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Author*Unverified author*
R Software ModuleIan.Hollidayrwasp_One Factor ANOVA.wasp
Title produced by softwareOne-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)
Date of computationThu, 25 Nov 2010 11:57:17 +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/25/t12906862762auimjmfqc3obie.htm/, Retrieved Sat, 03 Dec 2022 15:10:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=100830, Retrieved Sat, 03 Dec 2022 15:10:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
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]
-   PD          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [compendium 8.4] [2010-11-25 11:57:17] [15eab2c43e9c2ac9cd9b0628e098f0c4] [Current]
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Dataseries X:
1	1	6	36	88
3	1	8	56	94
3	1	8	48	90
3	1	7	32	73
1	1	5	44	68
2	1	7	39	80
3	1	8	34	86
3	1	9	41	86
2	1	9	50	91
1	1	3	39	79
1	1	9	62	96
3	1	7	52	92
3	1	9	37	72
3	1	8	50	96
1	1	6	41	70
3	1	7	55	86
3	1	8	41	87
3	1	9	56	88
2	1	7	39	79
2	1	6	52	90
1	1	8	46	95
1	1	7	44	85
3	1	8	41	90
1	1	9	50	115
1	1	9	50	84
3	1	7	44	79
1	1	4	52	94
2	1	7	54	97
3	1	7	44	86
2	1	9	52	111
2	1	7	37	87
3	1	9	52	98
1	1	10	50	87
3	1	5	36	68
3	1	6	50	88
1	1	9	52	82
2	1	9	55	111
1	1	8	31	75
1	1	6	36	94
2	1	6	49	95
1	1	5	42	80
1	1	8	37	95
3	1	8	41	68
2	1	5	30	94
2	1	6	52	88
1	1	9	30	84
1	1	4	44	101
1	1	8	66	98
2	1	9	48	78
3	1	7	43	109
3	1	7	57	102
3	1	6	46	81
3	2	9	54	97
2	2	9	48	75
2	2	8	48	97
3	2	6	62	101
3	2	10	58	101
2	2	8	58	95
2	2	7	62	95
3	2	8	46	95
2	2	3	34	90
3	2	8	66	107
3	2	10	52	92
2	2	7	55	86
2	2	5	55	70
3	2	10	57	95
3	2	5	56	96
2	2	8	55	91
1	2	9	56	87
2	2	6	54	92
2	2	9	55	97
2	2	8	46	102
2	2	5	52	91
3	2	8	32	68
2	2	3	44	88
3	2	7	46	97
1	2	8	59	90
3	2	10	46	101
3	2	9	46	94
1	2	10	54	101
1	2	9	66	109
2	2	8	56	100
1	2	8	59	103
3	2	8	57	94
1	2	9	52	97
3	2	4	48	85
2	2	6	44	75
3	2	7	41	77
1	2	4	50	87
1	2	9	48	78
2	2	7	48	108
3	2	8	59	97
3	2	8	46	106
1	2	7	54	107
2	2	7	55	95
2	2	9	54	107
3	2	8	59	115
3	2	8	44	101
1	2	9	54	85
3	2	9	52	90
3	2	10	66	115
3	2	7	44	95
2	2	8	57	97
3	2	5	39	112
3	2	9	60	97
3	2	8	45	77
3	2	7	41	90
1	2	8	50	94
3	2	8	39	103
1	2	7	43	77
3	2	6	48	98
3	2	7	37	90
3	2	7	58	111
2	2	6	46	77
3	2	6	43	88
2	2	7	44	75
3	2	9	34	92
2	2	6	30	78
2	2	10	50	106
2	2	4	39	80
3	2	8	37	87
3	2	7	55	92
3	2	5	39	86
3	2	9	36	85
2	2	8	43	90
1	2	9	50	101
3	2	8	55	94
3	2	8	43	86
3	3	9	60	86
2	3	8	48	90
1	3	9	30	75
3	3	7	43	86
3	3	6	39	91
3	3	8	52	97
2	3	6	39	91
3	3	5	39	70
2	3	3	56	98
3	3	6	59	96
3	3	8	46	95
2	3	7	57	100
3	3	8	50	95
3	3	6	54	97
3	3	9	50	97
3	3	9	60	92
1	3	10	59	115
3	3	7	41	88
2	3	5	48	87
3	3	8	59	100
3	3	9	60	98
1	3	8	56	102
3	3	4	51	96




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100830&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100830&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ANOVA Model
MC30VRB ~ MVRBIQ0
means45.4814.1644.78

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
MC30VRB  ~  MVRBIQ0 \tabularnewline
means & 45.481 & 4.164 & 4.78 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100830&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]MC30VRB  ~  MVRBIQ0[/C][/ROW]
[ROW][C]means[/C][C]45.481[/C][C]4.164[/C][C]4.78[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100830&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100830&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
MC30VRB ~ MVRBIQ0
means45.4814.1644.78







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MVRBIQ02639.163319.5824.6150.011
Residuals14810248.82369.249

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MVRBIQ0 & 2 & 639.163 & 319.582 & 4.615 & 0.011 \tabularnewline
Residuals & 148 & 10248.823 & 69.249 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100830&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]MVRBIQ0[/C][C]2[/C][C]639.163[/C][C]319.582[/C][C]4.615[/C][C]0.011[/C][/ROW]
[ROW][C]Residuals[/C][C]148[/C][C]10248.823[/C][C]69.249[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100830&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100830&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)
MVRBIQ02639.163319.5824.6150.011
Residuals14810248.82369.249







Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-14.1640.6187.710.017
3-14.78-0.1549.7140.06
3-20.616-4.0735.3050.948

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2-1 & 4.164 & 0.618 & 7.71 & 0.017 \tabularnewline
3-1 & 4.78 & -0.154 & 9.714 & 0.06 \tabularnewline
3-2 & 0.616 & -4.073 & 5.305 & 0.948 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100830&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]4.164[/C][C]0.618[/C][C]7.71[/C][C]0.017[/C][/ROW]
[ROW][C]3-1[/C][C]4.78[/C][C]-0.154[/C][C]9.714[/C][C]0.06[/C][/ROW]
[ROW][C]3-2[/C][C]0.616[/C][C]-4.073[/C][C]5.305[/C][C]0.948[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100830&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100830&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
2-14.1640.6187.710.017
3-14.78-0.1549.7140.06
3-20.616-4.0735.3050.948







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group20.0010.999
148

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 0.001 & 0.999 \tabularnewline
  & 148 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100830&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]0.001[/C][C]0.999[/C][/ROW]
[ROW][C] [/C][C]148[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100830&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100830&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)
Group20.0010.999
148



Parameters (Session):
par1 = 4 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 4 ; 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')