Stata 15 help for ttest

[R] ttest -- t tests (mean-comparison tests)

Syntax

One-sample t test

ttest varname == # [if] [in] [, level(#)]

Two-sample t test using groups

ttest varname [if] [in] , by(groupvar) [options1]

Two-sample t test using variables

ttest varname1 == varname2 [if] [in], unpaired [unequal welch level(#)]

Paired t test

ttest varname1 == varname2 [if] [in] [, level(#)]

Immediate form of one-sample t test

ttesti #obs #mean #sd #val [, level(#)]

Immediate form of two-sample t test

ttesti #obs1 #mean1 #sd1 #obs2 #mean2 #sd2 [, options2]

options1 Description ------------------------------------------------------------------------- Main * by(groupvar) variable defining the groups unequal unpaired data have unequal variances welch use Welch's approximation level(#) set confidence level; default is level(95) ------------------------------------------------------------------------- * by(groupvar) is required.

options2 Description ------------------------------------------------------------------------- Main unequal unpaired data have unequal variances welch use Welch's approximation level(#) set confidence level; default is level(95) -------------------------------------------------------------------------

by is allowed with ttest; see [D] by.

Menu

ttest

Statistics > Summaries, tables, and tests > Classical tests of hypotheses > t test (mean-comparison test)

ttesti

Statistics > Summaries, tables, and tests > Classical tests of hypotheses > t test calculator

Description

ttest performs t tests on the equality of means. The test can be performed for one sample against a hypothesized population mean. Two-sample tests can be conducted for paired and unpaired data. The assumption of equal variances can be optionally relaxed in the unpaired two-sample case.

ttesti is the immediate form of ttest; see immed.

Options

+------+ ----+ Main +-------------------------------------------------------------

by(groupvar) specifies the groupvar that defines the two groups that ttest will use to test the hypothesis that their means are equal. Specifying by(groupvar) implies an unpaired (two sample) t test. Do not confuse the by() option with the by prefix; you can specify both.

unpaired specifies that the data be treated as unpaired. The unpaired option is used when the two sets of values to be compared are in different variables.

unequal specifies that the unpaired data not be assumed to have equal variances.

welch specifies that the approximate degrees of freedom for the test be obtained from Welch's formula (1947) rather than from Satterthwaite's approximation formula (1946), which is the default when unequal is specified. Specifying welch implies unequal.

level(#) specifies the confidence level, as a percentage, for confidence intervals. The default is level(95) or as set by set level.

Examples

. sysuse auto (setup) . ttest mpg==20 (one-sample t test)

. webuse fuel3 (setup) . ttest mpg, by(treated) (two-sample t test using groups)

. webuse fuel (setup) . ttest mpg1==mpg2 (two-sample t test using variables)

(no setup required) . ttesti 24 62.6 15.8 75 (immediate form; n=24, m=62.6, sd=15.8; test m=75)

Video examples

One-sample t test in Stata

t test for two independent samples in Stata

t test for two paired samples in Stata

One-sample t-test calculator

Two-sample t-test calculator

Stored results

ttest and ttesti store the following in r():

Scalars r(N_1) sample size n_1 r(N_2) sample size n_2 r(p_l) lower one-sided p-value r(p_u) upper one-sided p-value r(p) two-sided p-value r(se) estimate of standard error r(t) t statistic r(sd_1) standard deviation for first variable r(sd_2) standard deviation for second variable r(sd) combined standard deviation r(mu_1) x_1 bar, mean for population 1 r(mu_2) x_2 bar, mean for population 2 r(df_t) degrees of freedom r(level) confidence level

References

Satterthwaite, F. E. 1946. An approximate distribution of estimates of variance components. Biometrics Bulletin 2: 110-114.

Welch, B. L. 1947. The generalization of `student's' problem when several different population variances are involved. Biometrika 34: 28-35.


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