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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 computationSun, 16 Dec 2012 08:41:10 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t13556656759f2pwji4fpob028.htm/, Retrieved Tue, 10 Feb 2026 11:39:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200349, Retrieved Tue, 10 Feb 2026 11:39:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact345

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)] [Paper 5 ANOVA T20] [2012-12-16 13:18:59] [b55499be1096a6a81b713a3785359e21]
- R P     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Paper 5 ANOVA T40] [2012-12-16 13:41:10] [b126d3b292555ea554033ae826bcef2a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
'Yes'	'Treatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'Treatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'Yes'	'Treatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'UsedStats'	0	'Yes'	'Bad'
'Yes'	'Treatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'UsedStats'	0	'Yes'	'Good'
'No'	'Treatment'	NA	'UsedStats'	0	'Yes'	'Good'
'Yes'	'Treatment'	NA	'UsedStats'	1	'Yes'	'Bad'
'Yes'	'Treatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'Treatment'	NA	'UsedStats'	1	'Yes'	'Good'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Bad'
'Yes'	'NoTreatment'	NA	'UsedStats'	0	'Yes'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Good'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Good'
'No'	'Treatment'	NA	'UsedStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	0	'Yes'	'Bad'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Bad'
'No'	'Treatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'Yes'	'Treatment'	NA	'UsedStats'	0	'Yes'	'Bad'
'No'	'NoTreatment'	NA	'UsedStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Good'
'No'	'Treatment'	NA	'NoStats'	0	'Yes'	'Bad'
'No'	'NoTreatment'	NA	'UsedStats'	1	'Yes'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	0	'No'	'Good'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Good'
'Yes'	'Treatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'Treatment'	NA	'UsedStats'	0	'No'	'Bad'
'Yes'	'Treatment'	NA	'UsedStats'	1	'Yes'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	1	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'Treatment'	NA	'UsedStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	0	'Yes'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'Yes'	'Treatment'	NA	'UsedStats'	1	'Yes'	'Good'
'Yes'	'Treatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	0	'Yes'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'Yes'	'Treatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'Treatment'	NA	'UsedStats'	1	'Yes'	'Bad'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	0	'No'	'Good'
'Yes'	'NoTreatment'	NA	'UsedStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'Treatment'	NA	'NoStats'	0	'Yes'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'UsedStats'	0	'Yes'	'Good'
'No'	'Treatment'	NA	'UsedStats'	1	'No'	'Good'
'No'	'Treatment'	NA	'NoStats'	0	'Yes'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'Yes'	'NoTreatment'	NA	'UsedStats'	0	'No'	'Good'
'No'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'No'	'NoTreatment'	NA	'UsedStats'	1	'No'	'Bad'
'No'	'NoTreatment'	NA	'NoStats'	0	'Yes'	'Good'
'Yes'	'NoTreatment'	NA	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'Yes'	NA	'Treatment'	'UsedStats'	0	'No'	'Good'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'No'	NA	'NoTreatment'	'NoStats'	0	'Yes'	'Bad'
'Yes'	NA	'Treatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'Yes'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'Treatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'Yes'	NA	'Treatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'Treatment'	'UsedStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'Treatment'	'UsedStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'Treatment'	'UsedStats'	0	'Yes'	'Bad'
'No'	NA	'Treatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'UsedStats'	0	'No'	'Bad'
'Yes'	NA	'Treatment'	'UsedStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'Treatment'	'UsedStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'UsedStats'	0	'Yes'	'Good'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'No'	NA	'Treatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'Yes'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'Yes'	NA	'NoTreatment'	'UsedStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'UsedStats'	0	'Yes'	'Good'
'Yes'	NA	'Treatment'	'UsedStats'	0	'Yes'	'Good'
'No'	NA	'Treatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'UsedStats'	1	'No'	'Good'
'No'	NA	'Treatment'	'UsedStats'	0	'No'	'Good'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'Yes'	'Good'
'No'	NA	'NoTreatment'	'NoStats'	0	'Yes'	'Bad'
'No'	NA	'Treatment'	'NoStats'	0	'No'	'Good'
'No'	NA	'Treatment'	'UsedStats'	0	'No'	'Bad'
'No'	NA	'Treatment'	'NoStats'	0	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'NoStats'	0	'No'	'Bad'
'No'	NA	'NoTreatment'	'NoStats'	0	'Yes'	'Good'
'No'	NA	'NoTreatment'	'NoStats'	0	'No'	'Good'
'Yes'	NA	'NoTreatment'	'UsedStats'	1	'No'	'Bad'
'Yes'	NA	'NoTreatment'	'UsedStats'	1	'Yes'	'Bad'
'Yes'	NA	'NoTreatment'	'UsedStats'	0	'No'	'Bad'

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'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200349&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200349&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200349&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'Herman Ole Andreas Wold' @ wold.wessa.net






ANOVA Model
CorrectAnalysis ~ T40
means0.0440.0040.217

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
CorrectAnalysis  ~  T40 \tabularnewline
means & 0.044 & 0.004 & 0.217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200349&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]CorrectAnalysis  ~  T40[/C][/ROW]
[ROW][C]means[/C][C]0.044[/C][C]0.004[/C][C]0.217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200349&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200349&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
CorrectAnalysis ~ T40
means0.0440.0040.217






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
T4020.9050.4536.7280.002
Residuals15110.160.067 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
T40 & 2 & 0.905 & 0.453 & 6.728 & 0.002 \tabularnewline
Residuals & 151 & 10.16 & 0.067 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200349&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]T40[/C][C]2[/C][C]0.905[/C][C]0.453[/C][C]6.728[/C][C]0.002[/C][/ROW]
[ROW][C]Residuals[/C][C]151[/C][C]10.16[/C][C]0.067[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200349&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200349&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)
T4020.9050.4536.7280.002
Residuals15110.160.067






Tukey Honest Significant Difference Comparisons
difflwruprp adj
NoTreatment-NA0.004-0.1040.1110.997
Treatment-NA0.2170.0690.3650.002
Treatment-NoTreatment0.2130.0640.3630.003

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
NoTreatment-NA & 0.004 & -0.104 & 0.111 & 0.997 \tabularnewline
Treatment-NA & 0.217 & 0.069 & 0.365 & 0.002 \tabularnewline
Treatment-NoTreatment & 0.213 & 0.064 & 0.363 & 0.003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200349&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]NoTreatment-NA[/C][C]0.004[/C][C]-0.104[/C][C]0.111[/C][C]0.997[/C][/ROW]
[ROW][C]Treatment-NA[/C][C]0.217[/C][C]0.069[/C][C]0.365[/C][C]0.002[/C][/ROW]
[ROW][C]Treatment-NoTreatment[/C][C]0.213[/C][C]0.064[/C][C]0.363[/C][C]0.003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200349&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200349&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
NoTreatment-NA0.004-0.1040.1110.997
Treatment-NA0.2170.0690.3650.002
Treatment-NoTreatment0.2130.0640.3630.003






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group26.7280.002
151 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 6.728 & 0.002 \tabularnewline
  & 151 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200349&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]6.728[/C][C]0.002[/C][/ROW]
[ROW][C] [/C][C]151[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200349&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200349&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)
Group26.7280.002
151


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 5 ; 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){
'Tukey Plot'
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<-leveneTest(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')