CImulen_data: CImulen_data

In sigbertklinke/exams2moodle: Access routines to XML Moodle files and helper routines to simplify generating exercises for Moodle from the 'exams' package

CImulen_data

Description

Data generation for the necessary sample size of a confidence interval for the population mean value. Either the estimation error e or the length of the interval l must be given (l=2*e). It is ensured that the computed s deviates from sigma.

Usage

CImulen_data(
  sigma,
  e = NULL,
  l = NULL,
  conf.level = c(0.9, 0.95, 0.99),
  nmin = 30,
  size = NA,
  u = c(seq(0.1, 0.4, 0.001), seq(0.6, 0.9, 0.001)),
  full = FALSE
)

Arguments

sigma	numeric: vector of possible variance
e	numeric: vector of estimation errors
l	numeric: vector of lengths of the interval
conf.level	numeric: vector of confidence levels of the interval (default: c(0.9, 0.95, 0.99))
nmin	numeric: minimal value of necessary observation (default: 30)
size	numeric: sample size for computing a sample standard deviation. Default NA means that the solution of estimation is used
u	numeric: vector of quantiles to sample the sample standard deviation (default: c(seq(0.15, 0.45, 0.001), seq(0.55, 0.85, 0.001)))
full	logical: if TRUE then a data frame with possible solution is returned, otherwise a list with a randomly choosen solution (default: FALSE)

Value

a data frame or a list with

• e estimation error

• sigma population variance

• conf.level confidence level

• l interval length

• x 1-alpha/2

• q z_{1-alpha/2}

• q2 z^2_{1-alpha/2}

• n computed minimal sample size

• N the smallest integer not less than n

• s sample standard deviation

Examples

# one solution
CImulen_data (1:10, e=(1:10)/10)
# all solutions
mul <- CImulen_data (1:10, e=(1:10)/10, full=TRUE)
str(mul)

sigbertklinke/exams2moodle documentation built on July 6, 2023, 3:26 p.m.