Soluation+to+8.2 - = = < 0.0036 0.037 | | 0.09730 1.96....

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2. (a) According to the regression results in column (1), the house price is expected to increase by 21% ( = 100% × 0.00042 × 500 ) with an additional 500 square feet and other factors held constant. The 95% confidence interval for the percentage change is 100% × 500 × (0.00042 ± 1.96 × 0.000038) = [17.276% to 24.724%]. (b)Because the regressions in columns (1) and (2) have the same dependent variable, 2 R can be used to compare the fit of these two regressions. The log-log regression in column (2) has the higher 2 , R so it is better so use ln( Size ) to explain house prices. (c)The house price is expected to increase by 7.1% ( = 100% × 0.071 × 1). The 95% confidence interval for this effect is 100% × (0.071 ± 1.96 × 0.034) = [0.436% to 13.764%]. (d)The house price is expected to increase by 0.36% (100% × 0.0036 × 1 = 0.36%) with an additional bedroom while other factors are held constant. The effect is not statistically significant at a 5% significance level:
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Unformatted text preview: = = < 0.0036 0.037 | | 0.09730 1.96. t Note that this coefficient measures the effect of an additional bedroom holding the size of the house constant. (e)The quadratic term ln( Size ) 2 is not important. The coefficient estimate is not statistically significant at a 5% significance level: = = < 0.0078 0.14 | | 0.05571 1.96. t (f) The house price is expected to increase by 7.1% ( = 100% 0.071 1) when a swimming pool is added to a house without a view and other factors are held constant. The house price is expected to increase by 7.32% ( = 100% (0.071 1 + 0.0022 1) ) when a swimming pool is added to a house with a view and other factors are held constant. The difference in the expected percentage change in price is 0.22%. The difference is not statistically significant at a 5% significance level: = = < 0.0022 0.10 | | 0.022 1.96. t...
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This note was uploaded on 02/15/2010 for the course REGRESSION IRAS10 taught by Professor Unknown during the Spring '10 term at Rutgers.

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