constant
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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Question
100%
- Interpret the constant and its p-value.
![We model energy use per capita of 131 countries in a.given.year as follows:
Lutpes.rS = Bo + B,Lnypcpenn + B2Lnypcpenn2 + B3LnGasprice +
BĄLnAnnualprecip + B5TempColdest + B6TempWarmest + B,ffrents + B3LnPop +
B,LnLand + D_incomegroups +u
The variables are defined as follows:
Lutpes PS = log of total primary energy consumption per capita (ktoe)
Lnypepenu =log of GDP per capita (USD)
Lnypcpenn2 = square of log of GDP per capita (USD)
Ln gasprice = log of pump price for gasoline (USD/liter)
LUAnnualprecip log of annual precipitation (mm)
Tempacoldest= average temperature for the coldest month in a year (in C)
Temp.warmeşt= average temperature for the warmest month in a year (in C)
ffrents = Fossil Fuel Rents (% of GDP)
LnPop = log of population (in millions)
LnLand = log of land area (in km2)
LIncomegroup =refers to income groups "1", "2" and "3", low, mid and high income
countries.
**Log" always refers to natural logs or "In" here.
Linear regression
Number of obs
131
F(11, 119)
143.13
Prob > F
0.0000
R-squared
0.8941
Root MSE
.36402
Robust
Intpes_pc
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
Inypcpenn
Inypcpenn2
In_gasprice
-.9599562
4732335
-2.03
0.045
-1.897006
-.0229066
.0954567
.0256192
3.73
0.000
.0447282
.1461852
-.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
.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
Log referrers to natural logarithm!](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F253d4224-4653-41cc-b97d-690b921b919c%2F6f6e131f-411c-4d4d-b915-bc86c1325f50%2Fbrudb7u_processed.png&w=3840&q=75)
Transcribed Image Text:We model energy use per capita of 131 countries in a.given.year as follows:
Lutpes.rS = Bo + B,Lnypcpenn + B2Lnypcpenn2 + B3LnGasprice +
BĄLnAnnualprecip + B5TempColdest + B6TempWarmest + B,ffrents + B3LnPop +
B,LnLand + D_incomegroups +u
The variables are defined as follows:
Lutpes PS = log of total primary energy consumption per capita (ktoe)
Lnypepenu =log of GDP per capita (USD)
Lnypcpenn2 = square of log of GDP per capita (USD)
Ln gasprice = log of pump price for gasoline (USD/liter)
LUAnnualprecip log of annual precipitation (mm)
Tempacoldest= average temperature for the coldest month in a year (in C)
Temp.warmeşt= average temperature for the warmest month in a year (in C)
ffrents = Fossil Fuel Rents (% of GDP)
LnPop = log of population (in millions)
LnLand = log of land area (in km2)
LIncomegroup =refers to income groups "1", "2" and "3", low, mid and high income
countries.
**Log" always refers to natural logs or "In" here.
Linear regression
Number of obs
131
F(11, 119)
143.13
Prob > F
0.0000
R-squared
0.8941
Root MSE
.36402
Robust
Intpes_pc
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
Inypcpenn
Inypcpenn2
In_gasprice
-.9599562
4732335
-2.03
0.045
-1.897006
-.0229066
.0954567
.0256192
3.73
0.000
.0447282
.1461852
-.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
.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
Log referrers to natural logarithm!
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