The rest, 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)įinding the commands “help regress” “regress postestimation” and you will find most of them (and more) thereĩ 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 variableġ0 Stata: calculating the correlation matrix (“corr” or “pwcorr”) and VIF statistics (“vif”)ġ1 Misspecification tests (replaces: all relevant predictor variables included) Also run “ovtest, rhs” here. Cure: use multi-level analyses part 2 of this course 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!). Independent errors: having information about the value of a residual should not give you information about the value of other residuals Errors are distributed normallyĦ 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.
#Manual stata 12 pdf manuals
Stata manuals You have all these as pdf! Check the folder /Stata12/docsĪSSUMPTION 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 SPSSĪssumption checking in “normal” multiple regression with Stataĥ 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.
Stata manuals You have all these as pdf! Check the folder /Stata12/docs."- Presentation transcript:
0 kommentar(er)
