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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_hypothesismean4.wasp
Title produced by softwareTesting Mean with known Variance - Sample Size
Date of computationThu, 06 Nov 2008 06:00:43 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/06/t1225976517e3h7jnc1s7kazcm.htm/, Retrieved Sun, 19 May 2024 07:14:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22097, Retrieved Sun, 19 May 2024 07:14:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Testing Mean with known Variance - Type II Error] [Case: the Pork Qu...] [2008-11-06 12:38:52] [38f43994ada0e6172896e12525dcc585]
F RMP     [Testing Mean with known Variance - Sample Size] [Case: the Pork Qu...] [2008-11-06 13:00:43] [284c7cdb9fcda2adcbb08e211682c8d6] [Current]
F           [Testing Mean with known Variance - Sample Size] [PorkQT Q4] [2008-11-11 21:04:30] [d32f94eec6fe2d8c421bd223368a5ced]
Feedback Forum
2008-11-15 17:55:47 [Kevin Neelen] [reply] 
Er is hier wederom gebruik gemaakt van de juiste methode, namelijk 'Testing Mean with known Variance - Sample Size'.
Uit deze tabel kunnen we zien dat als we een pakkans van 95% willen realiseren, er 32467 stalen onderzocht zouden moeten worden. Het spreekt voor zich dat dit niet haalbaar is.
De conclusie van de student, dat het bijna onmogelijk is om zoveel testen te doen, is dus wederom juist.
2008-11-21 22:39:32 [Kim Wester] [reply] 
De conclusie van de student is juist.
Toevoeging: 32.466 steekproeven uitvoeren is een aanzienlijk aantal waarbij rekening moet worden gehouden met tijd, geld en nauwkeurigheidsfouten. Daarnaast zullen misschien niet eens zoveel varkens het bedrijf passeren.
2008-11-24 23:00:07 [Martjin De Swert] [reply] 
Correct antwoord. We hebben via de methode van de testing mean with known variance - sample size zelf de waarschijnlijkheid van detectie van fraude bepaald en bijgevolg bekwamen we een sample size van 32466 gevallen.

Dit is praktisch en economisch onhaalbaar.

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Dataset

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22097&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22097&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22097&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Testing Mean with known Variance
population variance0.012
null hypothesis about mean0.15
alternative hypothesis about mean0.152
type I error0.05
type II error0.05
sample size32466.5214491449

\begin{tabular}{lllllllll}
\hline
Testing Mean with known Variance \tabularnewline
population variance & 0.012 \tabularnewline
null hypothesis about mean & 0.15 \tabularnewline
alternative hypothesis about mean & 0.152 \tabularnewline
type I error & 0.05 \tabularnewline
type II error & 0.05 \tabularnewline
sample size & 32466.5214491449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22097&T=1

[TABLE]
[ROW][C]Testing Mean with known Variance[/C][/ROW]
[ROW][C]population variance[/C][C]0.012[/C][/ROW]
[ROW][C]null hypothesis about mean[/C][C]0.15[/C][/ROW]
[ROW][C]alternative hypothesis about mean[/C][C]0.152[/C][/ROW]
[ROW][C]type I error[/C][C]0.05[/C][/ROW]
[ROW][C]type II error[/C][C]0.05[/C][/ROW]
[ROW][C]sample size[/C][C]32466.5214491449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22097&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22097&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Testing Mean with known Variance
population variance0.012
null hypothesis about mean0.15
alternative hypothesis about mean0.152
type I error0.05
type II error0.05
sample size32466.5214491449


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.012 ; par2 = 0.15 ; par3 = 0.152 ; par4 = 0.05 ; par5 = 0.05 ;
Parameters (R input):
par1 = 0.012 ; par2 = 0.15 ; par3 = 0.152 ; par4 = 0.05 ; par5 = 0.05 ;
R code (references can be found in the software module):
par1<-as.numeric(par1)
par2<-as.numeric(par2)
par3<-as.numeric(par3)
par4<-as.numeric(par4)
par5<-as.numeric(par5)
c <- 'NA'
csn <- abs(qnorm(par5))
if (par2 == par3)
{
conclusion <- 'Error: the null hypothesis and alternative hypothesis must not be equal.'
}
ua <- abs(qnorm(par4))
ub <- qnorm(par5)
c <- (par2+ua/ub*(-par3))/(1-(ua/ub))
sqrtn <- ua*sqrt(par1)/(c - par2)
samplesize <- sqrtn * sqrtn
ua
ub
c
sqrtn
samplesize
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ht_mean_knownvar.htm','Testing Mean with known Variance','learn more about Statistical Hypothesis Testing about the Mean when the Variance is known'),2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'population variance',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'null hypothesis about mean',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alternative hypothesis about mean',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'type I error',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'type II error',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('ht_mean_knownvar.htm#ex4','sample size','example'),header=TRUE)
a<-table.element(a,samplesize)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')