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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 12:31:33 +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/t1418301125vena1bbt2pkqpe6.htm/, Retrieved Thu, 16 May 2024 06:28:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265889, Retrieved Thu, 16 May 2024 06:28:40 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

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-       [Paired and Unpaired Two Samples Tests about the Mean] [Two sample T-test EX] [2014-12-11 12:31:33] [d71ad52285d92a63edfc83f9fb1da7a1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7.5 5
2.5 3
6 7.5
6.5 7
1 6
1 6
5.5 1
8.5 6
6.5 5
4.5 1
2 6.5
5 0
0.5 3.5
5 7.5
5 3.5
2.5 6
5.5 3.5
3.5 7.5
4 6.5
0.5 3.5
6.5 4
4.5 7.5
7.5 4.5
5.5 0
4 3.5
4 5.5
5.5 5
2.5 4.5
5.5 2.5
0.5 7.5
3.5 7
2.5 0
4.5 4.5
4.5 3
4.5 1.5
2.5 3.5
5 2.5
0 5.5
5 8
6.5 1
5 5
4.5 4.5
5.5 3
7.5 3
5 8
7 2.5
4.5 7
8.5 0
3.5 1
6 3.5
1.5 5.5
9 5.5
3.5 8.5
4 7
6.5 9.5
7.5 6
5 9
5.5 7.5
1 7.5
6.5 6
NA 10.5
6.5 8.5
7 10.5
1.5 6.5
0.5 9.5
7.5 8.5
9 7.5
9.5 5
8 8
10 10
7 7
8.5 9.5
9 7
9.5 6
4 7
6 7
8 3.5
5.5 8
7.5 10
7 5.5
7.5 6
8 6.5
7 6.5
7 8.5
6 4
10 9.5
2.5 8
9 8.5
8 7
8.5 9
6 8
9 8
8 8
8 9
9 8.5
5.5 7
5 9.5
7 8.5
5.5 7.5
9 7
2 8.5
8.5 7
9 8
8.5 3.5
10 8.5
9 10
8 7.5
10 6.5
7.5 5
7.5 4
6 8
10 10.5
3 6.5
10 8
5.5 9
10 8.5
6 9.5
5 3
4.5 6
7.5 0.5
5 6.5
8 7.5
5.5 4.5
7.5 8
9.5 9
8.5 7.5
6.5 8.5
6.5 7
10.5 9.5
8 6.5
10 9.5
9.5 6
9 8
10 9.5
4.5 8
4.5 9
0.5 5
4.5 NA
5.5 NA
6 NA
8.5 NA
8.5 NA
5.5 NA
7 NA
5 NA
3.5 NA
5 NA
5 NA
1.5 NA
8 NA

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265889&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265889&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265889&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 time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 16.01342281879195
Mean of Sample 26.2992700729927
t-stat-0.944351986629876
df284
p-value0.345792504906722
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.881650585725349,0.309956077323841]
F-test to compare two variances
F-stat0.994910589385981
df148
p-value0.973921776548716
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.714017343790623,1.38306963415425]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 6.01342281879195 \tabularnewline
Mean of Sample 2 & 6.2992700729927 \tabularnewline
t-stat & -0.944351986629876 \tabularnewline
df & 284 \tabularnewline
p-value & 0.345792504906722 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.881650585725349,0.309956077323841] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.994910589385981 \tabularnewline
df & 148 \tabularnewline
p-value & 0.973921776548716 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.714017343790623,1.38306963415425] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265889&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]6.01342281879195[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]6.2992700729927[/C][/ROW]
[ROW][C]t-stat[/C][C]-0.944351986629876[/C][/ROW]
[ROW][C]df[/C][C]284[/C][/ROW]
[ROW][C]p-value[/C][C]0.345792504906722[/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][-0.881650585725349,0.309956077323841][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.994910589385981[/C][/ROW]
[ROW][C]df[/C][C]148[/C][/ROW]
[ROW][C]p-value[/C][C]0.973921776548716[/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][0.714017343790623,1.38306963415425][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265889&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265889&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 16.01342281879195
Mean of Sample 26.2992700729927
t-stat-0.944351986629876
df284
p-value0.345792504906722
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.881650585725349,0.309956077323841]
F-test to compare two variances
F-stat0.994910589385981
df148
p-value0.973921776548716
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.714017343790623,1.38306963415425]






Welch Two Sample t-test (unpaired)
Mean of Sample 16.01342281879195
Mean of Sample 26.2992700729927
t-stat-0.944250549599557
df281.874549674787
p-value0.34585028209948
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.881733818852193,0.310039310450684]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 6.01342281879195 \tabularnewline
Mean of Sample 2 & 6.2992700729927 \tabularnewline
t-stat & -0.944250549599557 \tabularnewline
df & 281.874549674787 \tabularnewline
p-value & 0.34585028209948 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.881733818852193,0.310039310450684] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265889&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]6.01342281879195[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]6.2992700729927[/C][/ROW]
[ROW][C]t-stat[/C][C]-0.944250549599557[/C][/ROW]
[ROW][C]df[/C][C]281.874549674787[/C][/ROW]
[ROW][C]p-value[/C][C]0.34585028209948[/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][-0.881733818852193,0.310039310450684][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265889&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265889&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 16.01342281879195
Mean of Sample 26.2992700729927
t-stat-0.944250549599557
df281.874549674787
p-value0.34585028209948
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.881733818852193,0.310039310450684]






Wicoxon rank sum test with continuity correction (unpaired)
W9436
p-value0.269554257600783
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.126732964287464
p-value0.2017758341579
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.126732964287464
p-value0.2017758341579

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 9436 \tabularnewline
p-value & 0.269554257600783 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.126732964287464 \tabularnewline
p-value & 0.2017758341579 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.126732964287464 \tabularnewline
p-value & 0.2017758341579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265889&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]9436[/C][/ROW]
[ROW][C]p-value[/C][C]0.269554257600783[/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]0.126732964287464[/C][/ROW]
[ROW][C]p-value[/C][C]0.2017758341579[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.126732964287464[/C][/ROW]
[ROW][C]p-value[/C][C]0.2017758341579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265889&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265889&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)
W9436
p-value0.269554257600783
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.126732964287464
p-value0.2017758341579
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.126732964287464
p-value0.2017758341579


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 = 0.0 ;
R code (references can be found in the software module):
par6 <- '0.0'
par5 <- 'unpaired'
par4 <- 'two.sided'
par3 <- '0.95'
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)
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')