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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 computationTue, 16 Dec 2014 13:14:44 +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/16/t14187357133akdshs0ykvpwlh.htm/, Retrieved Thu, 16 May 2024 07:48:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269496, Retrieved Thu, 16 May 2024 07:48:32 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Notched Boxplots] [1.1 AMSI - boxplot] [2014-12-09 11:18:21] [4d39cf209776852399955073f9d0ee7a]
- R  D  [Notched Boxplots] [1.1 AMSI : Notche...] [2014-12-10 13:41:48] [765bd0d5d4a0c852014c120c6930661d]
- RMPD      [Paired and Unpaired Two Samples Tests about the Mean] [1.1 Two sample T-...] [2014-12-16 13:14:44] [706bcb1d0c5210dc074174906fafd7a3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
51	69
56	48
67	NA
69	68
NA	74
56	67
55	65
NA	NA
67	74
NA	NA
NA	NA
76	69
64	NA
68	63
64	NA
65	NA
71	68
63	NA
60	70
NA	78
72	NA
70	NA
61	NA
61	74
62	NA
71	62
NA	69
51	65
56	67
70	NA
73	52
76	NA
NA	53
NA	NA
NA	NA
52	NA
NA	NA
NA	NA
NA	NA
57	76
NA	63
60	41
60	NA
NA	NA
62	NA
59	NA
61	NA
NA	72
NA	52
NA	65
NA	63
69	NA
NA	56
59	NA
NA	56
66	64
NA	NA
67	75
NA	NA
NA	NA
75	NA
NA	NA
69	NA
58	NA
60	65
74	NA
55	NA
NA	61
63	71
NA	72
NA	62
NA	NA
68	66
NA	63
62	NA
NA	NA
NA	NA
NA	NA
NA	71
72	NA
62	NA
75	NA
58	NA
NA	NA
NA	NA
47	64
NA	78
NA	60
NA	NA
NA	NA
19	68
50	NA
NA	68
NA	60
79	65
NA	64
71	69
48	NA
66	NA
NA	NA
74	NA
66	NA
71	74
NA	NA
NA	NA
NA	76
53	NA
60	NA
NA	73
70	NA
69	76
NA	55
NA	NA
NA	62
59	NA
72	78
NA	NA
NA	67
NA	75
71	59
74	NA
NA	70
NA	59
NA	NA
80	63
73	67
67	58
61	71
NA	NA
74	53
32	NA
69	NA
NA	64
NA	NA
64	67
NA	72
60	NA
59	NA
78	57
NA	62
60	74
NA	54
68	NA
73	66
NA	64
67	74
NA	71
65	NA
NA	NA
74	63
NA	NA
NA	70
55	66
49	NA
NA	78
53	NA
64	72
NA	NA
57	NA
NA	72
NA	58
67	84
70	67
NA	NA
75	NA
NA	63
NA	NA
65	55
NA	NA
NA	58
65	69
	54
	58
	67
	77
	80
	67
	NA
	71
	NA
	NA
	79
	76
	72
	81
	52
	76
	60
	NA
	77
	64
	67
	NA
	79
	40
	71
	73
	NA
	70
	66
	NA
	NA
	74
	58
	51
	75
	NA
	50
	NA
	77
	NA
	NA
	NA
	NA
	71
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA
	NA

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269496&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]1 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=269496&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269496&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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 164.2481203007519
Mean of Sample 266.3813559322034
t-stat-1.94290665791975
df249
p-value0.0531545938473179
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.29571019315088,0.0292389302478546]
F-test to compare two variances
F-stat1.33416590241456
df132
p-value0.111528530772824
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.935028484731291,1.89644028535688]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 64.2481203007519 \tabularnewline
Mean of Sample 2 & 66.3813559322034 \tabularnewline
t-stat & -1.94290665791975 \tabularnewline
df & 249 \tabularnewline
p-value & 0.0531545938473179 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-4.29571019315088,0.0292389302478546] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 1.33416590241456 \tabularnewline
df & 132 \tabularnewline
p-value & 0.111528530772824 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.935028484731291,1.89644028535688] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269496&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]64.2481203007519[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]66.3813559322034[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.94290665791975[/C][/ROW]
[ROW][C]df[/C][C]249[/C][/ROW]
[ROW][C]p-value[/C][C]0.0531545938473179[/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][-4.29571019315088,0.0292389302478546][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]1.33416590241456[/C][/ROW]
[ROW][C]df[/C][C]132[/C][/ROW]
[ROW][C]p-value[/C][C]0.111528530772824[/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][0.935028484731291,1.89644028535688][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269496&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269496&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 164.2481203007519
Mean of Sample 266.3813559322034
t-stat-1.94290665791975
df249
p-value0.0531545938473179
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.29571019315088,0.0292389302478546]
F-test to compare two variances
F-stat1.33416590241456
df132
p-value0.111528530772824
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.935028484731291,1.89644028535688]






Welch Two Sample t-test (unpaired)
Mean of Sample 164.2481203007519
Mean of Sample 266.3813559322034
t-stat-1.959667772694
df248.857563108743
p-value0.0511508053839117
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.27722045705479,0.0107491941517678]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 64.2481203007519 \tabularnewline
Mean of Sample 2 & 66.3813559322034 \tabularnewline
t-stat & -1.959667772694 \tabularnewline
df & 248.857563108743 \tabularnewline
p-value & 0.0511508053839117 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-4.27722045705479,0.0107491941517678] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269496&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]64.2481203007519[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]66.3813559322034[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.959667772694[/C][/ROW]
[ROW][C]df[/C][C]248.857563108743[/C][/ROW]
[ROW][C]p-value[/C][C]0.0511508053839117[/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][-4.27722045705479,0.0107491941517678][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269496&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269496&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 164.2481203007519
Mean of Sample 266.3813559322034
t-stat-1.959667772694
df248.857563108743
p-value0.0511508053839117
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.27722045705479,0.0107491941517678]






Wicoxon rank sum test with continuity correction (unpaired)
W6755.5
p-value0.0571595653300879
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.165031222123104
p-value0.0663565386939269
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0942398368803365
p-value0.635276546177908

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 6755.5 \tabularnewline
p-value & 0.0571595653300879 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.165031222123104 \tabularnewline
p-value & 0.0663565386939269 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.0942398368803365 \tabularnewline
p-value & 0.635276546177908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269496&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]6755.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.0571595653300879[/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]0.165031222123104[/C][/ROW]
[ROW][C]p-value[/C][C]0.0663565386939269[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.0942398368803365[/C][/ROW]
[ROW][C]p-value[/C][C]0.635276546177908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269496&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269496&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)
W6755.5
p-value0.0571595653300879
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.165031222123104
p-value0.0663565386939269
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0942398368803365
p-value0.635276546177908


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')