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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 16:03:14 +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/t1288627333zhhs9ilz1hqgod2.htm/, Retrieved Mon, 29 Apr 2024 09:28:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=90954, Retrieved Mon, 29 Apr 2024 09:28:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188

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]
-   PD  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [question 6 short ...] [2010-10-29 13:20:07] [c1605865773cc027e55b238d879a644c]
-         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-01 09:52:05] [22937c5b58c14f6c22964f32d64ff823]
F   P         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Workshop 5 Questi...] [2010-11-01 16:03:14] [514029464b0621595fe21c9fa38c7009] [Current]
-               [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-02 14:51:29] [5278e0a58c5de897b31ce79607e774d7]
Feedback Forum
2010-11-06 13:45:52 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De student heeft hier met de correcte gegevens een correcte berekening gemaakt, maar er ontbreekt een duidelijke interpretatie van de gegevens.

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.

Vervolgens bekijken we de Tukey Honest Significant Difference Comparisons. Bij de vergelijking van de S- treatment en de E- treatment zien we dat de P – waarde zeer klein is, hierdoor kunnen verwerpen dat beiden aan elkaar gelijk zijn, en we zien dat het gemiddelde van E groter is dan dat van S. De invloed van de E – treatment is dus positiever/ groter dan die van de S – treatment. Bij de vergelijking van de T – treatment en de E – treatment zien we hetzelfde patroon, namelijk de E- treatment die beter is dan de T- treatment.

We kunnen dus besluiten dat op korte termijn de example treatment de beste resultaten oplevert.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	'T'
0	'T'
1	'T'
0	'T'
0	'T'
0	'T'
0	'T'
1	'T'
1	'T'
0	'T'
0	'T'
1	'T'
1	'T'
1	'T'
0	'T'
0	'T'
0	'T'
0	'T'
1	'T'
0	'T'
0	'T'
0	'T'
0	'T'
-1	'T'
0	'T'
-1	'T'
0	'T'
0	'T'
0	'T'
0	'T'
-1	'T'
0	'T'
0	'T'
0	'T'
0	'T'
0	'T'
0	'T'
1	'E'
1	'E'
0	'E'
0	'E'
0	'E'
0	'E'
0	'E'
0	'E'
1	'E'
1	'E'
0	'E'
0	'E'
0	'E'
1	'E'
0	'E'
1	'E'
0	'E'
1	'E'
1	'E'
1	'E'
1	'E'
0	'E'
0	'E'
1	'E'
0	'E'
0	'E'
-1	'E'
0	'E'
1	'E'
1	'E'
0	'E'
0	'E'
0	'E'
1	'S'
1	'S'
1	'S'
1	'S'
0	'S'
-1	'S'
0	'S'
0	'S'
-1	'S'
0	'S'
0	'S'
0	'S'
0	'S'
-1	'S'
0	'S'
0	'S'
-1	'S'
0	'S'
0	'S'
0	'S'
0	'S'
0	'S'
0	'S'
1	'S'
0	'S'
0	'S'
0	'S'
0	'S'
0	'S'
0	'S'
0	'S'
0	'S'
0	'S'
0	'S'
1	'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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90954&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90954&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90954&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






ANOVA Model
post1-pre ~ treatment
means0.364-0.306-0.256

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
post1-pre  ~  treatment \tabularnewline
means & 0.364 & -0.306 & -0.256 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90954&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]post1-pre  ~  treatment[/C][/ROW]
[ROW][C]means[/C][C]0.364[/C][C]-0.306[/C][C]-0.256[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90954&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90954&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-pre ~ treatment
means0.364-0.306-0.256






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
treatment21.8250.9123.1990.045
Residuals10229.090.285 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
treatment & 2 & 1.825 & 0.912 & 3.199 & 0.045 \tabularnewline
Residuals & 102 & 29.09 & 0.285 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90954&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]1.825[/C][C]0.912[/C][C]3.199[/C][C]0.045[/C][/ROW]
[ROW][C]Residuals[/C][C]102[/C][C]29.09[/C][C]0.285[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90954&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90954&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)
treatment21.8250.9123.1990.045
Residuals10229.090.285






Tukey Honest Significant Difference Comparisons
difflwruprp adj
S-E-0.306-0.6150.0020.052
T-E-0.256-0.560.0490.118
T-S0.051-0.2490.350.914

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
S-E & -0.306 & -0.615 & 0.002 & 0.052 \tabularnewline
T-E & -0.256 & -0.56 & 0.049 & 0.118 \tabularnewline
T-S & 0.051 & -0.249 & 0.35 & 0.914 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90954&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]-0.306[/C][C]-0.615[/C][C]0.002[/C][C]0.052[/C][/ROW]
[ROW][C]T-E[/C][C]-0.256[/C][C]-0.56[/C][C]0.049[/C][C]0.118[/C][/ROW]
[ROW][C]T-S[/C][C]0.051[/C][C]-0.249[/C][C]0.35[/C][C]0.914[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90954&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90954&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-0.306-0.6150.0020.052
T-E-0.256-0.560.0490.118
T-S0.051-0.2490.350.914






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group21.110.334
102 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 1.11 & 0.334 \tabularnewline
  & 102 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90954&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]1.11[/C][C]0.334[/C][/ROW]
[ROW][C] [/C][C]102[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90954&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90954&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)
Group21.110.334
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')