Linear regression Number of obs 131 F(11, 119) 143.13 %3D Prob > F 0.0000 %3D R-squared 0.8941 %3D Root MSE .36402 %3D Robust Intpes_pc Coef. Std. Err. P>|t| [95% Conf. Interval] Inypcpenn -.9599562 .4732335 -2.03 0.045 -1.897006 -.0229066 Inypcpenn2 In_gasprice temp_coldest .0954567 .0256192 3.73 0.000 .0447282 .1461852 -.2392021 .0566569 -4.22 0.000 -.3513885 -.1270157 -.01857 .005238 -3.55 0.001 -.0289417 -.0081983 temp_warmest .0166677 .016009 1.04 0.300 -.0150318 .0483671 In_annualprecip .006395 .0539954 0.12 0.906 -.1005213 .1133114 ffrents 0028204 .0028546 0.99 0.325 -.0028319 .0084728 Inpop -.0469675 038338 -1.23 0.223 -.1228807 .0289456 Inland .0546541 .0328958 1.66 0.099 -.0104828 .119791 _Iincomegro_2 .1032733 .2283648 0.45 0.652 -.3489118 .5554584 _Iincomegro_3 -.0828519 .1007342 -0.82 0.412 -.2823156 .1166118 _cons 8.303336 2.40965 3.45 0.001 3.531989 13.07468

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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Given that 

Log(total primary energy consumption per capita)=8.303336 +-.9599562*log(GDP per capita USD) + .0954567*square of log(GDP per capita) + -.2392021*log(pump price for gasoline USD) + .006395*log(annual precipitation mm) 

Questions

1)Describe if MLR 5 is likely to hold or not ?

2)Describe if MLR 4 is likely to hold or not ?

Linear regression
Number of obs
131
F(11, 119)
143.13
%3D
Prob > F
0.0000
%3D
R-squared
0.8941
%3D
Root MSE
.36402
%3D
Robust
Intpes_pc
Сoef.
Std. Err.
P>|t|
[95% Conf. Interval]
Inypcpenn
-.9599562
.4732335
-2.03
0.045
-1.897006
-.0229066
Inypcpenn2
.0954567
0256192
3.73
0.000
.0447282
.1461852
In_gasprice
-.2392021
.0566569
-4.22
0.000
-.3513885
-.1270157
temp_coldest
-.01857
.005238
-3.55
0.001
-.0289417
-.0081983
temp_warmest
.0166677
.016009
1.04
0.300
-.0150318
.0483671
In_annualprecip
.006395
0539954
0.12
0.906
-.1005213
.1133114
ffrents
0028204
.0028546
0.99
0.325
-.0028319
.0084728
Inpop
-.0469675
.038338
-1.23
0.223
-.1228807
.0289456
Inland
.0546541
.0328958
1.66
0.099
-.0104828
.119791
_Iincomegro_2
_Iincomegro_3
.1032733
.2283648
0.45
0.652
-.3489118
5554584
-.0828519
.1007342
-0.82
0.412
-.2823156
.1166118
_cons
8.303336
2.40965
3.45
0.001
3.531989
13.07468
Transcribed Image Text:Linear regression Number of obs 131 F(11, 119) 143.13 %3D Prob > F 0.0000 %3D R-squared 0.8941 %3D Root MSE .36402 %3D Robust Intpes_pc Сoef. Std. Err. P>|t| [95% Conf. Interval] Inypcpenn -.9599562 .4732335 -2.03 0.045 -1.897006 -.0229066 Inypcpenn2 .0954567 0256192 3.73 0.000 .0447282 .1461852 In_gasprice -.2392021 .0566569 -4.22 0.000 -.3513885 -.1270157 temp_coldest -.01857 .005238 -3.55 0.001 -.0289417 -.0081983 temp_warmest .0166677 .016009 1.04 0.300 -.0150318 .0483671 In_annualprecip .006395 0539954 0.12 0.906 -.1005213 .1133114 ffrents 0028204 .0028546 0.99 0.325 -.0028319 .0084728 Inpop -.0469675 .038338 -1.23 0.223 -.1228807 .0289456 Inland .0546541 .0328958 1.66 0.099 -.0104828 .119791 _Iincomegro_2 _Iincomegro_3 .1032733 .2283648 0.45 0.652 -.3489118 5554584 -.0828519 .1007342 -0.82 0.412 -.2823156 .1166118 _cons 8.303336 2.40965 3.45 0.001 3.531989 13.07468
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