## Free Statistics

of Irreproducible Research!

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 computationSun, 14 Nov 2010 14:51:46 +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/2010/Nov/14/t1289746232lmzw0gfwuynmf3d.htm/, Retrieved Wed, 27 Sep 2023 08:28:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94544, Retrieved Wed, 27 Sep 2023 08:28:26 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Paired and Unpaired Two Samples Tests about the Mean] [Workshop 2 Questi...] [2010-11-14 14:51:46] [2980b4453f2452156691660add27a53b] [Current]
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Dataseries X:
0	1
0	1
1	1
1	1
1	1
1	0
1	1
0	1
0	1
0	0
1	0
1	1
0	0
0	1
1	1
0	1
0
0	0
0	1
0	1
0
0	0
0
0	1
1	1
1	1
1	1
0	1
0	0
0	1
0	0
1	1
1	1


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94544&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94544&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94544&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 Output view raw output of R engine Computing time 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24

 Two Sample t-test (paired) Difference: Mean1 - Mean2 0.0606060606060606 t-stat 0.494241650572172 df 32 p-value 0.624511470007546 H0 value 0 Alternative two.sided CI Level 0.95 CI [-0.189171564410635,0.310383685622756] F-test to compare two variances F-stat 0.977941176470588 df 32 p-value 0.95008608575776 H0 value 1 Alternative two.sided CI Level 0.95 CI [0.482993844012052,1.98008516359644]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (paired) \tabularnewline
Difference: Mean1 - Mean2 & 0.0606060606060606 \tabularnewline
t-stat & 0.494241650572172 \tabularnewline
df & 32 \tabularnewline
p-value & 0.624511470007546 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.189171564410635,0.310383685622756] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.977941176470588 \tabularnewline
df & 32 \tabularnewline
p-value & 0.95008608575776 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.482993844012052,1.98008516359644] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94544&T=1

[TABLE]
[ROW][C]Two Sample t-test (paired)[/C][/ROW]
[ROW][C]Difference: Mean1 - Mean2[/C][C]0.0606060606060606[/C][/ROW]
[ROW][C]t-stat[/C][C]0.494241650572172[/C][/ROW]
[ROW][C]df[/C][C]32[/C][/ROW]
[ROW][C]p-value[/C][C]0.624511470007546[/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][-0.189171564410635,0.310383685622756][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.977941176470588[/C][/ROW]
[ROW][C]df[/C][C]32[/C][/ROW]
[ROW][C]p-value[/C][C]0.95008608575776[/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][0.482993844012052,1.98008516359644][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94544&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94544&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 (paired) Difference: Mean1 - Mean2 0.0606060606060606 t-stat 0.494241650572172 df 32 p-value 0.624511470007546 H0 value 0 Alternative two.sided CI Level 0.95 CI [-0.189171564410635,0.310383685622756] F-test to compare two variances F-stat 0.977941176470588 df 32 p-value 0.95008608575776 H0 value 1 Alternative two.sided CI Level 0.95 CI [0.482993844012052,1.98008516359644]

 Welch Two Sample t-test (paired) Difference: Mean1 - Mean2 0.0606060606060606 t-stat 0.494241650572172 df 32 p-value 0.624511470007546 H0 value 0 Alternative two.sided CI Level 0.95 CI [-0.189171564410635,0.310383685622756]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (paired) \tabularnewline
Difference: Mean1 - Mean2 & 0.0606060606060606 \tabularnewline
t-stat & 0.494241650572172 \tabularnewline
df & 32 \tabularnewline
p-value & 0.624511470007546 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.189171564410635,0.310383685622756] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94544&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (paired)[/C][/ROW]
[ROW][C]Difference: Mean1 - Mean2[/C][C]0.0606060606060606[/C][/ROW]
[ROW][C]t-stat[/C][C]0.494241650572172[/C][/ROW]
[ROW][C]df[/C][C]32[/C][/ROW]
[ROW][C]p-value[/C][C]0.624511470007546[/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][-0.189171564410635,0.310383685622756][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94544&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94544&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 (paired) Difference: Mean1 - Mean2 0.0606060606060606 t-stat 0.494241650572172 df 32 p-value 0.624511470007546 H0 value 0 Alternative two.sided CI Level 0.95 CI [-0.189171564410635,0.310383685622756]

 Wicoxon rank sum test with continuity correction (paired) W 76.5 p-value 0.637934810590957 H0 value 0 Alternative two.sided Kolmogorov-Smirnov Test to compare Distributions of two Samples KS Statistic 0.0606060606060606 p-value 0.999999985300324 Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples KS Statistic 0.515151515151515 p-value 0.000314530792568268

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (paired) \tabularnewline
W & 76.5 \tabularnewline
p-value & 0.637934810590957 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.0606060606060606 \tabularnewline
p-value & 0.999999985300324 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.515151515151515 \tabularnewline
p-value & 0.000314530792568268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94544&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (paired)[/C][/ROW]
[ROW][C]W[/C][C]76.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.637934810590957[/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]0.0606060606060606[/C][/ROW]
[ROW][C]p-value[/C][C]0.999999985300324[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.515151515151515[/C][/ROW]
[ROW][C]p-value[/C][C]0.000314530792568268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94544&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94544&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 (paired) W 76.5 p-value 0.637934810590957 H0 value 0 Alternative two.sided Kolmogorov-Smirnov Test to compare Distributions of two Samples KS Statistic 0.0606060606060606 p-value 0.999999985300324 Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples KS Statistic 0.515151515151515 p-value 0.000314530792568268

 PNG link Postscript link PDF link

 PNG link Postscript link PDF link

 PNG link Postscript link PDF link

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0.0 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0.0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #column number of first samplepar2 <- as.numeric(par2) #column number of second samplepar3 <- as.numeric(par3) #confidence (= 1 - alpha)if (par5 == 'unpaired') paired <- FALSE else paired <- TRUEpar6 <- as.numeric(par6) #H0z <- 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 - m2newsam1 <- z[!is.na(z[,par1]),par1]newsam2 <- z[,par2]+mdiffnewsam2 <- 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')