"Analyzing Academic Performance: The Impact of Major, Course Level, and Professor Ratings on GPA at Michigan Technological University"

Overview

This project aims to analyze the factors influencing academic performance at Michigan Technological University (MTU). Specifically, it investigates the relationships between average course grades (GPA), major association, course level, and professor ratings. The study utilizes data from mtugrades.com and RateMyProfessors.com to perform statistical analyses and derive insights.

Objectives

• Analyze the correlation between average grades and professor ratings.
• Compare average grades across different academic majors.
• Evaluate the impact of course level on average grades.
• Determine the relationship between professor ratings and major association.

Data Sources

• mtugrades.com: Provides average GPA data for course sections.
• RateMyProfessors.com: Provides professor ratings.

Variables

1. Average Grade (GPA): Continuous variable (0.0 - 4.0 scale).
2. Major Association: Categorical variable (e.g., Engineering, Business, Computer Science).
3. Course Level: Categorical variable (1000-5000 level).
4. Professor Rating: Continuous variable (0-5 scale).

Methodology

• Data Collection: Gather data from mtugrades.com and RateMyProfessors.com.
• Statistical Analysis: Perform hypothesis testing and linear regression to analyze relationships between variables.
• Comparative Analysis: Compare average GPAs across different majors and course levels.

Hypotheses

1. Average Grades vs. Professor Ratings:
   • Null: No correlation between average grades and professor ratings.
   • Alternative: Positive correlation between average grades and professor ratings.
2. Major Association vs. Average Grades:
   • Null: All majors have similar average GPAs.
   • Alternative: At least one major has a significantly different average GPA.
3. Course Level vs. Average Grades:
   • Null: All course levels have similar average GPAs.
   • Alternative: Different course levels have significantly different average GPAs.

Tools and Technologies

• R: For statistical analysis and hypothesis testing.
• Excel: For data organization and preliminary analysis.