What is
Regression
Richard Cox
Objectives
Definition
What is Regression
Applications
Computers
and
Regression
Richard Cox
Department of Economics
Arizona State University
ECN 410, Applied Regression A
Regression Lecture 1 (Midterm 2)
We can to describe the linear relationship y=b0+b1X
Y is the dependent variable
X is the independent variable or explanatory variable
The slope is b1
The intercept is
Regression Lecutre 3 (Midterm 2)
The equation for the conditional mean of y given x is:
(y x)= 0 + 1 x
For an individual response or observation of y we can write:
y i= 0 + 1 x +e i
The
ei
are called
Life Expectancy compared to GDP Globally.
Series1
Series2
AVERAGE LIFE EXPECTANCY
(YEARS)
GDP vs Life Expectancy
100.00
80.00
60.00
40.00
20.00
0.00
$0.00
$20,000.00 $40,000.00 $60,000.00 $80,000.00 $
How the election would have turned out if my data was correct
Weighted percentage of sample based on voting probability
James G. Birney
0.13
Martin Van Buren
39.6
William Henry Harrison
60.27
Percenta
Fixed Effects Model of year, Population, Female density,
Population density between 35 and 44; 25 and 34, and GDP per
Capita on New iPhone price
Variables
year
FEMALE
POP(MIL)
POP3544
POP2534
GDPPC
Co
State
Alabama
Arkansas
Connecticut
Delaware
Georgia
Illinois
Indiana
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Mississippi
Missouri
New Hampshire
New Jersey
New York
North Carolina
Ohio
There is a table represents the data of GDP per Capital
and Life Expectancy around the 184 countries here.
According to the data, we can get
GDP of Per capital
Mean
Median
Standard Deviation
$14,347.9
Regression
Output
Richard Cox
Objectives
Output
Regression Output
Interpretation
Predicted
Values
Goodness of
Fit
Significance
Richard Cox
Department of Economics
Arizona State University
ECN 410, App
Hypothesis
Tests - Review
Richard Cox
Objectives
Background
Hypothesis Tests - Review
Hypothesis
Test
Richard Cox
Department of Economics
Arizona State University
ECN 410, Applied Regression Analysis
Model
Assumptions
and
Diagnostics
Richard Cox
Objectives
Model Assumptions and Diagnostics
Model
Assumptions
Normality
Constant
Variance
Correlated
Errors
Richard Cox
Department of Economics
Arizona S
Indicator
Variables and
Interaction
Terms
Richard Cox
Objectives
Indicator Variables and Interaction Terms
Indicator
Variables
Interactions
Seasonal
Effects
Richard Cox
Department of Economics
Arizona
Heteroskedasticity
& Related
Topics
Richard Cox
Objectives
Heteroskedasticity
Heteroskedasticity & Related Topics
Box-Cox
Weighted
Least Squares
Tests for
Heteroskedasticity
Richard Cox
Department of
Estimating
Elasticity
Richard Cox
Objectives
Log-log model
Estimating Elasticity
Elasticity
Transformations
Richard Cox
Department of Economics
Arizona State University
ECN 410, Applied Regression Ana
Data and
Regression
Richard Cox
Objectives
Data
Data and Regression
LEAST
SQUARES
Richard Cox
Department of Economics
Arizona State University
ECN 410, Applied Regression Analysis
1/ 17
Objectives
Dat
Regression Lecture 2 (Midterm 2)
SIMPLE REGRESSION
The regression line seems to fit the data well. The correlation coefficient, .95 (statistically significant), pvalue < .0001.
Intercept 60, populatio