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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 computationThu, 11 Dec 2014 13:12:53 +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/11/t1418303599cfxqgxdnkqjl100.htm/, Retrieved Sun, 19 May 2024 16:59:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265956, Retrieved Sun, 19 May 2024 16:59:55 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79

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-       [Paired and Unpaired Two Samples Tests about the Mean] [Two sample T-test PE] [2014-12-11 13:12:53] [d71ad52285d92a63edfc83f9fb1da7a1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.1 2
1.5 2.1
2 2.7
2 1.9
2.1 2.2
2 1.9
2.3 2
2.1 2
2.1 2.2
2.2 1.7
2.1 2.2
2.1 2
2.1 1.6
2 2.1
2.3 1.8
1.8 2.1
2.2 1.8
2 2.1
2 2.1
1.8 1.8
2.2 2.1
2.2 1.9
1.7 2.1
2.1 1
2.3 2.2
2 2.1
2 1.9
1.9 2
2 1.9
2 2
2 1.8
2.1 2
2 2
1.8 2
2 1.8
2.2 2
2.1 1.1
1.8 1.8
1.9 1.8
2.1 2
2 1.9
2.2 2.1
2 1.6
1.7 2.2
2 1.9
2 2
1.9 2.1
2 1.3
2.1 1.8
2 1.9
1.9 2.1
2.2 1.8
2.1 3
2.3 1.5
2.3 3
2.2 1.5
2.2 1.5
1.9 2.25
1.8 2.25
2 1.5
1.7 2.25
2.1 3
2.1 3
2 1.5
0.75 2.25
1.5 1.5
3 2.25
2.25 2.25
3 2.25
3 3
3 1.5
0.75 3
3 2.25
2.25 3
1.5 1.5
1.5 3
2.25 3
3 3
1.5 2.25
2.25 2.25
2.25 0.75
1.5 3
2.25 0.75
1.5 1.5
2.25 1.5
2.25 3
3 1.5
3 2.25
3 3
3 1.5
3 3
2.25 1.5
1.5 2.25
2.25 1.5
2.25 1.5
3 2.25
0.75 3
2.25 3
3 0.75
3 2.25
1.5 2.25
2.25 0.75
3 2.25
2.25 0.75
2.25 3
1.5 1.5
3 1.5
3 2.25
2.25 3
2.25 3
2.25 1.5
3 3
1.5 0.75
3 1.5
3 1.5
3 2.25
1.5 2.25
1.5 0.75
2.25 1.5
1.5 2.25
1.5 1.5
2.25 0.75
1.5 1.5
0.75 1.5
2.25 2.25
3 1.5
0.75 1.5
3 3
3 2.25
3 1.5
3 0.75
3 2.25
3 3
3 3
2.25 1.5
0.75 2.25
0.75 0.75
3 NA
2.25 NA
2.25 NA
3 NA
3 NA
3 NA
3 NA
2.25 NA
2.25 NA
3 NA
2.25 NA
2.25 NA
1.5 NA

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265956&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265956&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265956&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 time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 12.181
Mean of Sample 21.99270072992701
t-stat2.69568713636963
df285
p-value0.00744192189014642
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.050807903060115,0.32579063708587]
F-test to compare two variances
F-stat0.866918100113629
df149
p-value0.393590519616172
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.622439011974806,1.20438915192844]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 2.181 \tabularnewline
Mean of Sample 2 & 1.99270072992701 \tabularnewline
t-stat & 2.69568713636963 \tabularnewline
df & 285 \tabularnewline
p-value & 0.00744192189014642 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.050807903060115,0.32579063708587] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.866918100113629 \tabularnewline
df & 149 \tabularnewline
p-value & 0.393590519616172 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.622439011974806,1.20438915192844] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265956&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]2.181[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]1.99270072992701[/C][/ROW]
[ROW][C]t-stat[/C][C]2.69568713636963[/C][/ROW]
[ROW][C]df[/C][C]285[/C][/ROW]
[ROW][C]p-value[/C][C]0.00744192189014642[/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][0.050807903060115,0.32579063708587][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.866918100113629[/C][/ROW]
[ROW][C]df[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0.393590519616172[/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][0.622439011974806,1.20438915192844][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265956&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265956&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 12.181
Mean of Sample 21.99270072992701
t-stat2.69568713636963
df285
p-value0.00744192189014642
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.050807903060115,0.32579063708587]
F-test to compare two variances
F-stat0.866918100113629
df149
p-value0.393590519616172
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.622439011974806,1.20438915192844]






Welch Two Sample t-test (unpaired)
Mean of Sample 12.181
Mean of Sample 21.99270072992701
t-stat2.68696648644367
df277.712797032186
p-value0.00764525541071706
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.0503462332775206,0.326252306868465]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 2.181 \tabularnewline
Mean of Sample 2 & 1.99270072992701 \tabularnewline
t-stat & 2.68696648644367 \tabularnewline
df & 277.712797032186 \tabularnewline
p-value & 0.00764525541071706 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.0503462332775206,0.326252306868465] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265956&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]2.181[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]1.99270072992701[/C][/ROW]
[ROW][C]t-stat[/C][C]2.68696648644367[/C][/ROW]
[ROW][C]df[/C][C]277.712797032186[/C][/ROW]
[ROW][C]p-value[/C][C]0.00764525541071706[/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][0.0503462332775206,0.326252306868465][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265956&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265956&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 12.181
Mean of Sample 21.99270072992701
t-stat2.68696648644367
df277.712797032186
p-value0.00764525541071706
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.0503462332775206,0.326252306868465]






Wicoxon rank sum test with continuity correction (unpaired)
W12300.5
p-value0.00356836180294854
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.198588807785888
p-value0.0070514302420599
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.200486618004866
p-value0.00632665054467174

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 12300.5 \tabularnewline
p-value & 0.00356836180294854 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.198588807785888 \tabularnewline
p-value & 0.0070514302420599 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.200486618004866 \tabularnewline
p-value & 0.00632665054467174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265956&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]12300.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.00356836180294854[/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]0.198588807785888[/C][/ROW]
[ROW][C]p-value[/C][C]0.0070514302420599[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.200486618004866[/C][/ROW]
[ROW][C]p-value[/C][C]0.00632665054467174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265956&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265956&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)
W12300.5
p-value0.00356836180294854
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.198588807785888
p-value0.0070514302420599
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.200486618004866
p-value0.00632665054467174


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')