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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 computationSat, 30 Oct 2010 13:41:06 +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/Oct/30/t1288446031rubcgr1heoo251r.htm/, Retrieved Thu, 02 May 2024 13:55:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=90362, Retrieved Thu, 02 May 2024 13:55:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171

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)] [Golfballs] [2010-10-25 12:27:51] [b98453cac15ba1066b407e146608df68]
F   PD    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Experiment 2 - Q7] [2010-10-30 13:41:06] [a948b7c78e10e31abd3f68e640bbd8ba] [Current]
-           [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [workshop 5 questi...] [2010-10-30 19:29:09] [87d60b8864dc39f7ed759c345edfb471]
-             [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [ws 5 vraag 7] [2010-11-01 15:14:50] [af8eb90b4bf1bcfcc4325c143dbee260]
- R P           [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-02 16:47:18] [c2a9e95daa10045f9fd6252038bcb219]
-   P             [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [ws 5 task 7 - Cha...] [2010-11-02 22:15:00] [8214fe6d084e5ad7598b249a26cc9f06]
- R P         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [WS 5 Vraag 7] [2010-11-03 00:42:07] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2010-11-08 18:46:38 [] [reply] 
Voor deze vraag heb je de verkeerde gegevens gebruikt. Deze vraag heb ik opgelost aan de hand van het berekenen van een korte termijn en een lange termijn effect. Voor de korte termijn resultaten heb ik de data kolom treatment en kolom post 1 - pre gebruikt. En voor de lange termijn de data kolom treatment en kolom post 2 - pre. Zo kom je dan tot de conclusie dat er een positief verschil is tussen CSWE en C op korte termijn. En op lange termijn, moeten we telkens de nulhypothese aanvaarden en concluderen dat er op lange termijn geen significante verschillen zijn tussen de testen.
2010-11-15 20:30:28 [aa73f869d2b3fd095fa61a7cff2af29f] [reply] 
Om verder te bouwen op de feedback van de vorige studente: Op LT is er dus geen effect en de reden hiervoor is dat gemotiveerde studenten zelf op zoek gaan naar de oplossing van de test aan de hand van andere bronnen dan de toegewezen treatment.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	'WWE'
0	'WWE'
2	'WWE'
0	'WWE'
2	'WWE'
1	'WWE'
0	'WWE'
2	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
NA	'WWE'
1	'WWE'
NA	'WWE'
-1	'WWE'
0	'WWE'
1	'WWE'
1	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
1	'WWE'
2	'WWE'
2	'WWE'
0	'WWE'
0	'WWE'
1	'WWE'
1	'WWE'
1	'WWE'
1	'WWE'
1	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
NA	'WWE'
1	'WWE'
0	'WWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
NA	'CSWE'
1	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
1	'CSWE'
0	'CSWE'
NA	'CSWE'
1	'CSWE'
NA	'CSWE'
0	'CSWE'
1	'CSWE'
NA	'CSWE'
1	'CSWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
1	'CSWE'
0	'CSWE'
1	'CSWE'
1	'CSWE'
1	'CSWE'
0	'CSWE'
1	'CSWE'
2	'CSWE'
1	'CSWE'
NA	'CSWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
1	'CSWE'
0	'C'
1	'C'
-1	'C'
1	'C'
NA	'C'
0	'C'
0	'C'
1	'C'
0	'C'
NA	'C'
0	'C'
1	'C'
1	'C'
0	'C'
0	'C'
0	'C'
0	'C'
NA	'C'
0	'C'
1	'C'
0	'C'
1	'C'
0	'C'
0	'C'
0	'C'
0	'C'
0	'C'
0	'C'
0	'C'
0	'C'
0	'C'
1	'C'
0	'C'
0	'C'
0	'C'
1	'C'
0	'C'
NA	'C'
0	'C'

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=90362&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=90362&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90362&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
Dist ~ Brand
means0.2290.3030.298

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Dist  ~  Brand \tabularnewline
means & 0.229 & 0.303 & 0.298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90362&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Dist  ~  Brand[/C][/ROW]
[ROW][C]means[/C][C]0.229[/C][C]0.303[/C][C]0.298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90362&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90362&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
Dist ~ Brand
means0.2290.3030.298






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Brand22.11.052.7040.072
Residuals10239.6140.388 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Brand & 2 & 2.1 & 1.05 & 2.704 & 0.072 \tabularnewline
Residuals & 102 & 39.614 & 0.388 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90362&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]Brand[/C][C]2[/C][C]2.1[/C][C]1.05[/C][C]2.704[/C][C]0.072[/C][/ROW]
[ROW][C]Residuals[/C][C]102[/C][C]39.614[/C][C]0.388[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90362&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90362&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)
Brand22.11.052.7040.072
Residuals10239.6140.388






Tukey Honest Significant Difference Comparisons
difflwruprp adj
CSWE-C0.303-0.060.6650.121
WWE-C0.298-0.050.6450.108
WWE-CSWE-0.005-0.3610.3510.999

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
CSWE-C & 0.303 & -0.06 & 0.665 & 0.121 \tabularnewline
WWE-C & 0.298 & -0.05 & 0.645 & 0.108 \tabularnewline
WWE-CSWE & -0.005 & -0.361 & 0.351 & 0.999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90362&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]CSWE-C[/C][C]0.303[/C][C]-0.06[/C][C]0.665[/C][C]0.121[/C][/ROW]
[ROW][C]WWE-C[/C][C]0.298[/C][C]-0.05[/C][C]0.645[/C][C]0.108[/C][/ROW]
[ROW][C]WWE-CSWE[/C][C]-0.005[/C][C]-0.361[/C][C]0.351[/C][C]0.999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90362&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90362&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
CSWE-C0.303-0.060.6650.121
WWE-C0.298-0.050.6450.108
WWE-CSWE-0.005-0.3610.3510.999






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group23.2760.042
102 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 3.276 & 0.042 \tabularnewline
  & 102 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90362&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]3.276[/C][C]0.042[/C][/ROW]
[ROW][C] [/C][C]102[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90362&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90362&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)
Group23.2760.042
102


Figures (Output of Computation)

Input Parameters & R Code

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