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Author*Unverified author*
R Software Modulerwasp_hypothesisprop1.wasp
Title produced by softwareTesting Population Proportion - Critical Value
Date of computationSat, 02 May 2015 09:19:50 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/May/02/t1430554828y9dmo4i7khpji76.htm/, Retrieved Wed, 01 Dec 2021 23:43:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=279003, Retrieved Wed, 01 Dec 2021 23:43:35 +0000
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Estimated Impact171
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-       [Testing Population Proportion - Critical Value] [NVNS-NS] [2015-05-02 08:19:50] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 0 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=279003&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=279003&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=279003&T=0

As an alternative you can also use a 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 time0 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Testing Population Proportion (normal approximation)
Sample size15249
Sample Proportion0.00085
Null hypothesis0.00071
Type I error (alpha)0.05
1-sided critical value0.00106479829132419
1-sided testDo not reject the Null Hypothesis
2-sided Confidence Interval(sample proportion)[ 0.000427231760092493 , 0.00127276823990751 ]
2-sided testDo not reject the Null Hypothesis

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (normal approximation) \tabularnewline
Sample size & 15249 \tabularnewline
Sample Proportion & 0.00085 \tabularnewline
Null hypothesis & 0.00071 \tabularnewline
Type I error (alpha) & 0.05 \tabularnewline
1-sided critical value & 0.00106479829132419 \tabularnewline
1-sided test & Do not reject the Null Hypothesis \tabularnewline
2-sided Confidence Interval(sample proportion) & [ 0.000427231760092493 , 0.00127276823990751 ] \tabularnewline
2-sided test & Do not reject the Null Hypothesis \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=279003&T=1

[TABLE]
[ROW][C]Testing Population Proportion (normal approximation)[/C][/ROW]
[ROW][C]Sample size[/C][C]15249[/C][/ROW]
[ROW][C]Sample Proportion[/C][C]0.00085[/C][/ROW]
[ROW][C]Null hypothesis[/C][C]0.00071[/C][/ROW]
[ROW][C]Type I error (alpha)[/C][C]0.05[/C][/ROW]
[ROW][C]1-sided critical value[/C][C]0.00106479829132419[/C][/ROW]
[ROW][C]1-sided test[/C][C]Do not reject the Null Hypothesis[/C][/ROW]
[ROW][C]2-sided Confidence Interval(sample proportion)[/C][C][ 0.000427231760092493 , 0.00127276823990751 ][/C][/ROW]
[ROW][C]2-sided test[/C][C]Do not reject the Null Hypothesis[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=279003&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=279003&T=1

As an alternative you can also use a QR Code:  

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

Testing Population Proportion (normal approximation)
Sample size15249
Sample Proportion0.00085
Null hypothesis0.00071
Type I error (alpha)0.05
1-sided critical value0.00106479829132419
1-sided testDo not reject the Null Hypothesis
2-sided Confidence Interval(sample proportion)[ 0.000427231760092493 , 0.00127276823990751 ]
2-sided testDo not reject the Null Hypothesis







Testing Population Proportion (Agresti-Coull method)
Sample size15249
Sample Proportion0.00085
Null hypothesis0.00071
Type I error (alpha)0.05
Left 1-sided confidence interval[ 0.000572889754615231 , 1 ]
Right 1-sided confidence interval[ 0 , 0.00130420152363743 ]
2-sided Confidence Interval(sample proportion)[ 0.000534690020478373 , 0.00141673384379324 ]

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (Agresti-Coull method) \tabularnewline
Sample size & 15249 \tabularnewline
Sample Proportion & 0.00085 \tabularnewline
Null hypothesis & 0.00071 \tabularnewline
Type I error (alpha) & 0.05 \tabularnewline
Left 1-sided confidence interval & [ 0.000572889754615231 , 1 ] \tabularnewline
Right 1-sided confidence interval & [ 0 , 0.00130420152363743  ] \tabularnewline
2-sided Confidence Interval(sample proportion) & [ 0.000534690020478373 , 0.00141673384379324 ] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=279003&T=2

[TABLE]
[ROW][C]Testing Population Proportion (Agresti-Coull method)[/C][/ROW]
[ROW][C]Sample size[/C][C]15249[/C][/ROW]
[ROW][C]Sample Proportion[/C][C]0.00085[/C][/ROW]
[ROW][C]Null hypothesis[/C][C]0.00071[/C][/ROW]
[ROW][C]Type I error (alpha)[/C][C]0.05[/C][/ROW]
[ROW][C]Left 1-sided confidence interval[/C][C][ 0.000572889754615231 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval[/C][C][ 0 , 0.00130420152363743  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(sample proportion)[/C][C][ 0.000534690020478373 , 0.00141673384379324 ][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=279003&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=279003&T=2

As an alternative you can also use a QR Code:  

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

Testing Population Proportion (Agresti-Coull method)
Sample size15249
Sample Proportion0.00085
Null hypothesis0.00071
Type I error (alpha)0.05
Left 1-sided confidence interval[ 0.000572889754615231 , 1 ]
Right 1-sided confidence interval[ 0 , 0.00130420152363743 ]
2-sided Confidence Interval(sample proportion)[ 0.000534690020478373 , 0.00141673384379324 ]







Testing Population Proportion (Exact and Wilson method)
Sample size15249
Sample Proportion0.00085
Null hypothesis0.00071
Type I error (alpha)0.05
Left 1-sided confidence interval(Exact method)[ 0.000502402433655345 , 1 ]
Right 1-sided confidence interval(Exact method)[ 0 , 0.00135199206960017 ]
2-sided Confidence Interval(Exact method)[ 0.000452175896555022 , 0.00145420777505683 ]
Left 1-sided confidence interval(Wilson method)[ 0.000540429973704393 , 1 ]
Right 1-sided confidence interval(Wilson method)[ 0 , 0.00133666130454827 ]
2-sided Confidence Interval(Wilson method)[ 0.00049644610027278 , 0.00145497776399883 ]

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (Exact and Wilson method) \tabularnewline
Sample size & 15249 \tabularnewline
Sample Proportion & 0.00085 \tabularnewline
Null hypothesis & 0.00071 \tabularnewline
Type I error (alpha) & 0.05 \tabularnewline
Left 1-sided confidence interval(Exact method) & [ 0.000502402433655345 , 1 ] \tabularnewline
Right 1-sided confidence interval(Exact method) & [ 0 , 0.00135199206960017  ] \tabularnewline
2-sided Confidence Interval(Exact method) & [ 0.000452175896555022 , 0.00145420777505683 ] \tabularnewline
Left 1-sided confidence interval(Wilson method) & [ 0.000540429973704393 , 1 ] \tabularnewline
Right 1-sided confidence interval(Wilson method) & [ 0 , 0.00133666130454827  ] \tabularnewline
2-sided Confidence Interval(Wilson method) & [ 0.00049644610027278 , 0.00145497776399883 ] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=279003&T=3

[TABLE]
[ROW][C]Testing Population Proportion (Exact and Wilson method)[/C][/ROW]
[ROW][C]Sample size[/C][C]15249[/C][/ROW]
[ROW][C]Sample Proportion[/C][C]0.00085[/C][/ROW]
[ROW][C]Null hypothesis[/C][C]0.00071[/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][ 0.000502402433655345 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval(Exact method)[/C][C][ 0 , 0.00135199206960017  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(Exact method)[/C][C][ 0.000452175896555022 , 0.00145420777505683 ][/C][/ROW]
[ROW][C]Left 1-sided confidence interval(Wilson method)[/C][C][ 0.000540429973704393 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval(Wilson method)[/C][C][ 0 , 0.00133666130454827  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(Wilson method)[/C][C][ 0.00049644610027278 , 0.00145497776399883 ][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=279003&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=279003&T=3

As an alternative you can also use a QR Code:  

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

Testing Population Proportion (Exact and Wilson method)
Sample size15249
Sample Proportion0.00085
Null hypothesis0.00071
Type I error (alpha)0.05
Left 1-sided confidence interval(Exact method)[ 0.000502402433655345 , 1 ]
Right 1-sided confidence interval(Exact method)[ 0 , 0.00135199206960017 ]
2-sided Confidence Interval(Exact method)[ 0.000452175896555022 , 0.00145420777505683 ]
Left 1-sided confidence interval(Wilson method)[ 0.000540429973704393 , 1 ]
Right 1-sided confidence interval(Wilson method)[ 0 , 0.00133666130454827 ]
2-sided Confidence Interval(Wilson method)[ 0.00049644610027278 , 0.00145497776399883 ]



Parameters (Session):
par1 = 15249 ; par2 = 0.00085 ; par3 = 0.00071 ; par4 = 0.05 ;
Parameters (R input):
par1 = 15249 ; par2 = 0.00085 ; par3 = 0.00071 ; 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')