Part B

1. Your real estate agent recently stated that houses in the suburb typically had an average price around 1200 ($000), but have recently changed. Test his claim at the 5% level of significance. Show all your calculations. 
2. Run a simple linear regression using the House prices and the size data and
a) Interpret the coefficients.
b) Show the output from Excel including a scatter plot. 
3. What is the value of correlation coefficient? Interpret the correlation coefficient. 
4. Is the coefficient estimate for the Size statistically different than zero at the 5% level of significance? Set-up the correct hypothesis test using the results found in the table in question 2 using both the critical value and p-value approach. Interpret the coefficient estimate of the slope.
5. Run a multiple linear regression using the House prices, size and number of bedrooms data. Show the output from Excel.
6. Set up the multiple regression equation.
7. Interpret the slope coefficient estimates. Discuss whether the signs are what you are expecting and explain your reasoning. 
8. Interpret the value of the Adjusted R2. Is there a large difference between the R2 and the Adjusted R2? If so, what may explain the reasoning for this? 
9. Is the overall model/equation statistically significant at the 5% level of significance? Use the value of significance-F for testing.
10. Test each of the slopes individually at the 5% level of significance. Based on the results of the regressions, what other factors would have influenced the Property prices? Provide a couple possible examples and indicate their predicted relationship with the review score if they were included. Use the p-value approach for testing. 
11. Predict the average price of a property with a size of 600 square meters and has 3 bedrooms. Show the predicted regression equation.