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Subject index - Stata

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<strong>Subject</strong> <strong>index</strong> 17<br />

confidence interval, continued<br />

for tabulated proportions, [SVY] svy: tabulate<br />

twoway<br />

for totals, [R] total<br />

linear combinations, [SVY] svy postestimation<br />

set default, [R] level<br />

confidence levels, [R] level<br />

config, estat subcommand, [MV] mds<br />

postestimation<br />

configuration, [MV] Glossary<br />

configuration plot, [MV] mds postestimation plots,<br />

[MV] Glossary<br />

confirm<br />

existence command, [P] confirm<br />

file command, [P] confirm<br />

format command, [P] confirm<br />

matrix command, [P] confirm<br />

names command, [P] confirm<br />

number command, [P] confirm<br />

scalar command, [P] confirm<br />

variable command, [P] confirm<br />

confirm, datasignature subcommand,<br />

[D] datasignature<br />

confirmatory factor analysis, [MV] intro,<br />

[SEM] intro 5, [SEM] example 15,<br />

[SEM] example 30g, [SEM] Glossary<br />

conformability, [M-2] void, [M-6] Glossary, also see<br />

c-conformability, also see p-conformability, also<br />

see r-conformability<br />

confounding, [ST] Glossary<br />

confusion matrix, [MV] Glossary<br />

conj() function, [M-5] conj( )<br />

conj() function, [M-5] conj( )<br />

conjoint analysis, [R] rologit<br />

conjugate, [M-5] conj( ), [M-6] Glossary<br />

conjugate transpose, [M-2] op transpose, [M-5] conj( ),<br />

[M-6] Glossary<br />

connect() option, [G-3] cline options,<br />

[G-3] connect options, [G-4] connectstyle<br />

connected, graph twoway subcommand, [G-2] graph<br />

twoway connected<br />

connectstyle, [G-4] connectstyle<br />

conren, set subcommand, [R] set<br />

console,<br />

controlling scrolling of output, [P] more, [R] more<br />

obtaining input from, [P] display<br />

constant conditional-correlation model, [TS] mgarch,<br />

[TS] mgarch ccc<br />

constrained estimation, [R] constraint, [R] estimation<br />

options<br />

alternative-specific<br />

conditional logistic model, [R] asclogit<br />

multinomial probit regression, [R] asmprobit<br />

rank-ordered probit regression, [R] asroprobit<br />

ARCH, [TS] arch<br />

ARFIMA, [TS] arfima<br />

ARIMA and ARMAX, [TS] arima<br />

competing risks, [ST] stcrreg<br />

constrained estimation, continued<br />

complementary log-log regression, [R] cloglog<br />

dynamic factor model, [TS] dfactor<br />

fixed-effects models<br />

logit, [XT] xtlogit<br />

negative binomial, [XT] xtnbreg<br />

Poisson, [XT] xtpoisson<br />

GARCH model, [TS] mgarch ccc, [TS] mgarch<br />

dcc, [TS] mgarch dvech, [TS] mgarch vcc<br />

generalized linear models, [R] glm<br />

for binomial family, [R] binreg<br />

generalized negative binomial regression, [R] nbreg<br />

heckman selection model, [R] heckman,<br />

[R] heckoprobit<br />

interval regression, [R] intreg<br />

linear regression, [R] cnsreg<br />

seemingly unrelated, [R] sureg<br />

stochastic frontier, [R] frontier<br />

three-stage least squares, [R] reg3<br />

truncated, [R] truncreg<br />

logistic regression, [R] logistic, [R] logit, also see<br />

logit regression subentry<br />

conditional, [R] clogit<br />

multinomial, [R] mlogit<br />

ordered, [R] ologit<br />

skewed, [R] scobit<br />

stereotype, [R] slogit<br />

logit regression, [R] logit, also see logistic regression<br />

subentry<br />

for grouped data, [R] glogit<br />

nested, [R] nlogit<br />

maximum likelihood estimation, [R] ml<br />

multilevel mixed-effects, [ME] mecloglog,<br />

[ME] meglm, [ME] melogit, [ME] menbreg,<br />

[ME] meologit, [ME] meoprobit,<br />

[ME] mepoisson, [ME] meprobit<br />

multinomial<br />

logistic regression, [R] mlogit<br />

probit regression, [R] mprobit<br />

negative binomial regression, [R] nbreg<br />

truncated, [R] tnbreg<br />

zero-inflated, [R] zinb<br />

parametric survival models, [ST] streg<br />

Poisson regression, [R] poisson<br />

truncated, [R] tpoisson<br />

zero-inflated, [R] zip<br />

probit regression, [R] probit<br />

bivariate, [R] biprobit<br />

for grouped data, [R] glogit<br />

heteroskedastic, [R] hetprobit<br />

multinomial, [R] mprobit<br />

ordered, [R] oprobit<br />

with endogenous regressors, [R] ivprobit<br />

with sample selection, [R] heckprobit<br />

programming, [P] makecns

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