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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