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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 computationThu, 07 Dec 2017 15:25:25 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/07/t1512657128ztmdsohsnidibwp.htm/, Retrieved Wed, 15 May 2024 01:09:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308724, Retrieved Wed, 15 May 2024 01:09:34 +0000
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Original text written by user:
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Estimated Impact92
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-       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2017-12-07 14:25:25] [1fb90e819e5b19aec9e872ea972cd63e] [Current]
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Dataseries X:
1	7
1	8
1	10
1	10
1	5
0	7
1	8
1	9
1	8
1	8
0	8
0	10
0	10
1	8
1	10
0	9
1	7
0	8
0	10
1	4
1	9
1	5
1	4
1	9
0	6
0	8
1	10
1	9
1	8
1	7
0	10
1	6
1	9
1	5
1	4
0	8
0	2
0	10
0	5
0	8
0	8
1	8
0	7
1	10
1	10
1	7
1	10
1	10
1	8
1	7
1	8
0	6
1	10
0	9
0	9
0	7
1	8
0	6
1	7
1	9
1	7
1	7
1	7
1	10
0	4
1	10
1	3
1	8
1	10
1	8
0	6
1	6
0	10
1	10
1	7
1	8
1	7
1	9
1	2
1	10
1	10
1	7
1	8
0	8
1	10
0	8
1	8
1	7
1	8
1	8
1	10
1	9
1	9
0	4
1	5
1	10
0	9
0	6
1	8
1	8
1	9
1	8
1	4
1	5
1	10
1	10
1	9
1	9
1	6
1	7
0	4
0	6
0	4
0	4
1	10
0	6
1	9
1	7
1	4
1	8
1	8
1	8
0	7
0	6
0	5
1	5
0	7
0	4
1	8
1	7
0	6
0	8
0	5
0	3
0	5
0	10
0	7
0	4
0	2
0	6
0	3
0	8
0	9
0	5
1	6
1	2
1	6
1	10
0	8
1	10
0	8
1	8
0	6
0	9
1	9
0	9
0	8
0	8
0	10
1	3
1	6
1	9
0	3
0	4
1	5
1	9
0	8
1	5
0	4
0	5
1	7
1	7
1	8
0	8
1	6
0	7
0	9
0	9
0	6
0	9
0	9
1	8
1	6
1	6
0	10
0	8
1	10
1	8
0	7
0	7
0	8
1	8
0	7
0	2
1	5
1	7
0	5
1	5
0	10
0	8
0	7
1	6
0	6
0	5
0	7
0	8
0	7
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0	5
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0	10
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1	9
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0	9
0	9
0	10
0	6
0	9
0	9
1	6
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1	9
1	4
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1	3
0	9
0	10
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0	9
1	8
0	2
0	6
0	9
0	6
0	4
1	3
0	3
0	4
0	6
1	8
0	6
0	7
1	8
0	3
0	10
0	8
0	6
0	10
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0	10
0	6
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0	8
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1	7
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0	7
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0	7
0	7
0	7
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1	9
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0	9
0	8
0	7
0	9
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0	6
1	7
1	2
1	3
1	4
1	5
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1	7
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1	8
1	4
1	5
0	6
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1	9
1	6
1	8
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1	4
1	8
1	4
0	10
0	8
0	5
1	3
1	7
0	6
0	5
1	5
0	9
1	2
1	7
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1	6
1	7




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308724&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308724&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308724&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center







ANOVA Model
rel_adv ~ Programma
means7.004-0.145

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
rel_adv  ~  Programma \tabularnewline
means & 7.004 & -0.145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308724&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]rel_adv  ~  Programma[/C][/ROW]
[ROW][C]means[/C][C]7.004[/C][C]-0.145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308724&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308724&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
rel_adv ~ Programma
means7.004-0.145







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Programma12.2432.2430.490.484
Residuals4442034.4654.582

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Programma & 1 & 2.243 & 2.243 & 0.49 & 0.484 \tabularnewline
Residuals & 444 & 2034.465 & 4.582 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308724&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]Programma[/C][C]1[/C][C]2.243[/C][C]2.243[/C][C]0.49[/C][C]0.484[/C][/ROW]
[ROW][C]Residuals[/C][C]444[/C][C]2034.465[/C][C]4.582[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308724&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308724&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)
Programma12.2432.2430.490.484
Residuals4442034.4654.582







Tukey Honest Significant Difference Comparisons
difflwruprp adj
1-0-0.145-0.5520.2620.484

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
1-0 & -0.145 & -0.552 & 0.262 & 0.484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308724&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]1-0[/C][C]-0.145[/C][C]-0.552[/C][C]0.262[/C][C]0.484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308724&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308724&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
1-0-0.145-0.5520.2620.484







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group12.0650.151
444

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 1 & 2.065 & 0.151 \tabularnewline
  & 444 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308724&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]1[/C][C]2.065[/C][C]0.151[/C][/ROW]
[ROW][C] [/C][C]444[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308724&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308724&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)
Group12.0650.151
444



Parameters (Session):
par1 = 2 ; par2 = 1 ; par3 = TRUE ;
Parameters (R input):
par1 = 2 ; par2 = 1 ; par3 = TRUE ;
R code (references can be found in the software module):
par3 <- 'TRUE'
par2 <- '1'
par1 <- '2'
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')