Creates data for a t-test, for one mean, based on the test's properties.
ttest_data(
size = (3:20)^2,
mean = -5:5,
sd = seq(0.1, 1, by = 0.1),
reject = NA,
alternative = c("two.sided", "less", "greater"),
alpha = c(0.01, 0.05, 0.1),
z = seq(-4.49, 4.49, by = 0.01),
use.sigma = TRUE
)dt1(
size = (3:20)^2,
mean = -5:5,
sd = seq(0.1, 1, by = 0.1),
reject = NA,
alternative = c("two.sided", "less", "greater"),
alpha = c(0.01, 0.05, 0.1),
z = seq(-4.49, 4.49, by = 0.01),
use.sigma = TRUE
)
A list with the components:
mu0 hypothetical mean
sigma standard deviation in the population
sd vector of possible standard deviations in the sample
xbar mean in the sample
n sample size
alpha significance level
alternative specifying the alternative hypothesis (either two.sided, greater or less)
altsd alternative values usable for sd (if use.sigma==TRUE) or sigma (if use.sigma==FALSE)
numeric: vector of possible sample sizes (default (3:20)^2,)
numeric: vector of possible means (default -5:5)
numeric: vector of possible standard deviations (default sd=seq(0.1, 1, by=0.1)
logical: should x generate a lead for the rejection of the null hypothesis (default TRUE), if equals NA then this will be ignored
character: a character string specifying the alternative hypothesis, must be one of two.sided (default), greater or less
numeric: vector of significance levels (default c(0.01, 0.05, 0.1))
numeric: vector of possible \(z\) values (default seq(-4.49, 4.49, by=0.01))
logical: should the standard deviation of the population (default) or the sample be used?