Tahsine/maternal_health_stats_project

Statistical analysis of maternal health and family planning data in Benin and Senegal # Maternal Health & Family Planning Analysis Project This project conducts a statistical analysis on maternal health and family planning (PF) data from Benin and Senegal. It tests two primary hypotheses related to healthcare access and patient satisfaction. ## 📊 Hypotheses Analyzed - **H1: Distance and Consultations**: Investigating if greater distance to health centers reduces the number of prenatal consultations. (**Confirmed**) - **H2: Instruction level and Satisfaction**: Checking if higher education levels correlate with lower satisfaction due to higher expectations. (**Not Confirmed**) ## 📁 Project Structure - `maternal_health_analysis.ipynb`: The main Jupyter Notebook containing the full analysis, pedagogical justifications, and formal statistical tests. - `etrilabs_sante_maternelle_pf.csv`: The dataset used for the analysis. - `venv/`: (Optional) Python virtual environment for local execution. ## 🚀 Getting Started ### Prerequisites You will need Python 3.8+ and the following libraries: - `pandas` - `numpy` - `matplotlib` - `seaborn` - `scipy` ### Installation 1. Clone the repository: ```bash git clone github.com cd maternal_health_stats_project ``` 2. Create and activate a virtual environment: ```bash python -m venv venv # Windows .\venv\Scripts\activate # Unix/macOS source venv/bin/activate ``` 3. Install dependencies: ```bash pip install pandas numpy matplotlib seaborn scipy ``` ## 📝 Analysis Highlights - **Data Cleaning**: Detailed justifications for using listwise deletion vs. imputation to maintain scientific rigor. - **Statistical Tests**: Usage of Pearson correlation for H1 and Kruskal-Wallis (non-parametric ANOVA) for H2. - **Formal Notation**: Inclusion of Null ($H_0$) and Alternative ($H_1$) hypotheses in mathematical form.