📊 Schedule Impact Statistical Analysis

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

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🎯 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.

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📁 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

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📈 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

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📊 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.

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🖼️ Sample Visuals

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

Fig 2: Effectiveness score distribution for students using vs. not using planning tools

Fig 3: Scheduling satisfaction among AI tool users vs. non-users

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

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💡 Recommendations

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

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📝 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

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👨‍💻 Contributors

This project was collaboratively created by:

• Fatima Mohsin – GitHub
• Alveena Javed – GitHub
• M. Obaid ur Rehman – GitHub
• M. Zohaib Khan – GitHub

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

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📄 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.