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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_hypothesismean3.wasp
Title produced by softwareTesting Mean with known Variance - Type II Error
Date of computationThu, 06 Nov 2008 05:54:09 -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/t1225976210fddmy05uqjgcs83.htm/, Retrieved Sun, 19 May 2024 07:17:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22088, Retrieved Sun, 19 May 2024 07:17:15 +0000
QR Codes:

Original text written by user:In samenwerking met Kevin Engels, Katrien Bourdiaudhy, Lindsay Heyndrickx, Stéphanie Claes
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact177

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] [Q3 pork quality test] [2008-11-06 12:54:09] [35348cd8592af0baf5f138bd59921307] [Current]
Feedback Forum
2008-11-13 19:14:20 [Jeroen Aerts] [reply] 
De berekeningen en het antwoord zijn volledig juist en goed geargumenteerd.
2008-11-14 19:03:22 [Kevin Engels] [reply] 
Dit antwoord is correct, we werken hier inderdaad met de type 2-fout
2008-11-19 15:13:19 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
Correcte analyse van student.

We bekijken type II fout, deze is bij benadering 94% (0.93942747750307). Leverancier heeft dus slechts 6% kans om betrapt te worden, wat frauderen relatief aantrekkelijk maakt.

2008-11-19 18:06:27 [Stijn Van de Velde] [reply] 
Correct. Als er inderdaad gefraudeerd word, is de kans dat we dat ontdekken slechts 6%.
Het enige verweer dat we hier tegen hebben is a) onze steekproef vergroten zodat de variantie verkleind of b) nauwkeuriger meten.
De 94% kans dat we het niet ontdekken is immers veel te groot.
2008-11-22 18:56:31 [Stéphanie Claes] [reply] 
Het gaat hier inderdaad om een type II fout, we gaan de alternatieve hypothese vooropstellen om de type II fout te berekenen. We zien dus dat er een zeer klein kans is ( ongeveer 6 % ) dat de leverancier betrapt zal worden op fraude.
2008-11-22 18:56:38 [Stéphanie Claes] [reply] 
Het gaat hier inderdaad om een type II fout, we gaan de alternatieve hypothese vooropstellen om de type II fout te berekenen. We zien dus dat er een zeer klein kans is ( ongeveer 6 % ) dat de leverancier betrapt zal worden op fraude.
2008-11-23 12:15:13 [Jeroen Michel] [reply] 
Ook hier geef ik bovenstaande studenten gelijk. Er valt weinig toe te voegen aan de conclusie van de student.

Wanneer we er van uitgaan dat de getuigenis en we maken bovenstaande berekening, stellen we vast dat de fraude van de leverancier voor 94% (type II error) niet kan worden gededecteerd. Er bestaat voor ons dus slechts een kans van 6% om de leveranciers op de feiten te pakken. De leverancier heeft dus de mogelijkheid om een grootschalige fraude door te voeren.
2008-11-24 10:18:20 [Lindsay Heyndrickx] [reply] 
Dit is correct. Om de type 2 fout te verkleinen moet je de techniek verbeteren of de steekproef vergroten

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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=22088&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=22088&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22088&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=22088&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=22088&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22088&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')