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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.

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.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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A myopenmath.com MyOpenMath b Answered: Surveys can give useful information.. | bartleby 1800 1600 1400 1200 SUMMARY OUTPUT 1000 800 Regression Statistics Multiple R 0.908689165 600 R Square 0.825715999 400 Adjusted R Square 0.800818284 200 Standard Error 166.9198439 Observations 9 1000 2000 3000 4000 5000 6000 ANOVA df MS F Significance F Regression 1 924032.3089 924032.3 33.16433 0.000692097 Residual 7 195035.64 27862.23 Total 1119067.949 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -48.51516784 133.4610236 -0.36352 0.72695 -364.1003409 267.070005 -364.1003409 267.0700052 Sqft 0.281922541 0.048954677 5.758848 0.000692 0.166163124 0.39768196 0.166163124 0.397681958 What is the regression equation? Oy - 48.52 + 0.28x - 48.52 + 0.28x 48.52х + 0.28 y = - 48.52x + 0.28 Interpret 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.
Transcribed Image Text:A myopenmath.com MyOpenMath b Answered: Surveys can give useful information.. | bartleby 1800 1600 1400 1200 SUMMARY OUTPUT 1000 800 Regression Statistics Multiple R 0.908689165 600 R Square 0.825715999 400 Adjusted R Square 0.800818284 200 Standard Error 166.9198439 Observations 9 1000 2000 3000 4000 5000 6000 ANOVA df MS F Significance F Regression 1 924032.3089 924032.3 33.16433 0.000692097 Residual 7 195035.64 27862.23 Total 1119067.949 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -48.51516784 133.4610236 -0.36352 0.72695 -364.1003409 267.070005 -364.1003409 267.0700052 Sqft 0.281922541 0.048954677 5.758848 0.000692 0.166163124 0.39768196 0.166163124 0.397681958 What is the regression equation? Oy - 48.52 + 0.28x - 48.52 + 0.28x 48.52х + 0.28 y = - 48.52x + 0.28 Interpret 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.com MyOpenMath b Answered: Surveys can give useful information.. | bartleby 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. 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 regression line. 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 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. No, the regression line does not fit the points reasonably well. O Yes, all of the criteria are met. Submit Question
Transcribed Image Text:A myopenmath.com MyOpenMath b Answered: Surveys can give useful information.. | bartleby 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. 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 regression line. 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 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. No, the regression line does not fit the points reasonably well. O Yes, all of the criteria are met. Submit Question
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