Description Usage Arguments Details Value Author(s) References See Also Examples
Compute the test of hypothesis and compute confidence interval on the mean of a population when the standard deviation of the population is known.
1 2 3 4 5 6 7 8 9 |
x |
numeric vector of data values. Non-finite (e.g. infinite or missing) values will be omitted. |
... |
further arguments to be passed to or from methods. |
formula |
a formula of the form |
data |
an optional matrix or data frame (or similar: see
|
subset |
an optional vector specifying a subset of observations to be used. |
na.action |
a function which indicates what should happen when the data
contain |
y |
an optional numeric vector of data values: as with x non-finite values will be omitted. |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
paired |
a logical indicating whether you want a paired z-test. |
mu |
a number specifying the hypothesized mean of the population. |
sd_pop |
a number specifying the known standard deviation of the population. |
conf.level |
confidence level for the interval computation. |
Most introductory statistical texts introduce inference by using the z-test
and z-based confidence intervals based on knowing the population standard
deviation. However statistical packages often do not include functions to do
z-tests since the t-test is usually more appropriate for real world
situations. This function is meant to be used during that short period of
learning when the student is learning about inference using z-procedures,
but has not learned the t-based procedures yet. Once the student has
learned about the t-distribution the t.test()
function should be used
instead of this one (but the syntax is very similar, so this function should
be an appropriate introductory step to learning t.test()
).
The formula interface is only applicable for the 2-sample tests.
A list with class "htest
" containing the following components:
statistic |
the value of the z-statistic. |
p.value |
the p-value for the test |
conf.int |
a confidence interval for the mean appropriate to the specified alternative hypothesis. |
estimate |
the estimated mean or difference in means depending on whether it was a one-sample test or a two-sample test. |
null.value |
the specified hypothesized value of the mean or mean difference depending on whether it was a one-sample test or a two-sample test. |
stderr |
the standard error of the mean (difference). |
alternative |
a character string describing the alternative hypothesis. |
method |
a character string indicating what type of test was performed. |
data.name |
a character string giving the name(s) of the data. |
Andri Signorell andri@signorell.net,
based on R-Core code of t.test()
,
documentation partly from Greg Snow greg.snow@imail.org
Stahel, W. (2002) Statistische Datenanalyse, 4th ed, vieweg
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | x <- rnorm(25, 100, 5)
ZTest(x, mu = 99, sd_pop = 5)
# the classic interface
with(sleep, ZTest(extra[group == 1], extra[group == 2], sd_pop = 2))
# the formula interface
ZTest(extra ~ group, data = sleep, sd_pop = 2)
# Stahel (2002), pp. 186, 196
d.tyres <- data.frame(
A = c(44.5, 55, 52.5, 50.2, 45.3, 46.1, 52.1, 50.5, 50.6, 49.2),
B = c(44.9, 54.8, 55.6, 55.2, 55.6, 47.7, 53, 49.1, 52.3, 50.7)
)
with(d.tyres, ZTest(A, B, sd_pop = 3, paired = TRUE))
d.oxen <- data.frame(
ext = c(2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9),
int = c(6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1)
)
with(d.oxen, ZTest(int, ext, sd_pop = 1.8, paired = FALSE))
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