FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 19 Nov 2010 22:59:17 +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/2010/Nov/19/t1290207454katyo0hf06vr8hx.htm/, Retrieved Fri, 03 May 2024 22:10:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98145, Retrieved Fri, 03 May 2024 22:10:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136

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)] [Simulation experi...] [2010-10-26 10:02:07] [b98453cac15ba1066b407e146608df68]
F R PD    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [W2-Q6-1] [2010-11-19 22:59:17] [3213ed9efdb724e3c847d204cd8135dd] [Current]
Feedback Forum
2010-11-23 18:03:10 [Kim Smits] [reply] 
Doordat je de gegevens moet vergelijken, is het niet genoeg om enkel de gegevens van de post 1 score te gebruiken. Je moet deze vergelijken met de pre scores. Doordat je dit niet hebt gedaan bekom je een foutief antwoord en kan je dus geen juiste conclusies trekken.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1	4	0	2	'T'
1	1	0	0	2	'T'
0	1	4	1	1.5	'T'
0	0	0	0	0	'T'
1	1	0	1	1	'T'
1	1	0	1	2	'T'
1	1	0	1	2	'T'
0	1	0	1	1	'T'
0	1	4	1	2	'T'
1	1	1	0	2	'T'
0	0	4	0	2	'T'
0	1	0	1	0	'T'
0	1	2	1	0	'T'
0	1	0	0	2	'T'
0	0	0	NA	NA	'T'
1	1	0	1	2	'T'
1	1	1	0	2	'T'
1	1	0	1	0.5	'T'
0	1	0	1	2	'T'
0	0	2	1	0	'T'
1	1	2	1	2	'T'
1	1	1	0	0	'T'
0	0	2	NA	NA	'T'
1	0	0	NA	NA	'T'
1	1	3	1	2	'T'
1	0	0	1	0	'T'
1	1	0	NA	NA	'T'
0	0	0	NA	NA	'T'
0	0	1	0	2	'T'
1	1	0	1	1	'T'
1	0	0	0	0.5	'T'
1	1	4	0	2	'T'
0	0	0	1	0.5	'T'
0	0	1	NA	NA	'T'
0	0	0	1	0.5	'T'
1	1	0	NA	NA	'T'
1	1	4	0	2	'T'
0	1	1	1	0	'E'
0	1	0	1	1	'E'
1	1	4	1	2	'E'
1	1	0	1	1	'E'
1	1	4	1	2	'E'
1	1	0	0	0	'E'
1	1	0	1	0.5	'E'
0	0	0	1	0	'E'
0	1	4	1	2	'E'
0	1	0	0	0	'E'
1	1	0	0	1	'E'
1	1	4	1	2	'E'
0	0	4	0	0.5	'E'
0	1	0	1	2	'E'
1	1	1	1	2	'E'
0	1	0	1	2	'E'
0	0	4	NA	NA	'E'
0	1	0	0	0	'E'
0	1	2	1	0	'E'
0	1	0	1	0.5	'E'
0	1	4	NA	NA	'E'
0	0	4	0	2	'E'
0	0	0	NA	NA	'E'
0	1	0	1	0	'E'
1	1	4	1	2	'E'
1	1	0	1	1	'E'
1	0	0	1	0	'E'
0	0	2	1	2	'E'
0	1	0	0	1	'E'
0	1	0	1	2	'E'
0	0	0	0	0	'E'
1	1	4	1	1	'E'
1	1	4	1	2	'E'
0	1	2	0	0	'S'
0	1	0	0	0	'S'
0	1	0	0	0	'S'
0	1	4	0	0	'S'
1	1	0	1	2	'S'
1	0	0	1	2	'S'
0	0	1	1	2	'S'
1	1	2	1	2	'S'
1	0	0	1	2	'S'
1	1	2	1	2	'S'
0	0	0	1	2	'S'
0	0	4	1	2	'S'
0	0	4	1	2	'S'
1	0	0	1	2	'S'
0	0	0	NA	NA	'S'
0	0	4	1	2	'S'
1	0	0	NA	NA	'S'
1	1	4	1	2	'S'
0	0	2	1	2	'S'
0	0	2	NA	NA	'S'
1	1	0	0	0	'S'
1	1	0	1	2	'S'
1	1	4	NA	NA	'S'
0	1	0	1	2	'S'
1	1	0	1	2	'S'
1	1	0	1	2	'S'
1	1	4	1	2	'S'
1	1	4	1	2	'S'
0	0	0	NA	NA	'S'
0	0	0	0	0	'S'
1	1	2	0	0	'S'
0	0	1	1	2	'S'
0	0	0	0	0	'S'
0	0	2	1	2	'S'
0	1	1	0	0	'S'

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98145&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98145&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98145&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'Gwilym Jenkins' @ 72.249.127.135






ANOVA Model
post1 ~ treatment
means0.758-0.243-0.109

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
post1  ~  treatment \tabularnewline
means & 0.758 & -0.243 & -0.109 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98145&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]post1  ~  treatment[/C][/ROW]
[ROW][C]means[/C][C]0.758[/C][C]-0.243[/C][C]-0.109[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98145&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98145&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
post1 ~ treatment
means0.758-0.243-0.109






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
treatment21.0120.5062.2210.114
Residuals10223.2360.228 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
treatment & 2 & 1.012 & 0.506 & 2.221 & 0.114 \tabularnewline
Residuals & 102 & 23.236 & 0.228 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98145&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]treatment[/C][C]2[/C][C]1.012[/C][C]0.506[/C][C]2.221[/C][C]0.114[/C][/ROW]
[ROW][C]Residuals[/C][C]102[/C][C]23.236[/C][C]0.228[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98145&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98145&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)
treatment21.0120.5062.2210.114
Residuals10223.2360.228






Tukey Honest Significant Difference Comparisons
difflwruprp adj
S-E-0.243-0.5190.0320.095
T-E-0.109-0.3810.1630.608
T-S0.134-0.1330.4020.46

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
S-E & -0.243 & -0.519 & 0.032 & 0.095 \tabularnewline
T-E & -0.109 & -0.381 & 0.163 & 0.608 \tabularnewline
T-S & 0.134 & -0.133 & 0.402 & 0.46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98145&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]S-E[/C][C]-0.243[/C][C]-0.519[/C][C]0.032[/C][C]0.095[/C][/ROW]
[ROW][C]T-E[/C][C]-0.109[/C][C]-0.381[/C][C]0.163[/C][C]0.608[/C][/ROW]
[ROW][C]T-S[/C][C]0.134[/C][C]-0.133[/C][C]0.402[/C][C]0.46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98145&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98145&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:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
S-E-0.243-0.5190.0320.095
T-E-0.109-0.3810.1630.608
T-S0.134-0.1330.4020.46






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group22.2210.114
102 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 2.221 & 0.114 \tabularnewline
  & 102 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98145&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]2[/C][C]2.221[/C][C]0.114[/C][/ROW]
[ROW][C] [/C][C]102[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98145&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98145&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)
Group22.2210.114
102


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0.0 ;
Parameters (R input):
par1 = 2 ; par2 = 6 ; 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){
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<-levene.test(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')