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

Date of computation

Thu, 20 Dec 2012 18:14:51 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t13560453038hfyl06j45s4r2c.htm/, Retrieved Fri, 26 Apr 2024 00:18:49 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203205, Retrieved Fri, 26 Apr 2024 00:18:49 +0000

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Estimated Impact

126

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)] [workshop 5 taak 6 a] [2012-10-27 12:19:51] [dbae308bdff61c0f4902cc85498d0d35]
- R     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [WS 5 Q6] [2012-10-30 09:16:55] [8c30f4dd45e15fd207e4faf2fdf6253e]
- RM        [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-20 23:14:51] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Histogram

Boxplots

1	1	4	0	2	'T'	0	3	-1	1	4
1	1	0	0	2	'T'	0	-1	-1	1	0
0	1	4	1	1.5	'T'	1	4	1	1.5	5
0	0	0	0	0	'T'	0	0	0	0	0
1	1	0	1	1	'T'	0	-1	0	0	0
1	1	0	1	2	'T'	0	-1	0	1	0
1	1	0	1	2	'T'	0	-1	0	1	0
0	1	0	1	1	'T'	1	0	1	1	1
0	1	4	1	2	'T'	1	4	1	2	5
1	1	1	0	2	'T'	0	0	-1	1	1
0	0	4	0	2	'T'	0	4	0	2	4
0	1	0	1	0	'T'	1	0	1	0	1
0	1	2	1	0	'T'	1	2	1	0	3
0	1	0	0	2	'T'	1	0	0	2	1
0	0	0	NA	NA	'T'	0	0	NA	NA	0
1	1	0	1	2	'T'	0	-1	0	1	0
1	1	1	0	2	'T'	0	0	-1	1	1
1	1	0	1	0.5	'T'	0	-1	0	-0.5	0
0	1	0	1	2	'T'	1	0	1	2	1
0	0	2	1	0	'T'	0	2	1	0	2
1	1	2	1	2	'T'	0	1	0	1	2
1	1	1	0	0	'T'	0	0	-1	-1	1
0	0	2	NA	NA	'T'	0	2	NA	NA	2
1	0	0	NA	NA	'T'	-1	-1	NA	NA	-1
1	1	3	1	2	'T'	0	2	0	1	3
1	0	0	1	0	'T'	-1	-1	0	-1	-1
1	1	0	NA	NA	'T'	0	-1	NA	NA	0
0	0	0	NA	NA	'T'	0	0	NA	NA	0
0	0	1	0	2	'T'	0	1	0	2	1
1	1	0	1	1	'T'	0	-1	0	0	0
1	0	0	0	0.5	'T'	-1	-1	-1	-0.5	-1
1	1	4	0	2	'T'	0	3	-1	1	4
0	0	0	1	0.5	'T'	0	0	1	0.5	0
0	0	1	NA	NA	'T'	0	1	NA	NA	1
0	0	0	1	0.5	'T'	0	0	1	0.5	0
1	1	0	NA	NA	'T'	0	-1	NA	NA	0
1	1	4	0	2	'T'	0	3	-1	1	4
0	1	1	1	0	'E'	1	1	1	0	2
0	1	0	1	1	'E'	1	0	1	1	1
1	1	4	1	2	'E'	0	3	0	1	4
1	1	0	1	1	'E'	0	-1	0	0	0
1	1	4	1	2	'E'	0	3	0	1	4
1	1	0	0	0	'E'	0	-1	-1	-1	0
1	1	0	1	0.5	'E'	0	-1	0	-0.5	0
0	0	0	1	0	'E'	0	0	1	0	0
0	1	4	1	2	'E'	1	4	1	2	5
0	1	0	0	0	'E'	1	0	0	0	1
1	1	0	0	1	'E'	0	-1	-1	0	0
1	1	4	1	2	'E'	0	3	0	1	4
0	0	4	0	0.5	'E'	0	4	0	0.5	4
0	1	0	1	2	'E'	1	0	1	2	1
1	1	1	1	2	'E'	0	0	0	1	1
0	1	0	1	2	'E'	1	0	1	2	1
0	0	4	NA	NA	'E'	0	4	NA	NA	4
0	1	0	0	0	'E'	1	0	0	0	1
0	1	2	1	0	'E'	1	2	1	0	3
0	1	0	1	0.5	'E'	1	0	1	0.5	1
0	1	4	NA	NA	'E'	1	4	NA	NA	5
0	0	4	0	2	'E'	0	4	0	2	4
0	0	0	NA	NA	'E'	0	0	NA	NA	0
0	1	0	1	0	'E'	1	0	1	0	1
1	1	4	1	2	'E'	0	3	0	1	4
1	1	0	1	1	'E'	0	-1	0	0	0
1	0	0	1	0	'E'	-1	-1	0	-1	-1
0	0	2	1	2	'E'	0	2	1	2	2
0	1	0	0	1	'E'	1	0	0	1	1
0	1	0	1	2	'E'	1	0	1	2	1
0	0	0	0	0	'E'	0	0	0	0	0
1	1	4	1	1	'E'	0	3	0	0	4
1	1	4	1	2	'E'	0	3	0	1	4
0	1	2	0	0	'S'	1	2	0	0	3
0	1	0	0	0	'S'	1	0	0	0	1
0	1	0	0	0	'S'	1	0	0	0	1
0	1	4	0	0	'S'	1	4	0	0	5
1	1	0	1	2	'S'	0	-1	0	1	0
1	0	0	1	2	'S'	-1	-1	0	1	-1
0	0	1	1	2	'S'	0	1	1	2	1
1	1	2	1	2	'S'	0	1	0	1	2
1	0	0	1	2	'S'	-1	-1	0	1	-1
1	1	2	1	2	'S'	0	1	0	1	2
0	0	0	1	2	'S'	0	0	1	2	0
0	0	4	1	2	'S'	0	4	1	2	4
0	0	4	1	2	'S'	0	4	1	2	4
1	0	0	1	2	'S'	-1	-1	0	1	-1
0	0	0	NA	NA	'S'	0	0	NA	NA	0
0	0	4	1	2	'S'	0	4	1	2	4
1	0	0	NA	NA	'S'	-1	-1	NA	NA	-1
1	1	4	1	2	'S'	0	3	0	1	4
0	0	2	1	2	'S'	0	2	1	2	2
0	0	2	NA	NA	'S'	0	2	NA	NA	2
1	1	0	0	0	'S'	0	-1	-1	-1	0
1	1	0	1	2	'S'	0	-1	0	1	0
1	1	4	NA	NA	'S'	0	3	NA	NA	4
0	1	0	1	2	'S'	1	0	1	2	1
1	1	0	1	2	'S'	0	-1	0	1	0
1	1	0	1	2	'S'	0	-1	0	1	0
1	1	4	1	2	'S'	0	3	0	1	4
1	1	4	1	2	'S'	0	3	0	1	4
0	0	0	NA	NA	'S'	0	0	NA	NA	0
0	0	0	0	0	'S'	0	0	0	0	0
1	1	2	0	0	'S'	0	1	-1	-1	2
0	0	1	1	2	'S'	0	1	1	2	1
0	0	0	0	0	'S'	0	0	0	0	0
0	0	2	1	2	'S'	0	2	1	2	2
0	1	1	0	0	'S'	1	1	0	0	2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203205&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203205&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203205&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	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

ANOVA Model
post1-pre ~ post4
means	0.417	-0.417	-0.083	0.583	-0.315	-0.483

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
post1-pre  ~  post4 \tabularnewline
means & 0.417 & -0.417 & -0.083 & 0.583 & -0.315 & -0.483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203205&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]post1-pre  ~  post4[/C][/ROW]
[ROW][C]means[/C][C]0.417[/C][C]-0.417[/C][C]-0.083[/C][C]0.583[/C][C]-0.315[/C][C]-0.483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203205&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203205&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
post1-pre ~ post4
means	0.417	-0.417	-0.083	0.583	-0.315	-0.483

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
post4	5	3.658	0.732	2.657	0.027
Residuals	99	27.256	0.275

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
post4 & 5 & 3.658 & 0.732 & 2.657 & 0.027 \tabularnewline
Residuals & 99 & 27.256 & 0.275 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203205&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]post4[/C][C]5[/C][C]3.658[/C][C]0.732[/C][C]2.657[/C][C]0.027[/C][/ROW]
[ROW][C]Residuals[/C][C]99[/C][C]27.256[/C][C]0.275[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203205&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203205&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)
post4	5	3.658	0.732	2.657	0.027
Residuals	99	27.256	0.275

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
0.5-0	-0.417	-1.072	0.238	0.44
1-0	-0.083	-0.679	0.513	0.999
1.5-0	0.583	-0.973	2.14	0.885
2-0	-0.315	-0.695	0.065	0.164
NA-0	-0.483	-0.985	0.019	0.066
1-0.5	0.333	-0.435	1.102	0.805
1.5-0.5	1	-0.63	2.63	0.482
2-0.5	0.102	-0.514	0.718	0.997
NA-0.5	-0.067	-0.765	0.631	1
1.5-1	0.667	-0.941	2.274	0.833
2-1	-0.231	-0.784	0.322	0.828
NA-1	-0.4	-1.043	0.243	0.465
2-1.5	-0.898	-2.438	0.642	0.539
NA-1.5	-1.067	-2.642	0.508	0.368
NA-2	-0.169	-0.619	0.281	0.884

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
0.5-0 & -0.417 & -1.072 & 0.238 & 0.44 \tabularnewline
1-0 & -0.083 & -0.679 & 0.513 & 0.999 \tabularnewline
1.5-0 & 0.583 & -0.973 & 2.14 & 0.885 \tabularnewline
2-0 & -0.315 & -0.695 & 0.065 & 0.164 \tabularnewline
NA-0 & -0.483 & -0.985 & 0.019 & 0.066 \tabularnewline
1-0.5 & 0.333 & -0.435 & 1.102 & 0.805 \tabularnewline
1.5-0.5 & 1 & -0.63 & 2.63 & 0.482 \tabularnewline
2-0.5 & 0.102 & -0.514 & 0.718 & 0.997 \tabularnewline
NA-0.5 & -0.067 & -0.765 & 0.631 & 1 \tabularnewline
1.5-1 & 0.667 & -0.941 & 2.274 & 0.833 \tabularnewline
2-1 & -0.231 & -0.784 & 0.322 & 0.828 \tabularnewline
NA-1 & -0.4 & -1.043 & 0.243 & 0.465 \tabularnewline
2-1.5 & -0.898 & -2.438 & 0.642 & 0.539 \tabularnewline
NA-1.5 & -1.067 & -2.642 & 0.508 & 0.368 \tabularnewline
NA-2 & -0.169 & -0.619 & 0.281 & 0.884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203205&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]0.5-0[/C][C]-0.417[/C][C]-1.072[/C][C]0.238[/C][C]0.44[/C][/ROW]
[ROW][C]1-0[/C][C]-0.083[/C][C]-0.679[/C][C]0.513[/C][C]0.999[/C][/ROW]
[ROW][C]1.5-0[/C][C]0.583[/C][C]-0.973[/C][C]2.14[/C][C]0.885[/C][/ROW]
[ROW][C]2-0[/C][C]-0.315[/C][C]-0.695[/C][C]0.065[/C][C]0.164[/C][/ROW]
[ROW][C]NA-0[/C][C]-0.483[/C][C]-0.985[/C][C]0.019[/C][C]0.066[/C][/ROW]
[ROW][C]1-0.5[/C][C]0.333[/C][C]-0.435[/C][C]1.102[/C][C]0.805[/C][/ROW]
[ROW][C]1.5-0.5[/C][C]1[/C][C]-0.63[/C][C]2.63[/C][C]0.482[/C][/ROW]
[ROW][C]2-0.5[/C][C]0.102[/C][C]-0.514[/C][C]0.718[/C][C]0.997[/C][/ROW]
[ROW][C]NA-0.5[/C][C]-0.067[/C][C]-0.765[/C][C]0.631[/C][C]1[/C][/ROW]
[ROW][C]1.5-1[/C][C]0.667[/C][C]-0.941[/C][C]2.274[/C][C]0.833[/C][/ROW]
[ROW][C]2-1[/C][C]-0.231[/C][C]-0.784[/C][C]0.322[/C][C]0.828[/C][/ROW]
[ROW][C]NA-1[/C][C]-0.4[/C][C]-1.043[/C][C]0.243[/C][C]0.465[/C][/ROW]
[ROW][C]2-1.5[/C][C]-0.898[/C][C]-2.438[/C][C]0.642[/C][C]0.539[/C][/ROW]
[ROW][C]NA-1.5[/C][C]-1.067[/C][C]-2.642[/C][C]0.508[/C][C]0.368[/C][/ROW]
[ROW][C]NA-2[/C][C]-0.169[/C][C]-0.619[/C][C]0.281[/C][C]0.884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203205&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203205&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
	diff	lwr	upr	p adj
0.5-0	-0.417	-1.072	0.238	0.44
1-0	-0.083	-0.679	0.513	0.999
1.5-0	0.583	-0.973	2.14	0.885
2-0	-0.315	-0.695	0.065	0.164
NA-0	-0.483	-0.985	0.019	0.066
1-0.5	0.333	-0.435	1.102	0.805
1.5-0.5	1	-0.63	2.63	0.482
2-0.5	0.102	-0.514	0.718	0.997
NA-0.5	-0.067	-0.765	0.631	1
1.5-1	0.667	-0.941	2.274	0.833
2-1	-0.231	-0.784	0.322	0.828
NA-1	-0.4	-1.043	0.243	0.465
2-1.5	-0.898	-2.438	0.642	0.539
NA-1.5	-1.067	-2.642	0.508	0.368
NA-2	-0.169	-0.619	0.281	0.884

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	5	2.979	0.015
	99

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 2.979 & 0.015 \tabularnewline
  & 99 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203205&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]5[/C][C]2.979[/C][C]0.015[/C][/ROW]
[ROW][C] [/C][C]99[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203205&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203205&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	5	2.979	0.015
	99

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 7 ; par2 = 6 ; par3 = TRUE ;

Parameters (R input):

par1 = 7 ; par2 = 6 ; par3 = TRUE ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;

R code (references can be found in the software module):

par3 <- 'TRUE'
par2 <- '5'
par1 <- '7'
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