Groc Sales Mall Case Study

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Sales = -253.774 + 12.191 Customers

- Create a scatterplot between Sales and Customers. Add lines that show the different Mall categories and fit lines for what you got in Part 4.

[pic 5]

Two

- Use version 1 of the midterm exam data sets for this. Run a regression to predict total debt by primary income, secondary income, monthly payment for mortgage or rent, utility payments and family size.

[pic 6]

- Now define indicator variables for the quadrants of the city. Use NW as the base category.

- Run another regression where the three indicator variables for location are added to the model in #6.

[pic 7]

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- Compare the model from part 6 to the one from part 8.

- Interpret the coefficients on the location variables and test them for significance.

All else equal, a household in NE has .772 more total debt payments than one in the base category (NW). One in SE is .943 higher. One in SW is .080 lower than NW.

Note of these are significant amounts because all t-ratios are modest and p-values are .363 or higher.

- How much does adjusted R-square change from the model in #6?

The regular r-square goes from 0.813 to 0.817, but adjusted r-square drops from 0.800 to 0.795. This would be an indication that location does not really matter, at least the way we have modeled it.

- When you add these location variables, do any of the other variable coefficients change a great deal?

Not by a great deal. Because location does not really affect debt, this is not surprising.