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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, 20 Dec 2012 11:44:40 -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/20/t1356021942zedit0a9aemtcaz.htm/, Retrieved Sat, 20 Apr 2024 13:43:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202887, Retrieved Sat, 20 Apr 2024 13:43:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Testing Mean with unknown Variance - Critical Value] [WS4: Q2: H2] [2012-10-19 10:22:44] [e3c1bef87ca0a912c3f24fbd5a616a95]
- RMPD  [Testing Mean with known Variance - Sample Size] [WS4: Q9] [2012-10-19 11:32:12] [e3c1bef87ca0a912c3f24fbd5a616a95]
- RMPD      [Paired and Unpaired Two Samples Tests about the Mean] [Paper: Unpaired T...] [2012-12-20 16:44:40] [ac36efda2e34453d278f09f8b1b9a536] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
127	NA
108	NA
110	NA
NA	102
104	NA
140	NA
NA	112
115	NA
121	NA
112	NA
NA	118
NA	122
105	NA
111	NA
151	NA
NA	106
100	NA
NA	149
NA	122
115	NA
NA	86
124	NA
69	NA
NA	117
113	NA
123	NA
123	NA
84	NA
NA	97
121	NA
NA	132
119	NA
98	NA
NA	87
NA	101
NA	115
109	NA
NA	109
NA	159
129	NA
119	NA
119	NA
122	NA
NA	131
NA	120
82	NA
NA	86
NA	105
114	NA
NA	100
100	NA
99	NA
132	NA
82	NA
NA	132
107	NA
114	NA
110	NA
NA	105
NA	121
109	NA
106	NA
124	NA
NA	120
91	NA
NA	126
NA	138
NA	118
128	NA
98	NA
133	NA
NA	130
NA	103
124	NA
142	NA
96	NA
93	NA
NA	129
NA	150
NA	88
NA	125
92	NA
NA	NA
117	NA
NA	112
NA	144
130	NA
87	NA
92	NA
114	NA
81	NA
NA	127
115	NA
123	NA
115	NA
117	NA
NA	117
103	NA
NA	108
NA	139
113	NA
NA	97
117	NA
133	NA
NA	115
103	NA
NA	95
117	NA
113	NA
127	NA
126	NA
119	NA
97	NA
105	NA
140	NA
NA	91
112	NA
113	NA
NA	102
92	NA
98	NA
122	NA
100	NA
84	NA
142	NA
NA	124
137	NA
105	NA
NA	106
NA	125
104	NA
NA	130
NA	79
NA	108
NA	136
98	NA
120	NA
NA	108
NA	139
123	NA
90	NA
119	NA
105	NA
NA	110
135	NA
101	NA
NA	114
NA	118
NA	120
NA	108
114	NA
122	NA
132	NA
NA	130
NA	130
NA	112
114	NA
103	NA
115	NA
108	NA
NA	94
105	NA

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202887&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202887&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202887&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 time3 seconds
R Server'George Udny Yule' @ yule.wessa.net






Two Sample t-test (unpaired)
Mean of Sample 1112.081632653061
Mean of Sample 2115.857142857143
t-stat-1.4530135102895
df159
p-value0.148190738419312
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-8.90734022597904,1.35631981781575]
F-test to compare two variances
F-stat0.817822707570563
df97
p-value0.371087937564926
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.513340621219681,1.27333365936511]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 112.081632653061 \tabularnewline
Mean of Sample 2 & 115.857142857143 \tabularnewline
t-stat & -1.4530135102895 \tabularnewline
df & 159 \tabularnewline
p-value & 0.148190738419312 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-8.90734022597904,1.35631981781575] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.817822707570563 \tabularnewline
df & 97 \tabularnewline
p-value & 0.371087937564926 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.513340621219681,1.27333365936511] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202887&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]112.081632653061[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]115.857142857143[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.4530135102895[/C][/ROW]
[ROW][C]df[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C]0.148190738419312[/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][-8.90734022597904,1.35631981781575][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.817822707570563[/C][/ROW]
[ROW][C]df[/C][C]97[/C][/ROW]
[ROW][C]p-value[/C][C]0.371087937564926[/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][0.513340621219681,1.27333365936511][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202887&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202887&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 1112.081632653061
Mean of Sample 2115.857142857143
t-stat-1.4530135102895
df159
p-value0.148190738419312
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-8.90734022597904,1.35631981781575]
F-test to compare two variances
F-stat0.817822707570563
df97
p-value0.371087937564926
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.513340621219681,1.27333365936511]






Welch Two Sample t-test (unpaired)
Mean of Sample 1112.081632653061
Mean of Sample 2115.857142857143
t-stat-1.42149590551726
df122.658941590135
p-value0.157710209478887
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-9.0330683601238,1.48204795196051]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 112.081632653061 \tabularnewline
Mean of Sample 2 & 115.857142857143 \tabularnewline
t-stat & -1.42149590551726 \tabularnewline
df & 122.658941590135 \tabularnewline
p-value & 0.157710209478887 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-9.0330683601238,1.48204795196051] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202887&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]112.081632653061[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]115.857142857143[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.42149590551726[/C][/ROW]
[ROW][C]df[/C][C]122.658941590135[/C][/ROW]
[ROW][C]p-value[/C][C]0.157710209478887[/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][-9.0330683601238,1.48204795196051][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202887&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202887&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 1112.081632653061
Mean of Sample 2115.857142857143
t-stat-1.42149590551726
df122.658941590135
p-value0.157710209478887
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-9.0330683601238,1.48204795196051]






Wicoxon rank sum test with continuity correction (unpaired)
W2717
p-value0.20045527968864
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.143990929705215
p-value0.404332814182517
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0804988662131519
p-value0.964900130646732

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 2717 \tabularnewline
p-value & 0.20045527968864 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.143990929705215 \tabularnewline
p-value & 0.404332814182517 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.0804988662131519 \tabularnewline
p-value & 0.964900130646732 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202887&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]2717[/C][/ROW]
[ROW][C]p-value[/C][C]0.20045527968864[/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]0.143990929705215[/C][/ROW]
[ROW][C]p-value[/C][C]0.404332814182517[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.0804988662131519[/C][/ROW]
[ROW][C]p-value[/C][C]0.964900130646732[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202887&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202887&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)
W2717
p-value0.20045527968864
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.143990929705215
p-value0.404332814182517
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.0804988662131519
p-value0.964900130646732


Figures (Output of Computation)

Input Parameters & R Code

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')