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Description of Statistical Computation

Author's title

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, 01 Jun 2012 06:22:23 -0400
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/Jun/01/t1338546173po0yd08jm2a8a9b.htm/, Retrieved Thu, 02 May 2024 02:24:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=168659, Retrieved Thu, 02 May 2024 02:24:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76

Tree of Dependent Computations

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)] [] [2012-06-01 10:22:23] [898f528db62d66cb4fd17f9b6ea3eb9d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	2
-1	3
0	2
-1	3
0	3
-5	3
-1	1
0	3
0	4
0	0
-5	3
0	1
2	1
-2	6
-2	4
0	1
0	3
5	-1
2	1
1	4
4	4
-1	2
-2	1
0	4
-1	2
3	4
1	2
1	2
2	3
1	3
1	4
1	2
-1	4
0	5
-5	10
-1	2
1	2
1	-6
5	4
0	0
0	0
-2	3
1	2
0	0
-1	5
0	4
0	6
0	1
0	3
0	3
-1	2
2	4
1	2
2	3
0	1
4	3
-2	3
-1	2
0	5
0	3
0	2
0	4
0	2
0	2
1	3
3	2
3	0
0	2
2	4
-1	3
-2	2
-1	0
-2	0
0	5
0	0
4	-1
3	3
-1	3
0	2
2	-2
4	3
1	2
1	3
0	1
1	4
1	1
0	4
1	2
0	1
3	3
4	5
0	2
5	4
4	3
1	3
0	-1
0	-2
1	3
0	2
-2	2
0	5
-1	2
-4	6
0	3
1	1
-5	1
-9	2
1	-3
-5	2
-1	2
-4	3
1	3
2	3
3	0
-1	1
0	3
2	3
-2	-1
0	0
-2	3
3	4
-3	-1
1	-1
-5	0
1	4
-1	3
5	3
-1	3
-1	0
0	7
-5	5
-1	4
4	3
0	-2
-1	2
-1	0
-1	-2
-2	-1
-4	3
2	3
-2	1
-2	0
1	4
2	3
0	3
-6	0
0	8
0	0
0	3
2	1
-1	4
-1	0
1	3
-4	2
-2	0
-1	2
-1	3
2	3
-2	0
2	1
-4	4
1	-2
-2	2
1	3
1	4
0	0
-2	0
3	0
-1	5
-1	3
0	1
2	1
0	0
1	3
-5	-3
3	0
3	0
3	0
-1	3
-2	-1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168659&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168659&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168659&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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






ANOVA Model
R ~ A
means0.1250.4-21-0.2690.053-5-0.4550.3470.913-0.375-200

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline R ~ A \tabularnewline means & 0.125 & 0.4 & -2 & 1 & -0.269 & 0.053 & -5 & -0.455 & 0.347 & 0.913 & -0.375 & -2 & 0 & 0 \tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=168659&T=1

[TABLE]
[ROW]
ANOVA Model[/C][/ROW] [ROW]R ~ A[/C][/ROW] [ROW][C]means[/C][C]0.125[/C][C]0.4[/C][C]-2[/C][C]1[/C][C]-0.269[/C][C]0.053[/C][C]-5[/C][C]-0.455[/C][C]0.347[/C][C]0.913[/C][C]-0.375[/C][C]-2[/C][C]0[/C][C]0[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=168659&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168659&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:

ANOVA Model
R ~ A
means0.1250.4-21-0.2690.053-5-0.4550.3470.913-0.375-200






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
A1481.8775.8481.1090.353
Residuals166875.1235.272 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
A & 14 & 81.877 & 5.848 & 1.109 & 0.353 \tabularnewline
Residuals & 166 & 875.123 & 5.272 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168659&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]A[/C][C]14[/C][C]81.877[/C][C]5.848[/C][C]1.109[/C][C]0.353[/C][/ROW]
[ROW][C]Residuals[/C][C]166[/C][C]875.123[/C][C]5.272[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168659&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168659&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:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
A1481.8775.8481.1090.353
Residuals166875.1235.272






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=168659&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168659&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168659&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:

Must Include Intercept to use Tukey Test






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group130.7520.709
166 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 13 & 0.752 & 0.709 \tabularnewline
  & 166 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168659&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]13[/C][C]0.752[/C][C]0.709[/C][/ROW]
[ROW][C] [/C][C]166[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168659&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168659&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group130.7520.709
166


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = a-102189388_0.709199646022171_Fri Jun 1 06:09:59 2012 ; par2 = x23 ; par3 = ab715c8262983aba1fc03fb879e960d2 ; par4 = -4 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; 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')