FitLife is a mobile fitness app that offers workout programs…

FitLife is a mobile fitness app that offers workout programs to its users. The analytics team is modeling Monthly Active Users (in thousands) to understand the impact of their marketing efforts. They want to distinguish between users acquired through paid campaigns and their “organic” user base (users who join through word-of-mouth or app store search). The two explanatory variables are: Social Media Ads (X₁): Monthly spending on social media advertising (in thousands of dollars) Push Notifications (X₂): Number of promotional push notifications sent to users each month There are months when the company does not run any ads or send any push notifications. The regression results are below: Variable Beta Coef. (β) Std. Error p-value Intercept 18.75 3.10 < 0.001 Social Media Ads 1.85 0.40 0.002 Push Notifications 0.95 0.28 0.010 Interpret the intercept.

Model A uses only GPA and SAT Score as predictors. Model B a…

Model A uses only GPA and SAT Score as predictors. Model B adds Essay Score (rated 0–10 by reviewers) as an additional predictor. Both models are fit on the same 400 applicants. AIC (Akaike Information Criterion) is used to compare model fit. Model Predictors AIC Model A GPA, SAT Score 491.6 Model B GPA, SAT Score, Essay Score 480.1 Based on the AIC values above, which model should the admissions office prefer, and why?