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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 computationTue, 28 Jan 2020 10:02:38 +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/28/t1580202602o4fv66m2k6exorz.htm/, Retrieved Wed, 21 Apr 2021 09:07:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319049, Retrieved Wed, 21 Apr 2021 09:07:05 +0000
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Estimated Impact55
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-       [Paired and Unpaired Two Samples Tests about the Mean] [vraag 2] [2020-01-28 09:02:38] [43eb2330ebca6ad52336dea971844457] [Current]
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Dataseries X:
21 36
22 32
17 33
21 39
19 34
23 39
21 36
22 33
11 30
20 39
18 37
16 37
18 35
13 32
17 36
20 36
20 41
15 36
18 37
15 29
19 39
19 37
19 32
20 36
20 43
16 30
18 33
17 28
18 30
13 28
20 39
21 34
17 34
19 29
20 32
15 33
15 27
19 35
18 38
22 40
20 34
18 34
14 26
15 39
17 34
16 39
17 26
15 30
17 34
18 34
16 29
18 41
22 43
16 31
16 33
20 34
18 30
16 23
16 29
20 35
21 40
18 27
15 30
18 27
18 29
20 33
18 32
16 33
19 36
20 34
22 45
18 30
8 22
13 24
13 25
18 26
12 27
16 27
21 35
20 36
18 32
22 35
23 35
23 36
21 37
16 33
14 25
18 35
22 37
20 36
18 35
12 29
17 35
15 31
18 30
18 37
15 36
16 35
15 32
16 34
19 37
19 36
23 39
20 37
18 31
21 40
19 38
18 35
19 38
17 32
21 41
19 28
24 40
12 25
15 28
18 37
19 37
22 40
19 26
16 30
19 32
18 31
18 28
19 34
21 39
19 33
22 43
23 37
17 31
18 31
19 34
15 32
14 27
18 34
17 28
19 32
16 39
14 28
20 39
16 32
18 36
16 31
21 39
16 23
14 25
16 32
19 32
19 36
19 39
18 31
16 32
14 28
19 34
11 28
18 38
18 35
16 32
20 26
18 32
20 28
16 31
18 33
19 38
19 38
15 36
17 31
21 36
24 43
16 37
13 28
21 35
16 34
17 40
17 31
18 41
18 35
23 38
20 37
20 31




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=319049&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=319049&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319049&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 Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Two Sample t-test (unpaired)
Mean of Sample 117.9441340782123
Mean of Sample 233.5418994413408
t-stat-1.59379496199984
df356
p-value0.111869378769461
H0 value-14.96
Alternativetwo.sided
CI Level0.98
CI[-16.5328753092161,-14.6626554170409]
F-test to compare two variances
F-stat0.356724972441682
df178
p-value1.77719315418111e-11
H0 value1
Alternativetwo.sided
CI Level0.98
CI[0.251351901346012,0.506273098719639]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 17.9441340782123 \tabularnewline
Mean of Sample 2 & 33.5418994413408 \tabularnewline
t-stat & -1.59379496199984 \tabularnewline
df & 356 \tabularnewline
p-value & 0.111869378769461 \tabularnewline
H0 value & -14.96 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.98 \tabularnewline
CI & [-16.5328753092161,-14.6626554170409] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.356724972441682 \tabularnewline
df & 178 \tabularnewline
p-value & 1.77719315418111e-11 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.98 \tabularnewline
CI & [0.251351901346012,0.506273098719639] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319049&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]17.9441340782123[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]33.5418994413408[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.59379496199984[/C][/ROW]
[ROW][C]df[/C][C]356[/C][/ROW]
[ROW][C]p-value[/C][C]0.111869378769461[/C][/ROW]
[ROW][C]H0 value[/C][C]-14.96[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.98[/C][/ROW]
[ROW][C]CI[/C][C][-16.5328753092161,-14.6626554170409][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.356724972441682[/C][/ROW]
[ROW][C]df[/C][C]178[/C][/ROW]
[ROW][C]p-value[/C][C]1.77719315418111e-11[/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]0.98[/C][/ROW]
[ROW][C]CI[/C][C][0.251351901346012,0.506273098719639][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319049&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319049&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 117.9441340782123
Mean of Sample 233.5418994413408
t-stat-1.59379496199984
df356
p-value0.111869378769461
H0 value-14.96
Alternativetwo.sided
CI Level0.98
CI[-16.5328753092161,-14.6626554170409]
F-test to compare two variances
F-stat0.356724972441682
df178
p-value1.77719315418111e-11
H0 value1
Alternativetwo.sided
CI Level0.98
CI[0.251351901346012,0.506273098719639]







Welch Two Sample t-test (unpaired)
Mean of Sample 117.9441340782123
Mean of Sample 233.5418994413408
t-stat-1.59379496199984
df290.658048649962
p-value0.112068779397918
H0 value-14.96
Alternativetwo.sided
CI Level0.98
CI[-16.5338266919212,-14.6617040343357]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 17.9441340782123 \tabularnewline
Mean of Sample 2 & 33.5418994413408 \tabularnewline
t-stat & -1.59379496199984 \tabularnewline
df & 290.658048649962 \tabularnewline
p-value & 0.112068779397918 \tabularnewline
H0 value & -14.96 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.98 \tabularnewline
CI & [-16.5338266919212,-14.6617040343357] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319049&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]17.9441340782123[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]33.5418994413408[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.59379496199984[/C][/ROW]
[ROW][C]df[/C][C]290.658048649962[/C][/ROW]
[ROW][C]p-value[/C][C]0.112068779397918[/C][/ROW]
[ROW][C]H0 value[/C][C]-14.96[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.98[/C][/ROW]
[ROW][C]CI[/C][C][-16.5338266919212,-14.6617040343357][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319049&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319049&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 117.9441340782123
Mean of Sample 233.5418994413408
t-stat-1.59379496199984
df290.658048649962
p-value0.112068779397918
H0 value-14.96
Alternativetwo.sided
CI Level0.98
CI[-16.5338266919212,-14.6617040343357]







Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)
W13259
p-value0.00474702216914255
H0 value-14.96
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.977653631284916
p-value0
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.195530726256983
p-value0.00213256832415887

\begin{tabular}{lllllllll}
\hline
Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired) \tabularnewline
W & 13259 \tabularnewline
p-value & 0.00474702216914255 \tabularnewline
H0 value & -14.96 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.977653631284916 \tabularnewline
p-value & 0 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.195530726256983 \tabularnewline
p-value & 0.00213256832415887 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319049&T=3

[TABLE]
[ROW][C]Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]13259[/C][/ROW]
[ROW][C]p-value[/C][C]0.00474702216914255[/C][/ROW]
[ROW][C]H0 value[/C][C]-14.96[/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]0.977653631284916[/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]0.195530726256983[/C][/ROW]
[ROW][C]p-value[/C][C]0.00213256832415887[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319049&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319049&T=3

As an alternative you can also use a 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)
W13259
p-value0.00474702216914255
H0 value-14.96
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.977653631284916
p-value0
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.195530726256983
p-value0.00213256832415887



Parameters (Session):
par2 = grey ; par3 = FALSE ; par4 = 7-point Likert ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.98 ; par4 = two.sided ; par5 = unpaired ; par6 = -14.96 ;
R code (references can be found in the software module):
par6 <- '0.0'
par5 <- 'unpaired'
par4 <- 'two.sided'
par3 <- '0.98'
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')