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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 computationThu, 18 Dec 2014 14:39:39 +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/18/t1418913653jtrvyqegcrdv3t1.htm/, Retrieved Fri, 17 May 2024 17:36:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271009, Retrieved Fri, 17 May 2024 17:36:43 +0000
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Original text written by user:
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Estimated Impact115
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] [] [2014-12-18 14:39:39] [18673d63f90870b9c004059cd6229007] [Current]
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Dataseries X:
108	86
119	70
97	71
78	64
99	129
68	153
147	80
40	71
57	84
120	137
68	79
55	101
116	189
111	81
66	64
63	85
69	86
71	120
143	96
55	60
69	95
57	100
85	68
103	105
57	51
41	69
49	50
93	58
74	54
15	69
107	107
70	65
53	58
136	136
126	59
95	118
69	65
58	83
82	70
102	77
90	101
64	71
50	60
37	44
81	43
79	56
55	40
40	34
56	89
45	50
32	34.10777778
56	61.17305556
46	70.07638889
76	69.05555556
64	145.0247222
74	119.8325
57	147.3852778
45	215.3216667
30	29.85444444
62	159.7583333
51	57.07944444
36	42.38583333
23.41555556	46.63722222
23.54111111	28.87861111
83.55583333	97.08638889
77.11222222	65.82138889
45.80333333	86.30472222
60.535	71.54111111
178.2077778	108.5758333
162.8508333	76.09222222
75.48972222	39.95111111
94.35194444	123.0486111
45.22833333	70.99916667
78.10833333	101.7594444
115.8919444	97.23444444
32.40888889	45.64277778
50.47138889	19.48722222
118.2822222	140.2911111
89.18194444	77.58111111
76.35277778	97.81361111
74.96555556	40.05305556
56.88416667	80.11416667
60.22472222	75.70055556
64.69722222	78.95305556
58.01527778	95.09722222
79.52611111	49.17611111
92.53222222	49.17611111
86.60055556	120.1744444
80.33722222	93.86972222
85.91027778	87.3275
68.60166667	51.66722222
78.97444444	70.98055556
52.1325	60.88333333
68.5725	50.07722222
72.24444444	75.1775
42.99111111	87.22777778
50.76083333	78.54861111
66.70083333	121.1086111
30.29	42.78388889
69.92833333	69.01388889
52.15055556	48.95638889
68.58333333	55.65916667
71.96166667	100.1288889
58.24583333	55.45833333
57.01527778	58.58083333
49.51805556	95.52888889
63.7	42.60833333
89.65333333	47.79555556
95.54333333	90.68388889
101.8902778	93.91805556
39.53611111	52.33527778
67.32805556	37.98888889
78.22138889	26.69583333
86.05972222	64.67194444
38.12111111	140.2911111
22.84861111	67.67305556
77.29583333	80.19444444
25.87611111	62.64055556
62.32916667	53.42638889
73.82555556	69.57527778
113.7033333	55.84472222
63.70833333	99.56138889
31.11111111	51.44083333
105.1427778	51.66666667
64.35055556	77.58111111
61.93305556	55.45694444
57.91027778	46.99166667
75.89027778	60.34444444
70.6675	47.89916667
75.70666667	56.25277778
46.17	91.30944444
74.07138889	67.95138889
78.32833333	74.39666667
102.3936111	29.26194444
78.23305556	NA
97.90555556	NA
75.89027778	NA
72.60083333	NA
44.88944444	NA
82.93916667	NA
49.54	NA
77.32694444	NA
75.90111111	NA




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=271009&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=271009&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271009&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 (unpaired)
Mean of Sample 171.6412315455944
Mean of Sample 276.751357793209
t-stat-1.40553815957236
df275
p-value0.160990052663223
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-12.2674822297987,2.0472297345696]
F-test to compare two variances
F-stat0.717979937068053
df142
p-value0.0525163062204051
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.512674944963154,1.00362343485292]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 71.6412315455944 \tabularnewline
Mean of Sample 2 & 76.751357793209 \tabularnewline
t-stat & -1.40553815957236 \tabularnewline
df & 275 \tabularnewline
p-value & 0.160990052663223 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-12.2674822297987,2.0472297345696] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.717979937068053 \tabularnewline
df & 142 \tabularnewline
p-value & 0.0525163062204051 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.512674944963154,1.00362343485292] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271009&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]71.6412315455944[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]76.751357793209[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.40553815957236[/C][/ROW]
[ROW][C]df[/C][C]275[/C][/ROW]
[ROW][C]p-value[/C][C]0.160990052663223[/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][-12.2674822297987,2.0472297345696][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.717979937068053[/C][/ROW]
[ROW][C]df[/C][C]142[/C][/ROW]
[ROW][C]p-value[/C][C]0.0525163062204051[/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][0.512674944963154,1.00362343485292][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271009&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271009&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 (unpaired)
Mean of Sample 171.6412315455944
Mean of Sample 276.751357793209
t-stat-1.40553815957236
df275
p-value0.160990052663223
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-12.2674822297987,2.0472297345696]
F-test to compare two variances
F-stat0.717979937068053
df142
p-value0.0525163062204051
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.512674944963154,1.00362343485292]







Welch Two Sample t-test (unpaired)
Mean of Sample 171.6412315455944
Mean of Sample 276.751357793209
t-stat-1.39803413332784
df261.359189174217
p-value0.163288116664014
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-12.3075601131585,2.08730761792944]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 71.6412315455944 \tabularnewline
Mean of Sample 2 & 76.751357793209 \tabularnewline
t-stat & -1.39803413332784 \tabularnewline
df & 261.359189174217 \tabularnewline
p-value & 0.163288116664014 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-12.3075601131585,2.08730761792944] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271009&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]71.6412315455944[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]76.751357793209[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.39803413332784[/C][/ROW]
[ROW][C]df[/C][C]261.359189174217[/C][/ROW]
[ROW][C]p-value[/C][C]0.163288116664014[/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][-12.3075601131585,2.08730761792944][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271009&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271009&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 (unpaired)
Mean of Sample 171.6412315455944
Mean of Sample 276.751357793209
t-stat-1.39803413332784
df261.359189174217
p-value0.163288116664014
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-12.3075601131585,2.08730761792944]







Wicoxon rank sum test with continuity correction (unpaired)
W8952.5
p-value0.345901198406117
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.0948230873604008
p-value0.56268396339928
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.111679365410709
p-value0.354133352506939

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 8952.5 \tabularnewline
p-value & 0.345901198406117 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.0948230873604008 \tabularnewline
p-value & 0.56268396339928 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.111679365410709 \tabularnewline
p-value & 0.354133352506939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271009&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]8952.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.345901198406117[/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]0.0948230873604008[/C][/ROW]
[ROW][C]p-value[/C][C]0.56268396339928[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.111679365410709[/C][/ROW]
[ROW][C]p-value[/C][C]0.354133352506939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271009&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271009&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 (unpaired)
W8952.5
p-value0.345901198406117
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.0948230873604008
p-value0.56268396339928
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.111679365410709
p-value0.354133352506939



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')