Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_One Factor ANOVA.wasp

Title produced by software

One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)

Date of computation

Wed, 30 May 2012 07:26:19 -0400

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/May/30/t13383771972s7pu9ircko59ay.htm/, Retrieved Sat, 04 May 2024 04:23:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=168001, Retrieved Sat, 04 May 2024 04:23:17 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

126

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

-     [Histogram, QQplot and Density] [Female Weights (A...] [2012-05-30 09:42:23] [68da2a3ec537521c4857f5c0bd5ed145]
- RM D  [CARE Data - Boxplots and Scatterplot Matrix] [Weight Data] [2012-05-30 10:56:53] [68da2a3ec537521c4857f5c0bd5ed145]
- RM D    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Actual Vs Reporte...] [2012-05-30 11:25:17] [68da2a3ec537521c4857f5c0bd5ed145]
- R           [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Actual Vs Reporte...] [2012-05-30 11:26:19] [784e4f8c7c58de81885d4525a9d3f3e7] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168001&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168001&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168001&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'AstonUniversity' @ aston.wessa.net

ANOVA Model
weight ~ repwt
means	101	97.5	102	119	39	46	45.667	48	50	50.8	53.2	52.2	53.6	55.2	55.846	55.857	55	56.6	59.25	59.5	61.375	63	64.25	63.833	64	64.714	67	68.571	68.5	69.5	71.2	71.75	73	75.167	76.2	76.75	80	79	80.333	79	83	84	83	82	87.25	88	87	84	90	88	96	96	97

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
weight  ~  repwt \tabularnewline
means & 101 & 97.5 & 102 & 119 & 39 & 46 & 45.667 & 48 & 50 & 50.8 & 53.2 & 52.2 & 53.6 & 55.2 & 55.846 & 55.857 & 55 & 56.6 & 59.25 & 59.5 & 61.375 & 63 & 64.25 & 63.833 & 64 & 64.714 & 67 & 68.571 & 68.5 & 69.5 & 71.2 & 71.75 & 73 & 75.167 & 76.2 & 76.75 & 80 & 79 & 80.333 & 79 & 83 & 84 & 83 & 82 & 87.25 & 88 & 87 & 84 & 90 & 88 & 96 & 96 & 97 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168001&T=1

[TABLE]
[ROW]ANOVA Model[/C][/ROW]
[ROW]weight  ~  repwt[/C][/ROW]
[ROW][C]means[/C][C]101[/C][C]97.5[/C][C]102[/C][C]119[/C][C]39[/C][C]46[/C][C]45.667[/C][C]48[/C][C]50[/C][C]50.8[/C][C]53.2[/C][C]52.2[/C][C]53.6[/C][C]55.2[/C][C]55.846[/C][C]55.857[/C][C]55[/C][C]56.6[/C][C]59.25[/C][C]59.5[/C][C]61.375[/C][C]63[/C][C]64.25[/C][C]63.833[/C][C]64[/C][C]64.714[/C][C]67[/C][C]68.571[/C][C]68.5[/C][C]69.5[/C][C]71.2[/C][C]71.75[/C][C]73[/C][C]75.167[/C][C]76.2[/C][C]76.75[/C][C]80[/C][C]79[/C][C]80.333[/C][C]79[/C][C]83[/C][C]84[/C][C]83[/C][C]82[/C][C]87.25[/C][C]88[/C][C]87[/C][C]84[/C][C]90[/C][C]88[/C][C]96[/C][C]96[/C][C]97[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168001&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168001&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
weight ~ repwt
means	101	97.5	102	119	39	46	45.667	48	50	50.8	53.2	52.2	53.6	55.2	55.846	55.857	55	56.6	59.25	59.5	61.375	63	64.25	63.833	64	64.714	67	68.571	68.5	69.5	71.2	71.75	73	75.167	76.2	76.75	80	79	80.333	79	83	84	83	82	87.25	88	87	84	90	88	96	96	97

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
repwt	53	809871.733	15280.599	2998.199	0
Residuals	127	647.267	5.097

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
repwt & 53 & 809871.733 & 15280.599 & 2998.199 & 0 \tabularnewline
Residuals & 127 & 647.267 & 5.097 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168001&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]repwt[/C][C]53[/C][C]809871.733[/C][C]15280.599[/C][C]2998.199[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]127[/C][C]647.267[/C][C]5.097[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168001&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168001&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
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
repwt	53	809871.733	15280.599	2998.199	0
Residuals	127	647.267	5.097

Must Include Intercept to use Tukey Test

\begin{tabular}{lllllllll}
\hline
Must Include Intercept to use Tukey Test  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168001&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168001&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168001&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Must Include Intercept to use Tukey Test

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	52	1.04	0.42
	127

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 52 & 1.04 & 0.42 \tabularnewline
  & 127 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168001&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]52[/C][C]1.04[/C][C]0.42[/C][/ROW]
[ROW][C] [/C][C]127[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168001&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168001&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
	Df	F value	Pr(>F)
Group	52	1.04	0.42
	127

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 10 ;

Parameters (R input):

par1 = 1 ; par2 = 2 ; par3 = FALSE ;

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code