help ttest, ttesti dialogs: one-sample two-sample, paired
two-sample, unpaired
two-sample, by()
immediate dialogs: one-sample two-sample
-------------------------------------------------------------------------------
Title
[R] ttest -- Mean-comparison tests
Syntax
One-sample mean-comparison test
ttest varname == # [if] [in] [, level(#)]
Two-sample mean-comparison test (unpaired)
ttest varname1 == varname2 [if] [in], unpaired [unequal welch
level(#)]
Two-sample mean-comparison test (paired)
ttest varname1 == varname2 [if] [in] [, level(#)]
Two-group mean-comparison test
ttest varname [if] [in] , by(groupvar) [options1]
Immediate form of one-sample mean-comparison test
ttesti #obs #mean #sd #val [, level(#)]
Immediate form of two-sample mean-comparison 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)
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Menu
one-sample
Statistics > Summaries, tables, and tests > Classical tests of
hypotheses > One-sample mean-comparison test
two-sample, unpaired
Statistics > Summaries, tables, and tests > Classical tests of
hypotheses > Two-sample mean-comparison test
two-sample, paired
Statistics > Summaries, tables, and tests > Classical tests of
hypotheses > Mean-comparison test, paired data
two-group
Statistics > Summaries, tables, and tests > Classical tests of
hypotheses > Two-group mean-comparison test
immediate command: one-sample
Statistics > Summaries, tables, and tests > Classical tests of
hypotheses > One-sample mean-comparison calculator
immediate command: two-sample
Statistics > Summaries, tables, and tests > Classical tests of
hypotheses > Two-sample mean-comparison calculator
Description
ttest performs t tests on the equality of means. In the first form,
ttest tests that varname has a mean of #. In the second form, ttest
tests that varname1 and varname2 have the same mean, assuming unpaired
data. In the third form, ttest tests that varname1 and varname2 have the
same mean, assuming paired data. In the fourth form, ttest tests that
varname has the same mean within the two groups defined by groupvar.
ttesti is the immediate form of ttest; see immed.
For the equivalent of a two-sample t test with sampling weights
(pweights), use the svy: mean command with the over() option, and then
use lincom; also see [SVY] svy postestimation.
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 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 are to be treated as unpaired. The
unpaired option is used when the two set 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 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 mean-comparison test)
. webuse fuel (setup)
. ttest mpg1==mpg2 (two-sample mean-comparison test)
. webuse fuel3 (setup)
. ttest mpg, by(treated) (two-group mean-comparison test)
(no setup required)
. ttesti 24 62.6 15.8 75 (immediate form; n=24, m=62.6, sd=15.8;
test m=75)
Saved results
ttest and ttesti save 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(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
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.
Also see
Manual: [R] ttest
Help: [R] bitest, [R] ci, [MV] hotelling, [R] mean, [R] oneway, [R]
prtest, [R] sdtest
