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 computationTue, 02 Nov 2010 19:21:54 +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/2010/Nov/02/t1288725700w3nug8dk6avfa9c.htm/, Retrieved Sun, 28 Apr 2024 06:08:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=91991, Retrieved Sun, 28 Apr 2024 06:08:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Factor Analysis] [Sleep in Mammals ...] [2010-03-21 11:39:53] [b98453cac15ba1066b407e146608df68]
- RMPD  [Testing Mean with unknown Variance - Critical Value] [Hypothesis Test a...] [2010-10-19 11:45:26] [b98453cac15ba1066b407e146608df68]
F RMPD      [Paired and Unpaired Two Samples Tests about the Mean] [Q1, W5] [2010-11-02 19:21:54] [0605ea080d54454c99180f574351b8e4] [Current]
-    D        [Paired and Unpaired Two Samples Tests about the Mean] [Q2, W5] [2010-11-02 19:28:48] [b3140021f9a1a3896de9ecbfce0f1101]
-    D          [Paired and Unpaired Two Samples Tests about the Mean] [Q3, W5] [2010-11-02 19:32:03] [b3140021f9a1a3896de9ecbfce0f1101]
F    D          [Paired and Unpaired Two Samples Tests about the Mean] [Q3, W5] [2010-11-02 19:32:03] [b3140021f9a1a3896de9ecbfce0f1101]
F    D            [Paired and Unpaired Two Samples Tests about the Mean] [Q5 a, W5] [2010-11-02 20:03:56] [b3140021f9a1a3896de9ecbfce0f1101]
F    D              [Paired and Unpaired Two Samples Tests about the Mean] [Q5 b,W5] [2010-11-02 20:06:39] [b3140021f9a1a3896de9ecbfce0f1101]
F    D                [Paired and Unpaired Two Samples Tests about the Mean] [Q5 c , WW5] [2010-11-02 20:10:07] [b3140021f9a1a3896de9ecbfce0f1101]
F RM D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q6, KT] [2010-11-02 20:27:53] [b3140021f9a1a3896de9ecbfce0f1101]
-    D                  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q7, KT] [2010-11-02 20:53:17] [b3140021f9a1a3896de9ecbfce0f1101]
F    D                  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q7, KT] [2010-11-02 20:53:17] [b3140021f9a1a3896de9ecbfce0f1101]
F RM D                  [Two-Way ANOVA] [Q8] [2010-11-02 21:01:13] [b3140021f9a1a3896de9ecbfce0f1101]
F    D            [Paired and Unpaired Two Samples Tests about the Mean] [Q2, W5] [2010-11-02 21:14:39] [b3140021f9a1a3896de9ecbfce0f1101]
Feedback Forum
2010-11-03 16:15:26 [Pascal Wijnen] [reply] 
De interpretatie van de student is correct, maar heeft de verkeerde methode gebruikt. Er moest gebruik gemaakt worden van 'paired' gegevens.
2010-11-07 22:32:47 [] [reply] 
De student heeft inderdaad de juiste data gebruikt, maar de observaties zijn paired. Dit omdat de 2 steekproeven afhankelijk van elkaar zijn door dezelfde treatment, namelijk treatment 'E'. Wanneer je unpaired vervangt door paired kom je tot volgend resultaat voor treatment 'E': http://www.freestatistics.org/blog/index.php?v=date/2010/Oct/29/t1288338802yi62r3oisnbi9ra.htm/. De nulhypothese is gelijk aan 0 en 0 maakt niet deel uit van het 95% betrouwbaarheidsinterval. Dus we mogen de nulhypothese verwerpen. Er is degelijk een significant verschil tussen de resultaten van de post-test 1 en die van de pre-test voor treatment 'E'. De 'E' treatment heeft dus effect op de resultaten van de studenten.
2010-11-07 22:39:59 [] [reply] 
Zijn de assumpties voldaan?
- F-stat = 1,5 (indien 1 varianties gelijk). Maar p-value = 0,46. Dus 46% kans dat we ons vergissen als de varianties niet gelijk zijn aan elkaar. De p-value is ook groter dan de alpha-fout, dus geen significant verschil tussen de varianties.
- We spreken wel niet van een normaalverdeling, want de data bestaat enkel uit 1 en 0. Als je de QQ-plot bekijkt, zie je ook dat de punten niet op een lijn liggen. De gemiddeldes zijn wel normaal verdeeld. En de centrale limietstelling is van toepassing. De steekproef is random.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	1
0	1
1	1
1	1
1	1
1	1
1	1
0	0
0	1
0	1
1	1
1	1
0	0
0	1
1	1
0	1
0	0
0	1
0	1
0	1
0	1
0	0
0	0
0	1
1	1
1	1
1	0
0	0
0	1
0	1
0	0
1	1
1	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 12 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91991&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91991&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91991&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 time12 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Two Sample t-test (unpaired)
Mean of Sample 10.393939393939394
Mean of Sample 20.757575757575758
t-stat-3.16502627229978
df64
p-value0.00237413416331207
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.593159623326958,-0.134113103945769]
F-test to compare two variances
F-stat1.3
df32
p-value0.462180712842677
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.642054974596472,2.63217336032971]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 0.393939393939394 \tabularnewline
Mean of Sample 2 & 0.757575757575758 \tabularnewline
t-stat & -3.16502627229978 \tabularnewline
df & 64 \tabularnewline
p-value & 0.00237413416331207 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.593159623326958,-0.134113103945769] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 1.3 \tabularnewline
df & 32 \tabularnewline
p-value & 0.462180712842677 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.642054974596472,2.63217336032971] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91991&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]0.393939393939394[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]0.757575757575758[/C][/ROW]
[ROW][C]t-stat[/C][C]-3.16502627229978[/C][/ROW]
[ROW][C]df[/C][C]64[/C][/ROW]
[ROW][C]p-value[/C][C]0.00237413416331207[/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][-0.593159623326958,-0.134113103945769][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]1.3[/C][/ROW]
[ROW][C]df[/C][C]32[/C][/ROW]
[ROW][C]p-value[/C][C]0.462180712842677[/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][0.642054974596472,2.63217336032971][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91991&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91991&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 10.393939393939394
Mean of Sample 20.757575757575758
t-stat-3.16502627229978
df64
p-value0.00237413416331207
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.593159623326958,-0.134113103945769]
F-test to compare two variances
F-stat1.3
df32
p-value0.462180712842677
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.642054974596472,2.63217336032971]






Welch Two Sample t-test (unpaired)
Mean of Sample 10.393939393939394
Mean of Sample 20.757575757575758
t-stat-3.16502627229978
df62.9293680297398
p-value0.00238983571755029
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.59323485286005,-0.134037874412677]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 0.393939393939394 \tabularnewline
Mean of Sample 2 & 0.757575757575758 \tabularnewline
t-stat & -3.16502627229978 \tabularnewline
df & 62.9293680297398 \tabularnewline
p-value & 0.00238983571755029 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.59323485286005,-0.134037874412677] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91991&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]0.393939393939394[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]0.757575757575758[/C][/ROW]
[ROW][C]t-stat[/C][C]-3.16502627229978[/C][/ROW]
[ROW][C]df[/C][C]62.9293680297398[/C][/ROW]
[ROW][C]p-value[/C][C]0.00238983571755029[/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][-0.59323485286005,-0.134037874412677][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91991&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91991&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 10.393939393939394
Mean of Sample 20.757575757575758
t-stat-3.16502627229978
df62.9293680297398
p-value0.00238983571755029
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.59323485286005,-0.134037874412677]






Wicoxon rank sum test with continuity correction (unpaired)
W346.5
p-value0.00309157689817328
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.363636363636364
p-value0.025463957781115
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.393939393939394
p-value0.0119375646999563

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 346.5 \tabularnewline
p-value & 0.00309157689817328 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.363636363636364 \tabularnewline
p-value & 0.025463957781115 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.393939393939394 \tabularnewline
p-value & 0.0119375646999563 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91991&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]346.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.00309157689817328[/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]0.363636363636364[/C][/ROW]
[ROW][C]p-value[/C][C]0.025463957781115[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.393939393939394[/C][/ROW]
[ROW][C]p-value[/C][C]0.0119375646999563[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91991&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91991&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)
W346.5
p-value0.00309157689817328
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.363636363636364
p-value0.025463957781115
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.393939393939394
p-value0.0119375646999563


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)
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')