Assignment: Linear Regression in Real$-$World Problems

Objective: Apply linear regression to a real$-$world problem, analyze the results, and interpret the

findings. $|||$

Option $2$: Predicting College Admission Scores from Student Profiles

Scenario: A university wants to develop a tool to predict students' admission scores based on

high school GPA, standardized test scores, extracurricular involvement, and personal essays.

Questions: $$

What assumptions would you check before applying linear regression to this dataset, and why

are they important?

Describe how you would split the data for training and testing to avoid overfiting. What

methods might you use to improve model generalization?

Which features might have the highest predictive power, and how would you assess feature

importance?

Suppose you find multicollinearity among the features$-$how would you address it in your

model?

What social or ethical implications should be considered when building a predictive model for

college admissions?