Later, Ms. McDonald ran regression analysis in two different ways by transforming the data. Each is described below with appropriate computer output and graphics. II. Log(Price) versus Age: (Computer output, scatterplot, residual plot) Coef 1.43723 SE Coef 0.02881 0. 003569 Predictor 49. ৪9 0.000 -25.40 Constant OR0652 0.000

Later, Ms. McDonald ran regression analysis in two different ways by transforming the data. Each is described below with appropriate computer output and graphics. II. Log(Price) versus Age: (Computer output, scatterplot, residual plot) Coef 1.43723 SE Coef 0.02881 0. 003569 Predictor 49. ৪9 0.000 -25.40 Constant OR0652 0.000

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

Question: Give the equation of the model you chose in (b ) using the transformed variables .

III. Log(Price) versus Log(Age)
Predictor
Constant
Coef
1.77448
SE Coef
0.07822
22.69
0.000
Log Age
-1.23359
0.09209
-13.40
0.000
S - 0.0867773
91.84
R-Sq - 92.3%
R-Sq(adj) -

Later, Ms. McDonald ran regression analysis in two different ways by transforming the data. Each is
described below with appropriate computer output and graphics.
II. Log(Price) versus Age: (Computer output, scatterplot, residual plot)
SE Coef
0.02881
-0.090652 0.003569
Coef
1.43723
Predictor
T
49.89 0.000
-25.40 0.000
Constant
Age
S = 0.0470892
97.65
R-Sq = 97.74
R-Sq(adj) =
梦 $
a
hp
f12
fg
f6

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