Interpret the y-intercept of the line.O On average, each increase in 1 square foot of a house decreases its asking price by $48, 515.O On average, each increase in 1 square foot of a house increases its asking price by $282.On average, when x = 0, a house costs - $48, 515.O On average, when x = 0, a house has 282 square feet.OWe should not interpret the y intercept in this problem.-O We should interpret the y - intercept, but none of the above are correct.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 We can predict the home asking price correctly 90.87% of the time using square footage in a least-squares regression line.O90.87% of the sample variation in home asking price can be explained by the least-squares regressionline.O 82.57% of the sample variation in home asking price can be explained by the least-squares regressionline.O90.87% of the differences in home asking price are caused by differences in square footage.O We can predict the home asking price correctly 82.57% of the time using square footage in a least-squares regression line.Is it reasonable to use the regression equation to make a prediction for a 550 square foot house? Justifyyour answer.O Yes, all of the criteria are met.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. The output below shows a regression between the square footage and asking prfor nine homes for sale in Orange County, California in February 2010.1800160014001200SUMMARY OUTPUT1000800●Multiple R600R Square400Adjusted R Square200Standard ErrorObservations10002000300050006000ANOVASSMSFSignificance FRegression1924032.3089 924032.3 33.16433 0.000692097Residual7195035.64 27862.23Total81119067.949InterceptSqFtCoefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%-48.51516784 133.4610236 -0.36352 0.72695 -364.1003409 267.070005 -364.1003409 267.07000520.2819225410.166163124 0.3976819580.048954677 5.758848 0.000692 0.166163124 0.39768196What is the regression equation?Oy=- 48.52x +0.28Oy- 48.52 +0.28x=Oy=- 48.52x + 0.28Dŷ 48.52 +0.28x=-Interpret the y - intercept of the line.O On average, each increase in 1 square foot of a house decreases its asking price by $48, 515.O On average, each increase in 1 square foot of a house increases its asking price by $282.Regression Statistics=0.9086891650.8257159990.800818284166.91984399df00●●**4000

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Answered: The output below shows a regression…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter7: Analytic Trigonometry

Section7.6: The Inverse Trigonometric Functions

Problem 91E

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(e) Find the least-squares regression line treating square footage as the explanatory variable.
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A microcomputer manufacturer has developed a regression model relating his sales (y=$10,000s) with three independent variables. The three independent variables are price per unit(Price in $100s), advertising( ADV in $1000s) and the number of product lines (Lines). Part of the regression results is shown below. Coefficient Standard Error Intercept 1.0211 22.8752 Price(X1) -0.1524 0.1411 ADV (X2) 0.8849 0.2886 Lines(X3) -0.1463 1.5340 Source d.f. S.S. Regression 3 2708.61 Error 14 2840.51 Total 17 5549.12 What has been the sample size (n) for this analysis? Use the above results to find the estimated multiple…
A research department of an American automobile company wants to develop a model topredict gasoline mileage (measured in MPG) of the company’s vehicles by using theirhorsepower and weights (measured in pounds). To do this, it took a random sample of 50vehicles to perform a regression analysis as follows: SUMMARYOUTPUTRegression StatisticsMultiple R 0.865689R Square 0.749417Adjusted RSquare 0.738754Standard Error 4.176602Observations 50ANOVAdf SS MS FRegression a 2451.973702 1225.987 dResidual b 819.8680976 cTotal 49 3271.8418CoefficientsStandardError t StatIntercept 58.15708 2.658248208 21.87797Horsepower -0.11753 0.032643428 -3.60028Weight -0.00687 0.001401173 -4.90349(a) State the multiple regression equation. Interpret the meanings of the coefficients forhorsepower and weight.(b) Test the validity of this multiple regression equation at the significance level of 1%. Showyour reasoning.(c) The research department claims that the weight of the vehicle is negatively linearly related…
Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245 0.06838 0.06350 0.06847 t value2.259 0.884 -0.332 -0.946 Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: 0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 Which of the following is the correct interpretation of the Adjusted R-squared? The probability that our model…
Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245  0.06838 0.06350 0.06847 t value2.259 0.884  -0.332 -0.946 Pr(>|t|) 0.0264  0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: 0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 What is the estimate for the standard deviation of the residuals? 1.719 0.01983 -0.946 0.6342
Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245  0.06838 0.06350 0.06847 t value2.259 0.884  -0.332 -0.946 Pr(>|t|) 0.0264  0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 Which of the following is the correct conclusion for the F-test that was performed? There is strong evidence to…
Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245  0.06838 0.06350 0.06847 t value2.259 0.884  -0.332 -0.946 Pr(>|t|) 0.0264  0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 What is the test-statistic is used to test whether at least one of the explanatory variables is a significant predictor of…

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