Data

 
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    4489
1026    5611
767      3290
885      4113
1156    4883
1146    5425
892      4414
938      5506
769      3346
677      3673
1184    6542
1009    5088

Construct a scatter plot with this data.
Do you observe a relationship between both variables?
Use Excel to fit a linear regression line to the data. What is the fitted regression model? (Hint: You can follow the steps outlined on page 497 of the textbook.)
What is the slope? What does the slope tell us?Is the slope significant?
What is the intercept? Is it meaningful?
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?
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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