JMP output appears below for simple linear regression with data from the price, y (in $1000), of n = 28 Seattle home prices. The explanatory variable is the total number of square feet in the home. - Response Price ($000) Regression Plot 600 500 400 300 200 - Distributions 1000 1500 2000 2500 3000 3500 Square Feet Square Feet Summary of Fit RSquare RSquare Adi Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.560503 0.543599 356.8214 28 Analysis of Variance Sum of 1000 1500 2000 2500 3000 3500 Source DF F Ratio Squares Mean Square 1 249200.64 Model 249201 33.1585 - Summary Statistics Error 26 Prob > F C. Total 27 <.0001 Mean 1923.1071 Std Dev 653.11574 v Parameter Estimates Std Er Mean 123.42727 Term Estimate Std Error t Ratio Prob>lt Upper 95% Mean 2176.359 Lower 95% Mean 1669.8553 Intercept Square Feet 0.1470966 0.025545 73.938964 51.78554 1.43 0.1653 5.76 <.0001 28 (ooos) eoad

JMP output appears below for simple linear regression with data from the price, y (in $1000), of n = 28 Seattle home prices. The explanatory variable is the total number of square feet in the home. - Response Price ($000) Regression Plot 600 500 400 300 200 - Distributions 1000 1500 2000 2500 3000 3500 Square Feet Square Feet Summary of Fit RSquare RSquare Adi Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.560503 0.543599 356.8214 28 Analysis of Variance Sum of 1000 1500 2000 2500 3000 3500 Source DF F Ratio Squares Mean Square 1 249200.64 Model 249201 33.1585 - Summary Statistics Error 26 Prob > F C. Total 27 <.0001 Mean 1923.1071 Std Dev 653.11574 v Parameter Estimates Std Er Mean 123.42727 Term Estimate Std Error t Ratio Prob>lt Upper 95% Mean 2176.359 Lower 95% Mean 1669.8553 Intercept Square Feet 0.1470966 0.025545 73.938964 51.78554 1.43 0.1653 5.76 <.0001 28 (ooos) eoad

Name: See attached image for the introduction. How much variation in price h
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Description: See attached image for the introduction. How much variation in price has been explained by the total number of square feet? JMP output appears below for simple linear regression with data from the price, y (in $1000), ofn = 28 Seattle home prices. Th

MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

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Question

See attached image for the introduction.

How much variation in price has been explained by the total number of square feet?

JMP output appears below for simple linear regression with data from the price, y (in $1000), of
n = 28 Seattle home prices. The explanatory variable is the total number of square feet in the
home.
- Response Price ($000)
Regression Plot
600
500
400
300
200
- Distributions
1000
1500
2000
2500
3000
3500
Square Feet
Square Feet
Summary of Fit
RSquare
RSquare Adi
Root Mean Square Error
Mean of Response
Observations (or Sum Wgts)
0.560503
0.543599
356.8214
28
Analysis of Variance
Sum of
1000
1500
2000
2500
3000
3500
Source
DF
F Ratio
Squares Mean Square
1 249200.64
Model
249201 33.1585
- Summary Statistics
Error
26
Prob > F
C. Total
27
<.0001
Mean
1923.1071
Std Dev
653.11574
v Parameter Estimates
Std Er Mean
123.42727
Term
Estimate Std Error t Ratio Prob>lt
Upper 95% Mean 2176.359
Lower 95% Mean 1669.8553
Intercept
Square Feet 0.1470966 0.025545
73.938964 51.78554
1.43 0.1653
5.76 <.0001
28
(ooos) eoad

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