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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 computationWed, 17 Dec 2014 23:24:01 +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/17/t1418858666yh40mny9lz2ikyq.htm/, Retrieved Thu, 16 May 2024 12:51:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270738, Retrieved Thu, 16 May 2024 12:51:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact59

Tree of Dependent Computations

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] [2] [2014-12-17 23:24:01] [3c8f34fed408bc4f957cadcbcbd22146] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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NA	51
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NA	30
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NA	51
NA	56
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49	NA
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62	NA
NA	30
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56	NA
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NA	55
NA	41
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NA	54
NA	66
NA	55
NA	45
NA	51
NA	47
NA	42
NA	53
NA	53
NA	41
NA	55
NA	55
NA	46
NA	63
NA	43
NA	65
NA	59
NA	39
NA	44
60	NA
NA	57
67	NA
52	NA
52	NA
NA	69
NA	46
NA	46
53	NA
NA	40
NA	70
NA	54
NA	77
45	NA
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47	NA
NA	50
NA	66
NA	60
41	NA
53	NA
34	NA
NA	51
NA	69
NA	60
45	NA
NA	58
NA	39
NA	51
NA	52
NA	49
NA	63
44	NA
NA	51
NA	52
60	NA
53	NA
53	NA
NA	52
NA	31
51	NA
65	NA
51	NA
49	NA
NA	61
58	NA
62	NA
NA	54
52	NA
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50	NA
NA	65
53	NA
NA	56
NA	63
62	NA
66	NA
50	NA
NA	45
58	NA
NA	52
53	NA
NA	68
59	NA
58	NA
52	NA
NA	45
58	NA
NA	70
NA	69
71	NA
NA	46
58	NA
NA	39
46	NA
64	NA
67	NA
44	NA
NA	54
NA	41
NA	68
NA	63
NA	57
NA	61
NA	39
69	NA
64	NA
38	NA
NA	59
NA	51
59	NA
NA	51
NA	65
47	NA
NA	50
57	NA
NA	21
NA	47
51	NA
NA	37
67	NA
43	NA
NA	58
NA	51
NA	40
41	NA
58	NA
64	NA
NA	64
NA	58
50	NA
59	NA
55	NA
59	NA
58	NA
41	NA
NA	56
NA	63
77	NA
NA	60
58	NA
NA	64
NA	47
NA	46
62	NA
60	NA
NA	50
NA	46
NA	44
NA	58
56	NA
43	NA
54	NA
54	NA
56	NA
65	NA
66	NA
62	NA
NA	58
67	NA
NA	25
NA	56
53	NA
NA	56
NA	59
NA	46
49	NA
56	NA
76	NA
33	NA
NA	49
NA	53
NA	58
72	NA
51	NA
42	NA
69	NA
51	NA
NA	54
NA	52
NA	59
51	NA
67	NA
64	NA
58	NA
NA	53

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'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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270738&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' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270738&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270738&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 155.5747126436782
Mean of Sample 252.241935483871
t-stat2.40093510041588
df209
p-value0.0172292579723387
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.596273186159348,6.06928113345504]
F-test to compare two variances
F-stat0.744253046590087
df86
p-value0.145603229516881
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.506432426190437,1.10890127952052]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 55.5747126436782 \tabularnewline
Mean of Sample 2 & 52.241935483871 \tabularnewline
t-stat & 2.40093510041588 \tabularnewline
df & 209 \tabularnewline
p-value & 0.0172292579723387 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.596273186159348,6.06928113345504] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.744253046590087 \tabularnewline
df & 86 \tabularnewline
p-value & 0.145603229516881 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.506432426190437,1.10890127952052] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270738&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]55.5747126436782[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]52.241935483871[/C][/ROW]
[ROW][C]t-stat[/C][C]2.40093510041588[/C][/ROW]
[ROW][C]df[/C][C]209[/C][/ROW]
[ROW][C]p-value[/C][C]0.0172292579723387[/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][0.596273186159348,6.06928113345504][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.744253046590087[/C][/ROW]
[ROW][C]df[/C][C]86[/C][/ROW]
[ROW][C]p-value[/C][C]0.145603229516881[/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][0.506432426190437,1.10890127952052][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270738&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270738&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 155.5747126436782
Mean of Sample 252.241935483871
t-stat2.40093510041588
df209
p-value0.0172292579723387
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.596273186159348,6.06928113345504]
F-test to compare two variances
F-stat0.744253046590087
df86
p-value0.145603229516881
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.506432426190437,1.10890127952052]






Welch Two Sample t-test (unpaired)
Mean of Sample 155.5747126436782
Mean of Sample 252.241935483871
t-stat2.46377555435934
df200.185356070639
p-value0.0145918045037805
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.665385788512122,6.00016853110226]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 55.5747126436782 \tabularnewline
Mean of Sample 2 & 52.241935483871 \tabularnewline
t-stat & 2.46377555435934 \tabularnewline
df & 200.185356070639 \tabularnewline
p-value & 0.0145918045037805 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.665385788512122,6.00016853110226] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270738&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]55.5747126436782[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]52.241935483871[/C][/ROW]
[ROW][C]t-stat[/C][C]2.46377555435934[/C][/ROW]
[ROW][C]df[/C][C]200.185356070639[/C][/ROW]
[ROW][C]p-value[/C][C]0.0145918045037805[/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][0.665385788512122,6.00016853110226][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270738&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270738&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 155.5747126436782
Mean of Sample 252.241935483871
t-stat2.46377555435934
df200.185356070639
p-value0.0145918045037805
H0 value0
Alternativetwo.sided
CI Level0.95
CI[0.665385788512122,6.00016853110226]






Wicoxon rank sum test with continuity correction (unpaired)
W6328.5
p-value0.0322497422296713
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.138672599184279
p-value0.279186312088503
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.109566184649611
p-value0.571305469894342

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 6328.5 \tabularnewline
p-value & 0.0322497422296713 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.138672599184279 \tabularnewline
p-value & 0.279186312088503 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.109566184649611 \tabularnewline
p-value & 0.571305469894342 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270738&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]6328.5[/C][/ROW]
[ROW][C]p-value[/C][C]0.0322497422296713[/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]0.138672599184279[/C][/ROW]
[ROW][C]p-value[/C][C]0.279186312088503[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.109566184649611[/C][/ROW]
[ROW][C]p-value[/C][C]0.571305469894342[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270738&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270738&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)
W6328.5
p-value0.0322497422296713
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.138672599184279
p-value0.279186312088503
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.109566184649611
p-value0.571305469894342


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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')