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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 computationMon, 01 Nov 2010 10:02:45 +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/2010/Nov/01/t1288605698dm66xzmdvuomgp7.htm/, Retrieved Mon, 29 Apr 2024 08:40:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=90690, Retrieved Mon, 29 Apr 2024 08:40:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157

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)] [Q7 one-way Anova] [2010-11-01 10:02:45] [dcc54e7e6e8c80b7c45e040080afe6ab] [Current]
-   P       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-02 08:37:24] [049b50ae610f671f7417ed8e2d1295c1]
Feedback Forum
2010-11-06 10:07:02 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De student heeft hier gewerkt met de gegevens van Post 2, maar aangezien er in de opgave gevraagd werd om de invloed te meten zal werken met Post 1 - Pre voor het bepalen van de invloed op korte termijn en met Post 2 - pre voor het bepalen van de invloed op lange termijn.

Voor de korte termijn vindt men hier een voorbeeld berekend met de software: http://www.freestatistics.org/blog/date/2010/Nov/02/t1288707350srramzc62392ojq.htm/

Wanneer we de eerste tabel ANOVA Statistics bekijken, zien we dat de P – waarde zeer klein is, hieruit kunnen we besluiten dat we de nulhypothese –alle treatments zijn aan elkaar gelijk – kunnen verwerpen.

Wanneer we in de Tukey Honest Significant Difference Comparisons de verschillende treatments met elkaar vergelijken, zien we dat bij de vergelijking van CSWE met C de P– waarde zeer klein is. We mogen de nulhypothese dus verwerpen en besluiten dat de CSWE – treatment betere resultaten oplevert. Wanneer we vervolgens WWE vergelijken met CSWE kunnen we vaststellen dat ook hier de P – waarde eerder klein zijn en dat beide treatments dus niet aan elkaar gelijk zijn. We zien (in de kolom differences) dat CSWE betere resultaten oplevert. Wat betreft de korte termijn kunnen we dus concluderen dat CSWE de beste treatment is.

Voor de lange termijn is hier een uitgewerkt voorbeeld te vinden: http://www.freestatistics.org/blog/date/2010/Nov/02/t12887076748bb97nhbvuymoi3.htm/

Hier zien we een grote P- waarde waardoor we de nulhypothese - de invloed van de treatments is niet significant verschillend - mogen aanvaarden. We kunnen - gebaseerd op de gegevens die we hier hebben - dus stellen dat op lange termijn het type treatment dat de student krijgt van minder invloed is op het resultaat.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	'WWE'
0	'WWE'
1	'WWE'
0	'WWE'
1	'WWE'
1	'WWE'
0	'WWE'
1	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
NA	'WWE'
1	'WWE'
NA	'WWE'
0	'WWE'
0	'WWE'
1	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
1	'WWE'
1	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
1	'WWE'
0	'WWE'
0	'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'
0	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
0	'CSWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
1	'CSWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
NA	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'C'
1	'C'
0	'C'
1	'C'
NA	'C'
0	'C'
0	'C'
0	'C'
0	'C'
NA	'C'
0	'C'
0	'C'
0	'C'
0	'C'
0	'C'
0	'C'
0	'C'
NA	'C'
0	'C'
0	'C'
0	'C'
0	'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'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=90690&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=90690&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90690&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
Post2 ~ Treatment
means0.1140.0420.149

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Post2  ~  Treatment \tabularnewline
means & 0.114 & 0.042 & 0.149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90690&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Post2  ~  Treatment[/C][/ROW]
[ROW][C]means[/C][C]0.114[/C][C]0.042[/C][C]0.149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90690&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90690&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
Post2 ~ Treatment
means0.1140.0420.149






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Treatment20.4320.2161.4560.238
Residuals10215.130.148 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Treatment & 2 & 0.432 & 0.216 & 1.456 & 0.238 \tabularnewline
Residuals & 102 & 15.13 & 0.148 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90690&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]0.432[/C][C]0.216[/C][C]1.456[/C][C]0.238[/C][/ROW]
[ROW][C]Residuals[/C][C]102[/C][C]15.13[/C][C]0.148[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90690&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90690&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)
Treatment20.4320.2161.4560.238
Residuals10215.130.148






Tukey Honest Significant Difference Comparisons
difflwruprp adj
CSWE-C0.042-0.1820.2660.897
WWE-C0.149-0.0660.3630.23
WWE-CSWE0.107-0.1130.3270.482

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
CSWE-C & 0.042 & -0.182 & 0.266 & 0.897 \tabularnewline
WWE-C & 0.149 & -0.066 & 0.363 & 0.23 \tabularnewline
WWE-CSWE & 0.107 & -0.113 & 0.327 & 0.482 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90690&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]0.042[/C][C]-0.182[/C][C]0.266[/C][C]0.897[/C][/ROW]
[ROW][C]WWE-C[/C][C]0.149[/C][C]-0.066[/C][C]0.363[/C][C]0.23[/C][/ROW]
[ROW][C]WWE-CSWE[/C][C]0.107[/C][C]-0.113[/C][C]0.327[/C][C]0.482[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90690&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90690&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-C0.042-0.1820.2660.897
WWE-C0.149-0.0660.3630.23
WWE-CSWE0.107-0.1130.3270.482






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group21.4560.238
102 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 1.456 & 0.238 \tabularnewline
  & 102 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90690&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]1.456[/C][C]0.238[/C][/ROW]
[ROW][C] [/C][C]102[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90690&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90690&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)
Group21.4560.238
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')