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?

Due Day 7

Reply/respond to at least 2 of your classmates or your faculty member and/or address the following subjects:

What is the difference between ANOVA and Regression Analysis. When do we use each concept?

What is simple linear regression? How is a scatter plot related to simple linear regression?

What is ANOVA and/or a regression chart?

Why is the p-value important in analyzing the outcome of multiple regression analysis?

What is the difference between correlation and cause and effect?

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