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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, 15 Dec 2014 19:37:37 +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/2014/Dec/15/t1418672272qjo8x08jhnuew3p.htm/, Retrieved Thu, 16 May 2024 16:51:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268948, Retrieved Thu, 16 May 2024 16:51:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact54

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-15 19:37:37] [61a57b1a717662ce9f6e819e563a5fa9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
'Female' 26
'Female' 37
'Male' 67
'Male' 43
'Male' 52
'Female' 52
'Male' 43
'Male' 84
'Male' 67
'Male' 49
'Male' 70
'Female' 58
'Female' 68
'Female' 62
'Male' 43
'Female' 56
'Female' 74
'Male' 63
'Female' 58
'Male' 63
'Male' 53
'Male' 57
'Male' 64
'Female' 53
'Female' 29
'Female' 54
'Male' 58
'Male' 51
'Female' 54
'Male' 56
'Female' 47
'Male' 50
'Male' 35
'Male' 30
'Female' 68
'Male' 56
'Male' 43
'Male' 67
'Male' 62
'Male' 57
'Male' 54
'Male' 61
'Female' 56
'Female' 41
'Female' 53
'Male' 46
'Female' 51
'Female' 37
'Female' 42
'Male' 38
'Female' 66
'Male' 53
'Female' 49
'Female' 49
'Male' 59
'Female' 40
'Female' 63
'Male' 34
'Female' 32
'Female' 67
'Male' 61
'Female' 60
'Female' 63
'Male' 52
'Male' 16
'Male' 46
'Male' 56
'Female' 52
'Male' 55
'Male' 50
'Female' 59
'Male' 60
'Female' 52
'Female' 44
'Male' 67
'Male' 52
'Male' 55
'Male' 37
'Male' 54
'Male' 72
'Male' 51
'Male' 48
'Female' 60
'Male' 50
'Male' 63
'Male' 33
'Male' 67
'Male' 46
'Male' 54
'Female' 59
'Male' 61
'Male' 33
'Male' 47
'Male' 69
'Male' 52
'Female' 55
'Female' 41
'Male' 73
'Female' 52
'Female' 50
'Male' 51
'Female' 60
'Male' 56
'Male' 56
'Female' 29
'Male' 66
'Male' 66
'Male' 73
'Female' 55
'Female' 64
'Female' 40
'Female' 46
'Male' 58
'Female' 43
'Male' 61
'Female' 51
'Male' 50
'Female' 52
'Male' 54
'Female' 66
'Female' 61
'Male' 80
'Female' 51
'Male' 56
'Male' 56
'Male' 56
'Male' 53
'Male' 47
'Female' 25
'Male' 47
'Female' 46
'Female' 50
'Female' 39
'Male' 51
'Female' 58
'Male' 35
'Female' 58
'Female' 60
'Female' 62
'Female' 63
'Male' 53
'Male' 46
'Male' 67
'Male' 59
'Female' 64
'Female' 38
'Male' 50
'Female' 48
'Female' 48
'Female' 47
'Female' 66
'Male' 47
'Male' 63
'Female' 58
'Female' 44
'Male' 51
'Female' 43
'Male' 55
'Male' 38
'Female' 45
'Male' 50
'Male' 54
'Male' 57
'Female' 60
'Female' 55
'Female' 56
'Male' 49
'Male' 37
'Male' 59
'Male' 46
'Female' 51
'Female' 58
'Female' 64
'Male' 53
'Male' 48
'Female' 51
'Female' 47
'Female' 59
'Male' 62
'Male' 62
'Female' 51
'Female' 64
'Female' 52
'Male' 67
'Male' 50
'Male' 54
'Male' 58
'Female' 56
'Male' 63
'Male' 31
'Male' 65
'Female' 71
'Female' 50
'Male' 57
'Female' 47
'Male' 47
'Male' 57
'Female' 43
'Male' 41
'Female' 63
'Male' 63
'Male' 56
'Female' 51
'Male' 50
'Female' 22
'Male' 41
'Female' 59
'Male' 56
'Female' 66
'Female' 53
'Male' 42
'Male' 52
'Female' 54
'Male' 44
'Male' 62
'Female' 53
'Male' 50
'Female' 36
'Female' 76
'Male' 66
'Male' 62
'Female' 59
'Male' 47
'Female' 55
'Female' 58
'Male' 60
'Female' 44
'Female' 57
'Male' 45

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268948&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268948&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268948&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 time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






ANOVA Model
AMS.I ~ gender
means52.5241.222

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
AMS.I  ~  gender \tabularnewline
means & 52.524 & 1.222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268948&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]AMS.I  ~  gender[/C][/ROW]
[ROW][C]means[/C][C]52.524[/C][C]1.222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268948&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268948&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
AMS.I ~ gender
means52.5241.222






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
gender184.59584.5950.7570.385
Residuals22725357.562111.707 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
gender & 1 & 84.595 & 84.595 & 0.757 & 0.385 \tabularnewline
Residuals & 227 & 25357.562 & 111.707 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268948&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]gender[/C][C]1[/C][C]84.595[/C][C]84.595[/C][C]0.757[/C][C]0.385[/C][/ROW]
[ROW][C]Residuals[/C][C]227[/C][C]25357.562[/C][C]111.707[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268948&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268948&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)
gender184.59584.5950.7570.385
Residuals22725357.562111.707






Tukey Honest Significant Difference Comparisons
difflwruprp adj
Male-Female1.222-1.5453.9880.385

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
Male-Female & 1.222 & -1.545 & 3.988 & 0.385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268948&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]Male-Female[/C][C]1.222[/C][C]-1.545[/C][C]3.988[/C][C]0.385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268948&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268948&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
Male-Female1.222-1.5453.9880.385






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group10.0160.899
227 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 1 & 0.016 & 0.899 \tabularnewline
  & 227 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268948&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]1[/C][C]0.016[/C][C]0.899[/C][/ROW]
[ROW][C] [/C][C]227[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268948&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268948&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)
Group10.0160.899
227


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){
'Tukey Plot'
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<-leveneTest(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')