Hippocampus Size Analysis Project

Overview

This project aims to analyze the differences in hippocampus size between two groups: "Unaffect" and "Affected". Using data from an .rda file, the project calculates statistical measures and visualizes the distribution of values for both groups.

Features

• Statistical Analysis: Calculation of mean, variance, and median for each group.
• Data Visualization: Histograms, empirical distribution functions (ECDF), and kernel density estimates (KDE) for visualizing the data distribution.
• Empirical Distribution Functions: Both ECDF and PDF are plotted for a detailed understanding of data distribution.

Core Components

hypocampus_size.ipynb

• A Jupyter notebook for interactive data analysis and visualization.
• Allows users to explore the data and perform statistical analysis in an interactive manner.

Dependencies

Ensure the following Python packages are installed:

• pyreadr
• pandas
• matplotlib
• numpy
• seaborn
• statsmodels

You can install the dependencies using pip:

pip install pyreadr pandas matplotlib numpy seaborn statsmodels

Usage

1. Data Loading: The data is read from a specified .rda file and divided into two groups: "Unaffect" and "Affected".

2. Statistical Calculations: The notebook includes functions to calculate the mean, variance, and median for each group's standard deviation.

3. Data Visualization: The notebook provides cells to generate histograms, ECDF, and KDE plots for both groups.

Running the Notebook

To use the notebook:

1. Open hypocampus_size.ipynb in Jupyter Notebook or JupyterLab.

2. Run the cells in sequence to load the data, perform statistical calculations, and visualize the results.

Example Code

Example code snippets provided in the notebook include:

# Example for statistical calculations

group_unaffect = df['Unaffect']
group_affected = df['Affected']

unaffect_stats = calculate_stats(group_unaffect)
affected_stats = calculate_stats(group_affected)