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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, 28 Nov 2011 06:33:52 -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/2011/Nov/28/t1322480043k7z0c7i6btieylz.htm/, Retrieved Thu, 31 Oct 2024 22:48:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147655, Retrieved Thu, 31 Oct 2024 22:48:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi Square Measure of Association- Free Statistics Software (Calculator)] [One Way ANOVA wit...] [2009-11-29 13:09:19] [98fd0e87c3eb04e0cc2efde01dbafab6]
-   PD  [Chi Square Measure of Association- Free Statistics Software (Calculator)] [One Way ANOVA for...] [2009-12-01 13:05:10] [3fdd735c61ad38cbc9b3393dc997cdb7]
- R P     [Chi Square Measure of Association- Free Statistics Software (Calculator)] [CARE date with Tu...] [2009-12-01 18:33:48] [98fd0e87c3eb04e0cc2efde01dbafab6]
-   P       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [CARE Data with Tu...] [2010-11-23 12:09:38] [3fdd735c61ad38cbc9b3393dc997cdb7]
- RM          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [IQ and Mothers Age] [2011-11-21 16:34:08] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R PD            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2011-11-28 11:33:52] [5785344b7622c5aa2d3a8a0c8dd90052] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1		NA
1		88
2		94
2		90
2		73
1		68
2		80
2		86
3		86
3		91
1		79
3		96
2		92
3		72
2		96
1		70
2		86
2		87
3		88
2		79
1		90
2		95
2		85
2		NA
2		90
3		115
3		84
2		79
1		94
2		97
2		86
3		111
2		87
3		98
3		87
1		68
1		88
3		82
3		111
2		75
1		94
1		95
1		80
2		95
2		68
1		94
1		88
3		84
2		NA
1		101
2		98
3		78
2		109
2		102
1		81
3		97
3		75
2		97
1		NA
1		101
3		101
2		95
2		95
2		NA
2		95
1		90
2		107
3		92
2		86
1		70
3		95
1		96
2		91
3		87
1		92
3		97
2		102
1		91
2		68
1		88
2		97
2		90
3		101
3		94
3		101
3		109
2		100
2		103
2		94
3		97
1		85
1		75
2		77
1		87
3		78
2		108
2		97
NA		105
2		106
2		107
2		95
3		107
2		115
2		101
3		85
3		90
3		115
2		95
2		97
1		112
3		97
2		77
2		90
2		94
2		103
2		77
1		98
2		90
2		111
1		77
1		88
2		75
3		92
1		78
3		106
1		80
2		87
2		92
3		NA
NA		111
1		86
3		85
2		90
3		101
2		94
2		86
3		86
2		90
3		75
2		86
1		91
2		97
1		91
1		70
1		98
1		96
2		95
2		100
2		95
1		97
3		97
3		92
3		115
2		88
1		87
2		100
3		98
2		102
2		NA
1		96

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147655&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147655&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147655&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 time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






ANOVA Model
WISCRY7V ~ MWARM30
means87.454.8116.5520.55

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
WISCRY7V
  ~  MWARM30 \tabularnewline
means & 87.45 & 4.811 & 6.55 & 20.55 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147655&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]WISCRY7V
  ~  MWARM30[/C][/ROW]
[ROW][C]means[/C][C]87.45[/C][C]4.811[/C][C]6.55[/C][C]20.55[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147655&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147655&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
WISCRY7V ~ MWARM30
means87.454.8116.5520.55






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MWARM3031497.737499.2464.6210.004
Residuals14916099.204108.048 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MWARM30 & 3 & 1497.737 & 499.246 & 4.621 & 0.004 \tabularnewline
Residuals & 149 & 16099.204 & 108.048 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147655&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]MWARM30[/C][C]3[/C][C]1497.737[/C][C]499.246[/C][C]4.621[/C][C]0.004[/C][/ROW]
[ROW][C]Residuals[/C][C]149[/C][C]16099.204[/C][C]108.048[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147655&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147655&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)
MWARM3031497.737499.2464.6210.004
Residuals14916099.204108.048






Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-14.811-0.55610.1780.096
3-16.550.58312.5170.025
NA-120.550.98140.1190.036
3-21.739-3.5477.0250.828
NA-215.739-3.63335.1110.154
NA-314-5.54733.5470.249

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2-1 & 4.811 & -0.556 & 10.178 & 0.096 \tabularnewline
3-1 & 6.55 & 0.583 & 12.517 & 0.025 \tabularnewline
NA-1 & 20.55 & 0.981 & 40.119 & 0.036 \tabularnewline
3-2 & 1.739 & -3.547 & 7.025 & 0.828 \tabularnewline
NA-2 & 15.739 & -3.633 & 35.111 & 0.154 \tabularnewline
NA-3 & 14 & -5.547 & 33.547 & 0.249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147655&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]2-1[/C][C]4.811[/C][C]-0.556[/C][C]10.178[/C][C]0.096[/C][/ROW]
[ROW][C]3-1[/C][C]6.55[/C][C]0.583[/C][C]12.517[/C][C]0.025[/C][/ROW]
[ROW][C]NA-1[/C][C]20.55[/C][C]0.981[/C][C]40.119[/C][C]0.036[/C][/ROW]
[ROW][C]3-2[/C][C]1.739[/C][C]-3.547[/C][C]7.025[/C][C]0.828[/C][/ROW]
[ROW][C]NA-2[/C][C]15.739[/C][C]-3.633[/C][C]35.111[/C][C]0.154[/C][/ROW]
[ROW][C]NA-3[/C][C]14[/C][C]-5.547[/C][C]33.547[/C][C]0.249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147655&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147655&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
2-14.811-0.55610.1780.096
3-16.550.58312.5170.025
NA-120.550.98140.1190.036
3-21.739-3.5477.0250.828
NA-215.739-3.63335.1110.154
NA-314-5.54733.5470.249






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.8530.467
149 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 0.853 & 0.467 \tabularnewline
  & 149 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147655&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]3[/C][C]0.853[/C][C]0.467[/C][/ROW]
[ROW][C] [/C][C]149[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147655&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147655&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)
Group30.8530.467
149


Figures (Output of Computation)

Input Parameters & R Code

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