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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 computationMon, 15 Dec 2014 03:18:03 +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/15/t14186135911nubqanvhrc4mph.htm/, Retrieved Thu, 16 May 2024 14:50:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267944, Retrieved Thu, 16 May 2024 14:50:34 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

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-       [Paired and Unpaired Two Samples Tests about the Mean] [Smurf 419] [2014-12-15 03:18:03] [7ba19d107fbc5e986bea1d115fcbe5dd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
NA 26
NA 57
NA 37
67 NA
43 NA
NA 52
NA 52
NA 43
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67 NA
NA 49
70 NA
NA 52
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68 NA
NA 62
NA 43
NA 56
56 NA
NA 74
65 NA
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NA 53
29 NA
NA 54
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43 NA
NA 51
NA 53
NA 54
NA 56
61 NA
NA 47
39 NA
NA 48
NA 50
NA 35
NA 30
NA 68
NA 49
61 NA
NA 67
NA 47
NA 56
50 NA
NA 43
NA 67
NA 62
NA 57
41 NA
NA 54
NA 45
NA 48
NA 61
NA 56
41 NA
NA 43
NA 53
NA 44
NA 66
NA 58
NA 46
NA 37
NA 51
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NA 56
NA 66
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NA 42
NA 38
NA 66
NA 34
53 NA
NA 49
NA 55
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59 NA
NA 40
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NA 63
56 NA
NA 54
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34 NA
NA 69
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48 NA
NA 67
58 NA
NA 57
NA 42
64 NA
NA 58
NA 66
26 NA
61 NA
NA 52
51 NA
NA 55
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60 NA
NA 56
NA 63
NA 61
52 NA
NA 16
NA 46
NA 56
NA 52
NA 55
NA 50
NA 59
NA 60
NA 52
NA 44
NA 67
NA 52
NA 55
NA 37
NA 54
NA 72
NA 51
NA 48
NA 60
NA 50
NA 63
NA 33
NA 67
NA 46
54 NA
NA 59
NA 61
NA 33
NA 47
NA 69
NA 52
NA 55
NA 41
NA 73
NA 52
NA 50
NA 51
60 NA
NA 56
NA 56
NA 29
NA 66
NA 66
NA 73
NA 55
NA 64
NA 40
NA 46
NA 58
NA 43
NA 61
NA 51
NA 50
NA 52
NA 54
NA 66
NA 61
NA 80
NA 51
NA 56
NA 56
NA 56
NA 53
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NA 25
NA 47
46 NA
NA 50
NA 39
NA 51
NA 58
NA 35
NA 58
NA 60
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NA 63
NA 53
NA 46
NA 67
NA 59
NA 64
NA 38
NA 50
NA 48
NA 48
NA 47
NA 66
NA 47
NA 63
NA 58
NA 44
NA 51
NA 43
NA 55
NA 38
NA 45
NA 50
NA 54
NA 57
NA 60
NA 55
NA 56
NA 49
NA 37
NA 59
NA 46
NA 51
NA 58
NA 64
NA 53
NA 48
NA 51
NA 47
NA 59
NA 62
NA 62
NA 51
NA 64
NA 52
NA 67
NA 50
NA 54
58 NA
NA 56
NA 63
NA 31
NA 65
NA 71
NA 50
NA 57
NA 47
NA 47
NA 57
NA 43
NA 41
NA 63
NA 63
NA 56
NA 51
NA 50
NA 22
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NA 59
56 NA
66 NA
NA 53
42 NA
NA 52
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NA 66
NA 62
NA 59
NA 47
NA 55
NA 58
NA 60
NA 44
NA 57
45 NA
52.21621622 53.29045643

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'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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267944&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267944&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267944&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'Gertrude Mary Cox' @ cox.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 152.2162162163158
Mean of Sample 253.2904564315289
t-stat-0.598871454640539
df278
p-value0.549746282023089
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.60534583760714,2.45686540718086]
F-test to compare two variances
F-stat1.27025568884156
df37
p-value0.296130615230344
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.809710063903801,2.18135542628896]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 52.2162162163158 \tabularnewline
Mean of Sample 2 & 53.2904564315289 \tabularnewline
t-stat & -0.598871454640539 \tabularnewline
df & 278 \tabularnewline
p-value & 0.549746282023089 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-4.60534583760714,2.45686540718086] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 1.27025568884156 \tabularnewline
df & 37 \tabularnewline
p-value & 0.296130615230344 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.809710063903801,2.18135542628896] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267944&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]52.2162162163158[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]53.2904564315289[/C][/ROW]
[ROW][C]t-stat[/C][C]-0.598871454640539[/C][/ROW]
[ROW][C]df[/C][C]278[/C][/ROW]
[ROW][C]p-value[/C][C]0.549746282023089[/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][-4.60534583760714,2.45686540718086][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]1.27025568884156[/C][/ROW]
[ROW][C]df[/C][C]37[/C][/ROW]
[ROW][C]p-value[/C][C]0.296130615230344[/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][0.809710063903801,2.18135542628896][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267944&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267944&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 152.2162162163158
Mean of Sample 253.2904564315289
t-stat-0.598871454640539
df278
p-value0.549746282023089
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-4.60534583760714,2.45686540718086]
F-test to compare two variances
F-stat1.27025568884156
df37
p-value0.296130615230344
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.809710063903801,2.18135542628896]






Welch Two Sample t-test (unpaired)
Mean of Sample 152.2162162163158
Mean of Sample 253.2904564315289
t-stat-0.548812154594807
df46.6036995606186
p-value0.585754274641121
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-5.0128879940342,2.86440756360793]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 52.2162162163158 \tabularnewline
Mean of Sample 2 & 53.2904564315289 \tabularnewline
t-stat & -0.548812154594807 \tabularnewline
df & 46.6036995606186 \tabularnewline
p-value & 0.585754274641121 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-5.0128879940342,2.86440756360793] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267944&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]52.2162162163158[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]53.2904564315289[/C][/ROW]
[ROW][C]t-stat[/C][C]-0.548812154594807[/C][/ROW]
[ROW][C]df[/C][C]46.6036995606186[/C][/ROW]
[ROW][C]p-value[/C][C]0.585754274641121[/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][-5.0128879940342,2.86440756360793][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267944&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267944&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 152.2162162163158
Mean of Sample 253.2904564315289
t-stat-0.548812154594807
df46.6036995606186
p-value0.585754274641121
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-5.0128879940342,2.86440756360793]






Wicoxon rank sum test with continuity correction (unpaired)
W4490
p-value0.816685560718859
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.11439756415833
p-value0.78329230648586
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.143105698129622
p-value0.51178130302412

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 4490 \tabularnewline
p-value & 0.816685560718859 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.11439756415833 \tabularnewline
p-value & 0.78329230648586 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.143105698129622 \tabularnewline
p-value & 0.51178130302412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267944&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]4490[/C][/ROW]
[ROW][C]p-value[/C][C]0.816685560718859[/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]0.11439756415833[/C][/ROW]
[ROW][C]p-value[/C][C]0.78329230648586[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.143105698129622[/C][/ROW]
[ROW][C]p-value[/C][C]0.51178130302412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267944&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267944&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)
W4490
p-value0.816685560718859
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.11439756415833
p-value0.78329230648586
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.143105698129622
p-value0.51178130302412


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):
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')