Problem
Scenario
You are tracking daily completed rides on the Didi Chuxing rider app and see a decline right after a dispatch flow update. The series has strong weekday and holiday effects, so raw trend comparison is misleading.
Question
How would you use time-series trends to separate seasonality from a real product issue?
Example Data
metric·Daily completed ridespre_mean·102143post_mean·94429pre_period_days·56intervention_day·57post_period_days·14estimated_holiday_effect·-12400estimated_weekend_effect·-18500
What You Need to Show
- Separate recurring seasonal patterns from underlying trend
- Estimate whether the post-change drop remains after controlling for seasonality
- Test whether the intervention effect is statistically distinguishable from noise
- Translate the result into a product diagnosis
Practicing as: Data Analyst interview at McLeod SoftwareHi, I'll play your McLeod Software interviewer for the Data Analyst role. Candidates describe these interviews as mostly positive and moderately difficult, so expect me to be friendly and conversational. Take your time with the question above and answer like we're in the room.
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