Analyse the relationship between energy use per capita and GDP pc based on these insights. How does energy use per capita change with GDPpc? Prepare a graph!
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
- Analyse the relationship between energy use per capita and GDP pc based on these insights. How does energy use per capita change with GDPpc? Prepare a graph!
![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%2F71ccfa35-05c5-44de-849c-9961ed79488d%2Fpbdfumg_processed.png&w=3840&q=75)
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