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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, 17 Nov 2014 20:40:36 +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/Nov/17/t1416256858v4gtp8strm9a1e6.htm/, Retrieved Sun, 19 May 2024 10:43:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=255772, Retrieved Sun, 19 May 2024 10:43:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

Tree of Dependent Computations

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]
- RMPD            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-11-17 20:40:36] [0ce137e93756d78d5d30d64827f474a7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
88	36	3
94	36	1
90	56	3
73	48	3
68	32	3
80	44	1
86	39	2
86	34	3
91	41	3
79	50	2
96	39	1
92	62	1
72	52	3
96	37	3
70	50	3
86	41	1
87	55	3
88	41	3
79	56	3
90	39	2
95	52	2
85	46	1
90	44	1
115	48	3
84	41	3
79	50	1
94	50	1
97	44	3
86	52	1
111	54	2
87	44	3
98	52	2
87	37	2
68	52	3
88	50	1
82	36	3
111	50	3
75	52	1
94	55	2
95	31	1
80	36	1
95	49	2
68	42	1
94	37	1
88	41	3
84	30	2
101	52	2
98	30	1
78	41	1
109	44	1
102	66	1
81	48	2
97	43	3
75	57	3
97	46	3
101	54	3
101	48	2
95	48	2
95	52	2
95	62	3
90	58	3
107	58	2
92	62	2
86	48	2
70	46	3
95	34	2
96	66	3
91	52	3
87	55	2
92	55	2
97	57	3
102	56	3
91	55	2
68	56	1
88	54	2
97	55	2
90	46	2
101	52	2
94	32	3
101	44	2
109	46	3
100	59	1
103	46	3
94	46	3
97	54	1
85	66	1
75	56	2
77	59	1
87	57	3
78	52	1
108	48	3
97	44	2
105	41	3
106	50	1
107	48	1
95	48	2
107	59	3
115	34	1
101	46	3
85	54	1
90	55	2
115	54	2
95	59	3
97	44	3
112	54	1
97	52	3
77	66	3
90	44	3
94	57	2
103	39	3
77	60	3
98	45	3
90	41	3
111	50	1
77	39	3
88	43	1
75	48	3
92	37	3
78	58	3
106	46	2
80	43	3
87	44	2
92	34	3
111	30	2
86	50	2
85	39	2
90	37	3
101	55	3
94	48	1
86	41	1
86	39	3
90	36	3
75	43	2
86	50	1
91	55	3
97	43	3
91	60	3
70	48	2
98	30	1
96	43	3
95	39	3
100	52	3
95	39	2
97	39	3
97	56	2
92	59	3
115	46	3
88	57	2
87	50	3
100	54	3
98	50	3
102	60	3
96	59	1
92	41	3
92	48	2
92	59	3
92	60	3
92	56	1
92	56	1
92	51	3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255772&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255772&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255772&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 time1 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






ANOVA Model
WISCRY7V ~ MOMAGE
means91.051.6090.437

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
WISCRY7V  ~  MOMAGE \tabularnewline
means & 91.05 & 1.609 & 0.437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255772&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]WISCRY7V  ~  MOMAGE[/C][/ROW]
[ROW][C]means[/C][C]91.05[/C][C]1.609[/C][C]0.437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255772&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255772&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
WISCRY7V ~ MOMAGE
means91.051.6090.437






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MOMAGE260.82730.4130.2720.762
Residuals15717536.773111.699 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MOMAGE & 2 & 60.827 & 30.413 & 0.272 & 0.762 \tabularnewline
Residuals & 157 & 17536.773 & 111.699 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255772&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]60.827[/C][C]30.413[/C][C]0.272[/C][C]0.762[/C][/ROW]
[ROW][C]Residuals[/C][C]157[/C][C]17536.773[/C][C]111.699[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255772&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255772&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)
MOMAGE260.82730.4130.2720.762
Residuals15717536.773111.699






Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-11.609-3.8547.0720.766
3-10.437-4.4485.3220.976
3-2-1.172-5.9093.5650.828

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2-1 & 1.609 & -3.854 & 7.072 & 0.766 \tabularnewline
3-1 & 0.437 & -4.448 & 5.322 & 0.976 \tabularnewline
3-2 & -1.172 & -5.909 & 3.565 & 0.828 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255772&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]1.609[/C][C]-3.854[/C][C]7.072[/C][C]0.766[/C][/ROW]
[ROW][C]3-1[/C][C]0.437[/C][C]-4.448[/C][C]5.322[/C][C]0.976[/C][/ROW]
[ROW][C]3-2[/C][C]-1.172[/C][C]-5.909[/C][C]3.565[/C][C]0.828[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255772&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255772&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
2-11.609-3.8547.0720.766
3-10.437-4.4485.3220.976
3-2-1.172-5.9093.5650.828






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group20.830.438
157 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 0.83 & 0.438 \tabularnewline
  & 157 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=255772&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]0.83[/C][C]0.438[/C][/ROW]
[ROW][C] [/C][C]157[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=255772&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=255772&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)
Group20.830.438
157


Figures (Output of Computation)

Input Parameters & R Code

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