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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_hypothesismean4.wasp
Title produced by softwareTesting Mean with known Variance - Sample Size
Date of computationMon, 10 Nov 2008 05:48:48 -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/10/t1226321366lz8vfxp9phxjmcf.htm/, Retrieved Sun, 19 May 2024 11:29:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23009, Retrieved Sun, 19 May 2024 11:29:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181

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 - Sample Size] [pork quality test q4] [2008-11-10 12:48:48] [4940af498c7c54f3992f17142bd40069] [Current]
Feedback Forum
2008-11-18 09:57:08 [Jan Van Riet] [reply] 
Hier stel je de type 2 fout verkeerd in. De juiste waarden zijn:

Testing Mean with known Variance
population variance 0.012
null hypothesis about mean 0.15
alternative hypothesis about mean 0.152
type I error 0.05
type II error 0.05
sample size 32466.5214491449

Hierbij zie je dat de sample size (en dus ook de kostprijs)gevoelig gaat stijgen naar een aantal dat financieel praktisch niet haalbaar is.
2008-11-20 18:51:48 [Dorien Peeters] [reply] 
Hier ben ik het niet eens met de student.(stelt de type 2 fout verkeerd in)
We willen een type 2 fout bekomen van 5% in plaats van 94%. Oorspronkelijk was de steekproefgrootte 27, nu is deze 32466,52(om het verschil tussen 15% en 15,2% te detecteren) Als de type 2 fout 5% is, zouden we dus 95% van de fraudegevallen ontdekken. Maar dan duikt er een probleem op=>dit is niet haalbaar, het is te duur en duurt te lang.
2008-11-23 16:31:08 [Nathalie Boden] [reply] 
Hier had ik een type 2 error moeten invullen van 0.05 in plaats van 0.95. Hier voor zijn er afgronden 32466 stalen genomen. We kunnen vaststellen dat de kostprijs zo groot is dat het praktisch niet meer haalbaar is.

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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 time1 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 & 1 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=23009&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]1 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=23009&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23009&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 time1 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 mean0.15
type I error0.05
type II error0.95
sample size51.946434318632

\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 & 0.15 \tabularnewline
type I error & 0.05 \tabularnewline
type II error & 0.95 \tabularnewline
sample size & 51.946434318632 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23009&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]0.15[/C][/ROW]
[ROW][C]type I error[/C][C]0.05[/C][/ROW]
[ROW][C]type II error[/C][C]0.95[/C][/ROW]
[ROW][C]sample size[/C][C]51.946434318632[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23009&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23009&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 mean0.15
type I error0.05
type II error0.95
sample size51.946434318632


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.012 ; par2 = 0.15 ; par3 = 0.15 ; par4 = 0.05 ; par5 = 0.95 ;
Parameters (R input):
par1 = 0.012 ; par2 = 0.15 ; par3 = 0.15 ; par4 = 0.05 ; par5 = 0.95 ;
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')