Business Case – Statistical Analysis

Quantitative statistical analysis of a real-world business case using Python. Covers exploratory data analysis, hypothesis testing, and data visualization to support data-driven decision making.

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Overview

This project applies statistical methods to extract meaningful insights from a business dataset. It was developed as part of a university course (2023) focused on quantitative analysis and evidence-based business decision making.

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Tech Stack

Tool	Purpose
Python	Core analysis language
Jupyter Notebook	Interactive development environment
Pandas	Data manipulation and EDA
NumPy	Numerical computation
Matplotlib / Seaborn	Data visualization
SciPy / Stats	Hypothesis testing

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Project Structure

BusinessCase_Statistics/
├── notebooks/          # Jupyter notebooks with full analysis
├── data/               # Dataset(s) used
└── README.md

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Analysis Pipeline

1. Exploratory Data Analysis (EDA)

• Dataset overview: shape, types, missing values
• Descriptive statistics: mean, median, std, quartiles
• Distribution analysis and outlier detection

2. Hypothesis Testing

• Formulation of null and alternative hypotheses
• Selection of appropriate statistical tests
• Significance evaluation (p-values, confidence intervals)

3. Data Visualization

• Distribution plots and histograms
• Correlation heatmaps
• Business-oriented summary charts

4. Conclusions & Recommendations

• Statistically supported findings
• Actionable business insights derived from the data

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Running the Project

Prerequisites

pip install pandas numpy matplotlib seaborn scipy jupyter

Launch

jupyter notebook

Open the notebook inside the notebooks/ folder and run all cells sequentially.

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Concepts Demonstrated

• Descriptive and inferential statistics
• Hypothesis testing methodology
• Data cleaning and preprocessing
• Business insight extraction from quantitative data

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Author

Kevin Joan Delgado Pérez B.S. Robotics and Digital Systems Engineering — Tecnológico de Monterrey Minor in Advanced AI and Data Science