Answer the 2 questions from the page with the question starting with "Give a practical interpretation..." A myopenmath.comMyOpenMathb Answered: Surveys can give useful information.. | bartleby1800160014001200SUMMARY OUTPUT1000800Regression StatisticsMultiple R0.908689165600R Square0.825715999400Adjusted R Square0.800818284200Standard Error166.9198439Observations9100020003000400050006000ANOVAdfMSFSignificance FRegression1924032.3089 924032.3 33.164330.000692097Residual7195035.64 27862.23Total1119067.949Coefficients Standard Errort StatP-valueLower 95%Upper 95% Lower 95.0% Upper 95.0%Intercept-48.51516784133.4610236 -0.363520.72695-364.1003409 267.070005 -364.1003409 267.0700052Sqft0.2819225410.048954677 5.758848 0.0006920.166163124 0.397681960.166163124 0.397681958What is the regression equation?Oy- 48.52 + 0.28x- 48.52 + 0.28x48.52х + 0.28y = - 48.52x + 0.28Interpret the y – intercept of the line.O On average, when x =0, a house costs – $48, 515.O On average, each increase in 1 square foot of a house decreases its asking price by $48, 515.On average, when x =0, a house has 282 square feet.On average, each increase in 1 square foot of a house increases its asking price by $282.O We should not interpret the y – intercept in this problem.O We should interpret the y – intercept, but none of the above are correct. A myopenmath.comMyOpenMathb Answered: Surveys can give useful information.. | bartlebyGive a practical interpretation of the coefficient of determination.O 82.57% of the differences in home asking price are caused by differences in square footage.O 82.57% of the sample variation in home asking price can be explained by the least-squares regressionline.We can predict the home asking price correctly 90.87% of the time using square footage in a least-squares regression line.90.87% of the sample variation in home asking price can be explained by the least-squares regressionline.We can predict the home asking price correctly 82.57% of the time using square footage in a least-squares regression line.90.87% of the differences in home asking price are caused by differences in square footage.Is it reasonable to use the regression equation to make a prediction for a 550 square foot house? Justify youranswer.O No, r does not indicate that there is a reasonable amount of correlation.O No, this prediction is far outside the scope of available data.No, the regression line does not fit the points reasonably well.O Yes, all of the criteria are met.Submit Question

Name: Answer the 2 questions from the page with the question starting with "
Uploaded: 2024-03-20T06:38:44.000Z
Channel: Bartleby
Description: Answer the 2 questions from the page with the question starting with "Give a practical interpretation..." A myopenmath.comMyOpenMathb Answered: Surveys can give useful information.. | bartleby1800160014001200SUMMARY OUTPUT1000800Regression Statistics

The value of R2 is 0.8257, which is high. That is, approximately 82.57% of the variation in the…

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Give a practical interpretation of the coefficient of determination. O 82.57% of the differences in home asking price are caused by differences in square footage. O 82.57% of the sample variation in home asking price can be explained by the least-squares regression line. O We can predict the home asking price correctly 90.87% of the time using square footage in a least- squares regression line. O 90.87% of the sample variation in home asking price can be explained by the least-squares regression line. We can predict the home asking price correctly 82.57% of the time using square footage in a least- squares regression line. O 90.87% of the differences in home asking price are caused by differences in square footage. Is it reasonable to use the regression equation to make a prediction for a 550 square foot house? Justify your answer. O No, r does not indicate that there is a reasonable amount of correlation. O No, this prediction is far outside the scope of available data. O No, the regression line does not fit the points reasonably well. Yes, all of the criteria are met.