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Models help us describe and summarize relationships between variables. Understanding how process variables relate to each other helps businesses predict and improve performance. For example, a marketing manager might be interested in modeling the relationship between advertisement expenditures and sales revenues.
Consider the dataset below and respond to the questions that follow:
Advertisement (000) Sales (000)
1068 44891026 5611767 3290885 41131156 48831146 5425892 4414938 5506769 3346677 36731184 65421009 5088
Question 1: Construct a scatter plot with this data.
Question 2: Do you observe a relationship between both variables?Question 3: Use Excel to fit a linear regression line to the data. What is the fitted regression model?Question 4: What is the slope? What does the slope tell us?Is the slope significant?Question 5: What is the intercept? Is it meaningful?Question 6: What is the value of the regression coefficient,r? What is the value of the coefficient of determination, r^2? What does r^2 tell us?Question 7: Use the model to predict sales and the business spends $950,000 in advertisement. Does the model underestimate or overestimates ales?
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