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*The author of this computation has been verified*

R Software Module

rwasp_twosampletests_mean.wasp

Title produced by software

Paired and Unpaired Two Samples Tests about the Mean

Date of computation

Sat, 06 Dec 2014 21:54:35 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/06/t1417902892lc0vbdiomzj1ivb.htm/, Retrieved Thu, 16 May 2024 06:26:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263676, Retrieved Thu, 16 May 2024 06:26:29 +0000

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Original text written by user:

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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] [paper] [2014-12-06 21:54:35] [984e7ebcf70ed344d92ecabf69fab39c] [Current]

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Dataseries X:

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Histogram

Boxplots

11,3	NA
NA	9,6
NA	16,1
13,4	NA
NA	12,7
NA	12,3
NA	7,9
NA	12,3
NA	11,6
NA	6,7
NA	12,1
5,7	NA
8	NA
NA	13,3
NA	9,1
12,2	NA
8,8	NA
NA	14,6
NA	12,6
9,9	NA
10,5	NA
13,4	NA
10,9	NA
NA	4,3
10,3	NA
NA	11,8
NA	11,2
11,4	NA
8,6	NA
13,2	NA
NA	12,6
NA	5,6
NA	9,9
8,8	NA
NA	7,7
9	NA
NA	7,3
NA	11,4
NA	13,6
NA	7,9
NA	10,7
10,3	NA
NA	8,3
NA	9,6
NA	14,2
8,5	NA
13,5	NA
4,9	NA
6,4	NA
9,6	NA
11,6	NA
NA	11,1
16,6	NA
NA	12,6
NA	18,9
NA	11,6
NA	14,6
NA	13,85
14,85	NA
11,75	NA
18,45	NA
NA	15,9
19,9	NA
NA	10,95
18,45	NA
NA	15,1
15	NA
11,35	NA
NA	15,95
18,1	NA
NA	14,6
NA	17,6
NA	15,35
13,4	NA
13,9	NA
15,25	NA
NA	12,9
16,1	NA
17,35	NA
13,15	NA
12,15	NA
NA	12,6
NA	10,35
NA	15,4
NA	9,6
18,2	NA
13,6	NA
NA	14,85
14,1	NA
14,9	NA
16,25	NA
NA	13,6
15,65	NA
14,6	NA
NA	12,65
NA	11,9
19,2	NA
NA	16,6
NA	11,2
13,2	NA
NA	15,85
NA	11,15
15,65	NA
7,65	NA
15,2	NA
NA	15,6
NA	13,1
11,85	NA
NA	12,4
11,4	NA
NA	14,9
19,9	NA
NA	11,2
NA	14,6
NA	14,75
15,15	NA
16,85	NA
NA	7,85
12,6	NA
NA	7,85
NA	10,95
12,35	NA
NA	9,95
NA	14,9
16,65	NA
NA	13,4
13,95	NA
15,7	NA
NA	16,85
NA	10,95
15,35	NA
NA	12,2
15,1	NA
17,75	NA
NA	15,2
16,65	NA
NA	8,1
12,9	NA
7,4	NA
NA	12,2
12,8	NA
NA	7,4
NA	6,7
NA	12,6
14,8	NA
NA	13,3
NA	11,1
NA	8,2
NA	11,4
NA	6,4
NA	10,6
12	NA
6,3	NA
NA	11,9
9,3	NA
10	NA
NA	6,4
NA	13,8
10,8	NA
NA	13,8
NA	11,7
NA	10,9
NA	9,9
11,5	NA
8,3	NA
11,7	NA
NA	6,1
NA	9
NA	9,7
NA	10,8
NA	10,3
10,4	NA
NA	9,3
11,8	NA
NA	5,9
NA	11,4
NA	13
NA	10,8
11,3	NA
NA	11,8
12,7	NA
NA	10,9
NA	13,3
NA	10,1
NA	14,3
NA	9,3
12,5	NA
7,6	NA
NA	15,9
9,2	NA
11,1	NA
NA	13
NA	14,5
12,3	NA
11,4	NA
NA	7,3
12,6	NA
NA	NA
13	NA
13,2	NA
NA	7,7
NA	4,35
NA	12,7
NA	18,1
NA	17,85
NA	17,1
19,1	NA
NA	16,1
13,35	NA
18,4	NA
NA	14,7
NA	10,6
NA	12,6
NA	16,2
NA	13,6
NA	14,1
NA	14,5
16,15	NA
NA	14,75
NA	14,8
NA	12,45
NA	12,65
NA	17,35
NA	8,6
18,4	NA
NA	16,1
NA	17,75
NA	15,25
NA	17,65
15,6	NA
16,35	NA
17,65	NA
NA	13,6
11,7	NA
14,35	NA
14,75	NA
NA	18,25
9,9	NA
NA	16
NA	18,25
16,85	NA
NA	18,95
15,6	NA
17,1	NA
NA	16,1
NA	15,4
NA	15,4
NA	13,35
19,1	NA
7,6	NA
NA	19,1
14,75	NA
NA	19,25
13,6	NA
NA	12,75
NA	9,85
NA	15,25
11,9	NA
16,35	NA
NA	12,4
14,35	NA
NA	18,15
17,75	NA
NA	12,35
NA	15,6
19,3	NA
17,1	NA
NA	18,4
19,05	NA
18,55	NA
19,1	NA
NA	12,85
NA	9,5
NA	4,5
NA	13,6
NA	11,7
13,35	NA
NA	17,75
17,6	NA
NA	14,05
16,1	NA
NA	13,35
NA	11,85
11,95	NA
NA	13,2
7,7	NA
14,6	NA

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263676&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263676&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263676&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Two Sample t-test (unpaired)
Mean of Sample 1	13.4463709677419
Mean of Sample 2	12.541975308642
t-stat	2.23082761553651
df	284
p-value	0.0264734649243776
H0 value	0
Alternative	two.sided
CI Level	0.95
CI	[0.10640968510604,1.70238163309388]
F-test to compare two variances
F-stat	1.10128925862411
df	123
p-value	0.563700450898233
H0 value	1
Alternative	two.sided
CI Level	0.95
CI	[0.792096459106985,1.54274081875378]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 13.4463709677419 \tabularnewline
Mean of Sample 2 & 12.541975308642 \tabularnewline
t-stat & 2.23082761553651 \tabularnewline
df & 284 \tabularnewline
p-value & 0.0264734649243776 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.10640968510604,1.70238163309388] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 1.10128925862411 \tabularnewline
df & 123 \tabularnewline
p-value & 0.563700450898233 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.792096459106985,1.54274081875378] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263676&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]13.4463709677419[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]12.541975308642[/C][/ROW]
[ROW][C]t-stat[/C][C]2.23082761553651[/C][/ROW]
[ROW][C]df[/C][C]284[/C][/ROW]
[ROW][C]p-value[/C][C]0.0264734649243776[/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][0.10640968510604,1.70238163309388][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]1.10128925862411[/C][/ROW]
[ROW][C]df[/C][C]123[/C][/ROW]
[ROW][C]p-value[/C][C]0.563700450898233[/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][0.792096459106985,1.54274081875378][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263676&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263676&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 1	13.4463709677419
Mean of Sample 2	12.541975308642
t-stat	2.23082761553651
df	284
p-value	0.0264734649243776
H0 value	0
Alternative	two.sided
CI Level	0.95
CI	[0.10640968510604,1.70238163309388]
F-test to compare two variances
F-stat	1.10128925862411
df	123
p-value	0.563700450898233
H0 value	1
Alternative	two.sided
CI Level	0.95
CI	[0.792096459106985,1.54274081875378]

Welch Two Sample t-test (unpaired)
Mean of Sample 1	13.4463709677419
Mean of Sample 2	12.541975308642
t-stat	2.21653510665994
df	258.13135340882
p-value	0.0275269176657214
H0 value	0
Alternative	two.sided
CI Level	0.95
CI	[0.100919589879747,1.70787172832018]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 13.4463709677419 \tabularnewline
Mean of Sample 2 & 12.541975308642 \tabularnewline
t-stat & 2.21653510665994 \tabularnewline
df & 258.13135340882 \tabularnewline
p-value & 0.0275269176657214 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.100919589879747,1.70787172832018] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263676&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]13.4463709677419[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]12.541975308642[/C][/ROW]
[ROW][C]t-stat[/C][C]2.21653510665994[/C][/ROW]
[ROW][C]df[/C][C]258.13135340882[/C][/ROW]
[ROW][C]p-value[/C][C]0.0275269176657214[/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][0.100919589879747,1.70787172832018][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263676&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263676&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 1	13.4463709677419
Mean of Sample 2	12.541975308642
t-stat	2.21653510665994
df	258.13135340882
p-value	0.0275269176657214
H0 value	0
Alternative	two.sided
CI Level	0.95
CI	[0.100919589879747,1.70787172832018]

Wicoxon rank sum test with continuity correction (unpaired)
W	11513.5
p-value	0.0340498174398412
H0 value	0
Alternative	two.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic	0.132716049382716
p-value	0.168347817156249
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic	0.0631222620469932
p-value	0.942308855625158

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 11513.5 \tabularnewline
p-value & 0.0340498174398412 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.132716049382716 \tabularnewline
p-value & 0.168347817156249 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.0631222620469932 \tabularnewline
p-value & 0.942308855625158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263676&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]11513.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.0340498174398412[/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]0.132716049382716[/C][/ROW]
[ROW][C]p-value[/C][C]0.168347817156249[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.0631222620469932[/C][/ROW]
[ROW][C]p-value[/C][C]0.942308855625158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263676&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263676&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)
W	11513.5
p-value	0.0340498174398412
H0 value	0
Alternative	two.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic	0.132716049382716
p-value	0.168347817156249
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic	0.0631222620469932
p-value	0.942308855625158

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0 ;

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code