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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 computationFri, 12 Dec 2014 11:13:50 +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/12/t1418382932k3c8zyhj7y508x4.htm/, Retrieved Thu, 16 May 2024 17:05:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266518, Retrieved Thu, 16 May 2024 17:05:10 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [anova] [2014-12-12 11:13:50] [093d2a2ec26a339da390c0fc5c0644df] [Current]
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Dataseries X:
21 7.5
26 2.5
22 6.0
22 6.5
18 1.0
23 1.0
12 5.5
20 8.5
22 6.5
21 4.5
19 2.0
22 5.0
15 0.5
20 5.0
19 5.0
18 2.5
15 5.0
20 5.5
21 3.5
21 3.0
15 4.0
16 0.5
23 6.5
21 4.5
18 7.5
25 5.5
9 4.0
30 7.5
20 7.0
23 4.0
16 5.5
16 2.5
19 5.5
25 0.5
25 3.5
18 2.5
23 4.5
21 4.5
10 4.5
14 6.0
22 2.5
26 5.0
23 0.0
23 5.0
24 6.5
24 5.0
18 6.0
23 4.5
15 5.5
19 1.0
16 7.5
25 6.0
23 5.0
17 1.0
19 5.0
21 6.5
18 7.0
27 4.5
21 0.0
13 8.5
8 3.5
29 7.5
28 3.5
23 6.0
21 1.5
19 9.0
19 3.5
20 3.5
18 4.0
19 6.5
17 7.5
19 6.0
25 5.0
19 5.5
22 3.5
23 7.5
26 1.0
14 6.5
28 NA
16 6.5
24 6.5
20 7.0
12 3.5
24 1.5
22 4.0
12 7.5
22 4.5
20 0.0
10 3.5
23 5.5
17 5.0
22 4.5
24 2.5
18 7.5
21 7.0
20 0.0
20 4.5
22 3.0
19 1.5
20 3.5
26 2.5
23 5.5
24 8.0
21 1.0
21 5.0
19 4.5
8 3.0
17 3.0
20 8.0
11 2.5
8 7.0
15 0.0
18 1.0
18 3.5
19 5.5
19 5.5
23 0.5
22 7.5
21 9
25 9.5
30 8.5
17 7
27 8
23 10
23 7
18 8.5
18 9
23 9.5
19 4
15 6
20 8
16 5.5
24 9.5
25 7.5
25 7
19 7.5
19 8
16 7
19 7
19 6
23 10
21 2.5
22 9
19 8
20 6
20 8.5
3 6
23 9
14 8
23 8
20 9
15 5.5
13 5
16 7
7 5.5
24 9
17 2
24 8.5
24 9
19 8.5
25 9
20 7.5
28 10
23 9
27 7.5
18 6
28 10.5
21 8.5
19 8
23 10
27 10.5
22 6.5
28 9.5
25 8.5
21 7.5
22 5
28 8
20 10
29 7
25 7.5
25 7.5
20 9.5
20 6
16 10
20 7
20 3
23 6
18 7
25 10
18 7
19 3.5
25 8
25 10
25 5.5
24 6
19 6.5
26 6.5
10 8.5
17 4
13 9.5
17 8
30 8.5
25 5.5
4 7
16 9
21 8
23 10
22 8
17 6
20 8
20 5
22 9
16 4.5
23 8.5
16 7
0 9.5
18 8.5
25 7.5
23 7.5
12 5
18 7
24 8
11 5.5
18 8.5
14 7.5
23 9.5
24 7
29 8
18 8.5
15 3.5
29 6.5
16 6.5
19 10.5
22 8.5
16 8
23 10
23 10
19 9.5
4 9
20 10
24 7.5
20 4.5
4 4.5
24 0.5
22 6.5
16 4.5
3 5.5
15 5
24 6
17 4
20 8
27 10.5
23 8.5
26 6.5
23 8
17 8.5
20 5.5
22 7
19 5
24 3.5
19 5
23 9
15 8.5
27 5
26 9.5
22 3
22 1.5
18 6
15 0.5
22 6.5
27 7.5
10 4.5
20 8
17 9
23 7.5
19 8.5
13 7
27 9.5
23 6.5
16 9.5
25 6
2 8
26 9.5
20 8
23 8
22 9
24 5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266518&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266518&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266518&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 time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ANOVA Model
Ex ~ NUMERACYTOT
means9.55.2545.3757.5746.3125.4175.9255.91486.3454.9415.7836.9716.0836.8165.3757.8758.37.255.758.1676.8335.54.54

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Ex  ~  NUMERACYTOT \tabularnewline
means & 9.5 & 5.25 & 4 & 5.375 & 7.5 & 7 & 4 & 6.312 & 5.417 & 5.925 & 5.914 & 8 & 6.345 & 4.941 & 5.783 & 6.971 & 6.083 & 6.816 & 5.375 & 7.875 & 8.3 & 7.25 & 5.75 & 8.167 & 6.833 & 5.5 & 4.5 & 4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266518&T=1

[TABLE]
[ROW]ANOVA Model[/C][/ROW]
[ROW]Ex  ~  NUMERACYTOT[/C][/ROW]
[ROW][C]means[/C][C]9.5[/C][C]5.25[/C][C]4[/C][C]5.375[/C][C]7.5[/C][C]7[/C][C]4[/C][C]6.312[/C][C]5.417[/C][C]5.925[/C][C]5.914[/C][C]8[/C][C]6.345[/C][C]4.941[/C][C]5.783[/C][C]6.971[/C][C]6.083[/C][C]6.816[/C][C]5.375[/C][C]7.875[/C][C]8.3[/C][C]7.25[/C][C]5.75[/C][C]8.167[/C][C]6.833[/C][C]5.5[/C][C]4.5[/C][C]4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266518&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266518&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
Ex ~ NUMERACYTOT
means9.55.2545.3757.5746.3125.4175.9255.91486.3454.9415.7836.9716.0836.8165.3757.8758.37.255.758.1676.8335.54.54







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
NUMERACYTOT2811062.671395.09562.9680
Residuals2581618.8296.275

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
NUMERACYTOT & 28 & 11062.671 & 395.095 & 62.968 & 0 \tabularnewline
Residuals & 258 & 1618.829 & 6.275 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266518&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]NUMERACYTOT[/C][C]28[/C][C]11062.671[/C][C]395.095[/C][C]62.968[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]258[/C][C]1618.829[/C][C]6.275[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266518&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266518&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)
NUMERACYTOT2811062.671395.09562.9680
Residuals2581618.8296.275







Must Include Intercept to use Tukey Test

\begin{tabular}{lllllllll}
\hline
Must Include Intercept to use Tukey Test  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266518&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266518&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266518&T=3

As an alternative you can also use a QR Code:  

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

Must Include Intercept to use Tukey Test







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group270.8020.748
258

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 27 & 0.802 & 0.748 \tabularnewline
  & 258 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266518&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]27[/C][C]0.802[/C][C]0.748[/C][/ROW]
[ROW][C] [/C][C]258[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266518&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266518&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)
Group270.8020.748
258



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