crj 604 introduction - arizona state university linear regression (next week) 3. multiple regression...
TRANSCRIPT
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CRJ 604 Introduction
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Do babies delay crawling
when it’s cold outside?
Here’s the raw data for 12 birth-month groups of babies:
Temperature in 6th month Average crawling month
66 6.86
73 7.02
72 6.84
63 7.32
52 6.58
39 7.23
33 7.73
30 7.55
33 7.78
37 7.69
48 7.69
57 7.53
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Do babies delay crawling
when it’s cold outside?
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Do babies delay crawling
when it’s cold outside?
6.5
77
.58
cra
wl
30 40 50 60 70temp6
“scatter crawl temp6” produces this graph
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Do babies delay crawling
when it’s cold outside?
“twoway (scatter crawl temp6) (lfit crawl temp6)” adds a line of “best fit”
6.5
77
.58
30 40 50 60 70temp6
crawl Fitted values
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Do babies delay crawling
when it’s cold outside?
-This line of best fit is what linear regression does. It represents our best estimate of
the relationship between our two variables.
- To obtain a quantification of this line in stata: “reg crawl temp6”
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Do babies delay crawling
when it’s cold outside?
This regression produces the following relationship between
temperature and crawling month:
-Month start crawling = 8.21-.0177*(avg. temperature)
-So a 1 point decrease in temperature is associated with babies
delaying crawling .018 months (about half a day).
- With a 10 point increase in temp, baby should crawl 5 days
earlier.
-Phoenix babies must be super fast crawlers . . .
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Goals
From the syllabus:
1. Students will understand the theoretical issues involved in the basic linear regression model in its simplest form (bivariate regression) and multivariate form (multiple regression).
2. Students will also acquire fluency with the computer application (using Stata) of bivariate and multivariate regressions and probit/logit models, including testing assumptions and applying fixes.
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Plan for course
1. Overview of course (today)
2. Simple linear regression (next week)
3. Multiple regression
4. Violations of regression assumptions
5. Limited dependent variables
6. Propensity score matching
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Textbook: Wooldridge’s
Introductory Econometrics
“Econometrics” is simply the
term economists use for
statistics.
- Crime examples used in
book. I/we will
supplement these with
more examples.
- Data sets / solutions to
odd-numbered exercises
are online
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Online resources, this course
All course documents will be located at the following address:
http://www.public.asu.edu/~gasweete/crj604/
This includes the syllabus, lecture slides, datasets for the exercises, and solutions to odd-numbered exercises.
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Statistical package
All analysis for this class should be
done using Stata!
- Stata is installed in this classroom
- Outside of the classroom, you will
have to access Stata via Saguaro
- The details of this access are
forthcoming.
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The best place to learn Stata:
http://www.ats.ucla.edu/stat/stata/
A quick introduction:
http://data.princeton.edu/stata/
What I use:
Online resources, Stata
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Computer exercise C1.1
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Research questions
You will come to understand statistical approaches
to answering questions like these:
Is a particular rehabilitation program effective in
reducing recidivism?
Does gang membership increase crime?
Does juvenile arrest affect high school dropout?
Does inequality increase crime rates?
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Types of Data
Your approach to answering research questions is
constricted by the data to which you have
access.
Nonexperimental data: naturally occurring,
preferably collected in a systematic manner
Experimental data: random assignment of cases
to two or more conditions.
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Theory
Barring data restrictions, the way you
approach research questions is guided
by criminological theory.
E.g. Social control, strain, differential
association, social disorganization
These theories point to constructs that
account for crime.
For statistical analysis, we create variables
that are supposed to represent theoretical
constructs.
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Becker’s model of crime, in theory
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Becker’s model of crime, in practice
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Causality
Criminologists are often concerned with the causal effect of one variable on another.
Ceteris paribus, meaning “all else equal,” is essential for causal analysis. It’s also difficult to impossible to achieve.
Example: Law enforcement and crime
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Causality
Recall: Month start crawling = 8.21-
.0177*(avg. temperature)
Is it reasonable to assume that we have captured a causal relationship between temperature and crawling age?
What else might be correlated with crawling age and temperature?
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Next time:
Homework: Problems 1.2, C1.2, C1.4
Note: If I cannot provide access to Stata by
Friday, C1.2 and C1.4 will be done as an
in-class exercise next week.
Read: Wooldridge Chapters 1 & 2, and
appendices A through C if you haven’t
already.