Home Price Insights - Assignment Example

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.