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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_twosampletests_mean.wasp
Title produced by softwarePaired and Unpaired Two Samples Tests about the Mean
Date of computationSun, 14 Dec 2014 18:34:08 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/14/t1418582348pklvs1xzmjhqn11.htm/, Retrieved Thu, 16 May 2024 06:02:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267808, Retrieved Thu, 16 May 2024 06:02:37 +0000
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Estimated Impact81

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-       [Paired and Unpaired Two Samples Tests about the Mean] [] [2014-12-14 18:34:08] [18673d63f90870b9c004059cd6229007] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
NA	7.5
NA	6
NA	6.5
1	NA
NA	1
5.5	NA
8.5	NA
NA	6.5
NA	4.5
2	NA
NA	5
0.5	NA
5	NA
5	NA
2.5	NA
5	NA
5.5	NA
NA	3.5
NA	3
4	NA
0.5	NA
NA	6.5
NA	4.5
7.5	NA
NA	5.5
4	NA
NA	7.5
7	NA
NA	4
5.5	NA
2.5	NA
5.5	NA
NA	3.5
2.5	NA
NA	4.5
NA	4.5
4.5	NA
6	NA
NA	2.5
NA	5
NA	0
NA	5
NA	6.5
NA	5
6	NA
NA	4.5
5.5	NA
1	NA
7.5	NA
NA	6
NA	5
1	NA
5	NA
NA	6.5
7	NA
NA	4.5
NA	0
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3.5	NA
NA	7.5
NA	3.5
NA	6
NA	1.5
9	NA
3.5	NA
3.5	NA
4	NA
6.5	NA
7.5	NA
6	NA
NA	5
5.5	NA
NA	3.5
NA	7.5
6.5	NA
6.5	NA
NA	6.5
7	NA
3.5	NA
NA	1.5
NA	4
7.5	NA
NA	4.5
0	NA
3.5	NA
NA	5.5
5	NA
NA	4.5
NA	2.5
7.5	NA
NA	7
0	NA
4.5	NA
NA	3
1.5	NA
3.5	NA
NA	2.5
NA	5.5
NA	8
NA	1
NA	5
4.5	NA
3	NA
3	NA
8	NA
2.5	NA
7	NA
0	NA
1	NA
3.5	NA
5.5	NA
5.5	NA
NA	0.5
NA	7.5
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NA	9.5
NA	8.5
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NA	10
NA	7
8.5	NA
9	NA
NA	9.5
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8	NA
5.5	NA
NA	9.5
NA	7.5
NA	7
7.5	NA
8	NA
7	NA
7	NA
6	NA
NA	10
NA	2.5
NA	9
8	NA
6	NA
8.5	NA
6	NA
NA	9
NA	8
9	NA
5.5	NA
7	NA
5.5	NA
NA	9
2	NA
NA	8.5
NA	9
8.5	NA
NA	9
7.5	NA
NA	10
NA	9
NA	7.5
6	NA
NA	10.5
NA	8.5
8	NA
NA	10
NA	10.5
NA	6.5
NA	9.5
NA	8.5
NA	7.5
NA	5
NA	8
10	NA
NA	7
NA	7.5
NA	7.5
9.5	NA
6	NA
10	NA
7	NA
3	NA
NA	6
7	NA
NA	10
7	NA
3.5	NA
NA	8
NA	10
NA	5.5
NA	6
6.5	NA
NA	6.5
8.5	NA
4	NA
9.5	NA
8	NA
NA	8.5
NA	5.5
7	NA
9	NA
NA	8
NA	10
NA	8
6	NA
8	NA
5	NA
NA	9
4.5	NA
NA	8.5
9.5	NA
8.5	NA
NA	7.5
NA	7.5
5	NA
7	NA
NA	8
5.5	NA
8.5	NA
NA	9.5
NA	7
NA	8
8.5	NA
3.5	NA
NA	6.5
6.5	NA
10.5	NA
NA	8.5
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NA	7.5
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NA	6.5
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5.5	NA
NA	7
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NA	3.5
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6	NA
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NA	6.5
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4.5	NA
8	NA
9	NA
NA	7.5
8.5	NA
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NA	9.5
NA	6.5
9.5	NA
NA	6
8	NA
NA	9.5
8	NA
NA	8
NA	9
NA	5

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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267808&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267808&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267808&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'Herman Ole Andreas Wold' @ wold.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 15.85664335664336
Mean of Sample 26.52962962962963
t-stat-2.22928196762638
df276
p-value0.0266000639276216
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-1.26727552666739,-0.0786970193051565]
F-test to compare two variances
F-stat0.943586022751162
df142
p-value0.732328963065216
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.674283961046851,1.31826898648311]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 5.85664335664336 \tabularnewline
Mean of Sample 2 & 6.52962962962963 \tabularnewline
t-stat & -2.22928196762638 \tabularnewline
df & 276 \tabularnewline
p-value & 0.0266000639276216 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-1.26727552666739,-0.0786970193051565] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.943586022751162 \tabularnewline
df & 142 \tabularnewline
p-value & 0.732328963065216 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.674283961046851,1.31826898648311] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267808&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]5.85664335664336[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]6.52962962962963[/C][/ROW]
[ROW][C]t-stat[/C][C]-2.22928196762638[/C][/ROW]
[ROW][C]df[/C][C]276[/C][/ROW]
[ROW][C]p-value[/C][C]0.0266000639276216[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][-1.26727552666739,-0.0786970193051565][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.943586022751162[/C][/ROW]
[ROW][C]df[/C][C]142[/C][/ROW]
[ROW][C]p-value[/C][C]0.732328963065216[/C][/ROW]
[ROW][C]H0 value[/C][C]1[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][0.674283961046851,1.31826898648311][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267808&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267808&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:

Two Sample t-test (unpaired)
Mean of Sample 15.85664335664336
Mean of Sample 26.52962962962963
t-stat-2.22928196762638
df276
p-value0.0266000639276216
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-1.26727552666739,-0.0786970193051565]
F-test to compare two variances
F-stat0.943586022751162
df142
p-value0.732328963065216
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.674283961046851,1.31826898648311]






Welch Two Sample t-test (unpaired)
Mean of Sample 15.85664335664336
Mean of Sample 26.52962962962963
t-stat-2.22741394029163
df273.937212392573
p-value0.026732167592535
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-1.26779365447085,-0.0781788915016944]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 5.85664335664336 \tabularnewline
Mean of Sample 2 & 6.52962962962963 \tabularnewline
t-stat & -2.22741394029163 \tabularnewline
df & 273.937212392573 \tabularnewline
p-value & 0.026732167592535 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-1.26779365447085,-0.0781788915016944] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267808&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]5.85664335664336[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]6.52962962962963[/C][/ROW]
[ROW][C]t-stat[/C][C]-2.22741394029163[/C][/ROW]
[ROW][C]df[/C][C]273.937212392573[/C][/ROW]
[ROW][C]p-value[/C][C]0.026732167592535[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][-1.26779365447085,-0.0781788915016944][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267808&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267808&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:

Welch Two Sample t-test (unpaired)
Mean of Sample 15.85664335664336
Mean of Sample 26.52962962962963
t-stat-2.22741394029163
df273.937212392573
p-value0.026732167592535
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-1.26779365447085,-0.0781788915016944]






Wicoxon rank sum test with continuity correction (unpaired)
W8092
p-value0.0196538232866383
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.152861952861953
p-value0.0779097482415704
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0759388759388759
p-value0.818088290475449

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 8092 \tabularnewline
p-value & 0.0196538232866383 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.152861952861953 \tabularnewline
p-value & 0.0779097482415704 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.0759388759388759 \tabularnewline
p-value & 0.818088290475449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267808&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]8092[/C][/ROW]
[ROW][C]p-value[/C][C]0.0196538232866383[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributions of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.152861952861953[/C][/ROW]
[ROW][C]p-value[/C][C]0.0779097482415704[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.0759388759388759[/C][/ROW]
[ROW][C]p-value[/C][C]0.818088290475449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267808&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267808&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:

Wicoxon rank sum test with continuity correction (unpaired)
W8092
p-value0.0196538232866383
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.152861952861953
p-value0.0779097482415704
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0759388759388759
p-value0.818088290475449


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0.0 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0.0 ;
R code (references can be found in the software module):
par6 <- '0.0'
par5 <- 'unpaired'
par4 <- 'two.sided'
par3 <- '0.95'
par2 <- '2'
par1 <- '1'
par1 <- as.numeric(par1) #column number of first sample
par2 <- as.numeric(par2) #column number of second sample
par3 <- as.numeric(par3) #confidence (= 1 - alpha)
if (par5 == 'unpaired') paired <- FALSE else paired <- TRUE
par6 <- as.numeric(par6) #H0
z <- t(y)
if (par1 == par2) stop('Please, select two different column numbers')
if (par1 < 1) stop('Please, select a column number greater than zero for the first sample')
if (par2 < 1) stop('Please, select a column number greater than zero for the second sample')
if (par1 > length(z[1,])) stop('The column number for the first sample should be smaller')
if (par2 > length(z[1,])) stop('The column number for the second sample should be smaller')
if (par3 <= 0) stop('The confidence level should be larger than zero')
if (par3 >= 1) stop('The confidence level should be smaller than zero')
(r.t <- t.test(z[,par1],z[,par2],var.equal=TRUE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(v.t <- var.test(z[,par1],z[,par2],conf.level=par3))
(r.w <- t.test(z[,par1],z[,par2],var.equal=FALSE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(w.t <- wilcox.test(z[,par1],z[,par2],alternative=par4,paired=paired,mu=par6,conf.level=par3))
(ks.t <- ks.test(z[,par1],z[,par2],alternative=par4))
m1 <- mean(z[,par1],na.rm=T)
m2 <- mean(z[,par2],na.rm=T)
mdiff <- m1 - m2
newsam1 <- z[!is.na(z[,par1]),par1]
newsam2 <- z[,par2]+mdiff
newsam2 <- newsam2[!is.na(newsam2)]
(ks1.t <- ks.test(newsam1,newsam2,alternative=par4))
mydf <- data.frame(cbind(z[,par1],z[,par2]))
colnames(mydf) <- c('Variable 1','Variable 2')
bitmap(file='test1.png')
boxplot(mydf, notch=TRUE, ylab='value',main=main)
dev.off()
bitmap(file='test2.png')
qqnorm(z[,par1],main='Normal QQplot - Variable 1')
qqline(z[,par1])
dev.off()
bitmap(file='test3.png')
qqnorm(z[,par2],main='Normal QQplot - Variable 2')
qqline(z[,par2])
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.t$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.t$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.t$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.t$conf.int[1],',',r.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-test to compare two variances',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-stat',header=TRUE)
a<-table.element(a,v.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,v.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,v.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,v.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,v.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(v.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',v.t$conf.int[1],',',v.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Welch Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.w$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.w$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.w$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.w$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.w$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.w$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.w$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.w$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.w$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.w$conf.int[1],',',r.w$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Wicoxon rank sum test with continuity correction (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'W',header=TRUE)
a<-table.element(a,w.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,w.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,w.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,w.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributions of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks1.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks1.t$p.value)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')