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R Software Module

Ian.Hollidayrwasp_Two Factor ANOVA -V4.wasp

Title produced by software

Variability

Date of computation

Tue, 19 Apr 2011 17:22:35 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Apr/19/t13032337551e4snw0nt6yp307.htm/, Retrieved Thu, 06 Nov 2025 03:21:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=120619, Retrieved Thu, 06 Nov 2025 03:21:00 +0000

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Estimated Impact

459

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

-     [Variability] [Two-Way ANOVA] [2010-11-30 21:42:30] [74be16979710d4c4e7c6647856088456]
-       [Variability] [anova wk09] [2010-12-05 23:43:41] [920d86197c99e892f7cfc71aadebcde0]
- R  D      [Variability] [male ANOVA] [2011-04-19 17:22:35] [fb39ddc2de7cdbac58bd2783134ca01b] [Current]

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Dataseries X:

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Histogram

Boxplots

2.46459695	'gp1'	's500'
1.74291939	'gp1'	's500'
4.08496732	'gp1'	's500'
2.069716776	'gp1'	's500'
2.723311547	'gp1'	's500'
1.770152505	'gp1'	's500'
3.06372549	'gp1'	's500'
1.838235294	'gp1'	's500'
3.118191721	'gp1'	's500'
3.676470588	'gp1'	's500'
3.349673203	'gp1'	's500'
5.215141612	'gp1'	's500'
5.065359477	'gp1'	's500'
3.036492375	'gp1'	's500'
1.217351309	'gp1'	's1000'
1.067303052	'gp1'	's1000'
2.32310894	'gp1'	's1000'
3.167601641	'gp1'	's1000'
1.345910242	'gp1'	's1000'
1.251658825	'gp1'	's1000'
2.679002292	'gp1'	's1000'
1.971739655	'gp1'	's1000'
3.317649897	'gp1'	's1000'
5.794954156	'gp1'	's1000'
2.631876583	'gp1'	's1000'
4.05054892	'gp1'	's1000'
2.910483774	'gp1'	's1000'
2.61038726	'gp1'	's1000'
3.090958606	'gp1'	'l500'
1.647603486	'gp1'	'l500'
3.281590414	'gp1'	'l500'
2.069716776	'gp1'	'l500'
0.953159041	'gp1'	'l500'
4.180283224	'gp1'	'l500'
2.137799564	'gp1'	'l500'
2.246732026	'gp1'	'l500'
5.569172113	'gp1'	'l500'
2.900326797	'gp1'	'l500'
5.569172113	'gp1'	'l500'
4.547930283	'gp1'	'l500'
1.906318083	'gp1'	'l500'
3.90795207	'gp1'	'l500'
1.740258173	'gp1'	'l1000'
1.903124623	'gp1'	'l1000'
3.270524189	'gp1'	'l1000'
3.167601641	'gp1'	'l1000'
2.794743033	'gp1'	'l1000'
2.228857522	'gp1'	'l1000'
2.910483774	'gp1'	'l1000'
2.318961877	'gp1'	'l1000'
2.156095428	'gp1'	'l1000'
4.680525395	'gp1'	'l1000'
2.631876583	'gp1'	'l1000'
0.44147364	'gp1'	'l1000'
1.684461334	'gp1'	'l1000'
1.088792375	'gp1'	'l1000'
2.805010893	'gp2'	's500'
2.46459695	'gp2'	's500'
3.581154684	'gp2'	's500'
3.880718954	'gp2'	's500'
2.46459695	'gp2'	's500'
1.416122004	'gp2'	's500'
4.9291939	'gp2'	's500'
1.348039216	'gp2'	's500'
2.001633987	'gp2'	's500'
3.308823529	'gp2'	's500'
3.159041394	'gp2'	's500'
3.159041394	'gp2'	's500'
5.324074074	'gp2'	's500'
1.416122004	'gp2'	's500'
1.067303052	'gp2'	's1000'
2.391723972	'gp2'	's1000'
3.831885632	'gp2'	's1000'
4.706538786	'gp2'	's1000'
2.400395102	'gp2'	's1000'
1.483140306	'gp2'	's1000'
3.06053203	'gp2'	's1000'
2.413213295	'gp2'	's1000'
1.971739655	'gp2'	's1000'
4.191926047	'gp2'	's1000'
2.413213295	'gp2'	's1000'
1.148736277	'gp2'	's1000'
3.249034865	'gp2'	's1000'
2.366087586	'gp2'	's1000'
2.791394336	'gp2'	'l500'
1.974400871	'gp2'	'l500'
3.581154684	'gp2'	'l500'
3.880718954	'gp2'	'l500'
3.390522876	'gp2'	'l500'
4.044117647	'gp2'	'l500'
4.9291939	'gp2'	'l500'
2.369281046	'gp2'	'l500'
2.137799564	'gp2'	'l500'
5.160675381	'gp2'	'l500'
2.369281046	'gp2'	'l500'
1.674836601	'gp2'	'l500'
4.833877996	'gp2'	'l500'
0.558278867	'gp2'	'l500'
1.298784534	'gp2'	'l1000'
1.971739655	'gp2'	'l1000'
4.76195862	'gp2'	'l1000'
3.711997828	'gp2'	'l1000'
1.302931596	'gp2'	'l1000'
0.801514055	'gp2'	'l1000'
3.06053203	'gp2'	'l1000'
2.293325492	'gp2'	'l1000'
0.99868802	'gp2'	'l1000'
3.04318977	'gp2'	'l1000'
2.241675715	'gp2'	'l1000'
1.483140306	'gp2'	'l1000'
3.283342381	'gp2'	'l1000'
2.366087586	'gp2'	'l1000'

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' @ 193.190.124.24

\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' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120619&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' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120619&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120619&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' @ 193.190.124.24

ANOVA Model
xdf2$accuracy ~ xdf2$group * xdf2$cond
names	(Intercept)	xdf2$groupgp2	xdf2$condl500	xdf2$conds1000	xdf2$conds500	xdf2$groupgp2:xdf2$condl500	xdf2$groupgp2:xdf2$conds1000	xdf2$groupgp2:xdf2$conds500
means	2.3584	-0.028491	0.78507	0.23727	0.72866	0.0061208	0.053912	-0.11157

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
xdf2$accuracy ~ xdf2$group * xdf2$cond \tabularnewline
names & (Intercept) & xdf2$groupgp2 & xdf2$condl500 & xdf2$conds1000 & xdf2$conds500 & xdf2$groupgp2:xdf2$condl500 & xdf2$groupgp2:xdf2$conds1000 & xdf2$groupgp2:xdf2$conds500 \tabularnewline
means & 2.3584 & -0.028491 & 0.78507 & 0.23727 & 0.72866 & 0.0061208 & 0.053912 & -0.11157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120619&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]xdf2$accuracy ~ xdf2$group * xdf2$cond[/C][/ROW]
[ROW][C]names[/C][C](Intercept)[/C][C]xdf2$groupgp2[/C][C]xdf2$condl500[/C][C]xdf2$conds1000[/C][C]xdf2$conds500[/C][C]xdf2$groupgp2:xdf2$condl500[/C][C]xdf2$groupgp2:xdf2$conds1000[/C][C]xdf2$groupgp2:xdf2$conds500[/C][/ROW]
[ROW][C]means[/C][C]2.3584[/C][C]-0.028491[/C][C]0.78507[/C][C]0.23727[/C][C]0.72866[/C][C]0.0061208[/C][C]0.053912[/C][C]-0.11157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120619&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120619&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
xdf2$accuracy ~ xdf2$group * xdf2$cond
names	(Intercept)	xdf2$groupgp2	xdf2$condl500	xdf2$conds1000	xdf2$conds500	xdf2$groupgp2:xdf2$condl500	xdf2$groupgp2:xdf2$conds1000	xdf2$groupgp2:xdf2$conds500
means	2.3584	-0.028491	0.78507	0.23727	0.72866	0.0061208	0.053912	-0.11157

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
	1
xdf2$group	1	0.047931	0.047931	0.032165	0.85802
xdf2$cond	1	11.189	3.7298	2.5029	0.06336
xdf2$group:xdf2$cond	1	0.10309	0.034363	0.02306	0.99523
Residuals	104	154.98	1.4902

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
xdf2$group & 1 & 0.047931 & 0.047931 & 0.032165 & 0.85802 \tabularnewline
xdf2$cond & 1 & 11.189 & 3.7298 & 2.5029 & 0.06336 \tabularnewline
xdf2$group:xdf2$cond & 1 & 0.10309 & 0.034363 & 0.02306 & 0.99523 \tabularnewline
Residuals & 104 & 154.98 & 1.4902 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120619&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][/C][C]1[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]xdf2$group[/C][C]1[/C][C]0.047931[/C][C]0.047931[/C][C]0.032165[/C][C]0.85802[/C][/ROW]
[ROW][C]xdf2$cond[/C][C]1[/C][C]11.189[/C][C]3.7298[/C][C]2.5029[/C][C]0.06336[/C][/ROW]
[ROW][C]xdf2$group:xdf2$cond[/C][C]1[/C][C]0.10309[/C][C]0.034363[/C][C]0.02306[/C][C]0.99523[/C][/ROW]
[ROW][C]Residuals[/C][C]104[/C][C]154.98[/C][C]1.4902[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120619&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120619&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)
	1
xdf2$group	1	0.047931	0.047931	0.032165	0.85802
xdf2$cond	1	11.189	3.7298	2.5029	0.06336
xdf2$group:xdf2$cond	1	0.10309	0.034363	0.02306	0.99523
Residuals	104	154.98	1.4902

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
gp2-gp1	-0.041374	-0.49885	0.4161	0.85802
l500-l1000	0.78813	-0.063735	1.64	0.080378
s1000-l1000	0.26423	-0.58764	1.1161	0.84966
s500-l1000	0.67287	-0.17899	1.5247	0.17238
s1000-l500	-0.5239	-1.3758	0.32796	0.37987
s500-l500	-0.11525	-0.96712	0.73661	0.9848
s500-s1000	0.40865	-0.44322	1.2605	0.59505
gp2:l1000-gp1:l1000	-0.028491	-1.4558	1.3988	1
gp1:l500-gp1:l1000	0.78507	-0.64226	2.2124	0.68625
gp2:l500-gp1:l1000	0.7627	-0.66463	2.19	0.71694
gp1:s1000-gp1:l1000	0.23727	-1.1901	1.6646	0.99957
gp2:s1000-gp1:l1000	0.26269	-1.1646	1.69	0.99915
gp1:s500-gp1:l1000	0.72866	-0.69867	2.156	0.7615
gp2:s500-gp1:l1000	0.5886	-0.83873	2.0159	0.90578
gp1:l500-gp2:l1000	0.81356	-0.61377	2.2409	0.64595
gp2:l500-gp2:l1000	0.79119	-0.63614	2.2185	0.6777
gp1:s1000-gp2:l1000	0.26576	-1.1616	1.6931	0.99909
gp2:s1000-gp2:l1000	0.29118	-1.1361	1.7185	0.99836
gp1:s500-gp2:l1000	0.75715	-0.67018	2.1845	0.7244
gp2:s500-gp2:l1000	0.61709	-0.81024	2.0444	0.88231
gp2:l500-gp1:l500	-0.02237	-1.4497	1.405	1
gp1:s1000-gp1:l500	-0.5478	-1.9751	0.87953	0.93387
gp2:s1000-gp1:l500	-0.52237	-1.9497	0.90496	0.94819
gp1:s500-gp1:l500	-0.056411	-1.4837	1.3709	1
gp2:s500-gp1:l500	-0.19647	-1.6238	1.2309	0.99988
gp1:s1000-gp2:l500	-0.52543	-1.9528	0.9019	0.94659
gp2:s1000-gp2:l500	-0.5	-1.9273	0.92733	0.95888
gp1:s500-gp2:l500	-0.034041	-1.4614	1.3933	1
gp2:s500-gp2:l500	-0.1741	-1.6014	1.2532	0.99995
gp2:s1000-gp1:s1000	0.025421	-1.4019	1.4528	1
gp1:s500-gp1:s1000	0.49138	-0.93595	1.9187	0.96255
gp2:s500-gp1:s1000	0.35133	-1.076	1.7787	0.99466
gp1:s500-gp2:s1000	0.46596	-0.96137	1.8933	0.97199
gp2:s500-gp2:s1000	0.32591	-1.1014	1.7532	0.99665
gp2:s500-gp1:s500	-0.14006	-1.5674	1.2873	0.99999

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
gp2-gp1 & -0.041374 & -0.49885 & 0.4161 & 0.85802 \tabularnewline
l500-l1000 & 0.78813 & -0.063735 & 1.64 & 0.080378 \tabularnewline
s1000-l1000 & 0.26423 & -0.58764 & 1.1161 & 0.84966 \tabularnewline
s500-l1000 & 0.67287 & -0.17899 & 1.5247 & 0.17238 \tabularnewline
s1000-l500 & -0.5239 & -1.3758 & 0.32796 & 0.37987 \tabularnewline
s500-l500 & -0.11525 & -0.96712 & 0.73661 & 0.9848 \tabularnewline
s500-s1000 & 0.40865 & -0.44322 & 1.2605 & 0.59505 \tabularnewline
gp2:l1000-gp1:l1000 & -0.028491 & -1.4558 & 1.3988 & 1 \tabularnewline
gp1:l500-gp1:l1000 & 0.78507 & -0.64226 & 2.2124 & 0.68625 \tabularnewline
gp2:l500-gp1:l1000 & 0.7627 & -0.66463 & 2.19 & 0.71694 \tabularnewline
gp1:s1000-gp1:l1000 & 0.23727 & -1.1901 & 1.6646 & 0.99957 \tabularnewline
gp2:s1000-gp1:l1000 & 0.26269 & -1.1646 & 1.69 & 0.99915 \tabularnewline
gp1:s500-gp1:l1000 & 0.72866 & -0.69867 & 2.156 & 0.7615 \tabularnewline
gp2:s500-gp1:l1000 & 0.5886 & -0.83873 & 2.0159 & 0.90578 \tabularnewline
gp1:l500-gp2:l1000 & 0.81356 & -0.61377 & 2.2409 & 0.64595 \tabularnewline
gp2:l500-gp2:l1000 & 0.79119 & -0.63614 & 2.2185 & 0.6777 \tabularnewline
gp1:s1000-gp2:l1000 & 0.26576 & -1.1616 & 1.6931 & 0.99909 \tabularnewline
gp2:s1000-gp2:l1000 & 0.29118 & -1.1361 & 1.7185 & 0.99836 \tabularnewline
gp1:s500-gp2:l1000 & 0.75715 & -0.67018 & 2.1845 & 0.7244 \tabularnewline
gp2:s500-gp2:l1000 & 0.61709 & -0.81024 & 2.0444 & 0.88231 \tabularnewline
gp2:l500-gp1:l500 & -0.02237 & -1.4497 & 1.405 & 1 \tabularnewline
gp1:s1000-gp1:l500 & -0.5478 & -1.9751 & 0.87953 & 0.93387 \tabularnewline
gp2:s1000-gp1:l500 & -0.52237 & -1.9497 & 0.90496 & 0.94819 \tabularnewline
gp1:s500-gp1:l500 & -0.056411 & -1.4837 & 1.3709 & 1 \tabularnewline
gp2:s500-gp1:l500 & -0.19647 & -1.6238 & 1.2309 & 0.99988 \tabularnewline
gp1:s1000-gp2:l500 & -0.52543 & -1.9528 & 0.9019 & 0.94659 \tabularnewline
gp2:s1000-gp2:l500 & -0.5 & -1.9273 & 0.92733 & 0.95888 \tabularnewline
gp1:s500-gp2:l500 & -0.034041 & -1.4614 & 1.3933 & 1 \tabularnewline
gp2:s500-gp2:l500 & -0.1741 & -1.6014 & 1.2532 & 0.99995 \tabularnewline
gp2:s1000-gp1:s1000 & 0.025421 & -1.4019 & 1.4528 & 1 \tabularnewline
gp1:s500-gp1:s1000 & 0.49138 & -0.93595 & 1.9187 & 0.96255 \tabularnewline
gp2:s500-gp1:s1000 & 0.35133 & -1.076 & 1.7787 & 0.99466 \tabularnewline
gp1:s500-gp2:s1000 & 0.46596 & -0.96137 & 1.8933 & 0.97199 \tabularnewline
gp2:s500-gp2:s1000 & 0.32591 & -1.1014 & 1.7532 & 0.99665 \tabularnewline
gp2:s500-gp1:s500 & -0.14006 & -1.5674 & 1.2873 & 0.99999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120619&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]gp2-gp1[/C][C]-0.041374[/C][C]-0.49885[/C][C]0.4161[/C][C]0.85802[/C][/ROW]
[ROW][C]l500-l1000[/C][C]0.78813[/C][C]-0.063735[/C][C]1.64[/C][C]0.080378[/C][/ROW]
[ROW][C]s1000-l1000[/C][C]0.26423[/C][C]-0.58764[/C][C]1.1161[/C][C]0.84966[/C][/ROW]
[ROW][C]s500-l1000[/C][C]0.67287[/C][C]-0.17899[/C][C]1.5247[/C][C]0.17238[/C][/ROW]
[ROW][C]s1000-l500[/C][C]-0.5239[/C][C]-1.3758[/C][C]0.32796[/C][C]0.37987[/C][/ROW]
[ROW][C]s500-l500[/C][C]-0.11525[/C][C]-0.96712[/C][C]0.73661[/C][C]0.9848[/C][/ROW]
[ROW][C]s500-s1000[/C][C]0.40865[/C][C]-0.44322[/C][C]1.2605[/C][C]0.59505[/C][/ROW]
[ROW][C]gp2:l1000-gp1:l1000[/C][C]-0.028491[/C][C]-1.4558[/C][C]1.3988[/C][C]1[/C][/ROW]
[ROW][C]gp1:l500-gp1:l1000[/C][C]0.78507[/C][C]-0.64226[/C][C]2.2124[/C][C]0.68625[/C][/ROW]
[ROW][C]gp2:l500-gp1:l1000[/C][C]0.7627[/C][C]-0.66463[/C][C]2.19[/C][C]0.71694[/C][/ROW]
[ROW][C]gp1:s1000-gp1:l1000[/C][C]0.23727[/C][C]-1.1901[/C][C]1.6646[/C][C]0.99957[/C][/ROW]
[ROW][C]gp2:s1000-gp1:l1000[/C][C]0.26269[/C][C]-1.1646[/C][C]1.69[/C][C]0.99915[/C][/ROW]
[ROW][C]gp1:s500-gp1:l1000[/C][C]0.72866[/C][C]-0.69867[/C][C]2.156[/C][C]0.7615[/C][/ROW]
[ROW][C]gp2:s500-gp1:l1000[/C][C]0.5886[/C][C]-0.83873[/C][C]2.0159[/C][C]0.90578[/C][/ROW]
[ROW][C]gp1:l500-gp2:l1000[/C][C]0.81356[/C][C]-0.61377[/C][C]2.2409[/C][C]0.64595[/C][/ROW]
[ROW][C]gp2:l500-gp2:l1000[/C][C]0.79119[/C][C]-0.63614[/C][C]2.2185[/C][C]0.6777[/C][/ROW]
[ROW][C]gp1:s1000-gp2:l1000[/C][C]0.26576[/C][C]-1.1616[/C][C]1.6931[/C][C]0.99909[/C][/ROW]
[ROW][C]gp2:s1000-gp2:l1000[/C][C]0.29118[/C][C]-1.1361[/C][C]1.7185[/C][C]0.99836[/C][/ROW]
[ROW][C]gp1:s500-gp2:l1000[/C][C]0.75715[/C][C]-0.67018[/C][C]2.1845[/C][C]0.7244[/C][/ROW]
[ROW][C]gp2:s500-gp2:l1000[/C][C]0.61709[/C][C]-0.81024[/C][C]2.0444[/C][C]0.88231[/C][/ROW]
[ROW][C]gp2:l500-gp1:l500[/C][C]-0.02237[/C][C]-1.4497[/C][C]1.405[/C][C]1[/C][/ROW]
[ROW][C]gp1:s1000-gp1:l500[/C][C]-0.5478[/C][C]-1.9751[/C][C]0.87953[/C][C]0.93387[/C][/ROW]
[ROW][C]gp2:s1000-gp1:l500[/C][C]-0.52237[/C][C]-1.9497[/C][C]0.90496[/C][C]0.94819[/C][/ROW]
[ROW][C]gp1:s500-gp1:l500[/C][C]-0.056411[/C][C]-1.4837[/C][C]1.3709[/C][C]1[/C][/ROW]
[ROW][C]gp2:s500-gp1:l500[/C][C]-0.19647[/C][C]-1.6238[/C][C]1.2309[/C][C]0.99988[/C][/ROW]
[ROW][C]gp1:s1000-gp2:l500[/C][C]-0.52543[/C][C]-1.9528[/C][C]0.9019[/C][C]0.94659[/C][/ROW]
[ROW][C]gp2:s1000-gp2:l500[/C][C]-0.5[/C][C]-1.9273[/C][C]0.92733[/C][C]0.95888[/C][/ROW]
[ROW][C]gp1:s500-gp2:l500[/C][C]-0.034041[/C][C]-1.4614[/C][C]1.3933[/C][C]1[/C][/ROW]
[ROW][C]gp2:s500-gp2:l500[/C][C]-0.1741[/C][C]-1.6014[/C][C]1.2532[/C][C]0.99995[/C][/ROW]
[ROW][C]gp2:s1000-gp1:s1000[/C][C]0.025421[/C][C]-1.4019[/C][C]1.4528[/C][C]1[/C][/ROW]
[ROW][C]gp1:s500-gp1:s1000[/C][C]0.49138[/C][C]-0.93595[/C][C]1.9187[/C][C]0.96255[/C][/ROW]
[ROW][C]gp2:s500-gp1:s1000[/C][C]0.35133[/C][C]-1.076[/C][C]1.7787[/C][C]0.99466[/C][/ROW]
[ROW][C]gp1:s500-gp2:s1000[/C][C]0.46596[/C][C]-0.96137[/C][C]1.8933[/C][C]0.97199[/C][/ROW]
[ROW][C]gp2:s500-gp2:s1000[/C][C]0.32591[/C][C]-1.1014[/C][C]1.7532[/C][C]0.99665[/C][/ROW]
[ROW][C]gp2:s500-gp1:s500[/C][C]-0.14006[/C][C]-1.5674[/C][C]1.2873[/C][C]0.99999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120619&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120619&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
gp2-gp1	-0.041374	-0.49885	0.4161	0.85802
l500-l1000	0.78813	-0.063735	1.64	0.080378
s1000-l1000	0.26423	-0.58764	1.1161	0.84966
s500-l1000	0.67287	-0.17899	1.5247	0.17238
s1000-l500	-0.5239	-1.3758	0.32796	0.37987
s500-l500	-0.11525	-0.96712	0.73661	0.9848
s500-s1000	0.40865	-0.44322	1.2605	0.59505
gp2:l1000-gp1:l1000	-0.028491	-1.4558	1.3988	1
gp1:l500-gp1:l1000	0.78507	-0.64226	2.2124	0.68625
gp2:l500-gp1:l1000	0.7627	-0.66463	2.19	0.71694
gp1:s1000-gp1:l1000	0.23727	-1.1901	1.6646	0.99957
gp2:s1000-gp1:l1000	0.26269	-1.1646	1.69	0.99915
gp1:s500-gp1:l1000	0.72866	-0.69867	2.156	0.7615
gp2:s500-gp1:l1000	0.5886	-0.83873	2.0159	0.90578
gp1:l500-gp2:l1000	0.81356	-0.61377	2.2409	0.64595
gp2:l500-gp2:l1000	0.79119	-0.63614	2.2185	0.6777
gp1:s1000-gp2:l1000	0.26576	-1.1616	1.6931	0.99909
gp2:s1000-gp2:l1000	0.29118	-1.1361	1.7185	0.99836
gp1:s500-gp2:l1000	0.75715	-0.67018	2.1845	0.7244
gp2:s500-gp2:l1000	0.61709	-0.81024	2.0444	0.88231
gp2:l500-gp1:l500	-0.02237	-1.4497	1.405	1
gp1:s1000-gp1:l500	-0.5478	-1.9751	0.87953	0.93387
gp2:s1000-gp1:l500	-0.52237	-1.9497	0.90496	0.94819
gp1:s500-gp1:l500	-0.056411	-1.4837	1.3709	1
gp2:s500-gp1:l500	-0.19647	-1.6238	1.2309	0.99988
gp1:s1000-gp2:l500	-0.52543	-1.9528	0.9019	0.94659
gp2:s1000-gp2:l500	-0.5	-1.9273	0.92733	0.95888
gp1:s500-gp2:l500	-0.034041	-1.4614	1.3933	1
gp2:s500-gp2:l500	-0.1741	-1.6014	1.2532	0.99995
gp2:s1000-gp1:s1000	0.025421	-1.4019	1.4528	1
gp1:s500-gp1:s1000	0.49138	-0.93595	1.9187	0.96255
gp2:s500-gp1:s1000	0.35133	-1.076	1.7787	0.99466
gp1:s500-gp2:s1000	0.46596	-0.96137	1.8933	0.97199
gp2:s500-gp2:s1000	0.32591	-1.1014	1.7532	0.99665
gp2:s500-gp1:s500	-0.14006	-1.5674	1.2873	0.99999

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	7	0.58229	0.76897
	104

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 7 & 0.58229 & 0.76897 \tabularnewline
  & 104 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120619&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]7[/C][C]0.58229[/C][C]0.76897[/C][/ROW]
[ROW][C] [/C][C]104[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120619&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120619&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	7	0.58229	0.76897
	104

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;

Parameters (R input):

par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;

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

cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
cat3 <- as.numeric(par3)
intercept<-as.logical(par4)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
f2 <- as.character(x[,cat3])
xdf<-data.frame(x1,f1, f2)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
(V3 <-dimnames(y)[[1]][cat3])
mynames<- c(V1, V2, V3)
xdf2<-xdf
names(xdf2)<-mynames
names(xdf)<-c('R', 'A', 'B')
mynames <- c(V1, V2, V3)
if(intercept == FALSE)eval (substitute(lmout<-lm(xdf$R ~ xdf$A * xdf$B- 1, data = xdf), list(xdf=quote(xdf2),R=mynames[1],A=mynames[2],B=mynames[3]) ))else eval(substitute(lmout<-lm(xdf$R ~ xdf$A * xdf$B, data = xdf), list(xdf=quote(xdf2),R=mynames[1],A=mynames[2],B=mynames[3]) ))
oldnames<-names(lmout$coeff)
newnames<-gsub('xdf2$', '', oldnames)
(names(lmout$coeff)<-newnames)
(names(lmout$coefficients)<-newnames)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Model', length(lmout$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
callstr<-gsub('xdf2$', '',as.character(lmout$call$formula))
callstr<-paste(callstr[2], callstr[1], callstr[3])
a<-table.element(a,callstr ,length(lmout$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'names',,TRUE)
for(i in 1:length(lmout$coefficients)){
a<-table.element(a, names(lmout$coefficients[i]),,FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'means',,TRUE)
for(i in 1:length(lmout$coefficients)){
a<-table.element(a, signif(lmout$coefficients[i], digits=5),,FALSE)
}
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
(aov.xdf<-aov(lmout) )
(anova.xdf<-anova(lmout) )
rownames(anova.xdf)<-gsub('xdf2$','',rownames(anova.xdf))
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)
for(i in 1 : length(rownames(anova.xdf))-1){
a<-table.row.start(a)
a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, signif(anova.xdf$'Sum Sq'[i], digits=5),,FALSE)
a<-table.element(a, signif(anova.xdf$'Mean Sq'[i], digits=5),,FALSE)
a<-table.element(a, signif(anova.xdf$'F value'[i], digits=5),,FALSE)
a<-table.element(a, signif(anova.xdf$'Pr(>F)'[i], digits=5),,FALSE)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$'Df'[i+1],,FALSE)
a<-table.element(a, signif(anova.xdf$'Sum Sq'[i+1], digits=5),,FALSE)
a<-table.element(a, signif(anova.xdf$'Mean Sq'[i+1], digits=5),,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(R ~ A + B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups')
dev.off()
bitmap(file='designplot.png')
xdf2 <- xdf # to preserve xdf make copy for function
names(xdf2) <- c(V1, V2, V3)
plot.design(xdf2, main='Design Plot of Group Means')
dev.off()
bitmap(file='interactionplot.png')
interaction.plot(xdf$A, xdf$B, xdf$R, xlab=V2, ylab=V1, trace.label=V3, main='Possible Interactions Between Anova Groups')
dev.off()
if(intercept==TRUE){
thsd<-TukeyHSD(aov.xdf)
names(thsd) <- c(V2, V3, paste(V2, ':', V3, sep=''))
bitmap(file='TukeyHSDPlot.png')
layout(matrix(c(0,0,1,2,1,2,0,0,3,3,3,3), 2,6))
plot(thsd, las=1)
dev.off()
}
if(intercept==TRUE){
ntables<-length(names(thsd))
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(nt in 1:ntables){
for(i in 1:length(rownames(thsd[[nt]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[nt]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,signif(thsd[[nt]][i,j], digits=5), 1, FALSE)
}
a<-table.row.end(a)
}
} # end nt
a<-table.end(a)
table.save(a,file='hsdtable.tab')
}#end if hsd tables
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(lmout)
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,signif(lt.lmxdf[[i]][1], digits=5), 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