1. What is the current status of the housing stock in the county?

a. To do this you will create a one-variable summaryusing StatTools and analyze the age of the recently sold homes, their average price sold, the number of bedrooms, bathrooms and number of cars that can be garaged.

b. What does the skewness and kurtosis tell you about these data?

c. Would it be better to use the Interquartile range to analyze this data (not a yes or no answer) and if so why, or why not?

2. Doing twoQ-Q plots, do you consider the data for price and square footage to be normal or not, and why?

3. Doing a correlation in StatTools and using all six of the variables, how are each of these variables correlated to each other.  Again be specific.

4. Doing a scatterplot of price versus square footage and adding a trend line to the plot, what does this tell you about the data?  

a. Now do a scatterplot of price versus age and adding a trend line, what does this tell you about the question of new homes versus price?

5. Next do a multiple regressionusing price as the dependent variable, and all other variables as independent variables:

a. Do any of the variables have a t-value that is greater than the alpha (.05) for this assignment?  If so, delete them and rerun the regression and compare and contrast the old regression versus the new regression without one or more of the variables.

b. Is the F-ratio for this/these regressions significant?  Why?

c. Is the r-squared values for this/these regressions appear to be valid?  Does it show that it explained a sufficient amount of the total variation?

6. Using the coefficients from this regression estimate the selling prices for the following typical Prescott county homes:

7. Based on the projected selling price of these homes, will Mayor Pete convince the county commission to rezone the land for his brother-in-law, or does the county get a new park? Defend your answer based on the statistics that you have calculated in this case study. Your argument should be no more than 300 words.