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3.
“Stargazer is a new R package
that creates LaTeX code for well-formatted
regression tables, with
multiple models side-by-side, as
well as for summary statistics
tables. It can also output the
content of data frames directly
into LaTeX.”
If you want to go further in this
area you probably need to learn
some LaTeX.
LaTeX is the industry standard
for type set t ing tec hni cal
documents
4.
First you need load a TeX Package:
Then it is useful to have IDE:
If you do not like LyX:
MacTeX http://tug.org/mactex/
MikTeX http://miktex.org/
http://www.lyx.org/
http://en.wikipedia.org/wiki/Comparison_of_TeX_editors
6.
Install the Stargazer Package:
Stargazer is a going to give you LaTeX output which
you can paste and compile into a Table
7.
Install the Stargazer Package:
Stargazer is a going to give you LaTeX output which
you can paste and compile into a Table
8.
This is a very helpful website that you should consult
regularly (and follow on FB ) for all things
9.
Lets Consult the ‘Stargazer’ Tutorial
http://www.r-bloggers.com/stargazer-package-for-beautiful-
latex-tables-from-r-statistical-models-output/
10.
The ‘attitude’ data frame (which should be available with your
default installation of R)
Lets take a quick peak:
http://www.r-bloggers.com/stargazer-package-for-beautiful-
latex-tables-from-r-statistical-models-output/
11.
Applying the basic
command to the dataframe
get you a set of LaTeX
output as shown to the left
12.
(1) Open http://www.lyx.org/
(2) File > New
(3) Start a LaTeX Box
(4) Cut from R output + Then Paste the LaTeX Code in box
starting here: ending here:
(5) Then Hit this Button to See Output
14.
Download this as an alternative because it allows you to easily
override errors and push through to get a regression table
15.
Okay Lets Run a Few Regression Models
Now Lets Generate the LaTeX Code
16.
Put this above
documentclass{article}
begin{document}
The
Resulting
LaTeX
Code
Put this below
end{document}
17.
These Tables
are Typically
How
Regression
Output is
Reported
18.
A Quick Primer on Interpreting
Regression Output
19.
http://dss.princeton.edu/training/
We are Working Through Selected Examples From this Fabulous
Resource Created by Oscar Torres-Reyna @ Princeton
20.
A Quick Primer on Interpreting
Regression Output
How Should We Discuss the
R e l a t i o n s h i p B e twe e n
Independent Variables and
Dependent Variables?
We Think in a Ceteris
paribus Manner
(i.e. All Other Things
Being Equal)
21.
These are
dummy
variables for
the respective
regions
22.
How Should We Discuss the
R e l a t i o n s h i p B e twe e n
Independent Variables and
Dependent Variables?
We Think in a Ceteris paribus
Manner (All Other Things Being
Equal)
23.
How Should We Discuss the
R e l a t i o n s h i p B e twe e n
Independent Variables and
Dependent Variables?
We Think in a Ceteris paribus
Manner (All Other Things Being
Equal)
The Implies We Are Interested in a Thought Experiment:
If We Were To Change Some Independent Variable by 1 Unit
-- What Would Be the Corresponding Effect on Y?
This Should be Considered Both in the Case of a
Regular Variable and a Dummy/Indicator Variable
24.
The Implies We Are Interested in a
Thought Experiment:
If We Were To Change Some
Independent Variable by 1 Unit --
What Would Be the
Corresponding Effect on Y?
This Should be Considered Both in the Case of a Regular Variable
and a Dummy/Indicator Variable
Start with “College” Variable -
3.38 is the Beta Coefficient on College
25.
Thinking in a Ceteris Paribus
Manner
Start with “College” Variable -
3.38 is the Beta Coefficient on College
26.
Thinking in a Ceteris Paribus
Manner
Start with “College” Variable -
3.38 is the Beta Coefficient on College
Y = B0 + ( B1 * (X1) ) – ( B2 * (X2) ) + ( B3 * (X3) ) + ( B4 * (X4)) + ( B5 * (X5) ) +
( B6 * (X6) ) + ( B7 * (X7) ) + ( B8 * (X8) ) + ε
27.
Thinking in a Ceteris Paribus
Manner
Start with “College” Variable -
3.38 is the Beta Coefficient on College
Y = B0 + ( B1 * (X1) ) – ( B2 * (X2) ) + ( B3 * (X3) ) + ( B4 * (X4)) + ( B5 * (X5) ) +
( B6 * (X6) ) + ( B7 * (X7) ) + ( B8 * (X8) ) + ε
csat = 786.30 – 0.004*expense – 3.02*percent + 0.48*income + 2.30*high + 3.38*college
+ 76.84*1 if region2=true + 27.26* 1 if region3=true + 34.35* 1 if region4=true + ε
28.
Thinking in a Ceteris Paribus
Manner
Start with “College” Variable -
3.38 is the Beta Coefficient on College
Y = B0 + ( B1 * (X1) ) – ( B2 * (X2) ) + ( B3 * (X3) ) + ( B4 * (X4)) + ( B5 * (X5) ) +
( B6 * (X6) ) + ( B7 * (X7) ) + ( B8 * (X8) ) + ε
csat = 786.30 – 0.004*expense – 3.02*percent + 0.48*income + 2.30*high + 3.38*college
+ 76.84*1 if region2=true + 27.26* 1 if region3=true + 34.35* 1 if region4=true + ε
All Else Equal - For Each 1 Unit Change in
“College” there is a corresponding 3.38 Unit
Change in “Csat”
29.
Thinking in a Ceteris Paribus
Manner
Y = B0 + ( B1 * (X1) ) – ( B2 * (X2) ) + ( B3 * (X3) ) + ( B4 * (X4)) + ( B5 * (X5) ) +
( B6 * (X6) ) + ( B7 * (X7) ) + ( B8 * (X8) ) + ε
csat = 786.30 – 0.004*expense – 3.02*percent + 0.48*income + 2.30*high + 3.38*college
+ 76.84*1 if region2=true + 27.26* 1 if region3=true + 34.35* 1 if region4=true + ε
76.84 if region =2 is True
27.26 if region =3 is True
34.35 if region =4 is True
Otherwise if if region =1 is True
we retain the Default Coefficient Estimates
Notice that
there are
really 4
Separate
Models
Here
30.
Non Linearities and
Transformations
Okay This is the Interpretation in the Linear Case
Sometimes Data Does not Neatly Conform to Our Linearity
Assumption
From a Model / Prediction Standpoint, Failure to Adjust to Account
for Non-Linearity might lead to Type II Error
31.
Non Linearities and
Transformations
Simple Linear Model
Y = B0 + (B1 * (X1)) + ε
Polynomial Regression Model
_
Y = B0 (B1 * (X1)2) + ε
In this Case of X^2
this is a Negative quadratic Function
“Lin- Log” Model
Y = B0 + (B1 * (ln X1)) + ε
Dependent Variable is Linear
1 or More Indep Var is Log
32.
How Do We Determine that a
Transformation is Appropriate?
These Are the Variables From Our Model
33.
How Do We Determine that a
Transformation is Appropriate?
Mean
composite
SAT
score
Per pupil
expenditures
prim&sec
% HS
graduates
taking
SAT
Median
household
income,
$1,000
%
adults
HS
diploma
% adults
college
degree
Take
A
Look
at
this
34.
How Do We Determine that a
Transformation is Appropriate?
Plot the Relationship
Between X & Y and
Observe the
Relationship
L e t s L o o k at
“ C s a t ” a n d
“Percent”
35.
How Do We Determine that a
Transformation is Appropriate?
R e l a t i o n s h i p
looks non-Linear
-- “Curvilinear”
Aka
Curve
+
Line
36.
How Do We Determine that a
Transformation is Appropriate?
-300 -200 -100 0 100
Augmented component plus residual
0 20 40 60 80
% HS graduates taking SAT
The command acprplot (augmented
component-plus-residual plot) provides
a graphical way to examine linearity.
Run this command after running a
regression
regress csat percent
This is a Stata Command
There is an alternative in R
It Appears that a Polynomial (Quadratic) relationship probably exists
thus, it makes sense to add a square version of it
37.
How Do I Generate
a New Variable?
We Want to Generate a New Variable Called
“Percent Squared”
Here is How We Do This In R
38.
Okay Lets Feed This Back Into the
Regression Model
39.
Now We
Have Added
“Percent
Squared”
to the Model
R^2 is not
everything but
we can see the
impact of
alternative
specification of
the model on
R^2
40.
Other Transformations
We Might Have A Variable Whose Relationship was Non-Linear
and follow a Natural Log
Include in the Model and Look at the Corresponding Model Fit
NOTE YOU CAN ALSO TRANSFORM THE DEPENDENT VARIABLE
ln Y = B0 + (B1 * (X1)) + ε
41.
How To Understand
Log Transformed
Regression Output
Dependent Variable is not in Log Form, Independent Variable is in Log Form (aka Linear-Log)
“A 1 Percent Change in the Independent Variable is associated with a (.01* Beta) Change in
the Dependent Variable”
Dependent Variable is in Log Form, Independent Variable in Not in Log Form (aka Log-Linear)
“A Change in the Independent Variable by 1 unit is associated with a (100percent * Beta)
Change in the Dependent Variable”
Dependent Variable is in Log Form, Independent Variable in Not in Log Form (aka Log-Log)
“A Change in the Independent Variable by 1 unit is associated with a (Beta % Change) in the
Dependent Variable”
43.
Interaction Terms
Sometime X1 Impacts Y and X2 Impacts Y but when both X1 and
X2 are Present there is an additional impact (+ or - ) beyond
Y = B0 + (B1 * (X1)) + (B2 * (X2)) + (B3 * (X3)(X2) + ε
Income = B0 + B1 *Gender + B2 * Education + B3* Gender * Education + ε
Our Beta Three Term Gives Us the Effect of Gender and Education
Together
Assuming Gender is Binary in the Model - The Interaction Will
Explore the Differential Effect on Income By Gender
44.
A Visual Display of
Interaction Terms
Image From - Thomas Brambor, William Roberts Clark & Matt Golder, Understanding Interaction Models:
Improving Empirical Analyses, 14 Political Analysis 63 (2005)
45.
For More on
Interaction Terms ...
Thomas Brambor, William Roberts Clark & Matt Golder, Understanding Interaction
Models: Improving Empirical Analyses, 14 Political Analysis 63 (2005)
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