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Author*The author of this computation has been verified*
R Software Modulerwasp_One Factor ANOVA.wasp
Title produced by softwareOne-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)
Date of computationTue, 09 Dec 2014 17:36:57 +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/09/t1418146648gzl5y16qvglj450.htm/, Retrieved Thu, 31 Oct 2024 23:08:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264778, Retrieved Thu, 31 Oct 2024 23:08:39 +0000
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-     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-02 14:26:49] [b98453cac15ba1066b407e146608df68]
- RMP   [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-10-21 08:30:46] [32b17a345b130fdf5cc88718ed94a974]
- R         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-09 17:36:57] [6870495cd5e22452491bd29e9b20b7c8] [Current]
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Dataseries X:
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	1	1	1	1	0	2
'WWE'	0	0	0	0	0	0	0
'WWE'	0	1	1	1	1	0	2
'WWE'	0	0	1	0	1	1	1
'WWE'	0	0	0	0	0	0	0
'WWE'	0	1	1	1	1	0	2
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	NA	0	NA	NA	NA
'WWE'	0	0	1	0	1	1	1
'WWE'	1	1	NA	0	NA	NA	NA
'WWE'	1	0	0	-1	-1	0	-1
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	1	0	1	1	1
'WWE'	0	1	0	1	0	-1	1
'WWE'	0	0	0	0	0	0	0
'WWE'	1	1	0	0	-1	-1	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	1	0	1	0	-1	1
'WWE'	0	1	1	1	1	0	2
'WWE'	0	1	1	1	1	0	2
'WWE'	0	0	0	0	0	0	0
'WWE'	1	1	0	0	-1	-1	0
'WWE'	0	1	0	1	0	-1	1
'WWE'	0	1	0	1	0	-1	1
'WWE'	0	0	1	0	1	1	1
'WWE'	0	1	0	1	0	-1	1
'WWE'	0	1	0	1	0	-1	1
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	1	1	0	0	-1	-1	0
'WWE'	1	1	0	0	-1	-1	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	NA	0	NA	NA	NA
'WWE'	0	0	1	0	1	1	1
'WWE'	0	0	0	0	0	0	0
'CSWE'	0	0	0	0	0	0	0
'CSWE'	1	0	NA	-1	NA	NA	NA
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	1	NA	1	NA	NA	NA
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	0	1	0	1	1	1
'CSWE'	0	0	0	0	0	0	0
'CSWE'	1	1	0	0	-1	-1	0
'CSWE'	0	0	1	0	1	1	1
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	1	NA	1	NA	NA	NA
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	1	NA	1	NA	NA	NA
'CSWE'	1	1	0	0	-1	-1	0
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	0	NA	0	NA	NA	NA
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	0	NA	0	NA	NA	NA
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	0	1	0	1	1	1
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	1	1	0	0	-1	-1	0
'CSWE'	0	0	1	0	1	1	1
'CSWE'	0	1	1	1	1	0	2
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	0	NA	0	NA	NA	NA
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	1	NA	1	NA	NA	NA
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	1	0	1	0	-1	1
'CSWE'	0	1	0	1	0	-1	1
'C'	0	0	0	0	0	0	0
'C'	0	0	1	0	1	1	1
'C'	1	0	0	-1	-1	0	-1
'C'	0	0	1	0	1	1	1
'C'	0	0	NA	0	NA	NA	NA
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	1	0	1	0	-1	1
'C'	1	1	0	0	-1	-1	0
'C'	0	0	NA	0	NA	NA	NA
'C'	0	0	0	0	0	0	0
'C'	0	1	0	1	0	-1	1
'C'	0	1	0	1	0	-1	1
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	NA	0	NA	NA	NA
'C'	0	0	0	0	0	0	0
'C'	0	1	0	1	0	-1	1
'C'	1	1	0	0	-1	-1	0
'C'	0	1	0	1	0	-1	1
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	1	1	0	0	-1	-1	0
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	1	1	0	0	-1	-1	0
'C'	0	0	1	0	1	1	1
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	1	0	1	1	1
'C'	0	0	0	0	0	0	0
'C'	0	0	NA	0	NA	NA	NA
'C'	1	1	0	0	-1	-1	0




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=264778&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=264778&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264778&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 time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







ANOVA Model
post2-post1 ~ treat
means-0.171-0.1410.04

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
post2-post1  ~  treat \tabularnewline
means & -0.171 & -0.141 & 0.04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264778&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]post2-post1  ~  treat[/C][/ROW]
[ROW][C]means[/C][C]-0.171[/C][C]-0.141[/C][C]0.04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264778&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264778&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Model
post2-post1 ~ treat
means-0.171-0.1410.04







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
treat20.6110.3060.7390.48
Residuals10242.1890.414

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
treat & 2 & 0.611 & 0.306 & 0.739 & 0.48 \tabularnewline
Residuals & 102 & 42.189 & 0.414 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264778&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]treat[/C][C]2[/C][C]0.611[/C][C]0.306[/C][C]0.739[/C][C]0.48[/C][/ROW]
[ROW][C]Residuals[/C][C]102[/C][C]42.189[/C][C]0.414[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264778&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264778&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
treat20.6110.3060.7390.48
Residuals10242.1890.414







Tukey Honest Significant Difference Comparisons
difflwruprp adj
CSWE-C-0.141-0.5150.2330.644
WWE-C0.04-0.3190.3980.962
WWE-CSWE0.181-0.1860.5480.472

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
CSWE-C & -0.141 & -0.515 & 0.233 & 0.644 \tabularnewline
WWE-C & 0.04 & -0.319 & 0.398 & 0.962 \tabularnewline
WWE-CSWE & 0.181 & -0.186 & 0.548 & 0.472 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264778&T=3

[TABLE]
[ROW][C]Tukey Honest Significant Difference Comparisons[/C][/ROW]
[ROW][C] [/C][C]diff[/C][C]lwr[/C][C]upr[/C][C]p adj[/C][/ROW]
[ROW][C]CSWE-C[/C][C]-0.141[/C][C]-0.515[/C][C]0.233[/C][C]0.644[/C][/ROW]
[ROW][C]WWE-C[/C][C]0.04[/C][C]-0.319[/C][C]0.398[/C][C]0.962[/C][/ROW]
[ROW][C]WWE-CSWE[/C][C]0.181[/C][C]-0.186[/C][C]0.548[/C][C]0.472[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264778&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264778&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
CSWE-C-0.141-0.5150.2330.644
WWE-C0.04-0.3190.3980.962
WWE-CSWE0.181-0.1860.5480.472







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group21.2230.299
102

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 1.223 & 0.299 \tabularnewline
  & 102 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264778&T=4

[TABLE]
[ROW][C]Levenes Test for Homogeneity of Variance[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]Group[/C][C]2[/C][C]1.223[/C][C]0.299[/C][/ROW]
[ROW][C] [/C][C]102[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264778&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264778&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group21.2230.299
102



Parameters (Session):
par1 = 7 ; par2 = 1 ; par3 = TRUE ;
Parameters (R input):
par1 = 7 ; par2 = 1 ; par3 = TRUE ;
R code (references can be found in the software module):
par3 <- 'FALSE'
par2 <- '2'
par1 <- '7'
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
intercept<-as.logical(par3)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
xdf<-data.frame(x1,f1)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
names(xdf)<-c('Response', 'Treatment')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, data = xdf) )
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Model', length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'means',,TRUE)
for(i in 1:length(lmxdf$coefficients)){
a<-table.element(a, round(lmxdf$coefficients[i], digits=3),,FALSE)
}
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,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',,TRUE)
a<-table.element(a, 'Df',,FALSE)
a<-table.element(a, 'Sum Sq',,FALSE)
a<-table.element(a, 'Mean Sq',,FALSE)
a<-table.element(a, 'F value',,FALSE)
a<-table.element(a, 'Pr(>F)',,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, V2,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$Df[2],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(Response ~ Treatment, data=xdf, xlab=V2, ylab=V1)
dev.off()
if(intercept==TRUE){
'Tukey Plot'
thsd<-TukeyHSD(aov.xdf)
bitmap(file='TukeyHSDPlot.png')
plot(thsd)
dev.off()
}
if(intercept==TRUE){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tukey Honest Significant Difference Comparisons', 5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ', 1, TRUE)
for(i in 1:4){
a<-table.element(a,colnames(thsd[[1]])[i], 1, TRUE)
}
a<-table.row.end(a)
for(i in 1:length(rownames(thsd[[1]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
if(intercept==FALSE){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'TukeyHSD Message', 1,TRUE)
a<-table.row.end(a)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Must Include Intercept to use Tukey Test ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
library(car)
lt.lmxdf<-leveneTest(lmxdf)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Levenes Test for Homogeneity of Variance', 4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
for (i in 1:3){
a<-table.element(a,names(lt.lmxdf)[i], 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Group', 1, TRUE)
for (i in 1:3){
a<-table.element(a,round(lt.lmxdf[[i]][1], digits=3), 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
a<-table.element(a,lt.lmxdf[[1]][2], 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')