6.8.3: Hands-On Lab Exercise

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Page ID: 58313

Felix Amoruwa
De Anza College

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Lab: Statistical Analysis of Sales Data

Scenario:

You have a dataset of 1,000 daily sales records for an online retailer with: daily_revenue, num_orders, avg_order_value, day_of_week, promotion_active (yes/no), season.

Instructions:

Descriptive Stats (15 min): Compute mean, median, std dev, IQR for daily_revenue. Create a histogram. Is the distribution normal, skewed, or multimodal?
Probability Application (20 min): The average number of returns per day is 5. Using the Poisson distribution, calculate P(exactly 8 returns) and P(3 or fewer returns). Use Python's scipy.stats.
Confidence Interval (15 min): Compute a 95% CI for the mean daily_revenue. Interpret the interval. What happens to the interval width if you use 99% confidence instead?
Hypothesis Test (20 min): Test whether promotion days have significantly higher revenue than non-promotion days. State H₀ and Hₐ, compute the test statistic and p-value (t-test), and state your conclusion at α = 0.05.
Correlation Analysis (20 min): Compute correlation between num_orders and daily_revenue. Create a scatter plot. Is the relationship linear? Discuss whether this correlation implies causation.

Deliverables:

Descriptive statistics table, probability calculations with code, CI computation and interpretation, hypothesis test write-up, correlation analysis with scatter plot.

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