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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 computationMon, 10 Nov 2008 06:59:45 -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/t1226325639cqcfssf0zheb2fv.htm/, Retrieved Sun, 19 May 2024 10:43:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23062, Retrieved Sun, 19 May 2024 10:43:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198

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 Q4] [2008-11-10 13:59:45] [01c398ee8ca2f8c0964b19b0b10c7536] [Current]
Feedback Forum
2008-11-14 20:32:04 [Mehmet Yilmaz] [reply] 
De student geeft een correct antwoord.
2008-11-16 15:40:39 [Astrid Sniekers] [reply] 
Het antwoord van de student is juist.
2008-11-18 14:04:27 [Ilknur Günes] [reply] 
Correct 32446 steekproeven zijn onhaalbaar: het zou de firma te veel geld en tijd kosten
2008-11-23 17:11:59 [Elias Van Deun] [reply] 
Dit antwoord klopt volledig. 32466.5214491449 steekproeven zijn onhaalbaar!
2008-11-24 09:08:33 [Davy De Nef] [reply] 
Als we de pakkans willen verhogen van 7% (zie Q3) naar 95%, zullen we het aantal steekproeven moeten verhogen. Daarvoor moet de type II error van 93% (zie Q3) zakken naar 5%. Om dit te kunnen verwezenlijken komen we uit op 32466,52. Dit wil zeggen dat we 32467 steekproeven zullen moeten nemen. Een halve steekproef nemen is nogal moeilijk dus rond ik af naar boven. Zoveel steekproeven nemen kost veel tijd, bijgevolg kost het ook veel geld. We mogen er dus vanuit gaan dat dit, ook praktisch gezien, onmogelijk/onhaalbaar is.
2008-11-24 19:19:44 [Niels Herremans] [reply] 
Correct antwoord. Als we de pakkans van de fraude willen verhogen naar 95% moeten we het aantal steekproeven naar 32467 brengen ipv van 27. Dit is echter onrealistisch wegens de hogere kosten en de tijd.
2008-11-24 22:28:43 [Kristof Augustyns] [reply] 
Dit is een correcte oplossing.
De steekproeven zullen inderdaad verhoogd moeten worden tot sample size 32466.5214491449 en dan pas komt men aan 95% en dit is ook wel logisch.
Hoe meer steekproeven, hoe groter de pakkans.

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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 time3 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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23062&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]3 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=23062&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23062&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 time3 seconds
R Server'George Udny Yule' @ 72.249.76.132






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=23062&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=23062&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23062&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')