A reproducible R project that applies descriptive and inferential statistical techniques to analyze educational data, identify behavioral patterns, and generate insights that support evidence-based decision-making.
Educational institutions collect large volumes of student data every semester. Transforming this information into meaningful insights requires a structured statistical analysis process.
This project presents a complete analytical pipeline developed in R, combining descriptive statistics, probability models, sampling techniques, and data visualization to explore a real educational dataset.
Rather than presenting isolated statistical exercises, the project was designed as a complete analytical workflow following professional data analytics practices, ensuring reproducibility, maintainability, and clear project organization.
Educational institutions frequently need reliable statistical evidence to better understand student characteristics, study habits, and overall satisfaction.
Questions such as:
- How much time do students dedicate to studying?
- How satisfied are students with their academic programs?
- How representative are random samples?
- Can statistical models support evidence-based decision-making?
require more than descriptive reports—they require a structured analytical workflow.
The ability to answer these questions enables educational institutions to better understand student behavior and make data-informed decisions.
- Import and prepare educational data.
- Perform descriptive statistical analysis.
- Apply probability distributions.
- Evaluate sampling techniques.
- Generate automated statistical reports.
- Produce publication-quality visualizations.
- Organize the complete workflow following professional data analytics practices.
The analysis uses a dataset containing information from 250 university students.
| Variable | Description |
|---|---|
| Age | Student age |
| Gender | Student gender |
| Height | Height (cm) |
| Weight | Weight (kg) |
| Weekly Study Hours | Hours dedicated to studying |
| Weekly Food Expenses | Weekly food expenditure |
| Approved Courses | Number of completed courses |
| Career Satisfaction | Satisfaction level |
| Employment Status | Whether the student is employed |
| Smoking Habits | Smoking status |
| Number of Siblings | Household information |
The project follows a modular workflow where every stage of the analysis is implemented as an independent R script.
Executing the scripts sequentially automatically reproduces the complete analytical process, generating statistical reports and visualizations without requiring manual intervention.
student-performance-analytics-r/
│
├── data/
│ student_survey.xlsx
│
├── scripts/
│ 01_data_import.R
│ 02_descriptive_statistics.R
│ 03_probability_models.R
│ 04_sampling_analysis.R
│ 05_visualizations.R
│
├── reports/
│ Automatically generated CSV reports
│
├── images/
│ Banner
│ Workflow
│ Charts
│
└── README.md
- Frequency distributions
- Measures of central tendency
- Measures of dispersion
- Quartiles
The project applies three fundamental probability distributions:
- Binomial Distribution
- Poisson Distribution
- Normal Distribution
These models demonstrate practical applications of statistical inference using educational data.
A Simple Random Sampling (SRS) approach is used to evaluate how accurately sample statistics estimate population parameters.
The analysis compares:
- Population Mean
- Sample Means
- Sampling Variability
This section demonstrates one of the core concepts of statistical inference.
The project automatically generates publication-ready visualizations, including:
- Histogram
- Bar Chart
- Boxplot
All figures are exported automatically to the images directory.
The statistical analysis revealed several relevant patterns:
- Most students dedicate between 9 and 13 hours per week to studying.
- Career satisfaction is predominantly classified as Very Satisfied.
- Sample means closely approximate the population mean, demonstrating the effectiveness of random sampling.
- Probability distributions provide a practical framework for modeling uncertainty in educational datasets.
Executing the analytical pipeline automatically generates statistical outputs in CSV format.
Generated reports include:
- Continuous Frequency Table
- Career Satisfaction Frequency Table
- Descriptive Statistics
- Binomial Distribution Results
- Poisson Distribution Results
- Normal Distribution Results
- Sampling Results
- Sampling Summary
All reports are generated automatically during project execution.
Executing the analytical pipeline automatically produces the following deliverables.
- Frequency Tables
- Descriptive Statistics
- Probability Model Results
- Sampling Analysis Summaries
- Project Banner
- Analytical Workflow
- Histogram
- Bar Chart
- Boxplot
The project is organized as a modular analytical pipeline where each script performs a specific stage of the statistical workflow.
Running the scripts sequentially reproduces the complete analysis, including data preparation, statistical calculations, report generation, and visualization outputs.
| Category | Technologies |
|---|---|
| Programming Language | R |
| Libraries | readxl, here |
| Statistical Methods | Descriptive Statistics, Inferential Statistics, Probability Theory, Sampling Theory |
| Data Visualization | Base R Graphics |
The project follows a modular structure to ensure reproducibility.
Executing the scripts sequentially automatically generates:
- Statistical summaries
- Frequency tables
- Probability analyses
- Sampling results
- Visualizations
No manual modification of intermediate files is required.
Clone the repository.
Open the project using RStudio.
Run the scripts in the following order:
01_data_import.R
02_descriptive_statistics.R
03_probability_models.R
04_sampling_analysis.R
05_visualizations.R
The complete analytical workflow will automatically generate all reports and visualizations.
Potential future enhancements include:
- Interactive dashboards using Shiny
- Automated reports with R Markdown
- Additional statistical methods
- Enhanced exploratory visualizations
This project demonstrates practical experience in:
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Descriptive Statistics
- Inferential Statistics
- Probability Distributions
- Sampling Techniques
- Data Visualization
- Reproducible Research
- R Project Organization
Jorgelina Etchevest
Economist | Data Analytics
Current areas of interest include:
- Data Analytics
- Business Intelligence
- Statistical Analysis
- Machine Learning
Explore additional projects on my GitHub profile.