constant

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 28PPS
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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!
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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