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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 computationSun, 09 Nov 2008 13:52:05 -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/09/t1226263979in6s6n10qlsw8dv.htm/, Retrieved Sun, 19 May 2024 10:48:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22872, Retrieved Sun, 19 May 2024 10:48:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsVan Dooren Leen
Estimated Impact148

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...] [2008-11-09 16:47:46] [57850c80fd59ccfb28f882be994e814e]
F         [Testing Mean with known Variance - Sample Size] [Case pork quality...] [2008-11-09 20:52:05] [d175f84d503eb4f2a43145d5e67795b5] [Current]
Feedback Forum
2008-11-16 18:02:53 [006ad2c49b6a7c2ad6ab685cfc1dae56] [reply] 
De oplossing van dit probleem is meer stalen nemen. De proef wordt dan nauwkeuriger en de pakkans groter door de variantie te verkleinen.
Om dit te bereiken moet de steekproefgrootte naar 32466.5 verhoogd worden. Een steekproefgrootte van 32466.5 is echter niet haalbaar.

2008-11-18 16:45:33 [72e979bcc364082694890d2eccc1a66f] [reply] 
Om dit doel te bereiken moeten we de steekproef vergroten. We komen dan tot de conclusie dat we 32466 steekproeven nodig hebben. Dit is echter een veel te groot aantal steekproeven. Als we hiermee gaan werken wordt dit veel te duur en omslachtig. Dit is dus niet realiseerbaar.
2008-11-22 13:59:22 [Hannes Van Hoof] [reply] 
Van de tabel kan je aflezen dat er een steekproef van 32467 nodig is om de type 2 fout te verkleinen tot 5%. Dit is in de praktijk moeilijk haalbaar aangezien dit veel te duur is en te veel tijd in beslag neemt.
2008-11-23 23:15:20 [Anouk Greeve] [reply] 
We maken de proef nauwkeuriger en de pakkans groter door de variantie te verkleinen. Om dit te bereiken moet de steekproefgrootte naar 32466,5 verhoogd worden. Dit kost echter veel geld en tijd om de opgelegde nauwkeurigheid te verkrijgen. Een steekproefgrootte van 32466,5 is dus niet haalbaar

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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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22872&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22872&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22872&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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=22872&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=22872&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22872&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')