Data were collected from a random sample of 200 home sales from a community in 2013. Let "Price" denotes the selling pri

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Data Were Collected From A Random Sample Of 200 Home Sales From A Community In 2013 Let Price Denotes The Selling Pri 1 (102.02 KiB) Viewed 13 times

Data were collected from a random sample of 200 home sales from a community in 2013. Let "Price" denotes the selling price (in $1000s), "BDR" denotes the number of bedrooms, "Bath" denotes the number of bathrooms, "Hsize" denotes the size of the house (in square feet), "Lsize" denote the lot size (in square feet), "Age" denotes the age of the house (in years), and "Poor" denote a binary variable that is equal to 1 if the condition of the house is reported as "poor." An estimated regression yields as follows, with standard errors reported in parentheses: I. II. III. Price = 109.7 +0.567BDR +26.9Bath +0.239Hsize + 0.005Lsize + 0.1Age - 56.9Poor (22.1) (1.23) (9.76) (0.021) (0.00072) (0.23) (12.23) R² = 0.85, SER=458 Examine whether "BDR" is a statistically significant determinant of "Price". (2 marks) A homeowner purchases 2500 square feet from an adjacent lot. Construct a 95% confident interval for the change in the value of her house. (2 marks) To test whether "BDR" and "Age" are redundant variables, the Likelihood Ratio (F-form) has been conducted. Given that the F-statistics for omitting "BDR and "Age" from the regression is F = 2.38, state clearly your null and alternative hypotheses for the redundant variable test. Then use the F-statistics to check the hypothesis at the 10% significance level, and comment on your results. (3 marks)