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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareOne-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)
Date of computationTue, 16 Dec 2014 17:06:50 +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/16/t1418749660gvl3df4kiajy1bf.htm/, Retrieved Thu, 16 May 2024 17:04:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269848, Retrieved Thu, 16 May 2024 17:04:48 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93

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)] [] [2010-11-01 13:37:53] [b98453cac15ba1066b407e146608df68]
- RMP   [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-10-21 08:05:52] [32b17a345b130fdf5cc88718ed94a974]
-         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-10-30 15:24:59] [2fea329c6e322b1612c5dc504f90c0ef]
- RMPD        [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-16 17:06:50] [8a5be748fffbe1272db475ee7e612f22] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
3 24
3 17
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2 8
2 29
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2 26
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2 8
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2 8
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2 30
2 17
2 24
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2 30
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2 0
2 18
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2 29
2 15
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2 10
2 18
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2 26
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2 24
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2 27
2 20
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1 21
1 26
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1 12
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1 19
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1 14
1 28
1 16
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1 27
1 23
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1 23
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1 25
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1 3
1 23
1 14
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1 7
1 24
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1 24
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1 28
1 23
1 19
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1 25
1 25
1 23
1 17
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1 12
1 24
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1 14
1 23
1 18
1 29
1 16
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1 23
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1 4
1 20
1 20
1 4
1 24
1 16
1 3
1 24
1 23
1 17
1 20
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1 19
1 24
1 19
1 27
1 22
1 23
1 22
1 17
1 23
1 23
1 28
1 29
1 21
1 24
1 20
1 7
1 19
1 28
1 26
1 19
1 20
1 23
1 24
1 16
1 19
1 24
1 21
1 16
1 16
1 21
1 NA
1 28
1 16
1 23
1 26
1 29
1 18
1 19
1 19
1 16
1 16
1 16
1 18
1 22
1 14
1 20
1 15
1 22
1 16
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1 11
1 15
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1 28
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1 27
1 17
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1 22
1 13
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1 24
1 18
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1 15
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1 24
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1 16
1 15
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1 27
1 23
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1 14
1 24
1 16
1 22
1 13
1 17
1 23
1 22
1 23
1 26
1 14
1 24
1 21
1 16
1 11
1 NA
1 19
1 16
1 19
1 16
1 11
1 23
1 27
1 23
1 25
1 24
1 22
1 26
1 19
1 19
1 19
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1 21
1 26
1 23
1 21
1 22
1 26
1 27
1 17
1 22
1 19
1 14
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1 26

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269848&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269848&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269848&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






ANOVA Model
Groep_n ~ NUMERACYTOT
means22.5712.12.6192.3682.3042.1671.72.1181.9571.69821.8551.8911.8671.5331.6591.7781.7691.7141.81.51.66721.75331.6672.252.61.333

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Groep_n  ~  NUMERACYTOT \tabularnewline
means & 2 & 2.571 & 2.1 & 2.619 & 2.368 & 2.304 & 2.167 & 1.7 & 2.118 & 1.957 & 1.698 & 2 & 1.855 & 1.891 & 1.867 & 1.533 & 1.659 & 1.778 & 1.769 & 1.714 & 1.8 & 1.5 & 1.667 & 2 & 1.75 & 3 & 3 & 1.667 & 2.25 & 2.6 & 1.333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269848&T=1

[TABLE]
[ROW]ANOVA Model[/C][/ROW]
[ROW]Groep_n  ~  NUMERACYTOT[/C][/ROW]
[ROW][C]means[/C][C]2[/C][C]2.571[/C][C]2.1[/C][C]2.619[/C][C]2.368[/C][C]2.304[/C][C]2.167[/C][C]1.7[/C][C]2.118[/C][C]1.957[/C][C]1.698[/C][C]2[/C][C]1.855[/C][C]1.891[/C][C]1.867[/C][C]1.533[/C][C]1.659[/C][C]1.778[/C][C]1.769[/C][C]1.714[/C][C]1.8[/C][C]1.5[/C][C]1.667[/C][C]2[/C][C]1.75[/C][C]3[/C][C]3[/C][C]1.667[/C][C]2.25[/C][C]2.6[/C][C]1.333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269848&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269848&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
Groep_n ~ NUMERACYTOT
means22.5712.12.6192.3682.3042.1671.72.1181.9571.69821.8551.8911.8671.5331.6591.7781.7691.7141.81.51.66721.75331.6672.252.61.333






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
NUMERACYTOT312684.56486.599140.9070
Residuals702431.4360.615 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
NUMERACYTOT & 31 & 2684.564 & 86.599 & 140.907 & 0 \tabularnewline
Residuals & 702 & 431.436 & 0.615 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269848&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]31[/C][C]2684.564[/C][C]86.599[/C][C]140.907[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]702[/C][C]431.436[/C][C]0.615[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269848&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269848&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)
NUMERACYTOT312684.56486.599140.9070
Residuals702431.4360.615






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=269848&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269848&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269848&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)
Group300.8440.707
702 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 30 & 0.844 & 0.707 \tabularnewline
  & 702 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269848&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]30[/C][C]0.844[/C][C]0.707[/C][/ROW]
[ROW][C] [/C][C]702[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269848&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269848&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)
Group300.8440.707
702


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = FALSE ;
Parameters (R input):
par1 = 1 ; par2 = 80 ; par3 = 1e-08 ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
R code (references can be found in the software module):
par3 <- 'FALSE'
par2 <- '2'
par1 <- '1'
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')