Statistical Hypothesis Testing and Regression Analysis Dashboard

This interactive web application provides powerful tools for statistical analysis and visualization of three key datasets: Real Estate Pricing, Red Wine Quality, and White Wine Quality. Built with Streamlit, the dashboard enables users to perform in-depth statistical hypothesis testing and regression analysis through an intuitive interface.

📊 Try the Live Dashboard

Project Overview

The dashboard serves as a comprehensive analytical tool that helps users understand complex relationships within datasets through:

• Statistical hypothesis testing to validate relationships between variables
• Interactive regression analysis with customizable parameters
• Dynamic visualization of correlations and relationships
• User-friendly interface with intuitive navigation

Ways to Access

You can use this dashboard in two ways:

1. Live Web Application: Access the deployed version instantly through your browser:

   • Visit https://statistical-hypothesis-testing-and-regrysis-jkwgpk9btxfeqw4a59.streamlit.app/
   • No installation required
   • Start analyzing data immediately

2. Local Installation: Run the application on your local machine for development or offline use by following the installation instructions below.

Features

Real Estate Analysis

The real estate analysis module helps users understand property pricing factors through:

• Correlation matrices visualizing relationships between all features in the dataset
• Linear regression analysis examining how factors like house age, MRT station proximity, and convenience store density affect prices
• Interactive scatter plots with regression lines for visual analysis of feature relationships
• Detailed statistical outputs including p-values, R-squared values, and confidence intervals

Wine Quality Analysis

Both red and white wine datasets can be analyzed to understand quality determinants:

• Comprehensive correlation analysis of wine characteristics
• Regression modeling to identify key quality factors like alcohol content, acidity, and pH
• Interactive visualizations showing relationships between chemical properties and wine quality
• Comparative analysis between red and white wine characteristics

User Interface

The application features a carefully designed interface for optimal user experience:

• Intuitive sidebar navigation for seamless movement between analysis modules
• Interactive widgets allowing users to customize their analysis parameters
• Clear visualization controls for adjusting plot parameters
• Detailed explanations of statistical concepts and interpretations

Installation

1. Clone the repository:

git clone https://github.com/Yz210x4E/Statistical-Hypothesis-Testing-and-Regrysis.git
cd Statistical-Hypothesis-Testing-and-Regrysis

2. Create and activate a virtual environment (recommended):

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

3. Install required dependencies:

pip install -r requirements.txt

4. Launch the application:

streamlit run app_streamlit.py

Dataset Information

The application analyzes three distinct datasets from the UCI Machine Learning Repository:

1. Real Estate Pricing Dataset

   • Features include house age, distance to MRT stations, convenience store count
   • Target variable: House price per unit area
   • Used for property value prediction and feature importance analysis

2. Red Wine Quality Dataset

   • Contains physicochemical properties of red wines
   • Features include alcohol content, acidity levels, pH, and other chemical properties
   • Target variable: Wine quality score

3. White Wine Quality Dataset

   • Similar structure to red wine dataset but for white wines
   • Enables comparative analysis between red and white wine characteristics
   • Facilitates understanding of quality determinants across wine types

Technologies

The project leverages several powerful Python libraries:

• Streamlit: Powers the web interface and interactive components
• Pandas: Handles data manipulation and analysis
• StatsModels: Provides statistical modeling capabilities
• Seaborn & Matplotlib: Generate static visualizations
• Plotly: Creates interactive plots
• NumPy: Supports numerical computations

Contributing

We welcome contributions to improve the dashboard. To contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Contact

For questions, feedback, or collaboration opportunities:

• Yz210x4E
• Dionis Leka
• Xhesika Gjikola

Acknowledgments

• Epoka University for providing institutional support
• UCI Machine Learning Repository for the datasets
• The Streamlit team for their excellent framework

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Developed with ❤️ at Epoka University