ZTest: Z Test for Known Population Standard Deviation

Description

Compute the test of hypothesis and compute confidence interval on the mean of a population when the standard deviation of the population is known.

Usage

ZTest(x, ...)
"ZTest"(x, y = NULL, alternative = c("two.sided", "less", "greater"), paired = FALSE, mu = 0, sd_pop, conf.level = 0.95, ... )
"ZTest"(formula, data, subset, na.action, ...)

Arguments

numeric vector of data values. Non-finite (e.g. infinite or missing) values will be omitted.

an optional numeric vector of data values: as with x non-finite values will be omitted.

a number specifying the hypothesized mean of the population.

sd_pop

a number specifying the known standard deviation of the population.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter. For one-sample tests, alternative refers to the true mean of the parent population in relation to the hypothesized value of the mean.

paired

a logical indicating whether you want a paired z-test.

conf.level

confidence level for the interval computation.

formula

a formula of the form lhs ~ rhs where lhs gives the data values and rhs a factor with two levels giving the corresponding groups.

data

an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).

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 NAs. Defaults to getOption("na.action").

...

further arguments to be passed to or from methods.

Value

A list with class "htest" containing the following components:

Details

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).

References

Stahel, W. (2002) Statistische Datenanalyse, 4th ed, vieweg

Examples

Run this code

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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