Statistical Data Analysis • Research Project • Data Science

Does Mobile Detox Improve Sleep Quality?

A research-driven statistical analysis based on primary survey data exploring how mobile detox habits influence sleep quality using primary survey data, custom index construction, descriptive statistics, and hypothesis testing.

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Project Background

Academic Overview

This project was completed as part of the Statistical Modeling for Business Systems course at BMS College of Engineering. The study collected and analyzed primary survey responses to determine whether practicing mobile detox before sleep leads to measurably better sleep quality.

Research Question

Core Problem Statement

"Does reducing mobile phone usage before sleep significantly improve overall sleep quality?"

Study the relationship between mobile detox and sleep.
Construct a weighted Sleep Quality Index (SQI).
Perform Independent Two-Sample t-Test.

Key Metrics

Interactive Statistics Dashboard

Participants22 Responses

Primary Survey

Detox Group5 Subjects

Active Detox

Non-Detox Group17 Subjects

Control Group

Mean SQI (Detox)6.92 / 10

Higher Quality

Mean SQI (Non-Detox)5.11 / 10

Baseline

t-Statistic3.18

df = 20

Data Visualizations

Comparative Analytics

Primary Survey Sample (N = 22)● Hypothesis Test Validated

Detox Group (n = 5)Mean SQI: 6.92 / 10

Non-Detox Group (n = 17)Mean SQI: 5.11 / 10

Difference: +1.81 SQI Points (+35.4% improvement in sleep quality)

Process

Research Workflow Timeline

STEP 01

Survey Design

Crafted multi-variable questionnaire capturing digital usage before sleep & vital parameters.

STEP 02

Data Collection

Gathered 22 primary student responses via structured Google Forms.

STEP 03

Data Cleaning

Filtered missing data, normalized categorical ratings, and verified outlier distributions.

STEP 04

SQI Construction

Built a custom weighted Sleep Quality Index combining latency, restfulness, and hours.

STEP 05

Descriptive Analysis

Computed mean, variance, standard deviation, and sample distributions across groups.

STEP 06

Hypothesis Testing

Executed Independent Two-Sample t-Test to evaluate null hypothesis (H₀: μ₁ = μ₂).

STEP 07

Conclusion

Rejected H₀ at 10%, 5%, and 1% significance levels (p < 0.01).

Key Statistical Result

Significant Improvement Confirmed (p < 0.01)

Participants practicing mobile detox displayed a significantly higher average Sleep Quality Index than the non-detox group (6.92 vs 5.11). With a calculated t-statistic of 3.18 and 20 degrees of freedom, the statistical test successfully rejected the null hypothesis at 10%, 5%, and 1% significance levels.

My Contributions

✓Performed rigorous two-sample statistical calculations and t-test variance analysis.
✓Engineered the multi-variable weighted Sleep Quality Index (SQI) formula.
✓Formatted analytical charts and statistical summary tables for academic publication.
✓Conducted final peer review and data validation check prior to submission.
✓Collaborated with course team under Statistical Modeling for Business Systems.

Tools & Techniques

Google FormsExcel Data EngineStatistical AnalysisHypothesis TestingData CleaningData VisualizationResearch Methods

Learning Outcomes

Data Cleaning & PreprocessingFeature Engineering (Index Building)Statistical Thinking & ReasoningHypothesis Testing (t-Test, p-values)Academic Research MethodologyData Visualization & CommunicationExperimental Design Controls