Interactive Plotly presentation created for Coursera Developing Data Products assignment.

27-11-2025

This analysis examines factors influencing professor evaluation scores at the University of Texas at Austin, with particular focus on whether physical attractiveness predicts higher ratings. Using multiple linear regression on 463 course evaluations, I found that while beauty rating is a statistically significant predictor (p < 0.001), it explains only 3.5% of the variance in scores—suggesting minimal practical impact. The final model identifies seven significant predictors: gender, language of degree institution, age, class evaluation participation rate, course credits, beauty rating, and picture color. Key limitations include the single-institution sample and non-independent observations due to professors teaching multiple courses. This project demonstrates skills in exploratory data analysis, regression diagnostics, model selection via backward elimination, and communicating statistical findings to diverse audiences. Here, you will analyze the data from this study in order to learn what goes into a positive professor evaluation.

Interactive Leaflet map created for Coursera Developing Data Products assignment.