4. A study was conducted to investigate the relationship between the size of a house (in squarefeet) and the selling price of a house (in dollars). The response variable is price in dollars, and wewant to study if the covariate of the square footage helps explain the response.A random sample of 522 houses was used, and the linear regression output from R is below.price sqft, data = house)1m (formula=Coefficients:Estimate Std. Error t value Pr(>|t|)(Intercept) -81432.946 11551.846 -7.049 5.74e-12 ***sqft158.9504.875 32.605 <2e-16 ***Residual standard error: 79120 on 520 degrees of freedomMultiple R-squared: 0.6715, Adjusted R-squared: 0.6709a. Write out the estimated linear equation. What is the estimated expected selling price of ahouse that is 2000 square feet?b. Does the intercept have a useful interpretation in this study? Why or why not.c. Interpret the slope estimate in context of the model.

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MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

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variate of the square footage helps explain the response. 2 houses was used, and the linear regression output from R sqft, data = house) te Std. Error t value Pr (>|t|) 46 11551.846 50 -7.049 5.74e-12 *** 4.875 32.605 < 2e-16 ***

variate of the square footage helps explain the response. 2 houses was used, and the linear regression output from R sqft, data = house) te Std. Error t value Pr (>|t|) 46 11551.846 50 -7.049 5.74e-12 * 4.875 32.605 < 2e-16 *