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Summarize relationships between variables

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Reference no: EM132865051

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?

Reference no: EM132865051

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