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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, 22 Jan 2020 10:24:04 +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/2020/Jan/22/t1579685286rl6taj2p4ekke55.htm/, Retrieved Thu, 25 Apr 2024 03:37:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319011, Retrieved Thu, 25 Apr 2024 03:37:37 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

Tree of Dependent Computations

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-       [Paired and Unpaired Two Samples Tests about the Mean] [vraag 2] [2020-01-22 09:24:04] [a95555c5f1e039c0b5d8fab05580554f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10 10
9 15
12 14
14 14
6 8
13 19
12 17
13 18
6 10
12 15
10 16
9 12
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7 10
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15 20
10 9
12 12
10 13
12 16
11 12
11 14
12 15
15 19
12 16
11 16
9 14
11 14
11 14
9 13
15 18
12 15
9 15
12 15
12 13
9 14
9 15
11 14
12 19
12 16
12 16
12 12
6 10
11 11
12 13
9 14
11 11
9 11
10 16
10 9
9 16
12 19
11 13
9 15
9 14
12 15
6 11
10 14
12 15
11 17
14 16
8 13
9 15
10 14
10 15
10 14
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10 12
12 15
14 17
10 13
8 5
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11 15
6 9
9 9
12 15
12 14
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9 18
15 20
15 20
13 16
9 15
12 14
9 13
15 18
11 14
11 12
6 9
14 19
11 13
8 12
10 14
10 6
9 14
8 11
9 11
10 14
11 12
14 19
12 13
9 14
13 17
8 12
12 16
14 15
9 15
10 15
12 16
12 15
9 12
9 13
12 14
15 17
12 14
11 14
8 14
11 15
11 11
10 11
12 16
9 12
11 12
15 19
14 18
6 16
9 16
9 13
8 11
7 10
10 14
6 14
9 14
9 16
7 10
11 16
9 7
12 16
9 15
10 17
11 11
7 11
12 10
8 13
13 14
11 13
11 13
12 12
11 10
12 15
3 6
10 15
13 15
10 11
6 14
11 14
12 16
9 12
10 15
15 20
9 12
6 9
9 13
15 15
15 19
9 11
11 11
9 17
11 15
10 14
9 15
6 11
12 12
13 15
12 16
12 16

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=319011&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=319011&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319011&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 110.5083798882682
Mean of Sample 213.8212290502793
t-stat-12.0883022095465
df356
p-value2.01520919536714e-28
H0 value0
Alternativetwo.sided
CI Level0.99
CI[-4.02256983464993,-2.60312848937241]
F-test to compare two variances
F-stat0.636497700077938
df178
p-value0.00272786710923501
H0 value1
Alternativetwo.sided
CI Level0.99
CI[0.431859835328948,0.938103729641626]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 10.5083798882682 \tabularnewline
Mean of Sample 2 & 13.8212290502793 \tabularnewline
t-stat & -12.0883022095465 \tabularnewline
df & 356 \tabularnewline
p-value & 2.01520919536714e-28 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.99 \tabularnewline
CI & [-4.02256983464993,-2.60312848937241] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.636497700077938 \tabularnewline
df & 178 \tabularnewline
p-value & 0.00272786710923501 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.99 \tabularnewline
CI & [0.431859835328948,0.938103729641626] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319011&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]10.5083798882682[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]13.8212290502793[/C][/ROW]
[ROW][C]t-stat[/C][C]-12.0883022095465[/C][/ROW]
[ROW][C]df[/C][C]356[/C][/ROW]
[ROW][C]p-value[/C][C]2.01520919536714e-28[/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]0.99[/C][/ROW]
[ROW][C]CI[/C][C][-4.02256983464993,-2.60312848937241][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.636497700077938[/C][/ROW]
[ROW][C]df[/C][C]178[/C][/ROW]
[ROW][C]p-value[/C][C]0.00272786710923501[/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]0.99[/C][/ROW]
[ROW][C]CI[/C][C][0.431859835328948,0.938103729641626][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319011&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319011&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 110.5083798882682
Mean of Sample 213.8212290502793
t-stat-12.0883022095465
df356
p-value2.01520919536714e-28
H0 value0
Alternativetwo.sided
CI Level0.99
CI[-4.02256983464993,-2.60312848937241]
F-test to compare two variances
F-stat0.636497700077938
df178
p-value0.00272786710923501
H0 value1
Alternativetwo.sided
CI Level0.99
CI[0.431859835328948,0.938103729641626]






Welch Two Sample t-test (unpaired)
Mean of Sample 110.5083798882682
Mean of Sample 213.8212290502793
t-stat-12.0883022095465
df339.261442379015
p-value3.21531732414591e-28
H0 value0
Alternativetwo.sided
CI Level0.99
CI[-4.02275851470364,-2.60293980931871]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 10.5083798882682 \tabularnewline
Mean of Sample 2 & 13.8212290502793 \tabularnewline
t-stat & -12.0883022095465 \tabularnewline
df & 339.261442379015 \tabularnewline
p-value & 3.21531732414591e-28 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.99 \tabularnewline
CI & [-4.02275851470364,-2.60293980931871] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319011&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]10.5083798882682[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]13.8212290502793[/C][/ROW]
[ROW][C]t-stat[/C][C]-12.0883022095465[/C][/ROW]
[ROW][C]df[/C][C]339.261442379015[/C][/ROW]
[ROW][C]p-value[/C][C]3.21531732414591e-28[/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]0.99[/C][/ROW]
[ROW][C]CI[/C][C][-4.02275851470364,-2.60293980931871][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319011&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319011&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 110.5083798882682
Mean of Sample 213.8212290502793
t-stat-12.0883022095465
df339.261442379015
p-value3.21531732414591e-28
H0 value0
Alternativetwo.sided
CI Level0.99
CI[-4.02275851470364,-2.60293980931871]






Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)
W5777
p-value6.70467478906951e-26
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.558659217877095
p-value0
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.156424581005587
p-value0.0250534671137755

\begin{tabular}{lllllllll}
\hline
Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired) \tabularnewline
W & 5777 \tabularnewline
p-value & 6.70467478906951e-26 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.558659217877095 \tabularnewline
p-value & 0 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.156424581005587 \tabularnewline
p-value & 0.0250534671137755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319011&T=3

[TABLE]
[ROW][C]Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]5777[/C][/ROW]
[ROW][C]p-value[/C][C]6.70467478906951e-26[/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]0.558659217877095[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.156424581005587[/C][/ROW]
[ROW][C]p-value[/C][C]0.0250534671137755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319011&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319011&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)
W5777
p-value6.70467478906951e-26
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.558659217877095
p-value0
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.156424581005587
p-value0.0250534671137755


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.99 ; par4 = two.sided ; par5 = unpaired ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.99 ; par4 = two.sided ; par5 = unpaired ; par6 = 0 ;
R code (references can be found in the software module):
par6 <- ''
par5 <- 'unpaired'
par4 <- 'two.sided'
par3 <- '0.99'
par2 <- '2'
par1 <- '1'
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')