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Author*The author of this computation has been verified*
R Software ModuleIan.Hollidayrwasp_One Factor ANOVA.wasp
Title produced by softwareChi Square Measure of Association- Free Statistics Software (Calculator)
Date of computationThu, 10 Dec 2009 14:44:20 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/10/t126045284032nlx4dij1c3238.htm/, Retrieved Thu, 28 Mar 2024 15:39:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65384, Retrieved Thu, 28 Mar 2024 15:39:48 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
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]
-   PD        [Chi Square Measure of Association- Free Statistics Software (Calculator)] [] [2009-12-10 13:44:20] [3011a93587a9e2726436e943c7460ff1] [Current]
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Dataseries X:
88	3
94	3
90	3
73	1
68	2
80	3
86	3
86	2
91	1
79	1
96	3
92	3
72	3
96	1
70	3
86	3
87	3
88	2
79	2
90	1
95	1
85	3	
90	1
115	1
84	3
79	1
94	2
97	3
86	2
111	2
87	3
98	1
87	3
68	3
88	1
82	2
111	1
75	1
94	2
95	1
80	1
95	3
68	2
94	2
88	1
84	1	
101	1
98	2
78	3
109	3
102	3
81	3
97	2
75	2
97	2	
101	3
101	2
95	2
95	2	
95	2
90	3
107	3
92	2
86	2
70	3
95	3
96	2
91	1
87	2
92	2
97	2
102	2
91	3
68	2
88	3
97	3
90	3
101	1
94	1
101	2
109	1
100	3
103	3
94	2
97	3
85	1
75	1
77	2
87	3
78	1
108	3
97	1
105	2
106	2
107	3
95	3
107	1
115	3
101	3
85	3
90	2
115	3
95	3
97	3
112	3
97	1
77	3
90	1
94	3
103	3
77	3	
90	3
111	2
77	3
88	2
75	2
92	2
78	3
106	3
80	1
87	1
92	3	
111	2
86	1
85	3
90	3
101	3
94	2
86	1
86	3
90	3
75	3
86	2
91	3
97	2
91	3
70	3
98	2
96	3
95	3
100	3
95	3
97	1
97	3
92	2
115	3
88	3
87	1
100	1
98	3
	




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65384&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65384&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65384&T=0

As an alternative you can also use a 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'Gwilym Jenkins' @ 72.249.127.135







ANOVA Model
RespVar ~ AgeGroup
means90.9720.6560.817

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
RespVar  ~  AgeGroup \tabularnewline
means & 90.972 & 0.656 & 0.817 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65384&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]RespVar  ~  AgeGroup[/C][/ROW]
[ROW][C]means[/C][C]90.972[/C][C]0.656[/C][C]0.817[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65384&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65384&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Model
RespVar ~ AgeGroup
means90.9720.6560.817







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
AgeGroup216.3248.1620.0690.933
Residuals14717412.85118.455

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
AgeGroup & 2 & 16.324 & 8.162 & 0.069 & 0.933 \tabularnewline
Residuals & 147 & 17412.85 & 118.455 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65384&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]AgeGroup[/C][C]2[/C][C]16.324[/C][C]8.162[/C][C]0.069[/C][C]0.933[/C][/ROW]
[ROW][C]Residuals[/C][C]147[/C][C]17412.85[/C][C]118.455[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65384&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65384&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
AgeGroup216.3248.1620.0690.933
Residuals14717412.85118.455







Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-10.656-5.1666.4770.962
3-10.817-4.4566.0890.929
3-20.161-4.8195.140.997

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2-1 & 0.656 & -5.166 & 6.477 & 0.962 \tabularnewline
3-1 & 0.817 & -4.456 & 6.089 & 0.929 \tabularnewline
3-2 & 0.161 & -4.819 & 5.14 & 0.997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65384&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]0.656[/C][C]-5.166[/C][C]6.477[/C][C]0.962[/C][/ROW]
[ROW][C]3-1[/C][C]0.817[/C][C]-4.456[/C][C]6.089[/C][C]0.929[/C][/ROW]
[ROW][C]3-2[/C][C]0.161[/C][C]-4.819[/C][C]5.14[/C][C]0.997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65384&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65384&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-10.656-5.1666.4770.962
3-10.817-4.4566.0890.929
3-20.161-4.8195.140.997







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group20.1530.859
147

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 0.153 & 0.859 \tabularnewline
  & 147 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65384&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]0.153[/C][C]0.859[/C][/ROW]
[ROW][C] [/C][C]147[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65384&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65384&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group20.1530.859
147



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()
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')