Finding help
Stata manuals
You have all these as pdf!
Check the folder /Stata12/docs
ASSUMPTION CHECKING
AND OTHER NUISANCES
• In regression analysis with Stata
• In logistic regression analysis with
Stata
NOTE: THIS WILL BE EASIER IN
Stata THAN IT WAS IN SPSS
Assumption checking
in “normal” multiple regression
with Stata
Assumptions in
regression analysis
•No multi-collinearity
•All relevant predictor variables
included
•Homoscedasticity: all residuals are
from a distribution with the same variance
•Linearity: the “true” model should be
linear.
•Independent errors: having information
about the value of a residual should not
give you information about the value of
other residuals
•Errors are distributed normally
5
FIRST THE ONE THAT LEADS TO
NOTHING NEW IN STATA
(NOTE: SLIDE TAKEN LITERALLY FROM MMBR)
Independent errors: having information about
the value of a residual should not give you
information about the value of other residuals
Detect: ask yourself whether it is likely that
knowledge about one residual would tell you
something about the value of another residual.
Typical cases:
-repeated measures
-clustered observations
(people within firms /
pupils within schools)
Consequences: as for heteroscedasticity
Usually, your confidence intervals are estimated
too small (think about why that is!).
Cure: use multi-level analyses
 part 2 of this course
6
The rest, in Stata:
Example:
the Stata “auto.dta” data set
sysuse auto
corr
vif
(correlation)
(variance inflation factors)
ovtest (omitted variable test)
hettest (heterogeneity test)
predict e, resid
swilk (test for normality)
Finding the commands
• “help regress”
•  “regress postestimation”
and you will find most of them
(and more) there
Multi-collinearity
A strong correlation between two or more of
your predictor variables
You don’t want it, because:
1. It is more difficult to get higher R’s
2. The importance of predictors can be difficult to
establish (b-hats tend to go to zero)
3. The estimates for b-hats are unstable under slightly
different regression attempts (“bouncing beta’s”)
Detect:
1. Look at correlation matrix of predictor variables
2. calculate VIF-factors while running regression
Cure:
Delete variables so that multi-collinearity
disappears, for instance by combining them
into a single variable
9
Stata: calculating the correlation matrix
(“corr” or “pwcorr”) and VIF statistics (“vif”)
10
Misspecification tests
(replaces: all relevant predictor
variables included)
Also run “ovtest, rhs” here. Both tests should be non-significant.
Note that there are two ways to interpret
“all relevant predictor variables included”
11
Homoscedasticity: all residuals
are from a distribution with the
same variance
Consequences: Heteroscedasticiy does not necessarily
lead to biases in your estimated coefficients (b-hat),
but it does lead to biases in the estimate of the width
of the confidence interval, and the estimation
procedure itself is not efficient.
12
Testing for heteroscedasticity in Stata
• Your residuals should have the same variance for
all values of Y  hettest
• Your residuals should have the same variance for
all values of X  hettest, rhs
Errors distributed normally
Errors should be distributed normally
(just the errors, not the variables themselves!)
Detect: look at the residual plots, test for
normality, or save residuals and test directly
Consequences: rule of thumb: if n>600, no
problem. Otherwise confidence intervals are
wrong.
Cure: try to fit a better model (or use more
difficult ways of modeling instead - ask an
expert).
14
Errors distributed normally
First calculate the errors (after
regress):
predict e, resid
Then test for normality
swilk e
Assumption checking in
logistic regression
with Stata
Note: based on
http://www.ats.ucla.edu/stat/stata/web
books/logistic/chapter3/statalog3.ht
m
Assumptions in
logistic regression
• Y is 0/1
• Independence of errors (as in
multiple regression)
• No cases where you have
complete separation
(Stata will try to remove these cases automatically)
• Linearity in the logit (comparable
to “the true model should be
linear” in multiple regression) –
“specification error”
• No multi-collinearity (as in m.r.)
• What will happen if you try
logit y x1 x2 in this case?
This!
Because all cases with x==1 lead to y==1, the weight
of x should be +infinity. Stata therefore rightly
disregards these cases.
Do realize that, even though you do not see them in
the regression, these are extremely important cases!
(checking for)
multi-collinearity
• In regression, we had “vif”
• Here we need to download a
command that a user-created:
“collin” (try “findit collin” in Stata)
(checking for)
specification error
• The equivalent for “ovtest” is the command “linktest”
(checking for)
specification error – part 2
Further things to do:
• Check for useful transformations of
variables, and interaction effects
• Check for outliers / influential cases:
1) using a plot of stdres
(against n) and dbeta (against n)
2) using a plot of ldfbeta’s (against n)
3) using regress and diag
(but don’t tell anyone that I suggested this)
Checking for outliers
… check the file auto_outliers.do for this …
Try the taxi tipping data