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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_hypothesisprop1.wasp
Title produced by softwareTesting Population Proportion - Critical Value
Date of computationSun, 09 Nov 2008 13:15:31 -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/09/t1226262355816dbmolkiekbzs.htm/, Retrieved Tue, 14 May 2024 00:59:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22864, Retrieved Tue, 14 May 2024 00:59:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact193

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] [vraag 1] [2008-11-09 20:15:31] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
F         [Testing Population Proportion - Critical Value] [vraag 3] [2008-11-09 20:35:59] [c45c87b96bbf32ffc2144fc37d767b2e]
- RM      [Minimum Sample Size - Testing Proportions] [vraag 4] [2008-11-09 20:51:51] [c45c87b96bbf32ffc2144fc37d767b2e]
F           [Minimum Sample Size - Testing Proportions] [vraag 4] [2008-11-09 20:55:06] [c45c87b96bbf32ffc2144fc37d767b2e]
F RM D      [Bivariate Kernel Density Estimation] [vraag 1] [2008-11-09 21:10:08] [c45c87b96bbf32ffc2144fc37d767b2e]
F RM D      [Bivariate Kernel Density Estimation] [vraag 1] [2008-11-09 21:12:27] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMPD        [Partial Correlation] [vraag 1] [2008-11-24 20:29:41] [c45c87b96bbf32ffc2144fc37d767b2e]
- RM D      [Bivariate Kernel Density Estimation] [vraag 1] [2008-11-09 21:14:06] [c45c87b96bbf32ffc2144fc37d767b2e]
- RM D      [Bivariate Kernel Density Estimation] [vraag 1] [2008-11-09 21:16:55] [c45c87b96bbf32ffc2144fc37d767b2e]
- RM D      [Bivariate Kernel Density Estimation] [vraag 1] [2008-11-09 21:19:51] [c45c87b96bbf32ffc2144fc37d767b2e]
F RM D      [Hierarchical Clustering] [vraag 2] [2008-11-09 21:30:22] [c45c87b96bbf32ffc2144fc37d767b2e]
-   PD        [Hierarchical Clustering] [dendrogram] [2008-12-21 14:09:41] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMPD          [Histogram] [Histogram groep 1] [2008-12-21 16:48:39] [c45c87b96bbf32ffc2144fc37d767b2e]
-  M D          [Hierarchical Clustering] [] [2009-12-30 13:17:52] [d2d412c7f4d35ffbf5ee5ee89db327d4]
-   PD            [Hierarchical Clustering] [] [2009-12-30 14:13:32] [d2d412c7f4d35ffbf5ee5ee89db327d4]
Feedback Forum
2008-11-19 15:53:34 [] [reply] 
De student heeft deze vraag correct opgelost en gebruik gemaakt van de juiste berekenigsmethode.
2008-11-19 15:57:36 [Nathalie Koulouris] [reply] 
De student heeft deze vraag correct opgelost en gebruik gemaakt van de juiste berekenigsmethode.
2008-11-19 21:16:34 [Michaël De Kuyer] [reply] 
Naar mijn mening heb ik deze vraag goed beantwoord.

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 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=22864&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=22864&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22864&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 Population Proportion (normal approximation)
Sample size98
Sample Proportion0.8571
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)[ 0.765532692217387 , 0.948667307782613 ]
2-sided testReject the Null Hypothesis

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (normal approximation) \tabularnewline
Sample size & 98 \tabularnewline
Sample Proportion & 0.8571 \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) & [ 0.765532692217387 , 0.948667307782613 ] \tabularnewline
2-sided test & Reject the Null Hypothesis \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22864&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]0.8571[/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][ 0.765532692217387 , 0.948667307782613 ][/C][/ROW]
[ROW][C]2-sided test[/C][C]Reject the Null Hypothesis[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22864&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22864&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 Proportion0.8571
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)[ 0.765532692217387 , 0.948667307782613 ]
2-sided testReject the Null Hypothesis






Testing Population Proportion (Agresti-Coull method)
Sample size98
Sample Proportion0.8571
Null hypothesis0.69
Type I error (alpha)0.05
Left 1-sided confidence interval[ 0.771528112392745 , 1 ]
Right 1-sided confidence interval[ 0 , 0.923484273215444 ]
2-sided Confidence Interval(sample proportion)[ 0.753520986213301 , 0.933739396841794 ]

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (Agresti-Coull method) \tabularnewline
Sample size & 98 \tabularnewline
Sample Proportion & 0.8571 \tabularnewline
Null hypothesis & 0.69 \tabularnewline
Type I error (alpha) & 0.05 \tabularnewline
Left 1-sided confidence interval & [ 0.771528112392745 , 1 ] \tabularnewline
Right 1-sided confidence interval & [ 0 , 0.923484273215444  ] \tabularnewline
2-sided Confidence Interval(sample proportion) & [ 0.753520986213301 , 0.933739396841794 ] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22864&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]0.8571[/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][ 0.771528112392745 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval[/C][C][ 0 , 0.923484273215444  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(sample proportion)[/C][C][ 0.753520986213301 , 0.933739396841794 ][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22864&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22864&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 Proportion0.8571
Null hypothesis0.69
Type I error (alpha)0.05
Left 1-sided confidence interval[ 0.771528112392745 , 1 ]
Right 1-sided confidence interval[ 0 , 0.923484273215444 ]
2-sided Confidence Interval(sample proportion)[ 0.753520986213301 , 0.933739396841794 ]






Testing Population Proportion (Exact and Wilson method)
Sample size98
Sample Proportion0.8571
Null hypothesis0.69
Type I error (alpha)0.05
Left 1-sided confidence interval(Exact method)[ 0.785670522351887 , 1 ]
Right 1-sided confidence interval(Exact method)[ 0 , 0.911484312655266 ]
2-sided Confidence Interval(Exact method)[ 0.77188973535872 , 0.91960748793079 ]
Left 1-sided confidence interval(Wilson method)[ 0.789346381933444 , 1 ]
Right 1-sided confidence interval(Wilson method)[ 0 , 0.905666003674745 ]
2-sided Confidence Interval(Wilson method)[ 0.774338311301997 , 0.912922071753098 ]

\begin{tabular}{lllllllll}
\hline
Testing Population Proportion (Exact and Wilson method) \tabularnewline
Sample size & 98 \tabularnewline
Sample Proportion & 0.8571 \tabularnewline
Null hypothesis & 0.69 \tabularnewline
Type I error (alpha) & 0.05 \tabularnewline
Left 1-sided confidence interval(Exact method) & [ 0.785670522351887 , 1 ] \tabularnewline
Right 1-sided confidence interval(Exact method) & [ 0 , 0.911484312655266  ] \tabularnewline
2-sided Confidence Interval(Exact method) & [ 0.77188973535872 , 0.91960748793079 ] \tabularnewline
Left 1-sided confidence interval(Wilson method) & [ 0.789346381933444 , 1 ] \tabularnewline
Right 1-sided confidence interval(Wilson method) & [ 0 , 0.905666003674745  ] \tabularnewline
2-sided Confidence Interval(Wilson method) & [ 0.774338311301997 , 0.912922071753098 ] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22864&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]0.8571[/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][ 0.785670522351887 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval(Exact method)[/C][C][ 0 , 0.911484312655266  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(Exact method)[/C][C][ 0.77188973535872 , 0.91960748793079 ][/C][/ROW]
[ROW][C]Left 1-sided confidence interval(Wilson method)[/C][C][ 0.789346381933444 , 1 ][/C][/ROW]
[ROW][C]Right 1-sided confidence interval(Wilson method)[/C][C][ 0 , 0.905666003674745  ][/C][/ROW]
[ROW][C]2-sided Confidence Interval(Wilson method)[/C][C][ 0.774338311301997 , 0.912922071753098 ][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22864&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22864&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 Proportion0.8571
Null hypothesis0.69
Type I error (alpha)0.05
Left 1-sided confidence interval(Exact method)[ 0.785670522351887 , 1 ]
Right 1-sided confidence interval(Exact method)[ 0 , 0.911484312655266 ]
2-sided Confidence Interval(Exact method)[ 0.77188973535872 , 0.91960748793079 ]
Left 1-sided confidence interval(Wilson method)[ 0.789346381933444 , 1 ]
Right 1-sided confidence interval(Wilson method)[ 0 , 0.905666003674745 ]
2-sided Confidence Interval(Wilson method)[ 0.774338311301997 , 0.912922071753098 ]


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 98 ; par2 = 0.8571 ; par3 = 0.69 ; par4 = 0.05 ;
Parameters (R input):
par1 = 98 ; par2 = 0.8571 ; 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')