use "https://www3.nd.edu/~rwilliam/statafiles/logist.dta", clear
logit  grade gpa tuce psi
logit  grade gpa tuce psi, or nolog
logistic grade gpa tuce psi
logistic grade gpa tuce psi, coef
* Model 0: Intercept only
quietly logit  grade
est store M0

* Model 1: GPA added
quietly logit  grade gpa
est store M1

* Model 2: GPA + TUCE
quietly logit  grade gpa tuce
est store M2

* Model 3: GPA + TUCE + PSI
quietly logit  grade gpa tuce psi
est store M3

* Model 1 versus Model 0
lrtest M1 M0
* Model 2 versus Model 1
lrtest M2 M1
* Model 3 versus Model 2
lrtest M3 M2
* Model 3 versus Model 0
lrtest M3 M0

logit grade gpa tuce psi
lrdrop1
test psi
nestreg, lr store(m): logit  grade gpa tuce psi
lrtest m3 m1
nestreg, lr: logit  grade gpa (tuce psi)
lrtest m3 m1, stats
fitstat
estat class

quietly logit  grade gpa tuce psi

* get the predicted log odds for each case
predict logodds, xb

* get the odds for each case
gen odds = exp(logodds)

* get the predicted probability of success
predict p, p

list  grade gpa tuce psi logodds odds p

* Probability of getting an A
quietly logit  grade gpa tuce i.psi
margins psi, at(gpa = 3 tuce = 20)
margins psi, at(gpa = 4 tuce = 25)
margins psi, at(gpa = 4 tuce = 25) predict(xb)
margins psi, at(gpa = 4 tuce = 25) expression(exp(predict(xb)))
sw, pe(.05) lr: logit grade gpa tuce psi

* Generate predicted probability of success
predict p, p

* Generate standardized residuals
predict rstandard, rstandard

* Generate the deviance residual
predict dev, deviance

* Use the extremes command to identify large residuals
extremes  rstandard dev p grade gpa tuce psi

collin  gpa tuce psi if !missing(grade)

logit  grade gpa tuce psi, robust