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Scenario 1: Patagonia You work for Patagonia, a clothing retailer in Ventura, California. You have data from some of their in-store transactions. You want to develop a model to better understand how in-store activities can influence the customer experience and in turn transaction amounts. The data has the following variables: amount: transaction amount from a single purchase store_ad_low: "low" level of in-store advertising activity store_ad_med: "medium" level of in-store advertising activity Note: baseline case is "high" level of in-store advertising activity upsell: employers focus on suggesting additional goods when customers are browsing (1 = ws, 0) = no) checkout: presence or absence of small "add-on" goods located right near checkout (1 = yes, 0 = no) name: cashiers interact with the customers by name whenever possible (1 = yes, 0 = no) ## Call : ## la (formula = amount - . + name * upsell, data = patagonia] ## ## Residuals: ## Min 14 Median Max ## -54.78 -10.03 0.03 10.05 44.27 ## Coofficiants: ## Estimate Std. Error t walna Pr(It! ) ## (Intercept) 28. 5229 1. 0867 26.248