025 Good luck and enjoy the course! 📊🎓