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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 computationThu, 13 Nov 2008 06:32:05 -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/13/t1226583166640iawgxno69koc.htm/, Retrieved Tue, 28 May 2024 07:01:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24604, Retrieved Tue, 28 May 2024 07:01:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152

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] [sample size] [2008-11-13 13:32:05] [628d1df75cd8f2f5ef9dafa62752b4fe] [Current]
Feedback Forum
2008-11-15 16:18:07 [Philip Van Herck] [reply] 
Zelfde opmerking omtrent foute input. De juiste oplossing is: 32466.5. Dit betekent dat de steekproef verhoogd zou moeten worden tot 32466.5 wat praktisch gezien niet mogelijk is. Dit zou veel te veel tijd en geld kosten.
2008-11-17 13:58:41 [Stef Vermeiren] [reply] 
Wederom foute input.

De sample size wordt hier gesteld op 32466.5, wat praktisch niet haalbaar is.
2008-11-23 16:06:21 [Gilliam Schoorel] [reply] 
Om de type 2 fout te beperken tot 5 % zouden we 32 467 steekroeven moeten nemen, maar dit is niet haalbaar. Dit betekend dat men om de pakkans te vergroten tot 95%, men de de steekproefgrootte moet uitbreiden tot 32467 observaties. Je conclusie is weer gebaseerd op verkeerde input, maar toch goed geïnterpreteerd.
2008-11-24 11:03:42 [Sofie Sergoynne] [reply] 
Weer dezelfde fout als bij de vorige vragen... verkeerde outupt zorgt er dus voor dat je een verkeerd besluit neemt. Indien je 15% en 15.2% ingeeft al 0.15 en 0.152 dan bakom je 3.60 als sample size. We maken de proef nauwkeuriger en de pakkans groter door de variantie te verkleinen. Hierdoor gaat de steekproefgrootte moeten verhoogd worden Dit kost echter veel geld en tijd om de opgelegde nauwkeurigheid te verkrijgen. Je iterpretatie van de sample size is oko fout.

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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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24604&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24604&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24604&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Testing Mean with known Variance
population variance0.012
null hypothesis about mean15
alternative hypothesis about mean15.2
type I error0.05
type II error0.05
sample size3.24665214491452

\begin{tabular}{lllllllll}
\hline
Testing Mean with known Variance \tabularnewline
population variance & 0.012 \tabularnewline
null hypothesis about mean & 15 \tabularnewline
alternative hypothesis about mean & 15.2 \tabularnewline
type I error & 0.05 \tabularnewline
type II error & 0.05 \tabularnewline
sample size & 3.24665214491452 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24604&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]15[/C][/ROW]
[ROW][C]alternative hypothesis about mean[/C][C]15.2[/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]3.24665214491452[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24604&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24604&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 mean15
alternative hypothesis about mean15.2
type I error0.05
type II error0.05
sample size3.24665214491452


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.012 ; par2 = 15 ; par3 = 15.2 ; par4 = 0.05 ; par5 = 0.05 ;
Parameters (R input):
par1 = 0.012 ; par2 = 15 ; par3 = 15.2 ; 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')