Logistic regression title logistic regression data

Logistic regression
title: Logistic regression
data:
file is hsbdemo.dat;
variable:
names are
id female ses schtyp prog read write math science socst honors awards cid;
usevariables are honors female read math;
categorical = honors;
analysis:
estimator = ML;
! link = logit;
model:
honors on female read math;

Ordered probit regression
title: Ordered probit regression
data:
file is hsbdemo.dat;
variable:
names are
id female ses schtyp prog read write math science socst honors awards cid;
usevariables are ses female read math;
categorical = ses;
model:
ses on female read math;

Ordered logistic regression
title: Ordered logistic regression
data:
file is hsbdemo.dat;
variable:
names are
id female ses schtyp prog read write math science socst honors awards cid;
usevariables are ses female read math;
categorical = ses;
analysis:
estimator = ML;
model:
ses on female read math;

Multinomial logistic regression
title: Multinomial logistic regression
data:
file is hsbdemo.dat;
variable:
names are
id female ses schtyp prog read write math science socst honors awards cid;
usevariables are prog female read math;
nominal = prog;
analysis:
estimator = ML;
model:
prog on female read math;

Poisson regression
title: Poisson regression
data:
file is hsbdemo.dat;
variable:
names are
id female ses schtyp prog read write math science socst honors awards cid;
usevariables are
awards female read math;
count = awards (p);
model:
awards on female read math;

Negative binomial regression
title: Negative binomial regression
data:
file is hsbdemo.dat;
variable:
names are
id female ses schtyp prog read write math science socst honors awards cid;
usevariables are
awards female read math;
count = awards (nb);
model:
awards on female read math;

Zero-inflated Poisson regression
title: Zero-inflated Poisson regression
data:
file is hsbdemo.dat;
variable:
names are
id female ses schtyp prog read write math science socst honors awards cid;
usevariables are
awards female read math;
count = awards (pi);
model:
awards on female read math;
awards#1 on female read math;

Zero-inflated negative binomial
regression
title: Zero-inflated negative binomial regression
data:
file is hsbdemo.dat;
variable:
names are
id female ses schtyp prog read write math science socst honors awards cid;
usevariables are
awards female read math;
count = awards (nbi);
model:
awards on female read math;
awards#1 on female read math;

Review Questions
•
When do you use the categorical statement (in the variable command
block)?
•
When do you use the usevar statement (in the variable command
block)?
•
What key word “tells” Mplus to run a regression model?

Path Analysis
•
Path analysis is used to estimate a system of equations in which all of the
variables are observed.
•
Unlike models that include latent variables, path models assume perfect
measurement of the observed variables; only the structural relationships
between the observed variables are modeled.