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 computationThu, 18 Nov 2010 12:16:49 +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/18/t1290082718t4ix7md1hkm7b59.htm/, Retrieved Fri, 03 May 2024 02:55:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97065, Retrieved Fri, 03 May 2024 02:55:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170

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 13:40:20] [b98453cac15ba1066b407e146608df68]
-   P   [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-17 15:35:41] [29e492448d11757ae0fad5ef6e7f8e86]
F           [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Workshop 2 Vraag 6b] [2010-11-18 12:16:49] [ca0a9c6c6ac3cc5623c7945c1ccf8fd2] [Current]
Feedback Forum
2010-11-22 15:54:45 [Michaël Delbaere] [reply] 
De p-waarde is groter dan de type I error van 0.05. Dit wil zeggen dat je de nulhypothese niet mag verwerpen en dat er dus geen significant verschil bestaat tussen de verschillende treatments. Als je hier de tabel van de Tukey Honest Significant Difference Comparisons gaat bekijken, kan je bepalen welke treatment het meest geschikt is op de lange termijn.

S-E -0.067 => Hier is treatment E net een klein beetje beter dan treatment S

T-E -0.267 => Hier is treatment E duidelijk beter dan treatment T

T-S -0.2 => Hier is treatment S duidelijk beter dan treatment T.

Geen enkele treatment is significant beter dan de andere, maar je kan wel zien dat ook hier op lange termijn treatment E beter is dan de andere 2. Op de QQ plot zie je ook dat er geen significant verschil omdat er geen enkel interval buiten de 0 waarde valt. De 0 waarde zit duidelijk inbegrepen in alle intervallen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1	4	0	2	'T'	0	3	-1	1	4
1	1	0	0	2	'T'	0	-1	-1	1	0
0	1	4	1	1.5	'T'	1	4	1	1.5	5
0	0	0	0	0	'T'	0	0	0	0	0
1	1	0	1	1	'T'	0	-1	0	0	0
1	1	0	1	2	'T'	0	-1	0	1	0
1	1	0	1	2	'T'	0	-1	0	1	0
0	1	0	1	1	'T'	1	0	1	1	1
0	1	4	1	2	'T'	1	4	1	2	5
1	1	1	0	2	'T'	0	0	-1	1	1
0	0	4	0	2	'T'	0	4	0	2	4
0	1	0	1	0	'T'	1	0	1	0	1
0	1	2	1	0	'T'	1	2	1	0	3
0	1	0	0	2	'T'	1	0	0	2	1
0	0	0	NA	NA	'T'	0	0	NA	NA	0
1	1	0	1	2	'T'	0	-1	0	1	0
1	1	1	0	2	'T'	0	0	-1	1	1
1	1	0	1	0.5	'T'	0	-1	0	-0.5	0
0	1	0	1	2	'T'	1	0	1	2	1
0	0	2	1	0	'T'	0	2	1	0	2
1	1	2	1	2	'T'	0	1	0	1	2
1	1	1	0	0	'T'	0	0	-1	-1	1
0	0	2	NA	NA	'T'	0	2	NA	NA	2
1	0	0	NA	NA	'T'	-1	-1	NA	NA	-1
1	1	3	1	2	'T'	0	2	0	1	3
1	0	0	1	0	'T'	-1	-1	0	-1	-1
1	1	0	NA	NA	'T'	0	-1	NA	NA	0
0	0	0	NA	NA	'T'	0	0	NA	NA	0
0	0	1	0	2	'T'	0	1	0	2	1
1	1	0	1	1	'T'	0	-1	0	0	0
1	0	0	0	0.5	'T'	-1	-1	-1	-0.5	-1
1	1	4	0	2	'T'	0	3	-1	1	4
0	0	0	1	0.5	'T'	0	0	1	0.5	0
0	0	1	NA	NA	'T'	0	1	NA	NA	1
0	0	0	1	0.5	'T'	0	0	1	0.5	0
1	1	0	NA	NA	'T'	0	-1	NA	NA	0
1	1	4	0	2	'T'	0	3	-1	1	4
0	1	1	1	0	'E'	1	1	1	0	2
0	1	0	1	1	'E'	1	0	1	1	1
1	1	4	1	2	'E'	0	3	0	1	4
1	1	0	1	1	'E'	0	-1	0	0	0
1	1	4	1	2	'E'	0	3	0	1	4
1	1	0	0	0	'E'	0	-1	-1	-1	0
1	1	0	1	0.5	'E'	0	-1	0	-0.5	0
0	0	0	1	0	'E'	0	0	1	0	0
0	1	4	1	2	'E'	1	4	1	2	5
0	1	0	0	0	'E'	1	0	0	0	1
1	1	0	0	1	'E'	0	-1	-1	0	0
1	1	4	1	2	'E'	0	3	0	1	4
0	0	4	0	0.5	'E'	0	4	0	0.5	4
0	1	0	1	2	'E'	1	0	1	2	1
1	1	1	1	2	'E'	0	0	0	1	1
0	1	0	1	2	'E'	1	0	1	2	1
0	0	4	NA	NA	'E'	0	4	NA	NA	4
0	1	0	0	0	'E'	1	0	0	0	1
0	1	2	1	0	'E'	1	2	1	0	3
0	1	0	1	0.5	'E'	1	0	1	0.5	1
0	1	4	NA	NA	'E'	1	4	NA	NA	5
0	0	4	0	2	'E'	0	4	0	2	4
0	0	0	NA	NA	'E'	0	0	NA	NA	0
0	1	0	1	0	'E'	1	0	1	0	1
1	1	4	1	2	'E'	0	3	0	1	4
1	1	0	1	1	'E'	0	-1	0	0	0
1	0	0	1	0	'E'	-1	-1	0	-1	-1
0	0	2	1	2	'E'	0	2	1	2	2
0	1	0	0	1	'E'	1	0	0	1	1
0	1	0	1	2	'E'	1	0	1	2	1
0	0	0	0	0	'E'	0	0	0	0	0
1	1	4	1	1	'E'	0	3	0	0	4
1	1	4	1	2	'E'	0	3	0	1	4
0	1	2	0	0	'S'	1	2	0	0	3
0	1	0	0	0	'S'	1	0	0	0	1
0	1	0	0	0	'S'	1	0	0	0	1
0	1	4	0	0	'S'	1	4	0	0	5
1	1	0	1	2	'S'	0	-1	0	1	0
1	0	0	1	2	'S'	-1	-1	0	1	-1
0	0	1	1	2	'S'	0	1	1	2	1
1	1	2	1	2	'S'	0	1	0	1	2
1	0	0	1	2	'S'	-1	-1	0	1	-1
1	1	2	1	2	'S'	0	1	0	1	2
0	0	0	1	2	'S'	0	0	1	2	0
0	0	4	1	2	'S'	0	4	1	2	4
0	0	4	1	2	'S'	0	4	1	2	4
1	0	0	1	2	'S'	-1	-1	0	1	-1
0	0	0	NA	NA	'S'	0	0	NA	NA	0
0	0	4	1	2	'S'	0	4	1	2	4
1	0	0	NA	NA	'S'	-1	-1	NA	NA	-1
1	1	4	1	2	'S'	0	3	0	1	4
0	0	2	1	2	'S'	0	2	1	2	2
0	0	2	NA	NA	'S'	0	2	NA	NA	2
1	1	0	0	0	'S'	0	-1	-1	-1	0
1	1	0	1	2	'S'	0	-1	0	1	0
1	1	4	NA	NA	'S'	0	3	NA	NA	4
0	1	0	1	2	'S'	1	0	1	2	1
1	1	0	1	2	'S'	0	-1	0	1	0
1	1	0	1	2	'S'	0	-1	0	1	0
1	1	4	1	2	'S'	0	3	0	1	4
1	1	4	1	2	'S'	0	3	0	1	4
0	0	0	NA	NA	'S'	0	0	NA	NA	0
0	0	0	0	0	'S'	0	0	0	0	0
1	1	2	0	0	'S'	0	1	-1	-1	2
0	0	1	1	2	'S'	0	1	1	2	1
0	0	0	0	0	'S'	0	0	0	0	0
0	0	2	1	2	'S'	0	2	1	2	2
0	1	1	0	0	'S'	1	1	0	0	2

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97065&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97065&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97065&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'George Udny Yule' @ 72.249.76.132






ANOVA Model
post3-pre ~ Treatment
means0.3-0.067-0.267

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
post3-pre  ~  Treatment \tabularnewline
means & 0.3 & -0.067 & -0.267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97065&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]post3-pre  ~  Treatment[/C][/ROW]
[ROW][C]means[/C][C]0.3[/C][C]-0.067[/C][C]-0.267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97065&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97065&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
post3-pre ~ Treatment
means0.3-0.067-0.267






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Treatment21.1560.5781.3720.259
Residuals8736.6330.421 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Treatment & 2 & 1.156 & 0.578 & 1.372 & 0.259 \tabularnewline
Residuals & 87 & 36.633 & 0.421 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97065&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]1.156[/C][C]0.578[/C][C]1.372[/C][C]0.259[/C][/ROW]
[ROW][C]Residuals[/C][C]87[/C][C]36.633[/C][C]0.421[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97065&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97065&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)
Treatment21.1560.5781.3720.259
Residuals8736.6330.421






Tukey Honest Significant Difference Comparisons
difflwruprp adj
S-E-0.067-0.4660.3330.917
T-E-0.267-0.6660.1330.255
T-S-0.2-0.60.20.46

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
S-E & -0.067 & -0.466 & 0.333 & 0.917 \tabularnewline
T-E & -0.267 & -0.666 & 0.133 & 0.255 \tabularnewline
T-S & -0.2 & -0.6 & 0.2 & 0.46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97065&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]-0.067[/C][C]-0.466[/C][C]0.333[/C][C]0.917[/C][/ROW]
[ROW][C]T-E[/C][C]-0.267[/C][C]-0.666[/C][C]0.133[/C][C]0.255[/C][/ROW]
[ROW][C]T-S[/C][C]-0.2[/C][C]-0.6[/C][C]0.2[/C][C]0.46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97065&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97065&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-0.067-0.4660.3330.917
T-E-0.267-0.6660.1330.255
T-S-0.2-0.60.20.46






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group21.2470.292
87 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 1.247 & 0.292 \tabularnewline
  & 87 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97065&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]1.247[/C][C]0.292[/C][/ROW]
[ROW][C] [/C][C]87[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97065&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97065&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)
Group21.2470.292
87


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 9 ; par2 = 6 ; par3 = TRUE ;
Parameters (R input):
par1 = 9 ; 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')