Question
WearRight recently released multiple models of its new wearable, the WristWrap, across a wide range of urban markets. Each model of the WristWrap tracks physical
WearRight recently released multiple models of its new wearable, the WristWrap, across a wide range of urban markets. Each model of the WristWrap tracks physical activity and monitors heart rate, among many other tracking and alert features; the key distinction across models is the battery life. For normal activity, the battery life across the different models ranges from 1 to 7 days.
Following the advice of EKA consulting, WearRight charged varying prices among battery-life models across many different markets. Table A.6 provides summary statistics for the WristWraps pricing and battery life across all models and markets. Here, Unit Sales/1,000 is wearable unit sales per 1,000 in the observed market.
WearRight wants to understand how unit sales depend on price and battery life, as well as the trade-off between these two product features. To address these issues, it asks EKA consulting to run an analysis. EKA assumes the following data-generating process for the unit sales in market i:
EKA analysts then regressed Unit Sales (per 1,000) on the Price and Battery Life; the results of this regression are in Table A.7.
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Describe the results in Table A.6.
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Is variation in Price for each model across markets important? If so, why?
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Suppose instead that WearRight simply charged $10 more for each 1-day increase in battery life in every market. How would that affect the regression results in Table A.7?
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Explain the results for Battery Life in Table A.7.
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What does the point estimate mean?
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What does the p-value mean?
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What do the upper and lower bounds for the 95% confidence interval mean?
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Explain the results for the Price in Table A.7.
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What does the point estimate mean?
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What does the p-value mean?
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What do the upper and lower bounds for the 95% confidence interval mean?
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Predict the effect of raising the Battery Life by 1 day on Unit Sales per 1,000.
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Detail the line of reasoning necessary to make your prediction in Question 4.
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On average, how much is an extra day of Battery life worth? (hint: determine how much the price could increase along with a 1-day increase in Battery Life without reducing Unit Sales per 1,000).
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Suppose WearRight is concerned that there are inherent differences in market performance for the WristWrap, and the price/battery life combinations offered across markets are determined at least in part based on those differences.
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How would this data feature damage the line of reasoning you presented in Question 5?
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How might you append the data and the assumed data-generating process to remedy this problem?
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