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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 computationSat, 16 Dec 2017 19:40:20 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/16/t1513449665ceq0grbl2nng4ju.htm/, Retrieved Wed, 15 May 2024 04:22:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309935, Retrieved Wed, 15 May 2024 04:22:31 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact59

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

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2	'H'
7	'T'
0	'E'
0	'E'
7	'T'
0	'T'
5	'E'
0	'E'
6	'H'
0	'H'
30	'E'
5	'T'
10	'H'
0	'H'
5	'S'
5	'S'
7	'H'
0	'H'
3	'H'
0	'H'
30	'E'
0	'E'
3	'H'
7	'T'
0	'H'
10	'H'
5	'T'
4	'T'
0	'T'
4	'H'
5	'T'
0	'T'
0	'H'
16	'H'
0	'M'
6	'M'
7	'T'
4	'T'
0	'T'
3	'H'
4	'H'
0	'H'
3	'H'
0	'H'
3	'T'
6	'T'
22	'E'
8	'T'
5	'T'
3	'H'
0	'M'
6	'T'
4	'H'
4	'H'
7	'T'
7	'H'
5	'H'
41	'T'
4	'H'
0	'H'
8	'T'
3	'H'
3	'T'
10	'H'
10	'T'
5	'M'
1	'T'
4	'M'
50	'T'
0	'T'
11	'M'
43	'M'
20	'E'
3	'M'
3	'H'
4	'T'
2	'H'
5	'H'
23	'T'
6	'T'
5	'H'
43	'M'
5	'H'
7	'E'
48	'T'
4	'M'
3	'M'
75	'T'
40	'T'
10	'T'
8	'M'
34	'T'
0	'H'
7	'T'
23	'E'
9	'T'
5	'T'
0	'T'
0	'M'
14	'M'
9	'H'
7	'H'
5	'T'
3	'H'
52	'M'
9	'H'
0	'H'
43	'T'
7	'H'
0	'H'
6	'H'
41	'E'
9	'T'
1	'T'
135	'E'
10	'H'
0	'H'
45	'E'
25	'T'
5	'H'
4	'T'
4	'H'
10	'T'
0	'H'
5	'T'
0	'T'
5	'T'
20	'E'
0	'E'
60	'T'
0	'T'
30	'T'
0	'T'
50	'T'
0	'T'
42	'E'
42	'E'
22	'M'
12	'M'
0	'E'
9	'T'
7	'T'
20	'E'
3	'M'
9	'T'
0	'T'
4	'T'
5	'T'
10	'T'
79	'M'
30	'M'
60	'M'
0	'M'
19	'M'
0	'M'
40	'E'
8	'T'
0	'H'
7	'H'
7	'E'
15	'S'
2	'T'
10	'H'
0	'H'
50	'M'
1	'M'
1	'M'
50	'M'
2	'T'
0	'E'
40	'E'
4	'M'
8	'M'
15	'M'
0	'H'
0	'H'
10	'H'
75	'T'
6	'H'
5	'S'
2	'H'
2	'H'
7	'M'
9	'T'
0	'T'
4	'H'
3	'H'
21	'E'
0	'E'
35	'M'
25	'E'
0	'E'
60	'E'
52	'M'
5	'H'
5	'H'
4	'H'
0	'H'
125	'T'
20	'T'
0	'T'
4	'T'
0	'T'
5	'M'
45	'E'
5	'T'
3	'S'
0	'S'
18	'T'
40	'E'
32	'M'
7	'S'
0	'S'
66	'M'
8	'H'
0	'H'
24	'M'
8	'M'
26	'H'
27	'E'
28	'M'
11	'H'
5	'E'
9	'H'
9	'T'
5	'H'
20	'E'
0	'E'
7	'T'
6	'T'
4	'T'
10	'T'
8	'M'
8	'T'
0	'T'
26	'T'
4	'T'
4	'T'
0	'M'
0	'E'
7	'T'
40	'E'
5	'H'
0	'H'
5	'H'
41	'E'
54	'M'
0	'M'
3	'T'
3	'H'
48	'M'
0	'M'
34	'M'
10	'H'
3	'H'
0	'H'
10	'H'
0	'H'
8	'H'
5	'H'
3	'H'
0	'H'
5	'H'
8	'H'
17	'H'
9	'T'
0	'H'
8	'T'
21	'T'
8	'T'
9	'T'
21	'E'
13	'H'
0	'S'
8	'S'
5	'H'
0	'H'
3	'T'
14	'H'
0	'H'
7	'S'
8	'S'
40	'M'
8	'S'
6	'S'
10	'T'
25	'E'
1	'H'
44	'M'
2	'H'
60	'E'
2	'E'
3	'E'
21	'E'
7	'H'
4	'E'
0	'T'
20	'M'
0	'M'
60	'M'
4	'T'
70	'M'
0	'M'
6	'T'
0	'H'
10	'T'
48	'T'
2	'H'
5	'H'
14	'T'
2	'M'
3	'T'
8	'M'
3	'H'
0	'M'
7	'M'
5	'H'
5	'H'
0	'H'
8	'T'
10	'S'
10	'M'
2	'S'
0	'H'
2	'H'
8	'H'
5	'T'
9	'T'
7	'T'
2	'S'
10	'T'
0	'H'
6	'H'
4	'H'
10	'M'
8	'H'
4	'H'
4	'H'
4	'H'
18	'M'
24	'E'
39	'M'

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309935&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309935&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309935&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center






ANOVA Model
speed ~ Acctype
means19.533-14.1112.609-13.803-8.016

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
speed  ~  Acctype \tabularnewline
means & 19.533 & -14.111 & 2.609 & -13.803 & -8.016 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309935&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]speed  ~  Acctype[/C][/ROW]
[ROW][C]means[/C][C]19.533[/C][C]-14.111[/C][C]2.609[/C][C]-13.803[/C][C]-8.016[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309935&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309935&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
speed ~ Acctype
means19.533-14.1112.609-13.803-8.016






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Acctype4115795.70528948.926104.060
Residuals2616727756.088278.194 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Acctype & 4 & 115795.705 & 28948.926 & 104.06 & 0 \tabularnewline
Residuals & 2616 & 727756.088 & 278.194 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309935&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]Acctype[/C][C]4[/C][C]115795.705[/C][C]28948.926[/C][C]104.06[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]2616[/C][C]727756.088[/C][C]278.194[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309935&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309935&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)
Acctype4115795.70528948.926104.060
Residuals2616727756.088278.194






Tukey Honest Significant Difference Comparisons
difflwruprp adj
H-E-14.111-17.193-11.0280
M-E2.609-0.7095.9280.201
S-E-13.803-19.09-8.5160
T-E-8.016-11.161-4.8720
M-H16.7214.25619.1840
S-H0.308-4.4895.1041
T-H6.0943.8718.3180
S-M-16.413-21.364-11.4610
T-M-10.626-13.166-8.0850
T-S5.7870.9510.6230.01

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
H-E & -14.111 & -17.193 & -11.028 & 0 \tabularnewline
M-E & 2.609 & -0.709 & 5.928 & 0.201 \tabularnewline
S-E & -13.803 & -19.09 & -8.516 & 0 \tabularnewline
T-E & -8.016 & -11.161 & -4.872 & 0 \tabularnewline
M-H & 16.72 & 14.256 & 19.184 & 0 \tabularnewline
S-H & 0.308 & -4.489 & 5.104 & 1 \tabularnewline
T-H & 6.094 & 3.871 & 8.318 & 0 \tabularnewline
S-M & -16.413 & -21.364 & -11.461 & 0 \tabularnewline
T-M & -10.626 & -13.166 & -8.085 & 0 \tabularnewline
T-S & 5.787 & 0.95 & 10.623 & 0.01 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309935&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]H-E[/C][C]-14.111[/C][C]-17.193[/C][C]-11.028[/C][C]0[/C][/ROW]
[ROW][C]M-E[/C][C]2.609[/C][C]-0.709[/C][C]5.928[/C][C]0.201[/C][/ROW]
[ROW][C]S-E[/C][C]-13.803[/C][C]-19.09[/C][C]-8.516[/C][C]0[/C][/ROW]
[ROW][C]T-E[/C][C]-8.016[/C][C]-11.161[/C][C]-4.872[/C][C]0[/C][/ROW]
[ROW][C]M-H[/C][C]16.72[/C][C]14.256[/C][C]19.184[/C][C]0[/C][/ROW]
[ROW][C]S-H[/C][C]0.308[/C][C]-4.489[/C][C]5.104[/C][C]1[/C][/ROW]
[ROW][C]T-H[/C][C]6.094[/C][C]3.871[/C][C]8.318[/C][C]0[/C][/ROW]
[ROW][C]S-M[/C][C]-16.413[/C][C]-21.364[/C][C]-11.461[/C][C]0[/C][/ROW]
[ROW][C]T-M[/C][C]-10.626[/C][C]-13.166[/C][C]-8.085[/C][C]0[/C][/ROW]
[ROW][C]T-S[/C][C]5.787[/C][C]0.95[/C][C]10.623[/C][C]0.01[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309935&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309935&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
H-E-14.111-17.193-11.0280
M-E2.609-0.7095.9280.201
S-E-13.803-19.09-8.5160
T-E-8.016-11.161-4.8720
M-H16.7214.25619.1840
S-H0.308-4.4895.1041
T-H6.0943.8718.3180
S-M-16.413-21.364-11.4610
T-M-10.626-13.166-8.0850
T-S5.7870.9510.6230.01






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group4124.4170
2616 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 4 & 124.417 & 0 \tabularnewline
  & 2616 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309935&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]4[/C][C]124.417[/C][C]0[/C][/ROW]
[ROW][C] [/C][C]2616[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309935&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309935&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)
Group4124.4170
2616


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = 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){
'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')