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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 computationTue, 02 Nov 2010 15:38:59 +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/02/t1288712451ol50eytrnc88a7g.htm/, Retrieved Sun, 28 Apr 2024 10:46:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=91720, Retrieved Sun, 28 Apr 2024 10:46:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-02 15:38:59] [33ba4313a043c7c916d0d88da7cd101b] [Current]
Feedback Forum
2010-11-09 19:47:05 [23c3e34d843bca32d327eaf7dc6bdb2b] [reply] 
CSWE is de beste behandeling op korte termijn.

Er is geen effect op lange termijn omdat actieve studenten statistieke concepten toch kunnen leren maar het duurt gewoon langer. Inactieve studenten vergeten het wat de behandeling ook was.

De control C-group verbetert zijn resultaten beetje bij beetje op lange termijn. Dit was niet het geval voor CSWE. Daarom is CSWE-C negatief. Als het verschil hiervan significant was, dan zouden we een sterk catch-up effect hebben gehad.Hetzelfde voor het verschil tussen WWW-CSWE.

Post a new message

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'
1	'WWE'
-1	'WWE'
0	'WWE'
1	'WWE'
0	'WWE'
0	'WWE'
-1	'WWE'
0	'WWE'
0	'WWE'
1	'WWE'
1	'WWE'
0	'WWE'
-1	'WWE'
0	'WWE'
0	'WWE'
1	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
0	'WWE'
-1	'WWE'
-1	'WWE'
0	'WWE'
1	'WWE'
0	'WWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
0	'CSWE'
-1	'CSWE'
1	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
-1	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
1	'CSWE'
0	'CSWE'
0	'CSWE'
-1	'CSWE'
1	'CSWE'
1	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'CSWE'
0	'C'
1	'C'
-1	'C'
1	'C'
0	'C'
0	'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'
0	'C'
0	'C'
0	'C'
0	'C'
-1	'C'
1	'C'
0	'C'
0	'C'
0	'C'
1	'C'
0	'C'
-1	'C'

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 3 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91720&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91720&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91720&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 time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






ANOVA Model
Response ~ Treatment
means-0.0570.120.189

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response  ~  Treatment \tabularnewline
means & -0.057 & 0.12 & 0.189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91720&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response  ~  Treatment[/C][/ROW]
[ROW][C]means[/C][C]-0.057[/C][C]0.12[/C][C]0.189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91720&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91720&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
Response ~ Treatment
means-0.0570.120.189






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Treatment20.6590.331.0470.355
Residuals10232.1030.315 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Treatment & 2 & 0.659 & 0.33 & 1.047 & 0.355 \tabularnewline
Residuals & 102 & 32.103 & 0.315 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91720&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]0.659[/C][C]0.33[/C][C]1.047[/C][C]0.355[/C][/ROW]
[ROW][C]Residuals[/C][C]102[/C][C]32.103[/C][C]0.315[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91720&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91720&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)
Treatment20.6590.331.0470.355
Residuals10232.1030.315






Tukey Honest Significant Difference Comparisons
difflwruprp adj
CSWE-C0.12-0.2070.4460.659
WWE-C0.189-0.1240.5010.326
WWE-CSWE0.069-0.2510.3890.865

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
CSWE-C & 0.12 & -0.207 & 0.446 & 0.659 \tabularnewline
WWE-C & 0.189 & -0.124 & 0.501 & 0.326 \tabularnewline
WWE-CSWE & 0.069 & -0.251 & 0.389 & 0.865 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91720&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]0.12[/C][C]-0.207[/C][C]0.446[/C][C]0.659[/C][/ROW]
[ROW][C]WWE-C[/C][C]0.189[/C][C]-0.124[/C][C]0.501[/C][C]0.326[/C][/ROW]
[ROW][C]WWE-CSWE[/C][C]0.069[/C][C]-0.251[/C][C]0.389[/C][C]0.865[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91720&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91720&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-C0.12-0.2070.4460.659
WWE-C0.189-0.1240.5010.326
WWE-CSWE0.069-0.2510.3890.865






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group20.9340.396
102 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 0.934 & 0.396 \tabularnewline
  & 102 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91720&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]0.934[/C][C]0.396[/C][/ROW]
[ROW][C] [/C][C]102[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91720&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91720&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)
Group20.9340.396
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')