mistat-package: The Modern Industrial Statistics Package

In mistat: Data Sets, Functions and Examples from the Book: "Modern Industrial Statistics" by Kenett, Zacks and Amberti

The Modern Industrial Statistics Package

Description

This R package is providing all the data sets and statistical analysis of Modern Industrial Statistics, with applications using R, MINITAB and JMP by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2013. This second revised and expanded second edition.

Details

Package:	mistat
Type:	Package
Date:	2012-08-22
License:	GPL >= 2

Author(s)

Daniele Amberti

Maintainer: Daniele Amberti <[e-mail]>

See Also

Bootstrap Resampling, Quality Control Charts, Operating Characteristics of an Acceptance Sampling Plan, Quality Control Charts, Fractional Factorial 2-level designs.

Examples

data(OELECT)
data(OELECT1)

randomizationTest(list(a=OELECT, b=OELECT1), 
                  R=500, calc=mean, 
                  fun=function(x) x[1]-x[2],
                  seed=123)

Ps <- pistonSimulation(
  m = rep(60, 100),
  s = rep(0.02, 100),
  v0 = rep(0.01, 100),
  k = rep(5000, 100),
  p0 = rep(110000, 100),
  t = c(rep(296,35), 296*1.1^(1:65)),
  t0 = rep(360, 100),
  each = 1, 
  seed = 123,
  check = FALSE)

head(Ps)

cusumArl(mean= 0.0, 
         N=100,  
         limit=5000,
         seed=123)

powerCircuitSimulation(seed=123, each=3)

set.seed(123)

Ttf <- rgamma(50, 
              shape=2, 
              scale=100)

Ttr <- rgamma(50, 
              shape=2, 
              scale=1)
              
AvailEbd <- availDis(ttf=Ttf,  
                     ttr=Ttr, 
                     n=1000, seed=123)


RenewEbd <- renewDis(ttf=Ttf, 
                     ttr=Ttr, 
                     time=1000, 
                     n=1000)

mistat documentation built on March 7, 2023, 6:43 p.m.