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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_hypothesisprop1.wasp
Title produced by softwareTesting Population Proportion - Critical Value
Date of computationTue, 11 Nov 2008 03:05:00 -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/11/t12263981049nowvbxqm7mvcx1.htm/, Retrieved Sun, 19 May 2024 10:05:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23265, Retrieved Sun, 19 May 2024 10:05:29 +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 Population Proportion - Critical Value] [Testing Populatio...] [2008-11-11 10:05:00] [3b916296c2d2371d528ff188880e3d2b] [Current]
Feedback Forum
2008-11-21 13:16:29 [Natalie De Wilde] [reply] 
We gebruiken hier 1sided test omdat het eerlijk is om aan te nemen dat peer assessment geen invloed kan hebben op the succes ratio.
De sample proportion (0,857) is significant groter dan de nul hypothese (0,69)

Post a new message

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=23265&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=23265&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23265&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 Population Proportion (normal approximation)
Sample size98
Sample Proportion0.857142857
Null hypothesis0.69
Type I error (alpha)0.05
1-sided critical value0.766845707117296
1-sided testReject the Null Hypothesis
2-sided Confidence Interval(sample proportion)[ 0.765575549217387 , 0.948710164782613 ]
2-sided testReject the Null Hypothesis

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (normal approximation) \tabularnewline
Sample size & 98 \tabularnewline
Sample Proportion & 0.857142857 \tabularnewline
Null hypothesis & 0.69 \tabularnewline
Type I error (alpha) & 0.05 \tabularnewline
1-sided critical value & 0.766845707117296 \tabularnewline
1-sided test & Reject the Null Hypothesis \tabularnewline
2-sided Confidence Interval(sample proportion) & [ 0.765575549217387 , 0.948710164782613 ] \tabularnewline
2-sided test & Reject the Null Hypothesis \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23265&T=1

[TABLE]
[ROW][C]Testing Population Proportion (normal approximation)[/C][/ROW]
[ROW][C]Sample size[/C][C]98[/C][/ROW]
[ROW][C]Sample Proportion[/C][C]0.857142857[/C][/ROW]
[ROW][C]Null hypothesis[/C][C]0.69[/C][/ROW]
[ROW][C]Type I error (alpha)[/C][C]0.05[/C][/ROW]
[ROW][C]1-sided critical value[/C][C]0.766845707117296[/C][/ROW]
[ROW][C]1-sided test[/C][C]Reject the Null Hypothesis[/C][/ROW]
[ROW][C]2-sided Confidence Interval(sample proportion)[/C][C][ 0.765575549217387 , 0.948710164782613 ][/C][/ROW]
[ROW][C]2-sided test[/C][C]Reject the Null Hypothesis[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23265&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23265&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 Population Proportion (normal approximation)
Sample size98
Sample Proportion0.857142857
Null hypothesis0.69
Type I error (alpha)0.05
1-sided critical value0.766845707117296
1-sided testReject the Null Hypothesis
2-sided Confidence Interval(sample proportion)[ 0.765575549217387 , 0.948710164782613 ]
2-sided testReject the Null Hypothesis






Testing Population Proportion (Agresti-Coull method)
Sample size98
Sample Proportion0.857142857
Null hypothesis0.69
Type I error (alpha)0.05
Left 1-sided confidence interval[ 0.771569818001552 , 1 ]
Right 1-sided confidence interval[ 0 , 0.923525978824251 ]
2-sided Confidence Interval(sample proportion)[ 0.753562226647685 , 0.933780637276178 ]

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (Agresti-Coull method) \tabularnewline
Sample size & 98 \tabularnewline
Sample Proportion & 0.857142857 \tabularnewline
Null hypothesis & 0.69 \tabularnewline
Type I error (alpha) & 0.05 \tabularnewline
Left 1-sided confidence interval & [ 0.771569818001552 , 1 ] \tabularnewline
Right 1-sided confidence interval & [ 0 , 0.923525978824251  ] \tabularnewline
2-sided Confidence Interval(sample proportion) & [ 0.753562226647685 , 0.933780637276178 ] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23265&T=2

[TABLE]
[ROW][C]Testing Population Proportion (Agresti-Coull method)[/C][/ROW]
[ROW][C]Sample size[/C][C]98[/C][/ROW]
[ROW][C]Sample Proportion[/C][C]0.857142857[/C][/ROW]
[ROW][C]Null hypothesis[/C][C]0.69[/C][/ROW]
[ROW][C]Type I error (alpha)[/C][C]0.05[/C][/ROW]
[ROW][C]Left 1-sided confidence interval[/C][C][ 0.771569818001552 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval[/C][C][ 0 , 0.923525978824251  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(sample proportion)[/C][C][ 0.753562226647685 , 0.933780637276178 ][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23265&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23265&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:

Testing Population Proportion (Agresti-Coull method)
Sample size98
Sample Proportion0.857142857
Null hypothesis0.69
Type I error (alpha)0.05
Left 1-sided confidence interval[ 0.771569818001552 , 1 ]
Right 1-sided confidence interval[ 0 , 0.923525978824251 ]
2-sided Confidence Interval(sample proportion)[ 0.753562226647685 , 0.933780637276178 ]






Testing Population Proportion (Exact and Wilson method)
Sample size98
Sample Proportion0.857142857
Null hypothesis0.69
Type I error (alpha)0.05
Left 1-sided confidence interval(Exact method)[ 0.785719343294818 , 1 ]
Right 1-sided confidence interval(Exact method)[ 0 , 0.911518963115213 ]
2-sided Confidence Interval(Exact method)[ 0.771939502911796 , 0.919640487298203 ]
Left 1-sided confidence interval(Wilson method)[ 0.789394967950623 , 1 ]
Right 1-sided confidence interval(Wilson method)[ 0 , 0.90570082887518 ]
2-sided Confidence Interval(Wilson method)[ 0.774387569504507 , 0.912955294419355 ]

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (Exact and Wilson method) \tabularnewline
Sample size & 98 \tabularnewline
Sample Proportion & 0.857142857 \tabularnewline
Null hypothesis & 0.69 \tabularnewline
Type I error (alpha) & 0.05 \tabularnewline
Left 1-sided confidence interval(Exact method) & [ 0.785719343294818 , 1 ] \tabularnewline
Right 1-sided confidence interval(Exact method) & [ 0 , 0.911518963115213  ] \tabularnewline
2-sided Confidence Interval(Exact method) & [ 0.771939502911796 , 0.919640487298203 ] \tabularnewline
Left 1-sided confidence interval(Wilson method) & [ 0.789394967950623 , 1 ] \tabularnewline
Right 1-sided confidence interval(Wilson method) & [ 0 , 0.90570082887518  ] \tabularnewline
2-sided Confidence Interval(Wilson method) & [ 0.774387569504507 , 0.912955294419355 ] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23265&T=3

[TABLE]
[ROW][C]Testing Population Proportion (Exact and Wilson method)[/C][/ROW]
[ROW][C]Sample size[/C][C]98[/C][/ROW]
[ROW][C]Sample Proportion[/C][C]0.857142857[/C][/ROW]
[ROW][C]Null hypothesis[/C][C]0.69[/C][/ROW]
[ROW][C]Type I error (alpha)[/C][C]0.05[/C][/ROW]
[ROW][C]Left 1-sided confidence interval(Exact method)[/C][C][ 0.785719343294818 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval(Exact method)[/C][C][ 0 , 0.911518963115213  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(Exact method)[/C][C][ 0.771939502911796 , 0.919640487298203 ][/C][/ROW]
[ROW][C]Left 1-sided confidence interval(Wilson method)[/C][C][ 0.789394967950623 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval(Wilson method)[/C][C][ 0 , 0.90570082887518  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(Wilson method)[/C][C][ 0.774387569504507 , 0.912955294419355 ][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23265&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23265&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:

Testing Population Proportion (Exact and Wilson method)
Sample size98
Sample Proportion0.857142857
Null hypothesis0.69
Type I error (alpha)0.05
Left 1-sided confidence interval(Exact method)[ 0.785719343294818 , 1 ]
Right 1-sided confidence interval(Exact method)[ 0 , 0.911518963115213 ]
2-sided Confidence Interval(Exact method)[ 0.771939502911796 , 0.919640487298203 ]
Left 1-sided confidence interval(Wilson method)[ 0.789394967950623 , 1 ]
Right 1-sided confidence interval(Wilson method)[ 0 , 0.90570082887518 ]
2-sided Confidence Interval(Wilson method)[ 0.774387569504507 , 0.912955294419355 ]


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 98 ; par2 = 0.857142857 ; par3 = 0.69 ; par4 = 0.05 ;
Parameters (R input):
par1 = 98 ; par2 = 0.857142857 ; par3 = 0.69 ; par4 = 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)
if (par2 < par3)
{
ucv <- qnorm(par4)
} else {
ucv <- -qnorm(par4)
}
cv1 <- par3 + ucv * sqrt(par3 * (1-par3) / par1)
cv2low <- par2 - abs(qnorm(par4/2)) * sqrt(par3 * (1-par3) / par1)
cv2upp <- par2 + abs(qnorm(par4/2)) * sqrt(par3 * (1-par3) / par1)
z21 <- qnorm(par4/2)^2 / par1
z2 <- qnorm(par4/2)^2 / (2*par1)
z24 <- qnorm(par4/2)^2 / (4*par1^2)
cv2lowexact <- (par2 + z2 - abs(qnorm(par4/2)) * sqrt(par3 * (1-par3) / par1 + z24)) / (1 + z21)
cv2uppexact <- (par2 + z2 + abs(qnorm(par4/2)) * sqrt(par3 * (1-par3) / par1 + z24)) / (1 + z21)
z11 <- qnorm(par4)^2 / par1
z1 <- qnorm(par4)^2 / (2*par1)
z14 <- qnorm(par4)^2 / (4*par1^2)
cv1lowexact <- (par2 + z1 - abs(qnorm(par4)) * sqrt(par3 * (1-par3) / par1 + z14)) / (1 + z11)
cv1uppexact <- (par2 + z1 + abs(qnorm(par4)) * sqrt(par3 * (1-par3) / par1 + z14)) / (1 + z11)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Testing Population Proportion (normal approximation)',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,'Sample Proportion',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Null hypothesis',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Type I error (alpha)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1-sided critical value',header=TRUE)
a<-table.element(a,cv1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1-sided test',header=TRUE)
if (par2 < par3)
{
if (par2 < cv1)
{
a<-table.element(a,'Reject the Null Hypothesis')
} else {
a<-table.element(a,'Do not reject the Null Hypothesis')
}
} else {
if (par2 > cv1)
{
a<-table.element(a,'Reject the Null Hypothesis')
} else {
a<-table.element(a,'Do not reject the Null Hypothesis')
}
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'2-sided Confidence Interval
(sample proportion)',header=TRUE)
dum <- paste('[',cv2low)
dum <- paste(dum,',')
dum <- paste(dum,cv2upp)
dum <- paste(dum,']')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'2-sided test',header=TRUE)
if ((par3 < cv2low) | (par3 > cv2upp))
{
a<-table.element(a,'Reject the Null Hypothesis')
} else {
a<-table.element(a,'Do not reject the Null Hypothesis')
}
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Testing Population Proportion (Agresti-Coull method)',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,'Sample Proportion',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Null hypothesis',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Type I error (alpha)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Left 1-sided confidence interval',header=TRUE)
dum <- paste('[',cv1lowexact)
dum <- paste(dum,', 1 ]')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Right 1-sided confidence interval',header=TRUE)
dum <- paste('[ 0 ,',cv1uppexact)
dum <- paste(dum,' ]')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'2-sided Confidence Interval
(sample proportion)',header=TRUE)
dum <- paste('[',cv2lowexact)
dum <- paste(dum,',')
dum <- paste(dum,cv2uppexact)
dum <- paste(dum,']')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
library(Hmisc)
re <- binconf(par2*par1,par1,par4,method='exact')
re1 <- binconf(par2*par1,par1,par4*2,method='exact')
rw <- binconf(par2*par1,par1,par4,method='wilson')
rw1 <- binconf(par2*par1,par1,par4*2,method='wilson')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Testing Population Proportion (Exact and Wilson method)',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,'Sample Proportion',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Null hypothesis',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Type I error (alpha)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Left 1-sided confidence interval
(Exact method)',header=TRUE)
dum <- paste('[',re1[2])
dum <- paste(dum,', 1 ]')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Right 1-sided confidence interval
(Exact method)',header=TRUE)
dum <- paste('[ 0 ,',re1[3])
dum <- paste(dum,' ]')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'2-sided Confidence Interval
(Exact method)',header=TRUE)
dum <- paste('[',re[2])
dum <- paste(dum,',')
dum <- paste(dum,re[3])
dum <- paste(dum,']')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Left 1-sided confidence interval
(Wilson method)',header=TRUE)
dum <- paste('[',rw1[2])
dum <- paste(dum,', 1 ]')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Right 1-sided confidence interval
(Wilson method)',header=TRUE)
dum <- paste('[ 0 ,',rw1[3])
dum <- paste(dum,' ]')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'2-sided Confidence Interval
(Wilson method)',header=TRUE)
dum <- paste('[',rw[2])
dum <- paste(dum,',')
dum <- paste(dum,rw[3])
dum <- paste(dum,']')
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')