In a recent project, I spearheaded a team-based statistical analysis using JMP to explore the Ames Housing data. Our objective was to identify key factors influencing house prices through multiple linear regression. We meticulously cleaned a dataset of over 2,900 entries, ensuring the validity of variables through various statistical plots and transformations.

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Our process involved rigorous checks for multicollinearity and the application of forward stepwise regression for selecting the most relevant variables. We successfully delivered insightful data visualizations and interpreted complex model outputs, providing a data-driven perspective essential for real estate evaluations. I also coordinated the final presentation of our project, confidently communicating our findings and statistical methodologies to an academic audience.

 

Reflecting on this project, I realized the importance of collaboration and trust within a team. Working on such a large-scale project taught me that it's not feasible to tackle everything individually. I learned the value of relying on my teammates for timely delivery of critical tasks and managing expectations. This experience also broadened my perspective: different approaches can be equally effective, even if they differ from my own methods. Embracing these varied approaches not only enhanced our project but also enriched my personal growth and understanding of effective teamwork.