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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 05:39:50 -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/t1226320814v1tebh4a4mmtfle.htm/, Retrieved Sat, 18 May 2024 21:28:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23002, Retrieved Sat, 18 May 2024 21:28:11 +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 - Type II Error] [pork quality test q3] [2008-11-10 12:39:50] [4940af498c7c54f3992f17142bd40069] [Current]
Feedback Forum
2008-11-18 09:50:18 [Jan Van Riet] [reply] 
Je hebt hier een fout gemaakt:
De alternative hypothesis about mean moet op 0.152 staan, en niet op 0.15 zoals jij gedaan hebt (dan krijg je terug de standaardsituatie).

Omwille van een onverklaarbare reden kan ik mijn bekomen resultaat niet archiveren, daarom de volgende tabel :

Testing Mean with known Variance
sample size 27
population variance 0.012
sample mean 0.1546
null hypothesis about mean 0.15
type I error 0.05
alternative hypothesis about mean 0.152
Type II Error 0.93942747750307
2008-11-18 09:51:50 [Jan Van Riet] [reply] 
Hierdoor is de type 2 fout 94%, en is er slechts een kans van 6% dat we frauduleuze leveranciers kunnen betrappen.
Om deze fout klein te houden zullen we onze techniek moeten verbeteren of het aantal samples vergroten. Alleen dan gaat de variantie verkleinen.
2008-11-20 18:41:25 [Dorien Peeters] [reply] 
We gaan de type 2 fout gebruiken. Ik ben het hier deels eens met de student.We gaan eerst de alternatieve hypothese invullen (15,2%)
Ik kwam hier echter een type 2 fout tegen van 93% ( de student kwam hier op 95%)Dit wil zeggen dat er 93% kans is dat we de fraude niet zullen ontdekken(=veel te hoog). Hoe moeten we dit nu oplossen?(dit ontbrak nog bij de uitleg van de student)Je kan dit op 2 manieren oplossen:de variantie verkleinen(steekproef nauwkeuriger maken door meer metingen te doen) of door de meettechnieken te verbeteren.
2008-11-23 16:28:21 [Nathalie Boden] [reply] 
Hier heb ik een fout gemaakt. Ik had namelijk bij alternative hypothesis about mean 0.152 moeten invullen in plaats van 0.15. We gaan hier de type 2 fout gebruiken en de alternatieve hypothese vooropstellen. We zien dat 93% enorm veel is. Er zijn 2 manieren om dit op te lossen en dat is de meettechniek verbeteren of de steekproef vergroten (we gaan de variantie verkleinen en zo gaan de normale verdelingen smaller 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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23002&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23002&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23002&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'George Udny Yule' @ 72.249.76.132






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 mean0.15
Type II Error0.95

\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 & 0.15 \tabularnewline
Type II Error & 0.95 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23002&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]0.15[/C][/ROW]
[ROW][C]Type II Error[/C][C]0.95[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23002&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23002&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 mean0.15
Type II Error0.95


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 = 0.15 ;
Parameters (R input):
par1 = 27 ; par2 = 0.012 ; par3 = 0.1546 ; par4 = 0.15 ; par5 = 0.05 ; par6 = 0.15 ;
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')