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ASSUMPTION CHECKING. In regression analysis with Stata In multi-level analysis with Stata (not much extra) 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.

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Assumption checking
ASSUMPTION CHECKING

  • In regression analysis with Stata

  • In multi-level analysis with Stata (not much extra)

  • 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
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


FIRST THE ONE THAT LEADS TO

NOTHING NEW IN STATA

(NOTE: SLIDE TAKEN LITERALLY FROM MMBR)

Independent errors: havinginformationabout the value of a residualshouldnotgiveyouinformationabout the value of otherresiduals

Detect: askyourselfwhetherit is likelythatknowledgeaboutoneresidualwouldtellyousomethingabout the value of anotherresidual.

Typical cases:

-repeatedmeasures

-clusteredobservations

(peoplewithinfirms /

pupilswithin schools)

Consequences: as forheteroscedasticity

Usually, yourconfidenceintervals are estimatedtoosmall (thinkaboutwhythat is!).

Cure: usemulti-level analyses


In stata
In Stata:

Example:

the Stata “auto.dta” data set

sysuse auto

corr (correlation)

vif (variance inflation factors)

ovtest (omitted variable test)

hettest (heterogeneity test)

predict e, resid

swilk (test for normality)


Finding the commands
Finding the commands

  • “help regress”

  •  “regress postestimation”

    and you will find most of them (and more) there


Multi-collinearity

A strongcorrelationbetweentwoor more of your predictor variables

Youdon’t want it, because:

  • It is more difficult to gethigher R’s

  • The importance of predictorscanbedifficult to establish (b-hatstend to go to zero)

  • The estimatesforb-hats are unstableunderslightly different regressionattempts (“bouncingbeta’s”)

    Detect:

  • Look at correlation matrix of predictor variables

  • calculateVIF-factorswhile running regression

    Cure:

    Delete variables sothatmulti-collinearitydisappears, forinstancebycombiningtheminto a single variable


Stata calculating the correlation matrix corr and vif statistics vif
Stata: calculating the correlation matrix (“corr”) and VIF statistics (“vif”)


Misspecification tests replaces all relevant predictor variables included
Misspecificationtests(replaces: all relevant predictor variables included)


Homoscedasticity: all residuals are from a distribution with the samevariance

Consequences: Heteroscedasticiy does notnecessarilylead to biases in yourestimatedcoefficients (b-hat), butit does lead to biases in the estimate of the width of the confidence interval, and the estimation procedure itself is notefficient.


Testing for heteroscedasticity in stata
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
Errorsdistributednormally

Errors are distributednormally

(justthe errors, not the variables themselves!)

Detect: look at the residual plots, test fornormality

Consequences: rule of thumb: ifn>600, noproblem. Otherwiseconfidenceintervals are wrong.

Cure: try to fit a better model, oruse more difficultways of modelinginstead (askan expert).


Errors distributed normally1
Errorsdistributednormally

First calculate the errors:

predict e, resid

Then test for normality

swilke


Assumption checking

in multi-level multiple regression

with Stata


In multi level
In multi-level

  • Test all that you would test for multiple regression – poor man’s test: do this using multiple regression! (e.g. “hettest”)

    Add:

  • xttest0 (see last week)

    Add (extra):

    Test visually whether the normality assumption holds, but do this for the random 


Note extra material not on the exam bonus points if you know how to use it
Note: extra material(= not on the exam, bonus points if you know how to use it)

tab school, gen(sch_)

regy sch2 – sch28

gen coefs = .

for num 2/28: replace coefs =_b[schX] if _n==X

swilkcoefs


Assumption checking

in multi-level multiple regression

with Stata


Assumptions
Assumptions

  • Y is 0/1

  • Ratio of cases to variables should be “reasonable”

  • No cases where you have complete separation (Stata will remove these cases automatically)

  • Linearity in the logit (comparable to “the true model should be linear” in multiple regression)

  • Independence of errors (as in multiple regression)


Further things to do
Further things to do:

  • Check goodness of fit and prediction for different groups (as done in the do-file you have)

  • Check the correlation matrix for strong correlations between predictors (corr)

  • Check for outliers using regress and diag(but don’t tell anyone I suggested this)


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