Business Statistics

Understanding data through statistical analysis, hypothesis testing, and predictive modeling.

Course Overview

Foundations of data-driven decision making.

MGSC 2301 introduced the statistical methods that underpin modern business analytics — from descriptive summaries and probability distributions to inferential techniques used in real-world decision-making. The course emphasized both conceptual understanding and hands-on application through SPSS.

Across lectures, labs, and a capstone regression project, I developed the ability to formulate hypotheses, select appropriate tests, interpret results with confidence, and communicate findings clearly to non-technical audiences.

Skills Learned

Core competencies from this course.

Regression Analysis

ANOVA

Hypothesis Testing

SPSS

Statistical Inference

Featured Project

Can money buy wins in SEC football?

Capstone Project

SEC Football Spending vs. Performance Analysis

This project investigated whether athletic department spending influences competitive success in SEC football programs. Using data from 70 program-seasons between 2020 and 2024, regression analysis was used to evaluate the relationship between spending and final SEC standings.

Project Highlights

Analyzed 70 SEC program-seasons
Evaluated athletic department spending from 2020–2024
Built and interpreted a regression model
Conducted hypothesis testing using t and F statistics
Assessed statistical significance and model fit
Generated business and sports management insights

Key Metrics

Program Seasons

Largest Athletic Budget

$285M

R²

0.087

P-Value

0.013

Statistically Significant

Analysis

What the data revealed.

Statistically Significant

t = -2.55

p = .013

Reject H₀

Weak Predictive Power

R² = 8.74%

Most variation remains unexplained by spending alone.

Real-World Insight

Money helps performance, but coaching, recruiting, facilities, and program culture remain critical factors.

Exploratory Analysis

Exploring the data.

Regression Analysis

Regression analysis.

Statistical Evidence

Statistical evidence.

Key Findings

✓

Statistically Significant Relationship

p = 0.013

✓

Reject Null Hypothesis

t = -2.5517

✓

Spending Has Measurable Impact

Higher spending is associated with improved SEC standings.

⚠

Limited Predictive Power

R² = 0.0874

Spending explains only a small portion of team performance variation.

Insight

What the analysis actually means.

Although spending showed a statistically significant relationship with SEC performance, the low R² value indicates that financial investment alone does not explain success. Factors such as recruiting quality, coaching effectiveness, player development, team culture, and institutional support likely play a much larger role in determining outcomes. The analysis demonstrates that money matters, but it is only one piece of a much larger competitive equation.

Presentation

Full project report.

Scroll through the complete regression analysis — from data exploration and model specification to hypothesis testing and business insights.

SEC Football Spending vs. Performance Analysis

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Artifacts

Course deliverables and outputs.

Final ReportOpen Report →

Regression Analysis

Statistical Evidence

Research Findings

Key Takeaways

What I carried forward from this course.

Data tells a story — statistics proves it.

Statistical analysis transforms observations into evidence-based conclusions.

Models are only as good as their assumptions.

Understanding limitations is just as important as interpreting results.

Significance does not equal importance.

A relationship can be statistically significant while still having limited practical predictive power.