Given that,Log(total primary energy consumption per capita)= 8.303336 + -.95999562*log(GDP per capita USD) + .0954567*square of log(GDP per capita USD) + -0.2392021*log(pump price for gasoline USD litre) + -.01857(average temperature for the coldest month in a year (in C)) + .0546541*log(land area in km2). Questions1)Describe if MLR 4 is likely to hold or not?2) Describe if MLR 5 is likely to hold or not?3)Interpret the coefficient on gas price and carry out a t test to determine the significance of the coefficient 4)When is it better to use non linear models than linear models and what types of relationships are best modelled with this ? Linear regressionNumber of obs131F(11, 119)143.13%3DProb > F0.0000%3DR-squared0.8941%3DRoot MSE.36402%3DRobustIntpes_pcСoef.Std. Err.P>|t|[95% Conf. Interval]Inypcpenn-.9599562.4732335-2.030.045-1.897006-.0229066Inypcpenn2.095456702561923.730.000.0447282.1461852In_gasprice-.2392021.0566569-4.220.000-.3513885-.1270157temp_coldest-.01857.005238-3.550.001-.0289417-.0081983temp_warmest.0166677.0160091.040.300-.0150318.0483671In_annualprecip.00639505399540.120.906-.1005213.1133114ffrents0028204.00285460.990.325-.0028319.0084728Inpop-.0469675.038338-1.230.223-.1228807.0289456Inland.0546541.03289581.660.099-.0104828.119791_Iincomegro_2_Iincomegro_3.1032733.22836480.450.652-.34891185554584-.0828519.1007342-0.820.412-.2823156.1166118_cons8.3033362.409653.450.0013.53198913.07468

Answer for 1): MLR 4 will holds good as all first 4 coefficients included in the above equation are…

Answered: Linear regression Number of obs 131 %3D…

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

Given that,

Log(total primary energy consumption per capita)= 8.303336 + -.95999562*log(GDP per capita USD) + .0954567*square of log(GDP per capita USD) + -0.2392021*log(pump price for gasoline USD litre) + -.01857(average temperature for the coldest month in a year (in C)) + .0546541*log(land area in km2).

Questions

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

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

3)Interpret the coefficient on gas price and carry out a t test to determine the significance of the coefficient

4)When is it better to use non linear models than linear models and what types of relationships are best modelled with this ?

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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