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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_samplenorm.wasp
Title produced by softwareMinimum Sample Size - Testing Mean
Date of computationWed, 30 Oct 2013 15:34:28 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Oct/30/t1383161748m4ku3a8j3nwh7k1.htm/, Retrieved Thu, 02 May 2024 01:02:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=221329, Retrieved Thu, 02 May 2024 01:02:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact59

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Minimum Sample Size - Testing Mean] [Workshop 4 Task 10] [2013-10-30 19:34:28] [9e345f4af24c955bbdd99e7ffb840b0f] [Current]
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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 time2 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221329&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221329&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221329&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 time2 seconds
R Server'George Udny Yule' @ yule.wessa.net






Minimum Sample Size
Population Size20000
Margin of Error0.05
Confidence0.8
Power0.15
Population Variance0.13
z(alpha/2) + z(beta)2.31798495503839
z(alpha) + z(beta)1.8780546230667
Minimum Sample Size (2 sided test)275.563000370784
Minimum Sample Size (1 sided test)181.750989187765

\begin{tabular}{lllllllll}
\hline
Minimum Sample Size \tabularnewline
Population Size & 20000 \tabularnewline
Margin of Error & 0.05 \tabularnewline
Confidence & 0.8 \tabularnewline
Power & 0.15 \tabularnewline
Population Variance & 0.13 \tabularnewline
z(alpha/2) + z(beta) & 2.31798495503839 \tabularnewline
z(alpha) + z(beta) & 1.8780546230667 \tabularnewline
Minimum Sample Size (2 sided test) & 275.563000370784 \tabularnewline
Minimum Sample Size (1 sided test) & 181.750989187765 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221329&T=1

[TABLE]
[ROW][C]Minimum Sample Size[/C][/ROW]
[ROW][C]Population Size[/C][C]20000[/C][/ROW]
[ROW][C]Margin of Error[/C][C]0.05[/C][/ROW]
[ROW][C]Confidence[/C][C]0.8[/C][/ROW]
[ROW][C]Power[/C][C]0.15[/C][/ROW]
[ROW][C]Population Variance[/C][C]0.13[/C][/ROW]
[ROW][C]z(alpha/2) + z(beta)[/C][C]2.31798495503839[/C][/ROW]
[ROW][C]z(alpha) + z(beta)[/C][C]1.8780546230667[/C][/ROW]
[ROW][C]Minimum Sample Size (2 sided test)[/C][C]275.563000370784[/C][/ROW]
[ROW][C]Minimum Sample Size (1 sided test)[/C][C]181.750989187765[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221329&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221329&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:

Minimum Sample Size
Population Size20000
Margin of Error0.05
Confidence0.8
Power0.15
Population Variance0.13
z(alpha/2) + z(beta)2.31798495503839
z(alpha) + z(beta)1.8780546230667
Minimum Sample Size (2 sided test)275.563000370784
Minimum Sample Size (1 sided test)181.750989187765






Minimum Sample Size (for Infinite Populations)
Population Sizeinfinite
Margin of Error0.05
Confidence0.8
Power0.15
Population Variance0.13
z(alpha/2) + z(beta)2.31798495503839
z(alpha) + z(beta)1.8780546230667
Minimum Sample Size (2 sided test)279.398821092785
Minimum Sample Size (1 sided test)183.408636695555

\begin{tabular}{lllllllll}
\hline
Minimum Sample Size (for Infinite Populations) \tabularnewline
Population Size & infinite \tabularnewline
Margin of Error & 0.05 \tabularnewline
Confidence & 0.8 \tabularnewline
Power & 0.15 \tabularnewline
Population Variance & 0.13 \tabularnewline
z(alpha/2) + z(beta) & 2.31798495503839 \tabularnewline
z(alpha) + z(beta) & 1.8780546230667 \tabularnewline
Minimum Sample Size (2 sided test) & 279.398821092785 \tabularnewline
Minimum Sample Size (1 sided test) & 183.408636695555 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221329&T=2

[TABLE]
[ROW][C]Minimum Sample Size (for Infinite Populations)[/C][/ROW]
[ROW][C]Population Size[/C][C]infinite[/C][/ROW]
[ROW][C]Margin of Error[/C][C]0.05[/C][/ROW]
[ROW][C]Confidence[/C][C]0.8[/C][/ROW]
[ROW][C]Power[/C][C]0.15[/C][/ROW]
[ROW][C]Population Variance[/C][C]0.13[/C][/ROW]
[ROW][C]z(alpha/2) + z(beta)[/C][C]2.31798495503839[/C][/ROW]
[ROW][C]z(alpha) + z(beta)[/C][C]1.8780546230667[/C][/ROW]
[ROW][C]Minimum Sample Size (2 sided test)[/C][C]279.398821092785[/C][/ROW]
[ROW][C]Minimum Sample Size (1 sided test)[/C][C]183.408636695555[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221329&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221329&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Minimum Sample Size (for Infinite Populations)
Population Sizeinfinite
Margin of Error0.05
Confidence0.8
Power0.15
Population Variance0.13
z(alpha/2) + z(beta)2.31798495503839
z(alpha) + z(beta)1.8780546230667
Minimum Sample Size (2 sided test)279.398821092785
Minimum Sample Size (1 sided test)183.408636695555






Minimum Sample Size (Unknown Population Variance)
Population Size20000
Margin of Error0.05
Confidence0.8
Power0.15
Population Varianceunknown
t(alpha/2) + t(beta)2.32303709306346
t(alpha) + t(beta)1.88302971114983
Minimum Sample Size (2 sided test)276.748870218166
Minimum Sample Size (1 sided test)182.706396449993

\begin{tabular}{lllllllll}
\hline
Minimum Sample Size (Unknown Population Variance) \tabularnewline
Population Size & 20000 \tabularnewline
Margin of Error & 0.05 \tabularnewline
Confidence & 0.8 \tabularnewline
Power & 0.15 \tabularnewline
Population Variance & unknown \tabularnewline
t(alpha/2) + t(beta) & 2.32303709306346 \tabularnewline
t(alpha) + t(beta) & 1.88302971114983 \tabularnewline
Minimum Sample Size (2 sided test) & 276.748870218166 \tabularnewline
Minimum Sample Size (1 sided test) & 182.706396449993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221329&T=3

[TABLE]
[ROW][C]Minimum Sample Size (Unknown Population Variance)[/C][/ROW]
[ROW][C]Population Size[/C][C]20000[/C][/ROW]
[ROW][C]Margin of Error[/C][C]0.05[/C][/ROW]
[ROW][C]Confidence[/C][C]0.8[/C][/ROW]
[ROW][C]Power[/C][C]0.15[/C][/ROW]
[ROW][C]Population Variance[/C][C]unknown[/C][/ROW]
[ROW][C]t(alpha/2) + t(beta)[/C][C]2.32303709306346[/C][/ROW]
[ROW][C]t(alpha) + t(beta)[/C][C]1.88302971114983[/C][/ROW]
[ROW][C]Minimum Sample Size (2 sided test)[/C][C]276.748870218166[/C][/ROW]
[ROW][C]Minimum Sample Size (1 sided test)[/C][C]182.706396449993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221329&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221329&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Minimum Sample Size (Unknown Population Variance)
Population Size20000
Margin of Error0.05
Confidence0.8
Power0.15
Population Varianceunknown
t(alpha/2) + t(beta)2.32303709306346
t(alpha) + t(beta)1.88302971114983
Minimum Sample Size (2 sided test)276.748870218166
Minimum Sample Size (1 sided test)182.706396449993






Minimum Sample Size(Infinite Population, Unknown Population Variance)
Population Sizeinfinite
Margin of Error0.05
Confidence0.8
Power0.15
Population Varianceunknown
t(alpha/2) + t(beta)2.32296733009161
t(alpha) + t(beta)1.88298438156779
Minimum Sample Size (2 sided test)280.601215266993
Minimum Sample Size (1 sided test)184.372769423869

\begin{tabular}{lllllllll}
\hline
Minimum Sample Size(Infinite Population, Unknown Population Variance) \tabularnewline
Population Size & infinite \tabularnewline
Margin of Error & 0.05 \tabularnewline
Confidence & 0.8 \tabularnewline
Power & 0.15 \tabularnewline
Population Variance & unknown \tabularnewline
t(alpha/2) + t(beta) & 2.32296733009161 \tabularnewline
t(alpha) + t(beta) & 1.88298438156779 \tabularnewline
Minimum Sample Size (2 sided test) & 280.601215266993 \tabularnewline
Minimum Sample Size (1 sided test) & 184.372769423869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221329&T=4

[TABLE]
[ROW][C]Minimum Sample Size(Infinite Population, Unknown Population Variance)[/C][/ROW]
[ROW][C]Population Size[/C][C]infinite[/C][/ROW]
[ROW][C]Margin of Error[/C][C]0.05[/C][/ROW]
[ROW][C]Confidence[/C][C]0.8[/C][/ROW]
[ROW][C]Power[/C][C]0.15[/C][/ROW]
[ROW][C]Population Variance[/C][C]unknown[/C][/ROW]
[ROW][C]t(alpha/2) + t(beta)[/C][C]2.32296733009161[/C][/ROW]
[ROW][C]t(alpha) + t(beta)[/C][C]1.88298438156779[/C][/ROW]
[ROW][C]Minimum Sample Size (2 sided test)[/C][C]280.601215266993[/C][/ROW]
[ROW][C]Minimum Sample Size (1 sided test)[/C][C]184.372769423869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221329&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221329&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Minimum Sample Size(Infinite Population, Unknown Population Variance)
Population Sizeinfinite
Margin of Error0.05
Confidence0.8
Power0.15
Population Varianceunknown
t(alpha/2) + t(beta)2.32296733009161
t(alpha) + t(beta)1.88298438156779
Minimum Sample Size (2 sided test)280.601215266993
Minimum Sample Size (1 sided test)184.372769423869


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 20000 ; par2 = 0.05 ; par3 = 0.80 ; par4 = 0.13 ; par5 = 0.15 ;
Parameters (R input):
par1 = 20000 ; par2 = 0.05 ; par3 = 0.80 ; par4 = 0.13 ; par5 = 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)
(z <- abs(qnorm((1-par3)/2)) + abs(qnorm(1-par5)))
(z1 <- abs(qnorm(1-par3)) + abs(qnorm(1-par5)))
z2 <- z*z
z2one <- z1*z1
z24 <- z2 * par4
z24one <- z2one * par4
npop <- array(NA, 200)
ppop <- array(NA, 200)
for (i in 1:200)
{
ppop[i] <- i * 100
npop[i] <- ppop[i] * z24 / (z24 + (ppop[i] - 1) * par2*par2)
}
bitmap(file='pic1.png')
plot(ppop,npop, xlab='population size', ylab='sample size (2 sided test)', main = paste('Confidence',par3))
dumtext <- paste('Margin of error = ',par2)
dumtext <- paste(dumtext,' Population Var. = ')
dumtext <- paste(dumtext, par4)
mtext(dumtext)
grid()
dev.off()
par2sq <- par2 * par2
num <- par1 * z24
denom <- z24 + (par1 - 1) * par2sq
(n <- num/denom)
num1 <- par1 * z24one
denom1 <- z24one + (par1 - 1) * par2sq
(n1 <- num1/denom1)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Population Size',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Margin of Error',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Confidence',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Power',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Population Variance',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'z(alpha/2) + z(beta)',header=TRUE)
a<-table.element(a,z)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'z(alpha) + z(beta)',header=TRUE)
a<-table.element(a,z1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (2 sided test)',header=TRUE)
a<-table.element(a,n)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (1 sided test)',header=TRUE)
a<-table.element(a,n1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
(ni <- z24 / (par2sq))
(ni1 <- z24one / (par2sq))
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (for Infinite Populations)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Population Size',header=TRUE)
a<-table.element(a,'infinite')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Margin of Error',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Confidence',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Power',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Population Variance',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'z(alpha/2) + z(beta)',header=TRUE)
a<-table.element(a,z)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'z(alpha) + z(beta)',header=TRUE)
a<-table.element(a,z1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (2 sided test)',header=TRUE)
a<-table.element(a,ni)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (1 sided test)',header=TRUE)
a<-table.element(a,ni1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
(z <- abs(qt((1-par3)/2,n-1)) + abs(qt(1-par5,n-1)))
(z1 <- abs(qt(1-par3,n1-1)) + abs(qt(1-par5,n1-1)))
z2 <- z*z
z2one <- z1*z1
z24 <- z2 * par4
z24one <- z2one * par4
par2sq <- par2 * par2
num <- par1 * z24
denom <- z24 + (par1 - 1) * par2sq
(n <- num/denom)
num1 <- par1 * z24one
denom1 <- z24one + (par1 - 1) * par2sq
(n1 <- num1/denom1)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (Unknown Population Variance)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Population Size',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Margin of Error',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Confidence',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Power',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Population Variance',header=TRUE)
a<-table.element(a,'unknown')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t(alpha/2) + t(beta)',header=TRUE)
a<-table.element(a,z)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t(alpha) + t(beta)',header=TRUE)
a<-table.element(a,z1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (2 sided test)',header=TRUE)
a<-table.element(a,n)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (1 sided test)',header=TRUE)
a<-table.element(a,n1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
(z <- abs(qt((1-par3)/2,ni-1)) + abs(qt(1-par5,ni-1)))
(z1 <- abs(qt(1-par3,ni1-1)) + abs(qt(1-par5,ni1-1)))
z2 <- z*z
z2one <- z1*z1
z24 <- z2 * par4
z24one <- z2one * par4
(ni <- z24 / (par2sq))
(ni1 <- z24one / (par2sq))
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size
(Infinite Population, Unknown Population Variance)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Population Size',header=TRUE)
a<-table.element(a,'infinite')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Margin of Error',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Confidence',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Power',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Population Variance',header=TRUE)
a<-table.element(a,'unknown')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t(alpha/2) + t(beta)',header=TRUE)
a<-table.element(a,z)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t(alpha) + t(beta)',header=TRUE)
a<-table.element(a,z1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (2 sided test)',header=TRUE)
a<-table.element(a,ni)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Minimum Sample Size (1 sided test)',header=TRUE)
a<-table.element(a,ni1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')