Question: The Excel file named house.xls contains data, descriptive statistics, and regression output relating the selling price of a house in dollars to the size of the house in square feet and the age of the house in years.

1. Write out the regression equation, with specific intercept and slope estimates.
2. For one row of data, use the independent variable values to predict the value of the dependent variable. Indicate which row of data you are using. For this row, compute the "residual." What summary output in the regression is related to the "residual", and what is its value in this regression?
3. For each independent variable, interpret the numerical value of the respective slope coefficient. That is, relate the numerical value of each independent variable’s coefficient to the dependent variable. Be careful to include the proper units in your interpretation when analyzing the effect of each independent variable on the dependent variable. This is perhaps the most important part of your answer!
4. Evaluate the reliability of each of the two slope estimates. That is, are these estimates statistically significant? Use either the t-value approach or the p-value approach, or both, when analyzing statistical significance. Explain briefly. (Hint: Each of these is an hypothesis test of the respective slope coefficient, where the value of the slope coefficient is zero under a true null hypothesis).
5. Use the reported R-square value to evaluate the overall reliability of the regression. That is, what is the interpretation of the R-square value that is presented?
6. Write a short paragraph describing these regression results as though you were writing a report or an article. What "story" do the results tell you? How statistically reliable are the results? Your answer here will be redundant in the sense that much of what you say will have been said in earlier answers.
7. There are other important variables that affect the price of a house.
   1. Suggest one or more to include in the regression model. Can it be quantified?
   2. What would happen to the explanatory power of the model if this additional information were to be included?
   3. How (and perhaps where) would these results be reflected in a revised regression output?

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Deliverable: Word Document