Reference no: EM132810275
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. Show the fitted regression model?
-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? 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 on the advertisement. Does the model underestimate or overestimates ales? (answer in Exel)
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