Z test for known population standard deviation
Compute the test of hypothesis and compute confidence interval on the mean of a population when the standard deviation of the population is known.
z.test(x, mu = 0, stdev, alternative = c("two.sided", "less", "greater"),
sd = stdev, n=length(x), conf.level = 0.95, ...)x |
Vector of data values or the mean of the data. |
mu |
Hypothesized mean of the population. |
stdev |
Known standard deviation of the population. |
alternative |
Direction of the alternative hypothesis. |
sd |
Alternative to |
n |
The sample size if |
conf.level |
Confidence level for the interval computation. |
... |
Additional arguments are silently ignored. |
Many introductory statistical texts introduce inference by using the Z
test and Z based confidence intervals based on knowing the population
standard deviation. Most statistical packages 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).
An object of class htest containing the results
This function should be used for learning only, real data should
generally use t.test.
Greg Snow 538280@gmail.com
x <- rnorm(25, 100, 5) z.test(x, 99, 5)
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