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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 computationTue, 16 Dec 2014 21:03:45 +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/t14187639691p1zi0cguo4tfs3.htm/, Retrieved Thu, 31 Oct 2024 23:47:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269950, Retrieved Thu, 31 Oct 2024 23:47:24 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107
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)] [] [2014-12-16 21:03:45] [94ef54447bb0146b66f4a809a85d3ed7] [Current]
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Dataseries X:
0	83.00
0	74.25
0	58.75
0	92.25
0	99.50
0	92.25
0	75.00
0	56.75
0	90.50
0	67.00
0	69.50
0	76.25
0	80.50
0	86.75
0	65.75
0	60.75
0	91.00
0	68.00
0	70.50
0	74.50
0	81.25
0	78.25
0	73.00
0	96.00
0	66.00
0	78.25
0	38.25
0	76.00
0	59.25
0	57.00
0	99.50
0	75.75
0	84.25
0	63.00
0	61.75
0	83.25
0	69.75
0	78.50
0	76.75
0	75.50
0	88.75
0	83.25
0	95.50
0	66.75
0	92.00
0	80.75
0	92.00
0	78.00
0	81.75
0	88.25
0	58.50
0	71.75
0	73.75
0	49.50
0	84.25
0	78.00
0	85.50
0	95.50
0	38.00
0	73.75
0	68.00
0	59.50
0	81.75
0	71.75
0	88.75
0	96.50
0	85.50
0	95.25
0	92.75
0	95.50
0	66.75
0	88.00
0	80.50
0	59.75
0	38.50
0	73.00
1	63.00
1	94.50
1	58.00
1	73.00
1	69.25
1	79.50
1	54.75
1	75.50
1	79.75
1	73.00
1	88.00
1	76.75
1	64.50
1	63.00
1	51.75
1	77.00
1	48.00
1	74.25
1	68.00
1	63.25
1	59.50
1	83.00
1	56.00
1	79.25
1	55.75
1	78.00
1	65.50
1	62.00
1	74.50
1	56.00
1	73.00
1	73.75
1	39.25
1	39.25
1	54.75
1	49.75
1	74.50
1	67.00
1	84.25
1	54.75
1	61.00
1	76.00
1	40.50
1	21.75
1	63.50
1	90.50
1	89.25
1	85.50
1	80.50
1	73.50
1	53.00
1	63.00
1	81.00
1	68.00
1	70.50
1	72.50
1	73.75
1	74.00
1	62.25
1	63.25
1	86.75
1	43.00
1	80.50
1	88.75
1	76.25
1	88.25
1	68.00
1	91.25
1	80.00
1	91.25
1	94.75
1	80.50
1	77.00
1	77.00
1	66.75
1	95.50
1	96.25
1	63.75
1	49.25
1	76.25
1	62.00
1	90.75
1	61.75
1	78.00
1	92.00
1	64.25
1	47.50
1	22.50
1	68.00
1	58.50
1	88.75
1	70.25
1	66.75
1	59.25
1	66.00




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269950&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269950&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269950&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'Herman Ole Andreas Wold' @ wold.wessa.net







ANOVA Model
Totaalscoreperc ~ Gender
means76.33269.526

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Totaalscoreperc  ~  Gender \tabularnewline
means & 76.332 & 69.526 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269950&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Totaalscoreperc  ~  Gender[/C][/ROW]
[ROW][C]means[/C][C]76.332[/C][C]69.526[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269950&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269950&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
Totaalscoreperc ~ Gender
means76.33269.526







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Gender2902043.705451021.8522058.5920
Residuals16937026.608219.092

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Gender & 2 & 902043.705 & 451021.852 & 2058.592 & 0 \tabularnewline
Residuals & 169 & 37026.608 & 219.092 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269950&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]Gender[/C][C]2[/C][C]902043.705[/C][C]451021.852[/C][C]2058.592[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]169[/C][C]37026.608[/C][C]219.092[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269950&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269950&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)
Gender2902043.705451021.8522058.5920
Residuals16937026.608219.092







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=269950&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269950&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269950&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)
Group10.4060.525
169

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 1 & 0.406 & 0.525 \tabularnewline
  & 169 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269950&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]0.406[/C][C]0.525[/C][/ROW]
[ROW][C] [/C][C]169[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269950&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269950&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)
Group10.4060.525
169



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):
par3 <- 'FALSE'
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')