Free Statistics

of Irreproducible Research!

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 computationSun, 07 Dec 2014 21:40:57 +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/07/t1417989055ys6lqinwlcigaok.htm/, Retrieved Thu, 31 Oct 2024 23:26:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263907, Retrieved Thu, 31 Oct 2024 23:26:12 +0000
QR Codes:

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)] [] [2014-12-07 21:40:57] [a97fb05c06a04cb9398859e294d4eb9c] [Current]
Feedback Forum

Post a new message
Dataseries X:
2012 4.35
2012 12.7
2012 18.1
2012 17.85
2012 16.6
2012 12.6
2012 17.1
2012 19.1
2012 16.1
2012 13.35
2012 18.4
2012 14.7
2012 10.6
2012 12.6
2012 16.2
2012 13.6
2012 18.9
2012 14.1
2012 14.5
2012 16.15
2012 14.75
2012 14.8
2012 12.45
2012 12.65
2012 17.35
2012 8.6
2012 18.4
2012 16.1
2012 11.6
2012 17.75
2012 15.25
2012 17.65
2012 15.6
2012 16.35
2012 17.65
2012 13.6
2012 11.7
2012 14.35
2012 14.75
2012 18.25
2012 9.9
2012 16
2012 18.25
2012 16.85
2012 14.6
2012 13.85
2012 18.95
2012 15.6
2012 14.85
2012 11.75
2012 18.45
2012 15.9
2012 17.1
2012 16.1
2012 19.9
2012 10.95
2012 18.45
2012 15.1
2012 15
2012 11.35
2012 15.95
2012 18.1
2012 14.6
2012 15.4
2012 15.4
2012 17.6
2012 13.35
2012 19.1
2012 15.35
2012 7.6
2012 13.4
2012 13.9
2012 19.1
2012 15.25
2012 12.9
2012 16.1
2012 17.35
2012 13.15
2012 12.15
2012 12.6
2012 10.35
2012 15.4
2012 9.6
2012 18.2
2012 13.6
2012 14.85
2012 14.75
2012 14.1
2012 14.9
2012 16.25
2012 19.25
2012 13.6
2012 13.6
2012 15.65
2012 12.75
2012 14.6
2012 9.85
2012 12.65
2012 11.9
2012 19.2
2012 16.6
2012 11.2
2012 15.25
2012 11.9
2012 13.2
2012 16.35
2012 12.4
2012 15.85
2012 14.35
2012 18.15
2012 11.15
2012 15.65
2012 17.75
2012 7.65
2012 12.35
2012 15.6
2012 19.3
2012 15.2
2012 17.1
2012 15.6
2012 18.4
2012 19.05
2012 18.55
2012 19.1
2012 13.1
2012 12.85
2012 9.5
2012 4.5
2012 11.85
2012 13.6
2012 11.7
2012 12.4
2012 13.35
2012 11.4
2012 14.9
2012 19.9
2012 17.75
2012 11.2
2012 14.6
2012 17.6
2012 14.05
2012 16.1
2012 13.35
2012 11.85
2012 11.95
2012 14.75
2012 15.15
2012 13.2
2012 16.85
2012 7.85
2012 7.7
2012 12.6
2012 7.85
2012 10.95
2012 12.35
2012 9.95
2012 14.9
2012 16.65
2012 13.4
2012 13.95
2012 15.7
2012 16.85
2012 10.95
2012 15.35
2012 12.2
2012 15.1
2012 17.75
2012 15.2
2012 14.6
2012 16.65
2012 8.1
2011 12.9
2011 7.4
2011 12.2
2011 12.8
2011 7.4
2011 6.7
2011 12.6
2011 14.8
2011 13.3
2011 11.1
2011 8.2
2011 11.4
2011 6.4
2011 10.6
2011 12
2011 6.3
2011 11.3
2011 11.9
2011 9.3
2011 9.6
2011 10
2011 6.4
2011 13.8
2011 10.8
2011 13.8
2011 11.7
2011 10.9
2011 16.1
2011 13.4
2011 9.9
2011 11.5
2011 8.3
2011 11.7
2011 6.1
2011 9
2011 9.7
2011 10.8
2011 10.3
2011 10.4
2011 12.7
2011 9.3
2011 11.8
2011 5.9
2011 11.4
2011 13
2011 10.8
2011 12.3
2011 11.3
2011 11.8
2011 7.9
2011 12.7
2011 12.3
2011 11.6
2011 6.7
2011 10.9
2011 12.1
2011 13.3
2011 10.1
2011 5.7
2011 14.3
2011 8
2011 13.3
2011 9.3
2011 12.5
2011 7.6
2011 15.9
2011 9.2
2011 9.1
2011 11.1
2011 13
2011 14.5
2011 12.2
2011 12.3
2011 11.4
2011 8.8
2011 14.6
2011 7.3
2011 12.6
2011 NA
2011 13
2011 12.6
2011 13.2
2011 9.9
2011 7.7
2011 10.5
2011 13.4
2011 10.9
2011 4.3
2011 10.3
2011 11.8
2011 11.2
2011 11.4
2011 8.6
2011 13.2
2011 12.6
2011 5.6
2011 9.9
2011 8.8
2011 7.7
2011 9
2011 7.3
2011 11.4
2011 13.6
2011 7.9
2011 10.7
2011 10.3
2011 8.3
2011 9.6
2011 14.2
2011 8.5
2011 13.5
2011 4.9
2011 6.4
2011 9.6
2011 11.6
2011 11.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263907&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263907&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263907&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'Sir Maurice George Kendall' @ kendall.wessa.net







ANOVA Model
Totale_ptn ~ Jaar
means10.593.92

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Totale_ptn  ~  Jaar \tabularnewline
means & 10.59 & 3.92 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263907&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Totale_ptn  ~  Jaar[/C][/ROW]
[ROW][C]means[/C][C]10.59[/C][C]3.92[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263907&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263907&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
Totale_ptn ~ Jaar
means10.593.92







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Jaar11056.4661056.466131.6250
Residuals2842279.4748.026

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Jaar & 1 & 1056.466 & 1056.466 & 131.625 & 0 \tabularnewline
Residuals & 284 & 2279.474 & 8.026 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263907&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]Jaar[/C][C]1[/C][C]1056.466[/C][C]1056.466[/C][C]131.625[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]284[/C][C]2279.474[/C][C]8.026[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263907&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263907&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)
Jaar11056.4661056.466131.6250
Residuals2842279.4748.026







Tukey Honest Significant Difference Comparisons
difflwruprp adj
2012-20113.923.2474.5920

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2012-2011 & 3.92 & 3.247 & 4.592 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263907&T=3

[TABLE]
[ROW][C]Tukey Honest Significant Difference Comparisons[/C][/ROW]
[ROW][C] [/C][C]diff[/C][C]lwr[/C][C]upr[/C][C]p adj[/C][/ROW]
[ROW][C]2012-2011[/C][C]3.92[/C][C]3.247[/C][C]4.592[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263907&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263907&T=3

As an alternative you can also use a QR Code:  

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

Tukey Honest Significant Difference Comparisons
difflwruprp adj
2012-20113.923.2474.5920







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group12.580.109
284

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 1 & 2.58 & 0.109 \tabularnewline
  & 284 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263907&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]2.58[/C][C]0.109[/C][/ROW]
[ROW][C] [/C][C]284[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263907&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263907&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)
Group12.580.109
284



Parameters (Session):
par1 = 2 ; par2 = 1 ; par3 = TRUE ;
Parameters (R input):
par1 = 2 ; par2 = 1 ; par3 = TRUE ;
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')