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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, 13 Dec 2017 12:03:40 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/13/t1513164472qsxrv4oxhqlaubb.htm/, Retrieved Wed, 15 May 2024 23:40:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309252, Retrieved Wed, 15 May 2024 23:40:12 +0000
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User-defined keywords
Estimated Impact67

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-       [Paired and Unpaired Two Samples Tests about the Mean] [two sample t-test...] [2017-12-13 11:03:40] [32118082f2c8d57cb544554523dbaf9e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.7	62.4
88.9	67.4
96.5	76.1
89.5	67.4
85.4	74.5
84.3	72.6
83.7	60.5
86.2	66.1
90.7	76.5
95.7	76.8
95.6	77
97	71
97.2	74.8
86.6	73.7
88.4	80.5
81.4	71.8
86.9	76.9
84.9	79.9
83.7	65.9
86.8	69.5
88.3	75.1
92.5	79.6
94.7	75.2
94.5	68
98.7	72.8
88.6	71.5
95.2	78.5
91.3	76.8
91.7	75.3
89.3	76.7
88.7	69.7
91.2	67.8
88.6	77.5
94.6	82.5
96	75.3
94.3	70.9
102	76
93.4	73.7
96.7	79.7
93.7	77.8
91.6	73.3
89.6	78.3
92.9	71.9
94.1	67
92	82
97.5	83.7
92.7	74.8
100.7	80
105.9	74.3
95.3	76.8
99.8	89
91.3	81.9
90.8	76.8
87.1	88.9
91.4	75.8
86.1	75.5
87.1	89.1
92.6	88
96.6	85.9
105.3	89.3
102.4	82.9
98.2	81.2
98.6	90.5
92.6	86.4
87.9	81.8
84.1	91.3
86.7	73.4
84.4	76.6
86	91
90.4	87
92.9	89.7
105.8	90.7
106	86.5
99.1	86.6
99.9	98.8
88.1	84.4
87.8	91.4
87.1	95.7
85.9	78.5
86.5	81.7
84.1	94.3
92.1	98.5
93.3	95.4
98.9	91.7
103	92.8
98.4	90.5
100.7	102.2
92.3	91.8
89	95
88.9	102
85.5	88.9
90.1	89.6
87	97.9
97.1	108.6
101.5	100.8
103	95.1
106.1	101
96.1	100.9
94.2	102.5
89.1	105.4
85.2	98.4
86.5	105.3
88	96.5
88.4	88.1
87.9	107.9
95.7	107
94.8	92.5
105.2	95.7
108.7	85.2
96.1	85.5
98.3	94.7
88.6	86.2
90.8	88.8
88.1	93.4
91.9	83.4
98.5	82.9
98.6	96.7
100.3	96.2
98.7	92.8
110.7	92.8
115.4	90
105.4	95.4
108	108.3
94.5	96.3
96.5	95
91	109
94.1	92
96.4	92.3
93.1	107
97.5	105.5
102.5	105.4
105.7	103.9
109.1	99.2
97.2	102.2
100.3	121.5
91.3	102.3
94.3	110
89.5	105.9
89.3	91.9
93.4	100
91.9	111.7
92.9	104.9
93.7	103.3
100.1	101.8
105.5	100.8
110.5	104.2
89.5	116.5
90.4	97.9
89.9	100.7
84.6	107
86.2	96.3
83.4	96
82.9	104.5
81.8	107.4
87.6	102.4
94.6	94.9
99.6	98.8
96.7	96.8
99.8	108.2
83.8	103.8
82.4	102.3
86.8	107.2
91	102
85.3	92.6
83.6	105.2
94	113
100.3	105.6
107.1	101.6
100.7	101.7
95.5	102.7
92.9	109
79.2	105.5
82	103.3
79.3	108.6
81.5	98.2
76	90
73.1	112.4
80.4	111.9
82.1	102.1
90.5	102.4
98.1	101.7
89.5	98.7
86.5	114
77	105.1
74.7	98.3
73.4	110
72.5	96.5
69.3	92.2
75.2	112
83.5	111.4
90.5	107.5
92.2	103.4
110.5	103.5
101.8	107.4
107.4	117.6
95.5	110.2
84.5	104.3
81.1	115.9
86.2	98.9
91.5	101.9
84.7	113.5
92.2	109.5
99.2	110
104.5	114.2
113	106.9
100.4	109.2
101	124.2
84.8	104.7
86.5	111.9
91.7	119
94.8	102.9
95	106.3

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309252&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309252&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309252&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center






Two Sample t-test (unpaired)
Mean of Sample 192.5264150943396
Mean of Sample 292.9061320754717
t-stat-0.344721272967098
df422
p-value0.730475657300466
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-2.54486394027964,1.78542997801549]
F-test to compare two variances
F-stat0.339012493464766
df211
p-value1.75101407556316e-14
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.258642678379456,0.444356172946001]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 92.5264150943396 \tabularnewline
Mean of Sample 2 & 92.9061320754717 \tabularnewline
t-stat & -0.344721272967098 \tabularnewline
df & 422 \tabularnewline
p-value & 0.730475657300466 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-2.54486394027964,1.78542997801549] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.339012493464766 \tabularnewline
df & 211 \tabularnewline
p-value & 1.75101407556316e-14 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.258642678379456,0.444356172946001] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309252&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]92.5264150943396[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]92.9061320754717[/C][/ROW]
[ROW][C]t-stat[/C][C]-0.344721272967098[/C][/ROW]
[ROW][C]df[/C][C]422[/C][/ROW]
[ROW][C]p-value[/C][C]0.730475657300466[/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][-2.54486394027964,1.78542997801549][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.339012493464766[/C][/ROW]
[ROW][C]df[/C][C]211[/C][/ROW]
[ROW][C]p-value[/C][C]1.75101407556316e-14[/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][0.258642678379456,0.444356172946001][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309252&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309252&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 192.5264150943396
Mean of Sample 292.9061320754717
t-stat-0.344721272967098
df422
p-value0.730475657300466
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-2.54486394027964,1.78542997801549]
F-test to compare two variances
F-stat0.339012493464766
df211
p-value1.75101407556316e-14
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.258642678379456,0.444356172946001]






Welch Two Sample t-test (unpaired)
Mean of Sample 192.5264150943396
Mean of Sample 292.9061320754717
t-stat-0.344721272967097
df339.31598410353
p-value0.730517488021635
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-2.54638242213885,1.7869484598747]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 92.5264150943396 \tabularnewline
Mean of Sample 2 & 92.9061320754717 \tabularnewline
t-stat & -0.344721272967097 \tabularnewline
df & 339.31598410353 \tabularnewline
p-value & 0.730517488021635 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-2.54638242213885,1.7869484598747] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309252&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]92.5264150943396[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]92.9061320754717[/C][/ROW]
[ROW][C]t-stat[/C][C]-0.344721272967097[/C][/ROW]
[ROW][C]df[/C][C]339.31598410353[/C][/ROW]
[ROW][C]p-value[/C][C]0.730517488021635[/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][-2.54638242213885,1.7869484598747][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309252&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309252&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 192.5264150943396
Mean of Sample 292.9061320754717
t-stat-0.344721272967097
df339.31598410353
p-value0.730517488021635
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-2.54638242213885,1.7869484598747]






Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)
W20989.5
p-value0.240129293018185
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.240566037735849
p-value9.39135714816963e-06
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.226415094339623
p-value3.81197762909791e-05

\begin{tabular}{lllllllll}
\hline
Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired) \tabularnewline
W & 20989.5 \tabularnewline
p-value & 0.240129293018185 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.240566037735849 \tabularnewline
p-value & 9.39135714816963e-06 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.226415094339623 \tabularnewline
p-value & 3.81197762909791e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309252&T=3

[TABLE]
[ROW][C]Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]20989.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.240129293018185[/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]0.240566037735849[/C][/ROW]
[ROW][C]p-value[/C][C]9.39135714816963e-06[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.226415094339623[/C][/ROW]
[ROW][C]p-value[/C][C]3.81197762909791e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309252&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309252&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:

Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)
W20989.5
p-value0.240129293018185
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.240566037735849
p-value9.39135714816963e-06
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.226415094339623
p-value3.81197762909791e-05


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):
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)
myWlabel <- 'Wilcoxon Signed-Rank Test'
if (par5=='unpaired') myWlabel = 'Wilcoxon Rank-Sum Test (Mann–Whitney U test)'
a<-table.element(a,paste(myWlabel,' 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')