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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 computationSun, 16 Dec 2012 05:07:22 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t1355652479sbrnxree4z9561u.htm/, Retrieved Fri, 29 Mar 2024 05:08:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200201, Retrieved Fri, 29 Mar 2024 05:08:14 +0000
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Estimated Impact131
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-       [Paired and Unpaired Two Samples Tests about the Mean] [paper unpaired] [2012-12-16 10:07:22] [0e946099c50efe5d1ce2f1fa3105dbad] [Current]
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Dataseries X:
35.323	1
35.478	1
4.39	1
41.667	2
22.173	1
28.021	1
18.109	2
13.962	1
40.174	1
16.065	1
18.145	2
18.439	2
10.603	1
34.811	1
69.064	1
51.202	2
14.786	1
33.01	2
81.101	2
89.232	1
21.223	2
15.173	1
241.66	1
26.848	2
8.752	1
60.535	1
60.535	1
26.052	1
49.218	2
30.669	1
18.673	2
86	1
10.632	1
35.802	2
33.974	2
36.972	2
4.928	1
53.976	2
15.467	2
35.723	1
40.424	1
9.706	1
26.532	1
23.843	2
18.062	2
35.681	1
68.125	2
23.937	2
31.479	1
66.659	2
250.234	1
49.469	1
42.951	1
43.402	1
24.112	2
56.95	1
17.313	1
25.658	1
48.172	2
13.891	2
32.048	1
19.797	1
31.317	1
20.966	2
22.708	1
26.81	2
52.004	2
32.354	2
27.128	1
26.529	1
28.392	1
57.393	2
194.731	2
9.415	1
91.076	1
57.751	1
8.236	1
20.407	2
13.681	2
79.659	2
53.48	2
6.906	1
50.202	
37.877	1
85.903	2
35.351	2
283.801	1
5.974	1
3.441	1
51.987	1
13.22	1
1.455	2
18.187	1
21.29	1
5.686	1
4.944	1
32.789	2
50.494	1
35.162	2
38.095	2
19.172	1
24.5	2
20.573	1
42.042	1
302.912	2
25.027	1
16.488	2
32.36	1
6.193	1
37.7	1
6.343	1
23.025	1
48.578	1
21.564	1
33.697	1
10.831	2
19.172	1
21.075	1
33.189	2
60.5	1
33.686	1
40.838	1
13.491	1
106.637	1
35.897	1
7.314	2
49.094	1
14.667	1
54.179	2
145.846	2
18.56	1
23.525	2
21.804	2
26.301	2
41.33	2
10.5	1
13.338	1
60.31	2
34.256	2
48.267	1
41.559	1
32.45	1
10.951	1
22.561	2
57.095	1
19.105	1
13.151	2
27.426	2
15.355	2
13.82	2
47.21	1
110.349	1
34.985	1
27.257	2
23.556	2
50.108	2
18.158	1
87.357	1
18.187	1
28.33	1
13.474	2
26.244	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200201&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200201&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200201&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Two Sample t-test (unpaired)
Mean of Sample 121.8281296296296
Mean of Sample 219.8793086419753
t-stat0.469985253411575
df322
p-value0.638683690929378
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-6.2089449709325,10.1065869462411]
F-test to compare two variances
F-stat0.88299182143838
df161
p-value0.430696418147491
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.647608096577812,1.20392960008861]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 21.8281296296296 \tabularnewline
Mean of Sample 2 & 19.8793086419753 \tabularnewline
t-stat & 0.469985253411575 \tabularnewline
df & 322 \tabularnewline
p-value & 0.638683690929378 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-6.2089449709325,10.1065869462411] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.88299182143838 \tabularnewline
df & 161 \tabularnewline
p-value & 0.430696418147491 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.647608096577812,1.20392960008861] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200201&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]21.8281296296296[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]19.8793086419753[/C][/ROW]
[ROW][C]t-stat[/C][C]0.469985253411575[/C][/ROW]
[ROW][C]df[/C][C]322[/C][/ROW]
[ROW][C]p-value[/C][C]0.638683690929378[/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][-6.2089449709325,10.1065869462411][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.88299182143838[/C][/ROW]
[ROW][C]df[/C][C]161[/C][/ROW]
[ROW][C]p-value[/C][C]0.430696418147491[/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][0.647608096577812,1.20392960008861][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200201&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200201&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 121.8281296296296
Mean of Sample 219.8793086419753
t-stat0.469985253411575
df322
p-value0.638683690929378
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-6.2089449709325,10.1065869462411]
F-test to compare two variances
F-stat0.88299182143838
df161
p-value0.430696418147491
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.647608096577812,1.20392960008861]







Welch Two Sample t-test (unpaired)
Mean of Sample 121.8281296296296
Mean of Sample 219.8793086419753
t-stat0.469985253411575
df320.761438083402
p-value0.638684918779653
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-6.20906380721628,10.1067057825249]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 21.8281296296296 \tabularnewline
Mean of Sample 2 & 19.8793086419753 \tabularnewline
t-stat & 0.469985253411575 \tabularnewline
df & 320.761438083402 \tabularnewline
p-value & 0.638684918779653 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-6.20906380721628,10.1067057825249] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200201&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]21.8281296296296[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]19.8793086419753[/C][/ROW]
[ROW][C]t-stat[/C][C]0.469985253411575[/C][/ROW]
[ROW][C]df[/C][C]320.761438083402[/C][/ROW]
[ROW][C]p-value[/C][C]0.638684918779653[/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][-6.20906380721628,10.1067057825249][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200201&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200201&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 121.8281296296296
Mean of Sample 219.8793086419753
t-stat0.469985253411575
df320.761438083402
p-value0.638684918779653
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-6.20906380721628,10.1067057825249]







Wicoxon rank sum test with continuity correction (unpaired)
W13579.5
p-value0.581077281427649
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.0679012345679012
p-value0.849237211099793
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.487654320987654
p-value0

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 13579.5 \tabularnewline
p-value & 0.581077281427649 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.0679012345679012 \tabularnewline
p-value & 0.849237211099793 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.487654320987654 \tabularnewline
p-value & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200201&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]13579.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.581077281427649[/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]0.0679012345679012[/C][/ROW]
[ROW][C]p-value[/C][C]0.849237211099793[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.487654320987654[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200201&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200201&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 (unpaired)
W13579.5
p-value0.581077281427649
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.0679012345679012
p-value0.849237211099793
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.487654320987654
p-value0



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0.0 ;
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):
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')