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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, 21 Nov 2011 11:34:08 -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/21/t1321894084qubiazhu43uk1u2.htm/, Retrieved Thu, 06 Oct 2022 23:46:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145818, Retrieved Thu, 06 Oct 2022 23:46:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsANOVA example
Estimated Impact834
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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-    D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Week 8 Compendium] [2011-11-23 12:03:05] [ed537d185cce52b1ebf7e35bc2be238e]
-    D                  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Week 8 Compendium] [2011-11-23 12:21:21] [ed537d185cce52b1ebf7e35bc2be238e]
-                       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Week 8 Compendium] [2011-11-23 12:24:59] [ed537d185cce52b1ebf7e35bc2be238e]
- RM              [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Week 8 ANOVA] [2011-11-23 22:08:19] [0e3849a58d2010f680210bfa36dadcb4]
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-    D              [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Mothers verbal IQ...] [2011-11-24 13:13:00] [74be16979710d4c4e7c6647856088456]
-    D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [mothers verbal IQ...] [2011-11-24 13:19:02] [74be16979710d4c4e7c6647856088456]
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-    D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [effect of materna...] [2011-11-24 20:36:48] [74be16979710d4c4e7c6647856088456]
- R               [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Question one] [2011-11-24 12:04:25] [57084a6890c0bc671c16163e98194e4e]
- R  D            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Week 8 ANOVA] [2011-11-24 12:08:11] [0e3849a58d2010f680210bfa36dadcb4]
-    D              [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Week 8 ANOVA] [2011-11-25 18:38:05] [74be16979710d4c4e7c6647856088456]
-    D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Week 8 ANOVA] [2011-11-25 19:02:45] [74be16979710d4c4e7c6647856088456]
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-                     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Week 8 ANOVA] [2011-11-25 19:05:42] [74be16979710d4c4e7c6647856088456]
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[Truncated]
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Dataseries X:
36	3
36	1
56	3
48	3
32	3
44	1
39	2
34	3
41	3
50	2
39	1
62	1
52	3
37	3
50	3
41	1
55	3
41	3
56	3
39	2
52	2
46	1
44	1
48	3
41	3
50	1
50	1
44	3
52	1
54	2
44	3
52	2
37	2
52	3
50	1
36	3
50	3
52	1
55	2
31	1
36	1
49	2
42	1
37	1
41	3
30	2
52	2
30	1
41	1
44	1
66	1
48	2
43	3
57	3
46	3
54	3
48	2
48	2
52	2
62	3
58	3
58	2
62	2
48	2
46	3
34	2
66	3
52	3
55	2
55	2
57	3
56	3
55	2
56	1
54	2
55	2
46	2
52	2
32	3
44	2
46	3
59	1
46	3
46	3
54	1
66	1
56	2
59	1
57	3
52	1
48	3
44	2
41	3
50	1
48	1
48	2
59	3
34	1
46	3
54	1
55	2
54	2
59	3
44	3
54	1
52	3
66	3
44	3
57	2
39	3
60	3
45	3
41	3
50	1
39	3
43	1
48	3
37	3
58	3
46	2
43	3
44	2
34	3
30	2
50	2
39	2
37	3
55	3
48	1
41	1
39	3
36	3
43	2
50	1
55	3
43	3
60	3
48	2
30	1
43	3
39	3
52	3
39	2
39	3
56	2
59	3
46	3
57	2
50	3
54	3
50	3
60	3
59	1
41	3
48	2
59	3
60	3
56	1
56	1
51	3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145818&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145818&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145818&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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







ANOVA Model
MC30VRB ~ MOMAGE
means47.80.7680.213

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
MC30VRB  ~  MOMAGE \tabularnewline
means & 47.8 & 0.768 & 0.213 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145818&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]MC30VRB  ~  MOMAGE[/C][/ROW]
[ROW][C]means[/C][C]47.8[/C][C]0.768[/C][C]0.213[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145818&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145818&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 ~ MOMAGE
means47.80.7680.213







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MOMAGE213.7936.8960.0950.909
Residuals15711404.18272.638

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MOMAGE & 2 & 13.793 & 6.896 & 0.095 & 0.909 \tabularnewline
Residuals & 157 & 11404.182 & 72.638 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145818&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]MOMAGE[/C][C]2[/C][C]13.793[/C][C]6.896[/C][C]0.095[/C][C]0.909[/C][/ROW]
[ROW][C]Residuals[/C][C]157[/C][C]11404.182[/C][C]72.638[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145818&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145818&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)
MOMAGE213.7936.8960.0950.909
Residuals15711404.18272.638







Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-10.768-3.6375.1740.911
3-10.213-3.7264.1520.991
3-2-0.555-4.3753.2650.937

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2-1 & 0.768 & -3.637 & 5.174 & 0.911 \tabularnewline
3-1 & 0.213 & -3.726 & 4.152 & 0.991 \tabularnewline
3-2 & -0.555 & -4.375 & 3.265 & 0.937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145818&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]0.768[/C][C]-3.637[/C][C]5.174[/C][C]0.911[/C][/ROW]
[ROW][C]3-1[/C][C]0.213[/C][C]-3.726[/C][C]4.152[/C][C]0.991[/C][/ROW]
[ROW][C]3-2[/C][C]-0.555[/C][C]-4.375[/C][C]3.265[/C][C]0.937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145818&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145818&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
2-10.768-3.6375.1740.911
3-10.213-3.7264.1520.991
3-2-0.555-4.3753.2650.937







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group21.460.235
157

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 1.46 & 0.235 \tabularnewline
  & 157 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145818&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]1.46[/C][C]0.235[/C][/ROW]
[ROW][C] [/C][C]157[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145818&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145818&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)
Group21.460.235
157



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')