FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSat, 13 Dec 2014 23:56:31 +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/13/t1418515023bs1fxaoxb57ypuj.htm/, Retrieved Thu, 16 May 2024 13:35:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267320, Retrieved Thu, 16 May 2024 13:35:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68

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] [] [2010-11-01 13:07:12] [b98453cac15ba1066b407e146608df68]
- RMP   [Paired and Unpaired Two Samples Tests about the Mean] [] [2014-10-21 07:47:40] [32b17a345b130fdf5cc88718ed94a974]
-   PD      [Paired and Unpaired Two Samples Tests about the Mean] [Extrinsic Motivat...] [2014-12-13 23:56:31] [8188a2bb20af439749c29996b06d1031] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
NA	50
NA	62
NA	54
NA	71
NA	54
NA	65
NA	73
NA	52
NA	84
NA	42
NA	66
NA	65
NA	78
NA	73
NA	75
72	NA
NA	66
NA	70
61	NA
NA	81
NA	71
NA	69
NA	71
NA	72
NA	68
NA	70
68	NA
61	NA
NA	67
NA	76
NA	70
NA	60
NA	72
NA	69
NA	71
NA	62
NA	70
64	NA
NA	58
NA	76
NA	52
NA	59
NA	68
NA	76
65	NA
NA	67
NA	59
69	NA
NA	76
63	NA
75	NA
63	NA
NA	60
73	NA
NA	63
NA	70
75	NA
NA	66
63	NA
63	NA
NA	64
NA	70
NA	75
NA	61
NA	60
62	NA
NA	73
NA	61
NA	66
64	NA
NA	59
NA	64
60	NA
56	NA
NA	78
NA	67
59	NA
NA	66
68	NA
NA	71
66	NA
73	NA
72	NA
71	NA
59	NA
64	NA
66	NA
78	NA
68	NA
73	NA
62	NA
65	NA
68	NA
65	NA
60	NA
71	NA
65	NA
68	NA
64	NA
74	NA
69	NA
76	NA
68	NA
72	NA
67	NA
63	NA
59	NA
73	NA
66	NA
62	NA
69	NA
66	NA
NA	51
NA	56
NA	67
NA	69
57	NA
56	NA
NA	55
NA	63
NA	67
NA	65
NA	47
NA	76
NA	64
NA	68
NA	64
NA	65
71	NA
NA	63
NA	60
NA	68
NA	72
NA	70
NA	61
NA	61
NA	62
NA	71
NA	71
NA	51
56	NA
NA	70
NA	73
NA	76
NA	68
NA	48
NA	52
NA	60
NA	59
NA	57
NA	79
NA	60
NA	60
NA	59
62	NA
59	NA
NA	61
NA	71
57	NA
66	NA
63	NA
69	NA
NA	58
NA	59
48	NA
66	NA
73	NA
67	NA
61	NA
68	NA
75	NA
62	NA
69	NA
NA	58
NA	60
74	NA
NA	55
NA	62
63	NA
NA	69
58	NA
58	NA
NA	68
72	NA
62	NA
62	NA
65	NA
69	NA
66	NA
72	NA
62	NA
75	NA
58	NA
66	NA
55	NA
47	NA
NA	72
62	NA
64	NA
64	NA
NA	19
50	NA
NA	68
70	NA
NA	79
69	NA
NA	71
48	NA
73	NA
74	NA
66	NA
NA	71
NA	74
78	NA
NA	75
NA	53
60	NA
NA	70
69	NA
65	NA
NA	78
78	NA
NA	59
NA	72
NA	70
63	NA
NA	63
71	NA
NA	74
NA	67
NA	66
NA	62
80	NA
NA	73
NA	67
NA	61
73	NA
NA	74
NA	32
69	NA
NA	69
84	NA
64	NA
58	NA
59	NA
78	NA
NA	57
NA	60
NA	68
NA	68
NA	73
NA	69
67	NA
60	NA
NA	65
66	NA
74	NA
NA	81
72	NA
55	NA
49	NA
74	NA
53	NA
64	NA
65	NA
57	NA
51	NA
80	NA
67	NA
70	NA
74	NA
75	NA
70	NA
69	NA
65	NA
NA	55
71	NA
65	NA

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'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267320&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267320&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267320&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 165.8602941176471
Mean of Sample 265.2676056338028
t-stat0.605436103690954
df276
p-value0.545386775184351
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-1.33445853548548,2.51983550317396]
F-test to compare two variances
F-stat0.60540815978875
df135
p-value0.00349995998441795
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.433274641063818,0.846974179149581]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 65.8602941176471 \tabularnewline
Mean of Sample 2 & 65.2676056338028 \tabularnewline
t-stat & 0.605436103690954 \tabularnewline
df & 276 \tabularnewline
p-value & 0.545386775184351 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-1.33445853548548,2.51983550317396] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.60540815978875 \tabularnewline
df & 135 \tabularnewline
p-value & 0.00349995998441795 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.433274641063818,0.846974179149581] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267320&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]65.8602941176471[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]65.2676056338028[/C][/ROW]
[ROW][C]t-stat[/C][C]0.605436103690954[/C][/ROW]
[ROW][C]df[/C][C]276[/C][/ROW]
[ROW][C]p-value[/C][C]0.545386775184351[/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][-1.33445853548548,2.51983550317396][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.60540815978875[/C][/ROW]
[ROW][C]df[/C][C]135[/C][/ROW]
[ROW][C]p-value[/C][C]0.00349995998441795[/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][0.433274641063818,0.846974179149581][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267320&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267320&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 165.8602941176471
Mean of Sample 265.2676056338028
t-stat0.605436103690954
df276
p-value0.545386775184351
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-1.33445853548548,2.51983550317396]
F-test to compare two variances
F-stat0.60540815978875
df135
p-value0.00349995998441795
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.433274641063818,0.846974179149581]






Welch Two Sample t-test (unpaired)
Mean of Sample 165.8602941176471
Mean of Sample 265.2676056338028
t-stat0.608668022524334
df265.002855372783
p-value0.543265882612852
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-1.32457612061516,2.50995308830365]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 65.8602941176471 \tabularnewline
Mean of Sample 2 & 65.2676056338028 \tabularnewline
t-stat & 0.608668022524334 \tabularnewline
df & 265.002855372783 \tabularnewline
p-value & 0.543265882612852 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-1.32457612061516,2.50995308830365] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267320&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]65.8602941176471[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]65.2676056338028[/C][/ROW]
[ROW][C]t-stat[/C][C]0.608668022524334[/C][/ROW]
[ROW][C]df[/C][C]265.002855372783[/C][/ROW]
[ROW][C]p-value[/C][C]0.543265882612852[/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][-1.32457612061516,2.50995308830365][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267320&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267320&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 165.8602941176471
Mean of Sample 265.2676056338028
t-stat0.608668022524334
df265.002855372783
p-value0.543265882612852
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-1.32457612061516,2.50995308830365]






Wicoxon rank sum test with continuity correction (unpaired)
W9692
p-value0.957710549178469
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.088960231980116
p-value0.641559043829215
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.107808616404308
p-value0.394731816806678

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 9692 \tabularnewline
p-value & 0.957710549178469 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.088960231980116 \tabularnewline
p-value & 0.641559043829215 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.107808616404308 \tabularnewline
p-value & 0.394731816806678 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267320&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]9692[/C][/ROW]
[ROW][C]p-value[/C][C]0.957710549178469[/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]0.088960231980116[/C][/ROW]
[ROW][C]p-value[/C][C]0.641559043829215[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.107808616404308[/C][/ROW]
[ROW][C]p-value[/C][C]0.394731816806678[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267320&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267320&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)
W9692
p-value0.957710549178469
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.088960231980116
p-value0.641559043829215
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.107808616404308
p-value0.394731816806678


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 2 3 4 5 6 7 8 9 10 11 12 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 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')