asbio (version 0.2-1)

ci.mu.z: Z and t confidence intervals for mu.

Description

These functions calculate t and z confidence intervals for $\mu$. Z confidence intervals require specification (and thus knowledge) of $\sigma$. Both methods assume underlying normal distributions although this assumption becomes irrelevant for large sample sizes. Finite population corrections are provided if requested.

Usage

ci.mu.z(data, conf = 0.95, sigma = 1, summarized = FALSE, xbar = NULL,
fpc = FALSE, N = NULL, n = NULL)

ci.mu.t(data, conf = 0.95, summarized = FALSE, xbar = NULL, st.dev = NULL, 
fpc = FALSE, N = NULL, n = NULL)

Arguments

data

A vector of quantitative data. Required if summarized = FALSE

conf

Confidence level; 1 - alpha.

sigma

The population standard deviation.

summarized

A logical statement specifying whether statistical summaries are to be used. If summarized = FALSE, then the sample mean and the sample standard deviation (t.conf only) are calculated from the vector provided in data

xbar

The sample mean. Required if summarized = TRUE.

fpc

A logical statement specifying whether a finite population correction should be made. If fpc = TRUE then both the sample size n and the population size N must be specified.

N

The population size. Required if fpc=TRUE

st.dev

The sample standard deviation. Required if summarized=TRUE.

n

The sample size. Required if summarized = TRUE.

Value

• Returns a list of class = "ci". Default printed results are the paramter estimate and confidence bounds. Other invisible objects include:
• Margin
  the confidence margin.

Details

ci.mu.z and ci.mu.t calculate confidence intervals for either summarized data or a dataset provided in data. Finite population corrections are made if a user specifies fpc=TRUE and specifies some value for N.

References

Lohr, S. L. (1999) Stampling: design and analysis. Duxbury Press. Pacific Grove, USA.

See Also

pnorm, pt

Examples

#With summarized=FALSE 
x<-c(5,10,5,20,30,15,20,25,0,5,10,5,7,10,20,40,30,40,10,5,0,0,3,20,30)
ci.mu.z(x,conf=.95,sigma=4,summarized=FALSE)
ci.mu.t(x,conf=.95,summarized=FALSE)
#With summarized = TRUE
ci.mu.z(x,conf=.95,sigma=4,xbar=14.6,n=25,summarized=TRUE)
ci.mu.t(x,conf=.95,st.dev=4,xbar=14.6,n=25,summarized=TRUE)
#with finite population correction and summarized = TRUE
ci.mu.z(x,conf=.95,sigma=4,xbar=14.6,n=25,summarized=TRUE,fpc=TRUE,N=100)
ci.mu.t(x,conf=.95,st.dev=4,xbar=14.6,n=25,summarized=TRUE,fpc=TRUE,N=100)