Free Statistics

of Irreproducible Research!

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 computationWed, 17 Dec 2014 14:15:44 +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/17/t14188257500oeerj8anntqef7.htm/, Retrieved Thu, 16 May 2024 10:23:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270301, Retrieved Thu, 16 May 2024 10:23:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [I1 B/S] [2014-12-17 14:02:09] [118a39334d200089014f927b57d44a19]
- RMPD    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-12-17 14:15:44] [32fa21232fc319032ead83218c7d01c8] [Current]
Feedback Forum

Post a new message
Dataseries X:
15 1
21 1
30 1
20 1
14 1
18 1
19 1
25 1
23 1
17 1
21 1
21 1
8 1
29 1
20 1
19 1
22 1
23 1
24 1
12 1
22 1
12 1
22 1
20 1
10 1
23 1
17 1
22 1
24 1
18 1
21 1
20 1
20 1
22 1
19 1
20 1
26 1
23 1
24 1
21 1
21 1
19 1
8 1
17 1
20 1
11 1
8 1
15 1
18 1
18 1
19 1
19 1
30 1
17 1
24 1
20 1
25 1
20 1
27 1
18 1
28 1
21 1
27 1
22 1
28 1
25 1
21 1
22 1
28 1
20 1
29 1
20 1
20 1
23 1
18 1
18 1
19 1
25 1
25 1
25 1
24 1
19 1
26 1
10 1
17 1
13 1
17 1
30 1
4 1
16 1
21 1
22 1
20 1
22 1
23 1
16 1
0 1
18 1
25 1
18 1
18 1
24 1
29 1
15 1
22 1
23 1
24 1
22 1
15 1
17 1
20 1
27 1
26 1
23 1
23 1
15 1
26 1
22 1
18 1
15 1
22 1
27 1
10 1
20 1
17 1
23 1
19 1
13 1
27 1
23 1
16 1
25 1
2 1
26 1
20 1
22 1
24 1
21 0
26 0
22 0
22 0
18 0
23 0
12 0
20 0
22 0
21 0
19 0
22 0
15 0
20 0
19 0
18 0
20 0
21 0
15 0
16 0
23 0
21 0
18 0
25 0
9 0
23 0
16 0
16 0
19 0
25 0
25 0
18 0
23 0
21 0
10 0
22 0
26 0
23 0
23 0
24 0
24 0
23 0
15 0
16 0
19 0
18 0
27 0
13 0
28 0
23 0
21 0
19 0
19 0
18 0
19 0
17 0
25 0
19 0
26 0
14 0
16 0
20 0
24 0
23 0
22 0
21 0
25 0
27 0
23 0
23 0
18 0
18 0
23 0
19 0
15 0
20 0
16 0
25 0
25 0
19 0
19 0
16 0
19 0
19 0
23 0
21 0
22 0
19 0
20 0
3 0
23 0
14 0
23 0
20 0
15 0
13 0
16 0
7 0
24 0
17 0
24 0
24 0
19 0
28 0
23 0
19 0
23 0
25 0
25 0
20 0
16 0
20 0
25 0
25 0
23 0
17 0
20 0
16 0
23 0
12 0
24 0
11 0
14 0
23 0
18 0
29 0
16 0
19 0
16 0
23 0
19 0
4 0
20 0
20 0
4 0
24 0
16 0
3 0
24 0
23 0
17 0
20 0
22 0
19 0
24 0
19 0
27 0
22 0
23 0




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=270301&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=270301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270301&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' @ jenkins.wessa.net







ANOVA Model
NUMERACYTOT ~ B/S
means19.7990.391

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
NUMERACYTOT  ~  B/S \tabularnewline
means & 19.799 & 0.391 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270301&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]NUMERACYTOT  ~  B/S[/C][/ROW]
[ROW][C]means[/C][C]19.799[/C][C]0.391[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270301&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
NUMERACYTOT ~ B/S
means19.7990.391







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
B/S110.91910.9190.4140.52
Residuals2847489.02526.37

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
B/S & 1 & 10.919 & 10.919 & 0.414 & 0.52 \tabularnewline
Residuals & 284 & 7489.025 & 26.37 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270301&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]B/S[/C][C]1[/C][C]10.919[/C][C]10.919[/C][C]0.414[/C][C]0.52[/C][/ROW]
[ROW][C]Residuals[/C][C]284[/C][C]7489.025[/C][C]26.37[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270301&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270301&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)
B/S110.91910.9190.4140.52
Residuals2847489.02526.37







Tukey Honest Significant Difference Comparisons
difflwruprp adj
1-00.391-0.8051.5880.52

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
1-0 & 0.391 & -0.805 & 1.588 & 0.52 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270301&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]1-0[/C][C]0.391[/C][C]-0.805[/C][C]1.588[/C][C]0.52[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270301&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270301&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
1-00.391-0.8051.5880.52







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group10.9260.337
284

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 1 & 0.926 & 0.337 \tabularnewline
  & 284 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270301&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]0.926[/C][C]0.337[/C][/ROW]
[ROW][C] [/C][C]284[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270301&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270301&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)
Group10.9260.337
284



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = FALSE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
R code (references can be found in the software module):
par3 <- 'FALSE'
par2 <- '2'
par1 <- '1'
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')