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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 computationWed, 08 Dec 2010 18:37:19 +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/2010/Dec/08/t1291833483eyqeijqcmq3r311.htm/, Retrieved Fri, 03 May 2024 11:33:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107072, Retrieved Fri, 03 May 2024 11:33:40 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Paired and Unpaired Two Samples Tests about the Mean] [Dagelijkse omzet ...] [2010-10-25 11:22:12] [b98453cac15ba1066b407e146608df68]
-   PD  [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-10-29 18:44:51] [1251ac2db27b84d4a3ba43449388906b]
-    D      [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-08 18:37:19] [1638ccfec791c539017705f3e680eb33] [Current]
-    D        [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-08 19:25:10] [1251ac2db27b84d4a3ba43449388906b]
-   PD          [Paired and Unpaired Two Samples Tests about the Mean] [Two Sample T-Test...] [2010-12-09 18:03:24] [1251ac2db27b84d4a3ba43449388906b]
-    D        [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-08 19:29:13] [1251ac2db27b84d4a3ba43449388906b]
-    D          [Paired and Unpaired Two Samples Tests about the Mean] [Two Sample T-Test...] [2010-12-09 18:05:46] [1251ac2db27b84d4a3ba43449388906b]
-    D          [Paired and Unpaired Two Samples Tests about the Mean] [Two Sample T-Test...] [2010-12-21 16:20:34] [1251ac2db27b84d4a3ba43449388906b]
-                 [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-24 15:06:17] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P             [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-24 15:46:47] [dc30d19c3bc2be07fe595ad36c2cf923]
-               [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-24 15:08:02] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P           [Paired and Unpaired Two Samples Tests about the Mean] [] [2010-12-24 15:47:43] [dc30d19c3bc2be07fe595ad36c2cf923]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	5	5	8
1	3	0	3
0	0	3	6
3	7	6	9
3	4	5	8
1	1	2	5
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4	12	3	6
0	0	0	0
3	5	2	8
2	6	2	5
4	6	0	3
3	6	0	3
1	2	1	4
1	1	5	5
2	5	7	7
3	7	4	4
1	3	5	5
1	3	0	0
2	3	0	3
3	7	0	6
4	8	0	0
2	6	2	2
1	3	4	4
2	5	0	3
2	5	2	5
4	10	0	9
2	2	5	5
3	6	4	4
3	4	0	3
3	6	1	7
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3	6	1	1
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5	10	0	3
3	4	2	8
1	3	6	6
1	3	4	7
1	3	0	6
2	3	5	5
3	7	0	6
9	15	3	3
0	0	2	2
0	0	2	5
2	4	3	3
2	5	0	3
3	5	4	4
1	2	5	8
2	3	0	3
0	0	3	6
5	9	2	5
2	2	3	6
4	7	2	5
3	7	2	2
0	0	2	2
0	0	0	3
4	10	4	10
1	2	0	3
1	1	5	5
4	8	5	5
2	6	1	1
4	11	2	2
1	3	2	2
4	8	2	2
2	6	2	8
5	9	2	2
4	9	0	0
4	8	0	3
4	8	2	5
4	7	3	6
3	6	1	4
3	5	0	3
3	4	1	1
2	6	1	1
1	3	1	1
1	2	3	6
5	12	0	6
4	8	2	5
2	5	4	10
3	9	2	11
2	6	4	7
2	5	4	4
2	2	3	9
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3	7	4	4
2	5	1	1
3	6	4	10
4	7	2	5
3	8	0	3
3	6	0	0
0	0	4	10
1	1	1	1
2	5	0	3
2	5	4	4
3	5	2	11
4	7	0	0
4	7	4	4
1	1	4	7
2	3	6	6
2	4	2	2
3	8	1	1
3	6	0	3
3	6	0	3
1	2	0	9
1	2	0	6
1	3	2	2
1	3	0	3
0	0	0	3
1	2	1	4
3	8	0	3
3	8	4	7
0	0	5	5
2	5	5	5
5	9	3	3
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3	6	2	2
3	3	0	6
5	9	6	9
4	7	4	4
4	8	2	8
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3	7	0	3
0	0	2	5
2	5	1	1
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6	14	0	3
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1	2	1	1
6	8	0	0
2	4	2	2
1	2	0	0
3	6	0	3
1	3	0	0
2	5	1	1
4	9	0	3
1	3	2	2
2	3	0	3
0	0	0	0
5	10	0	3
2	4	0	0
1	2	0	0
1	3	1	1
4	10	0	3
3	7	1	1
0	0	0	0
3	6	0	0
3	8	1	4
0	0	2	2
2	4	0	0
5	10	0	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107072&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]4 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=107072&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107072&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 time4 seconds
R Server'George Udny Yule' @ 72.249.76.132






Two Sample t-test (paired)
Difference: Mean1 - Mean20.52258064516129
t-stat2.64940648996508
df154
p-value0.00890432522616291
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.132926649864355,0.912234640458226]
F-test to compare two variances
F-stat0.656961481552193
df154
p-value0.00951018946870159
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.478461735309106,0.902054138068998]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (paired) \tabularnewline
Difference: Mean1 - Mean2 & 0.52258064516129 \tabularnewline
t-stat & 2.64940648996508 \tabularnewline
df & 154 \tabularnewline
p-value & 0.00890432522616291 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.132926649864355,0.912234640458226] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.656961481552193 \tabularnewline
df & 154 \tabularnewline
p-value & 0.00951018946870159 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.478461735309106,0.902054138068998] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107072&T=1

[TABLE]
[ROW][C]Two Sample t-test (paired)[/C][/ROW]
[ROW][C]Difference: Mean1 - Mean2[/C][C]0.52258064516129[/C][/ROW]
[ROW][C]t-stat[/C][C]2.64940648996508[/C][/ROW]
[ROW][C]df[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]0.00890432522616291[/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][0.132926649864355,0.912234640458226][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.656961481552193[/C][/ROW]
[ROW][C]df[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]0.00951018946870159[/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][0.478461735309106,0.902054138068998][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107072&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107072&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 (paired)
Difference: Mean1 - Mean20.52258064516129
t-stat2.64940648996508
df154
p-value0.00890432522616291
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.132926649864355,0.912234640458226]
F-test to compare two variances
F-stat0.656961481552193
df154
p-value0.00951018946870159
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.478461735309106,0.902054138068998]






Welch Two Sample t-test (paired)
Difference: Mean1 - Mean20.52258064516129
t-stat2.64940648996508
df154
p-value0.00890432522616291
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.132926649864355,0.912234640458226]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (paired) \tabularnewline
Difference: Mean1 - Mean2 & 0.52258064516129 \tabularnewline
t-stat & 2.64940648996508 \tabularnewline
df & 154 \tabularnewline
p-value & 0.00890432522616291 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.132926649864355,0.912234640458226] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107072&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (paired)[/C][/ROW]
[ROW][C]Difference: Mean1 - Mean2[/C][C]0.52258064516129[/C][/ROW]
[ROW][C]t-stat[/C][C]2.64940648996508[/C][/ROW]
[ROW][C]df[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]0.00890432522616291[/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][0.132926649864355,0.912234640458226][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107072&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107072&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 (paired)
Difference: Mean1 - Mean20.52258064516129
t-stat2.64940648996508
df154
p-value0.00890432522616291
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.132926649864355,0.912234640458226]






Wicoxon rank sum test with continuity correction (paired)
W5419.5
p-value0.0111526119721863
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.251612903225806
p-value0.000109481382581822
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.251612903225806
p-value0.000109481382581822

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (paired) \tabularnewline
W & 5419.5 \tabularnewline
p-value & 0.0111526119721863 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.251612903225806 \tabularnewline
p-value & 0.000109481382581822 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.251612903225806 \tabularnewline
p-value & 0.000109481382581822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107072&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (paired)[/C][/ROW]
[ROW][C]W[/C][C]5419.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.0111526119721863[/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]0.251612903225806[/C][/ROW]
[ROW][C]p-value[/C][C]0.000109481382581822[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.251612903225806[/C][/ROW]
[ROW][C]p-value[/C][C]0.000109481382581822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107072&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107072&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 (paired)
W5419.5
p-value0.0111526119721863
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.251612903225806
p-value0.000109481382581822
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.251612903225806
p-value0.000109481382581822


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0.0 ;
Parameters (R input):
par1 = 1 ; par2 = 3 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0.0 ;
R code (references can be found in the software module):
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')