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...

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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)


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 sales?


  • Provide an example from your personal or professional life where this information can be useful.

Answered Same DayJun 17, 2021

Answer To: Models help us describe and summarize relationships between variables. Understanding how process...

Atreye answered on Jun 17 2021
142 Votes
Sheet1
                Advertisement    Sales
                1068    4489
                1026    5611
                767    3290
                885    4113
                1156    
4883
                1146    5425
                892    4414
                938    5506
                769    3346
                677    3673
                1184    6542
                1009    5088
                950    4675.494
    SUMMARY OUTPUT
    Regression Statistics
    Multiple R    0.8237332977
    R Square    0.6785365458
    Adjusted R Square    0.6463902004
    Standard...
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