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📊 Schedule Impact Statistical Analysis

License: MIT Platform: Colab

A statistical analysis exploring how different student scheduling styles — Fixed, Flexible, and Hybrid — influence key outcomes like productivity, satisfaction, planning effectiveness, and lifestyle habits.

🎯 Project Objective

To investigate the impact of schedule types on various factors like productivity, satisfaction, and health habits using student survey data. The project applies statistical testing (T-tests) and data visualization to uncover trends and actionable insights.

📁 Project Structure


schedule-impact-statistical-analysis/
│
├── schedule_analysis.ipynb       ← Main Colab notebook for all analysis
├── charts/                       ← Visual outputs
│   ├── demographics/
│   ├── observations/
│   └── conclusion/
├── data/
│   ├── schedule_survey_cleaned.xlsx
│   └── data_dictionary.md
├── summary_table.csv             ← Key findings summary
├── report/
│   └── schedule-impact-presentation.pdf
├── LICENSE
├── .gitignore
└── README.md

📈 Analysis Overview

  • 🔍 Data Collection: Survey of students (N ≈ 150), aged 18–23
  • 🧪 Methods Used:
    • Independent samples T-test
    • KDE plots, histograms, bar charts
    • Grouped statistical summaries
  • 📊 Focus Areas:
    • Productivity
    • Scheduling satisfaction & control
    • Mental and physical well-being
    • AI scheduling tool usage

📊 Key Visualizations

  • Demographics → Age, Gender, Schedule Type Distributions
  • Observations → Productivity, Health, and Satisfaction vs. Schedule Type
  • Conclusion → Summarized insights and correlations

See charts in the /charts/ folder.

🖼️ Sample Visuals

Observation 1: Productivity by Schedule
Fig 1: Productivity distribution across schedule types (Fixed vs. Flexible/Hybrid)

Observation 3: Planning Method Effectiveness
Fig 2: Effectiveness score distribution for students using vs. not using planning tools

Observation 5: AI Tool Satisfaction
Fig 3: Scheduling satisfaction among AI tool users vs. non-users

Observation 7: Flexibility vs Satisfaction
Fig 4: Scheduling satisfaction distribution by flexibility importance

🧠 Key Insights

# Observation Result Significant?
1 Schedule Type vs. Productivity No clear difference ❌ No (p = 0.869)
2 Control vs. Satisfaction More control → Higher satisfaction ✅ Yes (p = 0.0012)
3 Planning vs. Effectiveness Planned → More effective ✅ Yes (p = 0.0078)
4 Schedule vs. Healthy Lifestyle No significant relationship ❌ No (Z = 1.315)
5 AI Use vs. Satisfaction No clear impact observed ❌ No (Z = 1.037)
6 Schedule vs. Work-Life Balance No significant difference ❌ No (p = 0.0738)
7 Flexibility vs. Satisfaction High flexibility → Different satisfaction ✅ Yes (p = 0.0058)

🔗 Full summary in: summary_table.csv

💡 Recommendations

  • Promote flexible scheduling in academic settings
  • Encourage digital calendar use and AI tools
  • Educate students on planning habits for better well-being

📝 How to Reproduce

  1. Clone or fork this repository
  2. Open schedule_analysis.ipynb in Google Colab
  3. Ensure data/schedule_survey_cleaned.xlsx is available
  4. Run the notebook to regenerate analysis and charts

👨‍💻 Contributors

This project was collaboratively created by:

ℹ️ All members contributed equally to this project as part of a collaborative academic effort.

📄 License

This project is open-sourced under the MIT License – see LICENSE for details.
You are free to use, adapt, and distribute this work with attribution.