Schedule Impact Statistical Analysis
Real-world research on 185+ students
Explored the impact of Fixed vs. Flexible Class Schedules on Student Productivity, Academic Success, and Satisfaction. Conducted surveys of 185+ students (aged 18–23), performed independent samples t-tests, KDE analysis, and statistical hypothesis testing to uncover relationships between schedule types and key outcome variables.
Why it mattered
More perceived control → Higher satisfaction; Better planning → Higher effectiveness; Flexible scheduling → Different satisfaction patterns
What shipped
Explored the impact of Fixed vs. Flexible Class Schedules on Student Productivity, Academic Success, and Satisfaction. Conducted surveys of 185+ students (aged 18–23), performed independent samples t-tests, KDE analysis, and statistical hypothesis testing to uncover relationships between schedule types and key outcome variables.
Proof signal
7 key findings validated through statistical hypothesis testing; significant relationships discovered in control-satisfaction (p=0.0012), planning-effectiveness (p=0.0078), and flexibility-satisfaction (p=0.0058)
System flow
Survey Design -> Data Collection -> Cleaning & EDA -> T-Test Analysis -> KDE Visualization -> Hypothesis Validation -> Report Generation.
Honest maturity snapshot
Raw datasets, disconnected metrics, and hard-to-scan findings.
A structured analysis or dashboard with clear KPIs, visual patterns, and decision-ready insights.
Features
- Python integrated into the workflow.
- Pandas integrated into the workflow.
- NumPy integrated into the workflow.
- SciPy integrated into the workflow.
- Survey Design stage documented in the delivery flow.
- Data Collection stage documented in the delivery flow.
Engineering challenges
- Keeping the implementation clear enough to explain while still solving the core technical problem.
- Choosing a scope that could be shipped, tested, and documented.
- Turning technical work into proof a visitor can evaluate quickly.
Results
- 7 key findings validated through statistical hypothesis testing; significant relationships discovered in control-satisfaction (p=0.0012), planning-effectiveness (p=0.0078), and flexibility-satisfaction (p=0.0058)
- More perceived control → Higher satisfaction; Better planning → Higher effectiveness; Flexible scheduling → Different satisfaction patterns
- Made source code or technical proof available for review.
What I learned
- Tradeoff learned: Keeping the implementation clear enough to explain while still solving the core technical problem.
- Proof learned: 7 key findings validated through statistical hypothesis testing; significant relationships discovered in control-satisfaction (p=0.0012), planning-effectiveness (p=0.0078), and flexibility-satisfaction (p=0.0058)
- Next iteration: Add richer screenshots or a narrated demo.