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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 computationWed, 17 Dec 2014 14:09:47 +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/17/t14188254363t6rob6dt5on19q.htm/, Retrieved Thu, 16 May 2024 07:49:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270294, Retrieved Thu, 16 May 2024 07:49:09 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [bla] [2014-12-15 16:03:26] [1601e79f56036968990446024a6397e5]
- RMPD    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [aNoVa] [2014-12-17 14:09:47] [093d2a2ec26a339da390c0fc5c0644df] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0 4.3
1 4.35
1 4.5
0 4.9
0 5.6
0 5.7
1 5.9
1 6.1
1 6.3
1 6.4
1 6.4
0 6.4
1 6.7
0 6.7
1 7.3
0 7.3
1 7.4
1 7.4
1 7.6
1 7.6
0 7.65
1 7.7
0 7.7
1 7.7
0 7.85
0 7.85
0 7.9
0 7.9
0 8
0 8.1
1 8.2
1 8.3
0 8.3
0 8.5
0 8.6
1 8.6
0 8.8
0 8.8
1 9
0 9
0 9.1
1 9.2
1 9.3
1 9.3
1 9.3
1 9.5
0 9.6
0 9.6
0 9.6
0 9.6
1 9.7
1 9.85
1 9.9
0 9.9
0 9.9
1 9.9
0 9.95
1 10
1 10.1
1 10.3
0 10.3
0 10.3
0 10.35
1 10.4
0 10.5
1 10.6
1 10.6
0 10.7
1 10.8
1 10.8
1 10.8
1 10.9
1 10.9
0 10.9
0 10.95
0 10.95
0 10.95
1 11.1
1 11.1
0 11.1
0 11.15
0 11.2
0 11.2
0 11.2
0 11.3
1 11.3
0 11.35
1 11.4
1 11.4
1 11.4
0 11.4
0 11.4
0 11.4
1 11.5
0 11.6
0 11.6
0 11.6
1 11.7
1 11.7
1 11.7
1 11.7
0 11.75
1 11.8
1 11.8
0 11.8
0 11.85
1 11.85
1 11.9
0 11.9
1 11.9
1 11.95
1 12
0 12.1
0 12.15
1 12.2
0 12.2
0 12.2
0 12.3
0 12.3
1 12.3
1 12.35
0 12.35
1 12.4
0 12.4
1 12.45
1 12.5
1 12.6
1 12.6
0 12.6
0 12.6
0 12.6
1 12.6
0 12.6
0 12.6
1 12.65
0 12.65
0 12.7
1 12.7
1 12.7
1 12.75
1 12.8
1 12.85
1 12.9
0 12.9
1 13
1 13
1 13
0 13.1
0 13.15
1 13.2
0 13.2
0 13.2
1 13.2
1 13.3
1 13.3
0 13.3
1 13.35
1 13.35
1 13.35
1 13.35
0 13.4
0 13.4
0 13.4
0 13.4
0 13.5
0 13.6
1 13.6
1 13.6
0 13.6
0 13.6
1 13.6
1 13.6
1 13.8
1 13.8
0 13.85
0 13.9
0 13.95
1 14.05
1 14.1
0 14.1
0 14.2
1 14.3
1 14.35
1 14.35
1 14.5
1 14.5
0 14.6
0 14.6
0 14.6
0 14.6
0 14.6
1 14.6
1 14.7
1 14.75
1 14.75
1 14.75
0 14.75
1 14.8
1 14.8
0 14.85
0 14.85
0 14.9
0 14.9
0 14.9
0 15
0 15.1
0 15.1
0 15.15
0 15.2
0 15.2
1 15.25
0 15.25
1 15.25
0 15.35
0 15.35
1 15.4
1 15.4
0 15.4
1 15.6
1 15.6
1 15.6
0 15.6
0 15.65
0 15.65
0 15.7
0 15.85
1 15.9
0 15.9
0 15.95
1 16
0 16.1
1 16.1
1 16.1
1 16.1
0 16.1
1 16.1
1 16.15
1 16.2
0 16.25
1 16.35
1 16.35
0 16.6
0 16.6
0 16.65
0 16.65
1 16.85
0 16.85
0 16.85
1 17.1
1 17.1
1 17.1
1 17.35
0 17.35
0 17.6
1 17.6
1 17.65
1 17.65
1 17.75
1 17.75
1 17.75
0 17.75
1 17.85
1 18.1
0 18.1
1 18.15
0 18.2
1 18.25
1 18.25
1 18.4
1 18.4
1 18.4
0 18.45
0 18.45
1 18.55
0 18.9
1 18.95
1 19.05
1 19.1
1 19.1
1 19.1
1 19.1
0 19.2
1 19.25
1 19.3
0 19.9
0 19.9

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'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=270294&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=270294&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270294&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'Gwilym Jenkins' @ jenkins.wessa.net






ANOVA Model
TOT ~ coursebin
means12.72913.122

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
TOT  ~  coursebin \tabularnewline
means & 12.729 & 13.122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270294&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]TOT  ~  coursebin[/C][/ROW]
[ROW][C]means[/C][C]12.729[/C][C]13.122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270294&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270294&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
TOT ~ coursebin
means12.72913.122






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
coursebin247856.18223928.0912043.8440
Residuals2843324.911.707 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
coursebin & 2 & 47856.182 & 23928.091 & 2043.844 & 0 \tabularnewline
Residuals & 284 & 3324.9 & 11.707 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270294&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]coursebin[/C][C]2[/C][C]47856.182[/C][C]23928.091[/C][C]2043.844[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]284[/C][C]3324.9[/C][C]11.707[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270294&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270294&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)
coursebin247856.18223928.0912043.8440
Residuals2843324.911.707






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=270294&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270294&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270294&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)
Group10.7340.392
284 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 1 & 0.734 & 0.392 \tabularnewline
  & 284 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270294&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]0.734[/C][C]0.392[/C][/ROW]
[ROW][C] [/C][C]284[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270294&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270294&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)
Group10.7340.392
284


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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')