Stata 15 help for mean

[R] mean -- Estimate means


mean varlist [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model stdize(varname) variable identifying strata for standardization stdweight(varname) weight variable for standardization nostdrescale do not rescale the standard weight variable

if/in/over over(varlist[, nolabel]) group over subpopulations defined by varlist; optionally, suppress group labels

SE/Cluster vce(vcetype) vcetype may be analytic, cluster clustvar, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) noheader suppress table header nolegend suppress table legend display_options control column formats and line width

coeflegend display legend instead of statistics ------------------------------------------------------------------------- bootstrap, jackknife, mi estimate, rolling, statsby, and svy are allowed; see prefix. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. Weights are not allowed with the bootstrap prefix. aweights are not allowed with the jackknife prefix. vce() and weights are not allowed with the svy prefix. fweights, aweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [R] mean postestimation for features available after estimation.


Statistics > Summaries, tables, and tests > Summary and descriptive statistics > Means


mean produces estimates of means, along with standard errors.


+-------+ ----+ Model +------------------------------------------------------------

stdize(varname) specifies that the point estimates be adjusted by direct standardization across the strata identified by varname. This option requires the stdweight() option.

stdweight(varname) specifies the weight variable associated with the standard strata identified in the stdize() option. The standardization weights must be constant within the standard strata.

nostdrescale prevents the standardization weights from being rescaled within the over() groups. This option requires stdize() but is ignored if the over() option is not specified.

+------------+ ----+ if/in/over +-------------------------------------------------------

over(varlist [, nolabel]) specifies that estimates be computed for multiple subpopulations, which are identified by the different values of the variables in varlist.

When this option is supplied with one variable name, such as over( varname), the value labels of varname are used to identify the subpopulations. If varname does not have labeled values (or there are unlabeled values), the values themselves are used, provided that they are nonnegative integers. Noninteger values, negative values, and labels that are not valid Stata names are substituted with a default identifier.

When over() is supplied with multiple variable names, each subpopulation is assigned a unique default identifier.

nolabel specifies that value labels attached to the variables identifying the subpopulations be ignored.

+------------+ ----+ SE/Cluster +-------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (analytic), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

vce(analytic), the default, uses the analytically derived variance estimator associated with the sample mean.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

noheader prevents the table header from being displayed. This option implies nolegend.

nolegend prevents the table legend identifying the subpopulations from being displayed.

display_options: cformat(%fmt) and nolstretch; see [R] estimation options.

The following option is available with mean but is not shown in the dialog box:

coeflegend; see [R] estimation options.


--------------------------------------------------------------------------- Setup . webuse fuel

Estimate the average mileage of the cars without the fuel treatment (mpg1) and those with the fuel treatment (mpg2) . mean mpg1 mpg2

Stack mpg1 on top of mpg2, creating mpg . stack mpg1 mpg2, into(mpg) clear

Summarize mpg by type . mean mpg, over(_stack)

--------------------------------------------------------------------------- Setup . webuse hbp, clear

Stratify sample by age, race, and sex . egen strata = group(age race sex) if inlist(year, 1990, 1992)

Create standardization weight equal to sample size for each stratum . by strata, sort: gen stdw=_N

Compute standardized mean using the observed distribution of age, race, and sex as the standard . mean hbp, over(city year) stdize(strata) stdweight(stdw)

--------------------------------------------------------------------------- Setup . webuse highschool, clear

Estimate a population mean using survey data . svy: mean weight

Estimate mean of weight for each subpopulation identified by sex . svy: mean weight, over(sex)


Video example

Descriptive statistics in Stata

Stored results

mean stores the following in e():

Scalars e(N) number of observations e(N_over) number of subpopulations e(N_stdize) number of standard strata e(N_clust) number of clusters e(k_eq) number of equations in e(b) e(df_r) sample degrees of freedom e(rank) rank of e(V)

Macros e(cmd) mean e(cmdline) command as typed e(varlist) varlist e(stdize) varname from stdize() e(stdweight) varname from stdweight() e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(over) varlist from over() e(over_labels) labels from over() variables e(over_namelist) names from e(over_labels) e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(properties) b V e(estat_cmd) program used to implement estat e(marginsnotok) predictions disallowed by margins

Matrices e(b) vector of mean estimates e(V) (co)variance estimates e(_N) vector of numbers of nonmissing observations e(_N_stdsum) number of nonmissing observations within the standard strata e(_p_stdize) standardizing proportions e(error) error code corresponding to e(b)

Functions e(sample) marks estimation sample

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