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HW exer-regr-2 var

# HW exer-regr-2 var - statistic and critical value and...

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Applied Business Research Tools Two-Variable Regression Exercise You are planning to buy a 1000 sq. foot house in one of two subdivisions. The regression results from these two subdivisions are presented below. PRICE is the sales price of a house and lot in dollars and SQFT is the house's size in sq. feet . The first numbers in parentheses are t-statistics; the second numbers are p-values. subdivision A: PRICE = 42,000 + 30.0 SQFT (3.72) (7.67) t-stats (0.02) (0.003) p-values F = 18.76 (0.001) R 2 = .723 n=274 subdivision B: PRICE = 25,000 + 35.0 SQFT (4.76) (5.32) (0.008) (0.005) F = 21.73 (0.0007) R 2 = .718 n=122 1. What is the proportion of variation in housing prices explained by house size in each model? How do you know? 2. For sub-division A, test the hypothesis (at 5%) that larger houses sell for higher prices. Write the null and alternative hypotheses, determine the appropriate test-

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Unformatted text preview: statistic and critical value, and conclude. 3. For subdivision B, test the hypothesis that an increase in size of one-square foot is associated with an increase in price of \$45 against the alternative that it is not. Write the null and alternative hypotheses, determine the appropriate test-statistic and critical value, and conclude. ( Note: the estimated standard error of the estimated slope coefficient is 6.579). 4. How did I know that the estimated standard error of the estimated slope coefficient is 6.579 in #3? 5. What will each (1000-square foot) house cost if it is priced as the regressions say it should be? Show your work. 6. Which house should you buy if you are planning to add 1000 sq. feet to the house and then sell it? This addition will cost \$33 per sq. foot. Explain your answer....
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HW exer-regr-2 var - statistic and critical value and...

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