Scenario

You are analyzing whether exposure to a new in-app rider promotion is associated with higher 7-day ride completion. Using user-level data from 8,000 users, you fit a linear probability regression: $Y_i = \beta_0 + \beta_1 Promo_i + \beta_2 PriorRides_i + \beta_3 AppOpens_i + \beta_4 CityTier2_i + \epsilon_i$, where $Y_i=1$ if the user completed a ride within 7 days. The estimated coefficient on $Promo_i$ is 0.018 with standard error 0.006, and the model intercept is 0.112.

Question

How would you interpret the regression result, test whether the promotion has a statistically significant effect on 7-day ride completion, and explain what this does and does not imply for product growth decisions?