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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, 10 Dec 2014 18:04:24 +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/10/t14182346821er5wd0pglm4676.htm/, Retrieved Sun, 19 May 2024 14:40:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265552, Retrieved Sun, 19 May 2024 14:40:26 +0000
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-       [Paired and Unpaired Two Samples Tests about the Mean] [] [2014-12-10 18:04:24] [f8081e57f48fffedb891dd68b4ffae29] [Current]
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Dataseries X:
12.9 26
12.2 51
12.8 57
7.4 37
6.7 67
12.6 43
14.8 52
13.3 52
11.1 43
8.2 84
11.4 67
6.4 49
10.6 70
12 52
6.3 58
11.3 68
11.9 62
9.3 43
9.6 56
10 56
6.4 74
13.8 65
10.8 63
13.8 58
11.7 57
10.9 63
16.1 53
13.4 57
9.9 51
11.5 64
8.3 53
11.7 29
9 54
9.7 51
10.8 58
10.3 43
10.4 51
12.7 53
9.3 54
11.8 56
5.9 61
11.4 47
13 39
10.8 48
12.3 50
11.3 35
11.8 30
7.9 68
12.7 49
12.3 61
11.6 67
6.7 47
10.9 56
12.1 50
13.3 43
10.1 67
5.7 62
14.3 57
8 41
13.3 54
9.3 45
12.5 48
7.6 61
15.9 56
9.2 41
9.1 43
11.1 53
13 44
14.5 66
12.2 58
12.3 46
11.4 37
8.8 51
14.6 51
12.6 56
13 45
12.6 37
13.2 59
9.9 42
7.7 38
10.5 66
13.4 34
10.9 53
4.3 49
10.3 55
11.8 49
11.2 59
11.4 40
8.6 58
13.2 60
12.6 63
5.6 56
9.9 54
8.8 52
7.7 34
9 69
7.3 32
11.4 48
13.6 67
7.9 58
10.7 57
10.3 42
8.3 64
9.6 58
14.2 66
8.5 26
13.5 61
4.9 52
6.4 51
9.6 55
11.6 50
11.1 60
4.35 56
12.7 63
18.1 61
17.85 52
16.6 16
12.6 46
17.1 56
19.1 52
16.1 55
13.35 50
18.4 59
14.7 60
10.6 52
12.6 44
16.2 67
13.6 52
18.9 55
14.1 37
14.5 54
16.15 72
14.75 51
14.8 48
12.45 60
12.65 50
17.35 63
8.6 33
18.4 67
16.1 46
11.6 54
17.75 59
15.25 61
17.65 33
16.35 47
17.65 69
13.6 52
14.35 55
14.75 55
18.25 41
9.9 73
16 51
18.25 52
16.85 50
14.6 51
13.85 60
18.95 56
15.6 56
14.85 29
11.75 66
18.45 66
15.9 73
17.1 55
16.1 64
19.9 40
10.95 46
18.45 58
15.1 43
15 61
11.35 51
15.95 50
18.1 52
14.6 54
15.4 66
15.4 61
17.6 80
13.35 51
19.1 56
15.35 56
7.6 56
13.4 53
13.9 47
19.1 25
15.25 47
12.9 46
16.1 50
17.35 39
13.15 51
12.15 58
12.6 35
10.35 58
15.4 60
9.6 62
18.2 63
13.6 53
14.85 46
14.75 67
14.1 59
14.9 64
16.25 38
19.25 50
13.6 48
13.6 48
15.65 47
12.75 66
14.6 47
9.85 63
12.65 58
19.2 44
16.6 51
11.2 43
15.25 55
11.9 38
13.2 56
16.35 45
12.4 50
15.85 54
18.15 57
11.15 60
15.65 55
17.75 56
7.65 49
12.35 37
15.6 43
19.3 59
15.2 46
17.1 51
15.6 58
18.4 64
19.05 53
18.55 48
19.1 51
13.1 47
12.85 59
9.5 62
4.5 62
11.85 51
13.6 64
11.7 52
12.4 67
13.35 50
11.4 54
14.9 58
19.9 56
11.2 63
14.6 31
17.6 65
14.05 71
16.1 50
13.35 57
11.85 47
11.95 54
14.75 47
15.15 57
13.2 43
16.85 41
7.85 63
7.7 63
12.6 56
7.85 51
10.95 50
12.35 22
9.95 41
14.9 59
16.65 56
13.4 66
13.95 53
15.7 42
16.85 52
10.95 54
15.35 44
12.2 62
15.1 53
17.75 50
15.2 36
14.6 76
16.65 66
8.1 62




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265552&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265552&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265552&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 time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Two Sample t-test (paired)
Difference: Mean1 - Mean2-40.0789568345324
t-stat-61.5893556233729
df277
p-value1.11287402241872e-163
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-41.3599904274789,-38.7979232415858]
F-test to compare two variances
F-stat0.109424771598533
df277
p-value3.85123378697986e-64
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.0864206119134676,0.138552370485185]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (paired) \tabularnewline
Difference: Mean1 - Mean2 & -40.0789568345324 \tabularnewline
t-stat & -61.5893556233729 \tabularnewline
df & 277 \tabularnewline
p-value & 1.11287402241872e-163 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-41.3599904274789,-38.7979232415858] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.109424771598533 \tabularnewline
df & 277 \tabularnewline
p-value & 3.85123378697986e-64 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.0864206119134676,0.138552370485185] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265552&T=1

[TABLE]
[ROW][C]Two Sample t-test (paired)[/C][/ROW]
[ROW][C]Difference: Mean1 - Mean2[/C][C]-40.0789568345324[/C][/ROW]
[ROW][C]t-stat[/C][C]-61.5893556233729[/C][/ROW]
[ROW][C]df[/C][C]277[/C][/ROW]
[ROW][C]p-value[/C][C]1.11287402241872e-163[/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][-41.3599904274789,-38.7979232415858][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.109424771598533[/C][/ROW]
[ROW][C]df[/C][C]277[/C][/ROW]
[ROW][C]p-value[/C][C]3.85123378697986e-64[/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][0.0864206119134676,0.138552370485185][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265552&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265552&T=1

As an alternative you can also use a QR Code:  

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

Two Sample t-test (paired)
Difference: Mean1 - Mean2-40.0789568345324
t-stat-61.5893556233729
df277
p-value1.11287402241872e-163
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-41.3599904274789,-38.7979232415858]
F-test to compare two variances
F-stat0.109424771598533
df277
p-value3.85123378697986e-64
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.0864206119134676,0.138552370485185]







Welch Two Sample t-test (paired)
Difference: Mean1 - Mean2-40.0789568345324
t-stat-61.5893556233729
df277
p-value1.11287402241872e-163
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-41.3599904274789,-38.7979232415858]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (paired) \tabularnewline
Difference: Mean1 - Mean2 & -40.0789568345324 \tabularnewline
t-stat & -61.5893556233729 \tabularnewline
df & 277 \tabularnewline
p-value & 1.11287402241872e-163 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-41.3599904274789,-38.7979232415858] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265552&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (paired)[/C][/ROW]
[ROW][C]Difference: Mean1 - Mean2[/C][C]-40.0789568345324[/C][/ROW]
[ROW][C]t-stat[/C][C]-61.5893556233729[/C][/ROW]
[ROW][C]df[/C][C]277[/C][/ROW]
[ROW][C]p-value[/C][C]1.11287402241872e-163[/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][-41.3599904274789,-38.7979232415858][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265552&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265552&T=2

As an alternative you can also use a QR Code:  

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

Welch Two Sample t-test (paired)
Difference: Mean1 - Mean2-40.0789568345324
t-stat-61.5893556233729
df277
p-value1.11287402241872e-163
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-41.3599904274789,-38.7979232415858]







Wicoxon rank sum test with continuity correction (paired)
W1
p-value2.45565578062573e-47
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.996402877697842
p-value0
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.255395683453237
p-value2.66641373425358e-08

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (paired) \tabularnewline
W & 1 \tabularnewline
p-value & 2.45565578062573e-47 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.996402877697842 \tabularnewline
p-value & 0 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.255395683453237 \tabularnewline
p-value & 2.66641373425358e-08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265552&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (paired)[/C][/ROW]
[ROW][C]W[/C][C]1[/C][/ROW]
[ROW][C]p-value[/C][C]2.45565578062573e-47[/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]0.996402877697842[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.255395683453237[/C][/ROW]
[ROW][C]p-value[/C][C]2.66641373425358e-08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265552&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265552&T=3

As an alternative you can also use a QR Code:  

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

Wicoxon rank sum test with continuity correction (paired)
W1
p-value2.45565578062573e-47
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.996402877697842
p-value0
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.255395683453237
p-value2.66641373425358e-08



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 = 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')