📄 y_est0.hlp
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{smcl}
{p 0 4}
{help contents:Top}
> {help y_stat:Statistics}
> {help y_est:Estimation}
{bind:> {bf:Regression models}}
{p_end}
{hline}
{title:Help and category listings}
{p 4 8 4}
{bf:{help y_e_linreg:Linear regression & related}}{break}
OLS, 2SLS, 3SLS, multivariate regression, quantile regression, Box-Cox, ...;
the outcome variable is continuous
{p 4 8 4}
{bf:{help y_e_bin:Binary outcome data}}{break}
probit, logit, nested logit...;
the outcome variable is 0 or 1, meaning failure or success
{p 4 8 4}
{bf:{help y_e_mout:Multiple outcome data}}{break}
conditional logistic regression, ordered probit or logit, ...;
the outcome variable is 1, 2, ..., indicating the category of the outcome,
which might be ordered
{p 4 8 4}
{bf:{help y_e_count:Count data}}{break}
Poisson regression, negative binomial regression, ...;
the outcome variable is 0, 1, 2, ..., and that records the number
of occurrences of an event
{p 4 8 4}
{bf:{help y_estchoice:Choice models}}{break}
McFadden choice, nested logit, ...
{p 4 8 4}
{bf:{help y_heckman:Selection models}}{break}
Heckman selection models;
linear regression with selection, probit with selection
{p 4 8 4}
{bf:{help glm:Generalized linear models (GLM)}}{break}
GLM for continuous, binary, and count data; estimates using IRLS
or maximum likelihood
{p 4 8 4}
{bf:{help logistic_estimation_commands:Index of logistic estimation commands}}
INCLUDE help ypostnote
{hline}
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