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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 computationThu, 04 Dec 2014 18:52:15 +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/04/t1417719190flgzzfbxxfbq1o2.htm/, Retrieved Thu, 16 May 2024 20:17:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263393, Retrieved Thu, 16 May 2024 20:17:08 +0000
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-       [Paired and Unpaired Two Samples Tests about the Mean] [paper] [2014-12-04 18:52:15] [627bde65e5570be47fd7fc8a9f75ea40] [Current]
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Dataseries X:
12.9	11.3
7.4	9.6
12.2	16.1
12.8	13.4
7.4	12.7
6.7	12.3
12.6	7.9
14.8	12.3
13.3	11.6
11.1	6.7
8.2	12.1
11.4	5.7
6.4	8.0
10.6	13.3
12.0	9.1
6.3	12.2
11.9	8.8
9.3	14.6
10.0	12.6
6.4	9.9
13.8	10.5
10.8	13.4
13.8	10.9
11.7	4.3
10.9	10.3
9.9	11.8
11.5	11.2
8.3	11.4
11.7	8.6
6.1	13.2
9.0	12.6
9.7	5.6
10.8	9.9
10.3	8.8
10.4	7.7
9.3	9.0
11.8	7.3
5.9	11.4
11.4	13.6
13.0	7.9
10.8	10.7
11.3	10.3
11.8	8.3
12.7	9.6
10.9	14.2
13.3	8.5
10.1	13.5
14.3	4.9
9.3	6.4
12.5	9.6
7.6	11.6
15.9	11.1
9.2	16.6
11.1	12.6
13.0	18.9
14.5	11.6
12.3	14.6
11.4	13.85
7.3	14.85
12.6	11.75
NA	18.45
13.0	15.9
13.2	19.9
7.7	10.95
4.35	18.45
12.7	15.1
18.1	15
17.85	11.35
17.1	15.95
19.1	18.1
16.1	14.6
13.35	17.6
18.4	15.35
14.7	13.4
10.6	13.9
12.6	15.25
16.2	12.9
13.6	16.1
14.1	17.35
14.5	13.15
16.15	12.15
14.75	12.6
14.8	10.35
12.45	15.4
12.65	9.6
17.35	18.2
8.6	13.6
18.4	14.85
16.1	14.1
17.75	14.9
15.25	16.25
17.65	13.6
15.6	15.65
16.35	14.6
17.65	12.65
13.6	11.9
11.7	19.2
14.35	16.6
14.75	11.2
18.25	13.2
9.9	15.85
16	11.15
18.25	15.65
16.85	7.65
18.95	15.2
15.6	15.6
17.1	13.1
16.1	11.85
15.4	12.4
15.4	11.4
13.35	14.9
19.1	19.9
7.6	11.2
19.1	14.6
14.75	14.75
19.25	15.15
13.6	16.85
12.75	7.85
9.85	12.6
15.25	7.85
11.9	10.95
16.35	12.35
12.4	9.95
14.35	14.9
18.15	16.65
17.75	13.4
12.35	13.95
15.6	15.7
19.3	16.85
17.1	10.95
18.4	15.35
19.05	12.2
18.55	15.1
19.1	17.75
12.85	15.2
9.5	16.65
4.5	8.1
13.6
11.7
13.35
17.75
17.6
14.05
16.1
13.35
11.85
11.95
13.2
7.7
14.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263393&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263393&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263393&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net







Two Sample t-test (unpaired)
Mean of Sample 113.1151006711409
Mean of Sample 212.616
t-stat1.26770364287156
df297
p-value0.205896767052072
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.27570336435484,1.27390470663672]
F-test to compare two variances
F-stat1.14432825294612
df148
p-value0.412216745297989
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.828609264526377,1.58062431561469]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 13.1151006711409 \tabularnewline
Mean of Sample 2 & 12.616 \tabularnewline
t-stat & 1.26770364287156 \tabularnewline
df & 297 \tabularnewline
p-value & 0.205896767052072 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.27570336435484,1.27390470663672] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 1.14432825294612 \tabularnewline
df & 148 \tabularnewline
p-value & 0.412216745297989 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.828609264526377,1.58062431561469] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263393&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]13.1151006711409[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]12.616[/C][/ROW]
[ROW][C]t-stat[/C][C]1.26770364287156[/C][/ROW]
[ROW][C]df[/C][C]297[/C][/ROW]
[ROW][C]p-value[/C][C]0.205896767052072[/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][-0.27570336435484,1.27390470663672][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]1.14432825294612[/C][/ROW]
[ROW][C]df[/C][C]148[/C][/ROW]
[ROW][C]p-value[/C][C]0.412216745297989[/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][0.828609264526377,1.58062431561469][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263393&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263393&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 113.1151006711409
Mean of Sample 212.616
t-stat1.26770364287156
df297
p-value0.205896767052072
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.27570336435484,1.27390470663672]
F-test to compare two variances
F-stat1.14432825294612
df148
p-value0.412216745297989
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.828609264526377,1.58062431561469]







Welch Two Sample t-test (unpaired)
Mean of Sample 113.1151006711409
Mean of Sample 212.616
t-stat1.26741734524799
df295.382355890902
p-value0.206004356354616
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.275895749923607,1.27409709220549]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 13.1151006711409 \tabularnewline
Mean of Sample 2 & 12.616 \tabularnewline
t-stat & 1.26741734524799 \tabularnewline
df & 295.382355890902 \tabularnewline
p-value & 0.206004356354616 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.275895749923607,1.27409709220549] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263393&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]13.1151006711409[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]12.616[/C][/ROW]
[ROW][C]t-stat[/C][C]1.26741734524799[/C][/ROW]
[ROW][C]df[/C][C]295.382355890902[/C][/ROW]
[ROW][C]p-value[/C][C]0.206004356354616[/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][-0.275895749923607,1.27409709220549][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263393&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263393&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 113.1151006711409
Mean of Sample 212.616
t-stat1.26741734524799
df295.382355890902
p-value0.206004356354616
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.275895749923607,1.27409709220549]







Wicoxon rank sum test with continuity correction (unpaired)
W12060
p-value0.236665537369559
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.108322147651007
p-value0.344317940865429
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0743624161073826
p-value0.802890653408304

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 12060 \tabularnewline
p-value & 0.236665537369559 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.108322147651007 \tabularnewline
p-value & 0.344317940865429 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.0743624161073826 \tabularnewline
p-value & 0.802890653408304 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263393&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]12060[/C][/ROW]
[ROW][C]p-value[/C][C]0.236665537369559[/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]0.108322147651007[/C][/ROW]
[ROW][C]p-value[/C][C]0.344317940865429[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.0743624161073826[/C][/ROW]
[ROW][C]p-value[/C][C]0.802890653408304[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263393&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263393&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)
W12060
p-value0.236665537369559
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.108322147651007
p-value0.344317940865429
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0743624161073826
p-value0.802890653408304



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')