Homework Help is Here – Start Your Trial Now!
Math
Statistics
The output below shows a regression between the square footage and asking price (in thousands of dollars) for nine homes for sale in Orange County, California in February 2010. 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 Standard Error 166.9198439 200 Observations 9 1000 2000 3000 4000 5000 6000 ANOVA df SS Significance F 924032.3089 924032.3 33.16433 0.000692097 MS F Regression Residual 195035.64 27862.23 Total 8 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? y = 48.52x + 0.28 48.52 + 0.28x y = – 48.52 + 0.28x 48.52x +0.28 Interpret the y – intercept of the line. 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

The output below shows a regression between the square footage and asking price (in thousands of dollars) for nine homes for sale in Orange County, California in February 2010. 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 Standard Error 166.9198439 200 Observations 9 1000 2000 3000 4000 5000 6000 ANOVA df SS Significance F 924032.3089 924032.3 33.16433 0.000692097 MS F Regression Residual 195035.64 27862.23 Total 8 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? y = 48.52x + 0.28 48.52 + 0.28x y = – 48.52 + 0.28x 48.52x +0.28 Interpret the y – intercept of the line. 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

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2CYU
See similar textbooks
icon
Related questions
Question
The output below shows a regression between the square footage and asking price (in thousands of dollars) for nine homes for sale in Orange County, California in February 2010. 1800 1600 1400 1200 SUMMARY OUTPUT 1000 Regression Statistics 800 Multiple R 0.908689165 600 R Square 0.825715999 400 Adjusted R Square 0.800818284 Standard Error 166.9198439 200 Observations 1000 2000 3000 4000 5000 6000 ANOVA df Significance F 924032.3089 924032.3 33.16433 0.000692097 MS Regression 1 Residual 7 195035.64 27862.23 Total 8 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? y = 48.52x + 0.28 - 48.52 + 0.28x y = 48.52 + 0.28x 48.52x +0.28 Interpret the y – intercept of the line. 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.
Transcribed Image Text:The output below shows a regression between the square footage and asking price (in thousands of dollars) for nine homes for sale in Orange County, California in February 2010. 1800 1600 1400 1200 SUMMARY OUTPUT 1000 Regression Statistics 800 Multiple R 0.908689165 600 R Square 0.825715999 400 Adjusted R Square 0.800818284 Standard Error 166.9198439 200 Observations 1000 2000 3000 4000 5000 6000 ANOVA df Significance F 924032.3089 924032.3 33.16433 0.000692097 MS Regression 1 Residual 7 195035.64 27862.23 Total 8 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? y = 48.52x + 0.28 - 48.52 + 0.28x y = 48.52 + 0.28x 48.52x +0.28 Interpret the y – intercept of the line. 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.
On rage, when x = U, a house costs –$48, 515. On average, each increase in 1 square foot of a house decreases its asking price by $48, 515. We should not interpret the y – intercept in this problem. We should interpret the y – intercept, but none of the above are correct. Give a practical interpretation of the coefficient of determination. 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. 82.57% 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. No, r does not indicate that there is a reasonable amount of correlation. No, this prediction is far outside the scope of available data. No, the regression line does not fit the points reasonably well. Yes, all of the criteria are met.
Transcribed Image Text:On rage, when x = U, a house costs –$48, 515. On average, each increase in 1 square foot of a house decreases its asking price by $48, 515. We should not interpret the y – intercept in this problem. We should interpret the y – intercept, but none of the above are correct. Give a practical interpretation of the coefficient of determination. 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. 82.57% 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. No, r does not indicate that there is a reasonable amount of correlation. No, this prediction is far outside the scope of available data. No, the regression line does not fit the points reasonably well. Yes, all of the criteria are met.
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 2 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Similar questions
Recommended textbooks for you
Glencoe Algebra 1, Student Edition, 9780079039897…
Glencoe Algebra 1, Student Edition, 9780079039897…
Algebra
ISBN:
9780079039897
Author:
Carter
Publisher:
McGraw Hill
Linear Algebra: A Modern Introduction
Linear Algebra: A Modern Introduction
Algebra
ISBN:
9781285463247
Author:
David Poole
Publisher:
Cengage Learning
Functions and Change: A Modeling Approach to Coll…
Functions and Change: A Modeling Approach to Coll…
Algebra
ISBN:
9781337111348
Author:
Bruce Crauder, Benny Evans, Alan Noell
Publisher:
Cengage Learning
SEE MORE TEXTBOOKS