## Description

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)

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?

## Explanation & Answer

Attached. Please let me know if you have any questions or need revisions.

4489

5611

3290

4113

4883

5425

4414

5506

3346

3673

6542

5088

ScatterPlot of Sales Vs Advertisement

7000

6000

Sales ($'000)

Advertisement ($'000) Sales ($'000)

1068

1026

767

885

1156

1146

892

938

769

677

1184

1009

5000

4000

3000

2000

1000

0

0

200

400

600

800

Advertisement($'000)

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.823733298

R Square

0.678536546

Adjusted R Square

0.6463902

Standard Error

592.7335727

Observations

12

ANOVA

df

Regression

Residual

Total

Intercept

Advertisement ($'000)

SS

MS

F

Significance F

1 7415846 7415846 21.10774 0.000989

10 3513331 351333.1

11 10929177

Coeff...

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