Essays on Home Price Insights Assignment

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The paper "Home Price Insights" is a wonderful example of an assignment on macro and microeconomics.   The scatter plot shows a downhill pattern moving from left to right indicative of a negative relationship between the age of a home and the living price of a home. As the age of a home increases, the price of that home decreases. Question 2 Variable Sign Expectation Livadia +ve The sign is in line with my expectations since I would expect the price of a home to go up as the living area by square feet increases Age -ve The sign is in line with my expectations since I would expect the price of a home to decrease if its age at the time of sale is high Beds -ve The sign is not in line with my expectations since I would expect the price of a home to increase if it has more bedrooms Baths -ve The sign is not in line with my expectations since I would expect the price of a home to increase if it has more bathrooms Question 3 : : Test statistic: = Using a 5% significance level, with 1495 degrees of freedom, the critical value is 1.962 Since 1.962, we reject the null hypothesis.

At a 5% significance level, there is enough evidence to conclude that the price of a home depends on the living area. = Using a 5% significance level, with 1495 degrees of freedom, the critical value is 1.962 Since -0.141 > -1.962, we reject the null hypothesis. At a 5% significance level, there is enough evidence to conclude that the price of a home depends on its age at the time of sale. = Using a 5% significance level, with 1495 degrees of freedom, the critical value is 1.962 Since -7.895 < -1.962, we fail to reject the null hypothesis.

At the 5% significance level, there is no enough evidence to conclude that the price of a home depends on the number of bedrooms. = Using a 5% significance level, with 1495 degrees of freedom, the critical value is 1.962 Since -2.147 < -1.962, we fail to reject the null hypothesis. At the 5% significance level, there is no enough evidence to conclude that the price of a home depends on the number of bathrooms. Question 4 For 2 years old For 10 years old Difference in price = 1131.62 – 1040.96 = $90.672 95% Confidence interval is given by; 90.672 ± = 90.672 ± = 90.672 ± 0.664 = (90.008, 91.336).

The 95% Confidence interval implies that the difference in price of a home 2 years old and that which is 10 years old lies between 90.008 and 91.336. Question 5 : , : ,, , , or all are nonzero Test statistic: Critical value = = = 3.371 Computed value: = 6.8795e+010 6.8795e+010 < 3.371, we fail to reject the null hypothesis at 1% significance level and conclude that all and/or each of the variables, living area in square feet, age of home, number of bedrooms, number of bathrooms, do not have an influence on the price of a home.

The variables are not statistically significant. Question 6 Prior price; Expected price; Price difference = $21,360 Testing hypothesis; : : Test statistic: Critical value: = = Computed value: = = 9645.39 Since 9645.39 > , we reject the null hypothesis. At the 5% significance level, there is enough evidence to conclude that the increase in price is more than $20,000. Question 7 F-test; : : Test statistic: Critical value: = = 3.848 Livarea squared: Computed value: = 1.5e+13/1.3e+15 = 115.38 Since 115.38 > 3.848, we reject the null hypothesis.

At a 5% significance level, there is enough evidence to conclude that living area squared is a significant predictor of the price of a home. Age squared: Computed value: = 3.0e+14/1.3e+15 = 23.077 Since 23.077 > 3.848, we reject the null hypothesis. At a 5% significance level, there is enough evidence to conclude that age squared is a significant predictor of the price of a home. Question 8 From the model above, the log of the price of a home increases by 0.083 following a unit increase in living area square feet, it, however, decreases by 0.001 with a unit increase in living are square feet squared, decreases by 0.008 following a unit increase in age of a house, increases by 0.0001 following a unit increase in age squared and decreases by 0.075 following a unit increase in the number of bedrooms.

References

Freedman, D.H et al, 2007, Statistics, 4th edn, New York, W.W Norton & Company
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