Respond to the following in a minimum of 175 words: Models help us describe and summarize relationships between variables. Understanding how process variables relate to each other helps businesses...

Respond to the following in a minimum of 175 words:

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)

10684489

10265611

7673290

8854113

11564883

11465425

8924414

9385506

7693346

6773673

11846542

10095088

• 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?

Respond to the following in a minimum of 175 words: 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) 10684489 10265611 7673290 8854113 11564883 11465425 8924414 9385506 7693346 6773673 11846542 10095088 · 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?