“Regression
diagnostics” are methods for determining
whether a regression model fit to data
adequately represents the data. This
workshop will present diagnostics for
linear models fit by least squares, for
generalized-linear models fit by maximum
likelihood, for linear and
generalized-linear mixed-effects models,
and for linear regression models estimated
by instrumental variables. I assume some
familiarity with the various regression
models covered in the workshop. Primarily
to establish notation and basic results,
I prepared a brief review of
these topics; please read the
review prior to the workshop.
I’ll use the R statistical computing
environment for the presentation, and so I
also assume some familiarity with R. See
my ICPSR
R lectures for introductory material
on R along with a variety of references
and links to resources, including
installation instructions for R and
RStudio (a free programming editor for R).
In addition to the standard R
distribution, to follow along with the R
scripts for the workshop (see below), you
should install several contributed
packages: install.packages(c("car",
"effects", "ivreg", "lme4"))
The workshop is largely based on two
sources: Fox, Regression
Diagnostics, Second Edition
(Sage, 2020), and Fox and Weisberg, An
R Companion to Applied Regression,
Third Edition (Sage, 2019). You need not
have access to these books to follow the
workshop.
Register at Eventbrite:
2022 SORA-TABA Annual Workshop &
DLSPH Biostatistics Research Day.
The topics below don't precisely
correspond to the eight hours of the
workshop; in particular, the earlier
topics will likely take more time than the
later ones.
The
materials on this website may be
updated before the workshop, so please
(re-)download them the day before the
workshop.
I also recommend that your update your
R installation to the current version
and that you update all of your R
packages: update(ask=FALSE)
Data Files
|
| CIA.txt |
CIA
World Factbook data |
| Davis.txt |
Davis's
data on measured and reported
height and weight |
| Duncan.txt |
Duncan's
occupational-prestige data |
| Mroz.txt |
Mroz's
data on women's labor-force
participation |
|
