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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_hypothesismean3.wasp
Title produced by softwareTesting Mean with known Variance - Type II Error
Date of computationMon, 10 Nov 2008 06:30:32 -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/t1226324058jcjt6nfrhsb16dc.htm/, Retrieved Tue, 28 May 2024 12:25:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23045, Retrieved Tue, 28 May 2024 12:25:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145

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] [Pork Quality Q3] [2008-11-10 13:30:32] [01c398ee8ca2f8c0964b19b0b10c7536] [Current]
Feedback Forum
2008-11-14 20:31:39 [Mehmet Yilmaz] [reply] 
De student geeft een correct antwoord.
2008-11-16 15:40:13 [Astrid Sniekers] [reply] 
Het antwoord van de student is juist.

Als je type I-fout klein wilt houden en als je de type II-fout wil verkleinen moet u:
- de meettechniek verbeteren
- de steekproef (= 27) vergroten: op deze manier verkleint de variantie en versmalt de normaalverdeling
2008-11-18 14:03:34 [Ilknur Günes] [reply] 
Correct!
2008-11-23 17:09:58 [Elias Van Deun] [reply] 
Het antwoord is juist. Type 2 error betekent de kans dat we iemand die in werkelijkheid schuldig is, aanzien als onschuldig. In dit voorbeeld is de kans dat de leverancier niet gepakt wordt 93,94%, met andere woorden is het zéér verleidelijk om te frauderen.
2008-11-24 09:01:54 [Davy De Nef] [reply] 
Eerst en vooral moeten we ervan uit gaan dat de anonieme getuigenis van een werknemer klopt. Volgens hem zit er een vetgehalte van 15,2% in het vlees terwijl er contractueel overeengekomen was dat dit slechts 15% mocht zijn. De leverancier zou dus mogelijkerwijs frauderen met 0,2%. We moeten onderzoeken wat de kans is dat wij deze fraude niet ontdekken. Dat gebeurt aan de hand van de type II error. Deze bedraagt 0,9342 wat wil zeggen dat de kans 93,42% is, dat wij de fraude van 0,2% NIET ontdekken.
Anders bekeken wil dat zeggen dat de kans dat de leverancier gepakt wordt, 6,58% bedraagt. Met zo’n klein percentage als pakkans wordt het voor de leverancier wel heel verleidelijk om te gaan frauderen.
2008-11-24 19:18:30 [Niels Herremans] [reply] 
Correct antwoord. Er is 94% kans volgens type II error dat fraude niet kan ontdekt worden door ons. Als de leverancier fraude pleegt is de kans dus heel klein dat wij het te weten komen.
2008-11-24 22:24:58 [Kristof Augustyns] [reply] 
Het is hier correct.
De kans dat men fraude kan plegen met een geslaagd verloop is bijna 94% (type II fout).
De pakkans is dus maar 6% en dus helemaal niet veel.
Het is dus wel het proberen waard natuurlijk.
Ongeveer 1 kans op 20 om gepakt te worden.

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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'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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23045&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23045&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23045&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'Gwilym Jenkins' @ 72.249.127.135






Testing Mean with known Variance
sample size27
population variance0.012
sample mean0.1546
null hypothesis about mean0.15
type I error0.05
alternative hypothesis about mean0.152
Type II Error0.93942747750307

\begin{tabular}{lllllllll}
\hline
Testing Mean with known Variance \tabularnewline
sample size & 27 \tabularnewline
population variance & 0.012 \tabularnewline
sample mean & 0.1546 \tabularnewline
null hypothesis about mean & 0.15 \tabularnewline
type I error & 0.05 \tabularnewline
alternative hypothesis about mean & 0.152 \tabularnewline
Type II Error & 0.93942747750307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23045&T=1

[TABLE]
[ROW][C]Testing Mean with known Variance[/C][/ROW]
[ROW][C]sample size[/C][C]27[/C][/ROW]
[ROW][C]population variance[/C][C]0.012[/C][/ROW]
[ROW][C]sample mean[/C][C]0.1546[/C][/ROW]
[ROW][C]null hypothesis about mean[/C][C]0.15[/C][/ROW]
[ROW][C]type I error[/C][C]0.05[/C][/ROW]
[ROW][C]alternative hypothesis about mean[/C][C]0.152[/C][/ROW]
[ROW][C]Type II Error[/C][C]0.93942747750307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23045&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23045&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
sample size27
population variance0.012
sample mean0.1546
null hypothesis about mean0.15
type I error0.05
alternative hypothesis about mean0.152
Type II Error0.93942747750307


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 27 ; par2 = 0.012 ; par3 = 0.1546 ; par4 = 0.15 ; par5 = 0.05 ; par6 = 0.152 ;
Parameters (R input):
par1 = 27 ; par2 = 0.012 ; par3 = 0.1546 ; par4 = 0.15 ; par5 = 0.05 ; par6 = 0.152 ;
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)
par6<-as.numeric(par6)
c <- 'NA'
csn <- abs(qnorm(par5))
if (par3 == par4)
{
conclusion <- 'Error: the null hypothesis and sample mean must not be equal.'
}
if (par3 > par4)
{
c <- par4 + csn * sqrt(par2) / sqrt(par1)
}
if (par3 < par4)
{
c <- par4 - csn * sqrt(par2) / sqrt(par1)
}
p <- pnorm((c - par6) / (sqrt(par2/par1)))
p
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,'sample size',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'population variance',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'sample mean',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'null hypothesis about mean',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'type I error',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alternative hypothesis about mean',header=TRUE)
a<-table.element(a,par6)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('ht_mean_knownvar.htm#ex3','Type II Error','example'),header=TRUE)
a<-table.element(a,p)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')