In some situations we may wish to quantify confidence in the region above or below a mean estimate. For instance, a biologist working with an endangered species may be interested in saying: "I am 95 percent confident that the true mean number of offspring is above a particular threshold." In a one-sided situation, we essentially let our confidence be 1- 2\(\alpha\) (instead of 1 - \(\alpha\)). Thus, if our significance level for a two-tailed test is \(\alpha\), our one-tailed significance level will be 2\(\alpha\).
ci.mu.oneside(data, conf = 0.95, n = NULL, Var = NULL, xbar = NULL,
summarized = FALSE, N = NULL, fpc = FALSE, tail = "upper")Returns a list of class = "ci". Default output is a matrix with the sample mean and either the upper or lower confidence limit.
A vector of quantitative data. Required if summarized=TRUE.
Level of confidence; 1 - P(type I error).
Sample size. Required if summarized=TRUE.
Sample variance. Required if summarized=TRUE.
Sample mean. Required if summarized=TRUE.
Logical. Indicates whether summary statistics instead of raw data should be used.
Population size. Required if summarized=TRUE.
Logical. Indicating whether finite population corrections should be made.
Indicates what side the one sided confidence limit should be calculated for. Choices are "upper" or "lower".
Ken Aho
Bain, L. J., and Engelhardt, M. (1992) Introduction to Probability and Mathematical Statistics. Duxbury press, Belmont, CA, USA.
ci.mu.t