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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 18:53:05 -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/t1322524458srstz50s4wxvlvt.htm/, Retrieved Fri, 29 Mar 2024 02:36:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148078, Retrieved Fri, 29 Mar 2024 02:36:27 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84
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)] [iq at 30 months a...] [2011-11-28 23:51:18] [09253b89c68efd7a460a267273a9d6e3]
-   P               [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [mothers iq and ch...] [2011-11-28 23:53:05] [8e78b9caec05a843a8511780bd4770d3] [Current]
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Dataseries X:
2	36	88
2	56	94
3	48	90
1	32	73
1	44	68
2	39	80
2	34	86
1	41	86
3	50	91
1	39	79
3	62	96
2	52	92
1	37	72
2	50	96
2	41	70
2	55	86
1	41	87
3	56	88
2	39	79
1	52	90
1	46	95
1	44	85		
2	41	90
3	50	115
2	50	84
1	44	79
2	52	94
2	54	97
2	44	86
3	52	111
2	37	87
2	52	98
2	50	87
1	36	68
2	50	88
2	52	82
3	55	111
1	31	75
2	36	94
1	49	95
2	42	80
2	37	95
2	41	68
2	30	94
2	52	88
1	30	84		
2	44	101
2	66	98
1	48	78
3	43	109
1	57	102
1	46	81
1	54	97
2	48	75
2	48	97		
1	62	101
1	58	101
2	58	95
2	62	95		
2	46	95
1	34	90
3	66	107
2	52	92
1	55	86
1	55	70
2	57	95
1	56	96
2	55	91
2	56	87
2	54	92
2	55	97
3	46	102
1	52	91
2	32	68
1	44	88
2	46	97
1	59	90
2	46	101
2	46	94
3	54	101
3	66	109
2	56	100
2	59	103
2	57	94
2	52	97
2	48	85
2	44	75
1	41	77
1	50	87
1	48	78
3	48	108
2	59	97
2	34	105
2	46	106
2	54	107
1	55	95
2	54	107
2	59	115
1	44	101
1	54	85
2	52	90
3	66	115
2	44	95
1	57	97
1	39	112
1	60	97
1	45	77
2	41	90
2	50	94
3	39	103
2	43	77
2	48	98
2	37	90
3	58	111
1	46	77
3	43	88
1	44	75
2	34	92
2	30	78
2	50	106
1	39	80
2	37	87
1	55	92		
3	41	111
2	39	86
2	36	85
1	43	90
3	50	101
2	55	94
1	43	86
1	60	86
1	48	90
1	30	75
2	43	86
3	39	91
2	52	97
2	39	91
1	39	70
2	56	98
1	59	96
1	46	95
2	57	100
1	50	95
2	54	97
1	50	97
2	60	92
3	59	115
3	41	88
2	48	87
2	59	100
2	60	98
1	56	102
1	51	96




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'AstonUniversity' @ aston.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 & 2 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.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=148078&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]'AstonUniversity' @ aston.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=148078&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148078&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'AstonUniversity' @ aston.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 ~ MVRBIQ0
means87.0754.61715.697

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
WISCRY7V  ~  MVRBIQ0 \tabularnewline
means & 87.075 & 4.617 & 15.697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148078&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]WISCRY7V  ~  MVRBIQ0[/C][/ROW]
[ROW][C]means[/C][C]87.075[/C][C]4.617[/C][C]15.697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148078&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148078&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
WISCRY7V ~ MVRBIQ0
means87.0754.61715.697







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MVRBIQ023830.7641915.38220.8710
Residuals15013766.17791.775

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MVRBIQ0 & 2 & 3830.764 & 1915.382 & 20.871 & 0 \tabularnewline
Residuals & 150 & 13766.177 & 91.775 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148078&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]3830.764[/C][C]1915.382[/C][C]20.871[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]150[/C][C]13766.177[/C][C]91.775[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148078&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148078&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)
MVRBIQ023830.7641915.38220.8710
Residuals15013766.17791.775







Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-14.6170.588.6540.021
3-115.6979.94621.4490
3-211.085.60616.5550

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2-1 & 4.617 & 0.58 & 8.654 & 0.021 \tabularnewline
3-1 & 15.697 & 9.946 & 21.449 & 0 \tabularnewline
3-2 & 11.08 & 5.606 & 16.555 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148078&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]4.617[/C][C]0.58[/C][C]8.654[/C][C]0.021[/C][/ROW]
[ROW][C]3-1[/C][C]15.697[/C][C]9.946[/C][C]21.449[/C][C]0[/C][/ROW]
[ROW][C]3-2[/C][C]11.08[/C][C]5.606[/C][C]16.555[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148078&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148078&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
2-14.6170.588.6540.021
3-115.6979.94621.4490
3-211.085.60616.5550







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group21.5810.209
150

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 1.581 & 0.209 \tabularnewline
  & 150 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148078&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]1.581[/C][C]0.209[/C][/ROW]
[ROW][C] [/C][C]150[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148078&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148078&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)
Group21.5810.209
150



Parameters (Session):
par1 = 3 ; par2 = 1 ; par3 = TRUE ;
Parameters (R input):
par1 = 3 ; 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')