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Author

*The author of this computation has been verified*

R Software Module

Ian.Hollidayrwasp_Two Factor ANOVA -V2.wasp

Title produced by software

Two-Way ANOVA

Date of computation

Fri, 28 May 2010 23:09:47 +0000

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/2010/May/29/t1275088811hnvtj64f0av6c0q.htm/, Retrieved Fri, 07 Nov 2025 02:30:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=76643, Retrieved Fri, 07 Nov 2025 02:30:04 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

512

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

-     [Two-Way ANOVA] [two-way anova wit...] [2010-05-26 17:02:24] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R PD    [Two-Way ANOVA] [ANOVA with good l...] [2010-05-28 23:09:47] [a9208f4f8d3b118336aae915785f2bd9] [Current]
- R         [Variability] [ANOVA with better...] [2010-05-29 09:47:12] [98fd0e87c3eb04e0cc2efde01dbafab6] 
- R           [Variability] [ANOVA with better...] [2010-05-29 09:54:40] [98fd0e87c3eb04e0cc2efde01dbafab6] 
- R  D          [Variability] [compendium] [2010-05-30 20:52:28] [4edce1892c378475bb20c4acd224a51d] 
- R  D          [Variability] [Trimmed 10%] [2010-05-31 11:11:24] [856c65906cd78e3f7881668c6dfea87f] 
- R  D          [Variability] [Trimmed date 10%] [2010-05-31 11:11:52] [885328d98a95a442af53d0763bccf325] 
- RMPD          [] [] [1970-01-01 00:00:00] [166b3b50b14ad81e946931c96c6ff94f] 
- RMP           [Two-Way ANOVA] [] [2010-06-01 23:03:14] [3a572644bd86164a067a2945d748afb7] 
- R             [Variability] [blog 1] [2010-06-02 08:40:04] [c519646407a489a26f129bdc22b2e203] 
- R             [Variability] [SectionA] [2010-06-02 08:40:35] [4edce1892c378475bb20c4acd224a51d] 
- R             [Variability] [Test 1] [2010-06-02 08:40:25] [7d07ebb7f3978280240b500f174a2af2] 
- R  D            [Variability] [test 2] [2010-06-02 09:11:16] [7d07ebb7f3978280240b500f174a2af2] 
- RMPD            [Simple Linear Regression] [linear regression] [2010-06-02 09:42:39] [7d07ebb7f3978280240b500f174a2af2] 
- R             [Variability] [Question one] [2010-06-02 08:40:45] [6754037f2a7547483397efade45eb176] 
- R             [Variability] [Question 1] [2010-06-02 08:40:59] [153000c0b3bd367036e4d581452d08df] 
- R  D            [Variability] [Question 1ii] [2010-06-02 08:52:01] [153000c0b3bd367036e4d581452d08df] 
- R             [Variability] [Examcompendium] [2010-06-02 08:40:07] [175567c9546e50fd2412bc13fece161f] 
- R             [Variability] [Section A 1 - 1] [2010-06-02 08:40:52] [5cb86ecb659a3920b562748ed004e500] 
- R             [Variability] [adler] [2010-06-02 08:41:42] [f894c941edfbe86ae91b01acfc2a02b5] 
- R             [Variability] [exam] [2010-06-02 08:41:23] [5bdc9e4bd4169daeceaf774f5b9d9a20] 
- R             [Variability] [section A Q1] [2010-06-02 08:41:49] [256a42577f5eb7e9c8a1b74c73a90fa8] 
- R             [Variability] [june exam section...] [2010-06-02 08:41:39] [a2ec18f77143ca7c2255feafca790c81] 
- R             [Variability] [] [2010-06-02 08:41:42] [a6e410a5e6b2ff1e0abc686251520516] 
- R  D          [Variability] [exam1] [2010-06-02 08:41:53] [86674042f568b97a0cb1393bb670625c] 
- R             [Variability] [Adler Exam Reprod...] [2010-06-02 08:41:55] [814d7f27257f4c09e0e8a930c67f7fe6] 
- R             [Variability] [] [2010-06-02 08:42:17] [74be16979710d4c4e7c6647856088456] 
- R             [Variability] [Reproduced Analys...] [2010-06-02 08:42:28] [e92f8a4a2b7017be7b51e64bfca3a271] 
- R             [Variability] [reproduction of a...] [2010-06-02 08:42:14] [c3b05d290fad0f2bad0901abbf20f20e] 
- R             [Variability] [Adler Study] [2010-06-02 08:41:57] [74be16979710d4c4e7c6647856088456] 
- R             [Variability] [June exam ANOVA] [2010-06-02 08:42:16] [012a64ac316c94a67eaef3285dac2cf7] 
- R             [Variability] [] [2010-06-02 08:42:05] [a0fd591e63c5cd1c7fa328d28b50e124] 
- R             [Variability] [Adler Study] [2010-06-02 08:41:55] [06b133101696c6b5dc677840451c9976] 
- R  D            [Variability] [Adler Adjusted] [2010-06-02 09:17:20] [06b133101696c6b5dc677840451c9976] 
- R             [Variability] [Exam QA1] [2010-06-02 08:43:04] [2cb75f3785cab2383fe897d8b1eb3abc] 
- R  D            [Variability] [SUMMER EXAM QA ad...] [2010-06-02 09:21:13] [2cb75f3785cab2383fe897d8b1eb3abc] 
- R             [Variability] [AdvancedStats] [2010-06-02 08:43:30] [7ee8584ae92dbbc2a823887b8397aaa8] 
- R             [Variability] [Section A - ANOVA] [2010-06-02 08:43:24] [8431b2cca73e677c29fb8bfdfc230859] 
- R             [Variability] [Section a q1] [2010-06-02 08:44:23] [d85e8cd4dd2ccdf2c3dfa3761837f774] 
- R             [Variability] [] [2010-06-02 08:42:08] [5937f6f6887339314deaa19552a34d35] 
- R             [Variability] [ANOVA] [2010-06-02 08:44:10] [01668bebb17c5d247271c36324d8beb6] 
- R               [Variability] [ANOVA for attract...] [2010-06-02 09:08:14] [01668bebb17c5d247271c36324d8beb6] 
- R             [Variability] [statsexam] [2010-06-02 08:44:40] [66f61a2d5ef80b1eafe31e5651ad0889] 
- R             [Variability] [adler study] [2010-06-02 08:42:09] [74be16979710d4c4e7c6647856088456] 
- R             [Variability] [adler study] [2010-06-02 08:42:09] [74be16979710d4c4e7c6647856088456] 
- R             [Variability] [Adler] [2010-06-02 08:42:19] [f4cf89988ffad44ee0ca561abbab9122] 
- R             [Variability] [] [2010-06-02 08:45:50] [36cf82ea4074b55afa05ece289b9dfca] 
- R             [Variability] [] [2010-06-02 08:46:11] [bab33aa52cf59fc0c168bbba8dd04cc1] 
- R             [Variability] [Nacy Adler (1973)] [2010-06-02 08:40:58] [166b3b50b14ad81e946931c96c6ff94f] 
- R             [Variability] [q1a] [2010-06-02 08:40:47] [869dc8c90da15910a169a569d8b6a5c9] 

[Truncated]

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Dataseries X:

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Histogram

Boxplots

'GOOD'	'HIGH'	25
'GOOD'	'HIGH'	0
'GOOD'	'HIGH'	-16
'GOOD'	'HIGH'	5
'GOOD'	'HIGH'	11
'GOOD'	'HIGH'	-6
'GOOD'	'HIGH'	42
'GOOD'	'HIGH'	-2
'GOOD'	'HIGH'	-13
'GOOD'	'HIGH'	14
'GOOD'	'HIGH'	4
'GOOD'	'HIGH'	-22
'GOOD'	'HIGH'	19
'GOOD'	'HIGH'	6
'GOOD'	'HIGH'	-6
'GOOD'	'LOW'	-25
'GOOD'	'LOW'	-23
'GOOD'	'LOW'	-28
'GOOD'	'LOW'	-22
'GOOD'	'LOW'	-22
'GOOD'	'LOW'	-10
'GOOD'	'LOW'	-20
'GOOD'	'LOW'	-24
'GOOD'	'LOW'	-24
'GOOD'	'LOW'	-22
'GOOD'	'LOW'	-23
'GOOD'	'LOW'	-19
'GOOD'	'LOW'	-2
'GOOD'	'LOW'	12
'GOOD'	'LOW'	-8
'GOOD'	'LOW'	-17
'GOOD'	'LOW'	-30
'SCIENTIFIC'	'HIGH'	-19
'SCIENTIFIC'	'HIGH'	-24
'SCIENTIFIC'	'HIGH'	-4
'SCIENTIFIC'	'HIGH'	-24
'SCIENTIFIC'	'HIGH'	0
'SCIENTIFIC'	'HIGH'	-4
'SCIENTIFIC'	'HIGH'	5
'SCIENTIFIC'	'HIGH'	-1
'SCIENTIFIC'	'HIGH'	-9
'SCIENTIFIC'	'HIGH'	-5
'SCIENTIFIC'	'HIGH'	-6
'SCIENTIFIC'	'HIGH'	4
'SCIENTIFIC'	'HIGH'	-13
'SCIENTIFIC'	'HIGH'	-1
'SCIENTIFIC'	'HIGH'	-3
'SCIENTIFIC'	'HIGH'	-11
'SCIENTIFIC'	'HIGH'	-6
'SCIENTIFIC'	'HIGH'	-4
'SCIENTIFIC'	'LOW'	6
'SCIENTIFIC'	'LOW'	-5
'SCIENTIFIC'	'LOW'	14
'SCIENTIFIC'	'LOW'	-11
'SCIENTIFIC'	'LOW'	14
'SCIENTIFIC'	'LOW'	-5
'SCIENTIFIC'	'LOW'	-22
'SCIENTIFIC'	'LOW'	7
'SCIENTIFIC'	'LOW'	14
'SCIENTIFIC'	'LOW'	15
'SCIENTIFIC'	'LOW'	-6
'SCIENTIFIC'	'LOW'	9
'SCIENTIFIC'	'LOW'	-5
'NONE'	'HIGH'	-26
'NONE'	'HIGH'	-1
'NONE'	'HIGH'	22
'NONE'	'HIGH'	3
'NONE'	'HIGH'	-26
'NONE'	'HIGH'	4
'NONE'	'HIGH'	-21
'NONE'	'HIGH'	-19
'NONE'	'HIGH'	-12
'NONE'	'HIGH'	9
'NONE'	'HIGH'	-9
'NONE'	'HIGH'	-27
'NONE'	'HIGH'	-10
'NONE'	'HIGH'	-37
'NONE'	'HIGH'	0
'NONE'	'HIGH'	-10
'NONE'	'LOW'	-12
'NONE'	'LOW'	-4
'NONE'	'LOW'	13
'NONE'	'LOW'	-27
'NONE'	'LOW'	-7
'NONE'	'LOW'	-20
'NONE'	'LOW'	-4
'NONE'	'LOW'	-10
'NONE'	'LOW'	-3
'NONE'	'LOW'	-11
'NONE'	'LOW'	2
'NONE'	'LOW'	-9
'NONE'	'LOW'	20
'NONE'	'LOW'	9
'NONE'	'LOW'	-8
'NONE'	'LOW'	8
'NONE'	'LOW'	-6
'NONE'	'LOW'	6

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76643&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76643&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76643&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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

ANOVA Model
R ~ Exp * Inst
names	(Intercept)	ExpLOW	InstNONE	InstSCIENTIFIC	ExpLOW:InstNONE	ExpLOW:InstSCIENTIFIC
means	4.067	-22.125	-14.067	-11.011	28.625	30.993

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
R ~ Exp * Inst \tabularnewline
names & (Intercept) & ExpLOW & InstNONE & InstSCIENTIFIC & ExpLOW:InstNONE & ExpLOW:InstSCIENTIFIC \tabularnewline
means & 4.067 & -22.125 & -14.067 & -11.011 & 28.625 & 30.993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76643&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]R ~ Exp * Inst[/C][/ROW]
[ROW][C]names[/C][C](Intercept)[/C][C]ExpLOW[/C][C]InstNONE[/C][C]InstSCIENTIFIC[/C][C]ExpLOW:InstNONE[/C][C]ExpLOW:InstSCIENTIFIC[/C][/ROW]
[ROW][C]means[/C][C]4.067[/C][C]-22.125[/C][C]-14.067[/C][C]-11.011[/C][C]28.625[/C][C]30.993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76643&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76643&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
R ~ Exp * Inst
names	(Intercept)	ExpLOW	InstNONE	InstSCIENTIFIC	ExpLOW:InstNONE	ExpLOW:InstSCIENTIFIC
means	4.067	-22.125	-14.067	-11.011	28.625	30.993

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
	1
Exp	1	165.945	165.945	1.042	0.31
Inst	1	295.635	147.817	0.928	0.399
Exp:Inst	1	4729.436	2364.718	14.844	0
Residuals	91	14496.242	159.299

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Exp & 1 & 165.945 & 165.945 & 1.042 & 0.31 \tabularnewline
Inst & 1 & 295.635 & 147.817 & 0.928 & 0.399 \tabularnewline
Exp:Inst & 1 & 4729.436 & 2364.718 & 14.844 & 0 \tabularnewline
Residuals & 91 & 14496.242 & 159.299 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76643&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]Exp[/C][C]1[/C][C]165.945[/C][C]165.945[/C][C]1.042[/C][C]0.31[/C][/ROW]
[ROW][C]Inst[/C][C]1[/C][C]295.635[/C][C]147.817[/C][C]0.928[/C][C]0.399[/C][/ROW]
[ROW][C]Exp:Inst[/C][C]1[/C][C]4729.436[/C][C]2364.718[/C][C]14.844[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]91[/C][C]14496.242[/C][C]159.299[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76643&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76643&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
Exp	1	165.945	165.945	1.042	0.31
Inst	1	295.635	147.817	0.928	0.399
Exp:Inst	1	4729.436	2364.718	14.844	0
Residuals	91	14496.242	159.299

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
LOW-HIGH	-2.616	-7.707	2.475	0.31
NONE-GOOD	1.124	-6.283	8.531	0.931
SCIENTIFIC-GOOD	4.169	-3.41	11.747	0.393
SCIENTIFIC-NONE	3.045	-4.423	10.513	0.597
LOW:GOOD-HIGH:GOOD	-22.125	-35.142	-9.109	0
HIGH:NONE-HIGH:GOOD	-14.067	-27.273	-0.861	0.03
LOW:NONE-HIGH:GOOD	-7.567	-20.413	5.28	0.526
HIGH:SCIENTIFIC-HIGH:GOOD	-11.011	-23.857	1.835	0.136
LOW:SCIENTIFIC-HIGH:GOOD	-2.144	-16.068	11.78	0.998
HIGH:NONE-LOW:GOOD	8.059	-4.74	20.858	0.45
LOW:NONE-LOW:GOOD	14.559	2.132	26.986	0.012
HIGH:SCIENTIFIC-LOW:GOOD	11.114	-1.313	23.542	0.107
LOW:SCIENTIFIC-LOW:GOOD	19.982	6.444	33.52	0.001
LOW:NONE-HIGH:NONE	6.5	-6.125	19.125	0.666
HIGH:SCIENTIFIC-HIGH:NONE	3.056	-9.57	15.681	0.981
LOW:SCIENTIFIC-HIGH:NONE	11.923	-1.797	25.643	0.126
HIGH:SCIENTIFIC-LOW:NONE	-3.444	-15.693	8.804	0.963
LOW:SCIENTIFIC-LOW:NONE	5.423	-7.951	18.797	0.845
LOW:SCIENTIFIC-HIGH:SCIENTIFIC	8.868	-4.507	22.242	0.391

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
LOW-HIGH & -2.616 & -7.707 & 2.475 & 0.31 \tabularnewline
NONE-GOOD & 1.124 & -6.283 & 8.531 & 0.931 \tabularnewline
SCIENTIFIC-GOOD & 4.169 & -3.41 & 11.747 & 0.393 \tabularnewline
SCIENTIFIC-NONE & 3.045 & -4.423 & 10.513 & 0.597 \tabularnewline
LOW:GOOD-HIGH:GOOD & -22.125 & -35.142 & -9.109 & 0 \tabularnewline
HIGH:NONE-HIGH:GOOD & -14.067 & -27.273 & -0.861 & 0.03 \tabularnewline
LOW:NONE-HIGH:GOOD & -7.567 & -20.413 & 5.28 & 0.526 \tabularnewline
HIGH:SCIENTIFIC-HIGH:GOOD & -11.011 & -23.857 & 1.835 & 0.136 \tabularnewline
LOW:SCIENTIFIC-HIGH:GOOD & -2.144 & -16.068 & 11.78 & 0.998 \tabularnewline
HIGH:NONE-LOW:GOOD & 8.059 & -4.74 & 20.858 & 0.45 \tabularnewline
LOW:NONE-LOW:GOOD & 14.559 & 2.132 & 26.986 & 0.012 \tabularnewline
HIGH:SCIENTIFIC-LOW:GOOD & 11.114 & -1.313 & 23.542 & 0.107 \tabularnewline
LOW:SCIENTIFIC-LOW:GOOD & 19.982 & 6.444 & 33.52 & 0.001 \tabularnewline
LOW:NONE-HIGH:NONE & 6.5 & -6.125 & 19.125 & 0.666 \tabularnewline
HIGH:SCIENTIFIC-HIGH:NONE & 3.056 & -9.57 & 15.681 & 0.981 \tabularnewline
LOW:SCIENTIFIC-HIGH:NONE & 11.923 & -1.797 & 25.643 & 0.126 \tabularnewline
HIGH:SCIENTIFIC-LOW:NONE & -3.444 & -15.693 & 8.804 & 0.963 \tabularnewline
LOW:SCIENTIFIC-LOW:NONE & 5.423 & -7.951 & 18.797 & 0.845 \tabularnewline
LOW:SCIENTIFIC-HIGH:SCIENTIFIC & 8.868 & -4.507 & 22.242 & 0.391 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76643&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]LOW-HIGH[/C][C]-2.616[/C][C]-7.707[/C][C]2.475[/C][C]0.31[/C][/ROW]
[ROW][C]NONE-GOOD[/C][C]1.124[/C][C]-6.283[/C][C]8.531[/C][C]0.931[/C][/ROW]
[ROW][C]SCIENTIFIC-GOOD[/C][C]4.169[/C][C]-3.41[/C][C]11.747[/C][C]0.393[/C][/ROW]
[ROW][C]SCIENTIFIC-NONE[/C][C]3.045[/C][C]-4.423[/C][C]10.513[/C][C]0.597[/C][/ROW]
[ROW][C]LOW:GOOD-HIGH:GOOD[/C][C]-22.125[/C][C]-35.142[/C][C]-9.109[/C][C]0[/C][/ROW]
[ROW][C]HIGH:NONE-HIGH:GOOD[/C][C]-14.067[/C][C]-27.273[/C][C]-0.861[/C][C]0.03[/C][/ROW]
[ROW][C]LOW:NONE-HIGH:GOOD[/C][C]-7.567[/C][C]-20.413[/C][C]5.28[/C][C]0.526[/C][/ROW]
[ROW][C]HIGH:SCIENTIFIC-HIGH:GOOD[/C][C]-11.011[/C][C]-23.857[/C][C]1.835[/C][C]0.136[/C][/ROW]
[ROW][C]LOW:SCIENTIFIC-HIGH:GOOD[/C][C]-2.144[/C][C]-16.068[/C][C]11.78[/C][C]0.998[/C][/ROW]
[ROW][C]HIGH:NONE-LOW:GOOD[/C][C]8.059[/C][C]-4.74[/C][C]20.858[/C][C]0.45[/C][/ROW]
[ROW][C]LOW:NONE-LOW:GOOD[/C][C]14.559[/C][C]2.132[/C][C]26.986[/C][C]0.012[/C][/ROW]
[ROW][C]HIGH:SCIENTIFIC-LOW:GOOD[/C][C]11.114[/C][C]-1.313[/C][C]23.542[/C][C]0.107[/C][/ROW]
[ROW][C]LOW:SCIENTIFIC-LOW:GOOD[/C][C]19.982[/C][C]6.444[/C][C]33.52[/C][C]0.001[/C][/ROW]
[ROW][C]LOW:NONE-HIGH:NONE[/C][C]6.5[/C][C]-6.125[/C][C]19.125[/C][C]0.666[/C][/ROW]
[ROW][C]HIGH:SCIENTIFIC-HIGH:NONE[/C][C]3.056[/C][C]-9.57[/C][C]15.681[/C][C]0.981[/C][/ROW]
[ROW][C]LOW:SCIENTIFIC-HIGH:NONE[/C][C]11.923[/C][C]-1.797[/C][C]25.643[/C][C]0.126[/C][/ROW]
[ROW][C]HIGH:SCIENTIFIC-LOW:NONE[/C][C]-3.444[/C][C]-15.693[/C][C]8.804[/C][C]0.963[/C][/ROW]
[ROW][C]LOW:SCIENTIFIC-LOW:NONE[/C][C]5.423[/C][C]-7.951[/C][C]18.797[/C][C]0.845[/C][/ROW]
[ROW][C]LOW:SCIENTIFIC-HIGH:SCIENTIFIC[/C][C]8.868[/C][C]-4.507[/C][C]22.242[/C][C]0.391[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76643&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76643&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
LOW-HIGH	-2.616	-7.707	2.475	0.31
NONE-GOOD	1.124	-6.283	8.531	0.931
SCIENTIFIC-GOOD	4.169	-3.41	11.747	0.393
SCIENTIFIC-NONE	3.045	-4.423	10.513	0.597
LOW:GOOD-HIGH:GOOD	-22.125	-35.142	-9.109	0
HIGH:NONE-HIGH:GOOD	-14.067	-27.273	-0.861	0.03
LOW:NONE-HIGH:GOOD	-7.567	-20.413	5.28	0.526
HIGH:SCIENTIFIC-HIGH:GOOD	-11.011	-23.857	1.835	0.136
LOW:SCIENTIFIC-HIGH:GOOD	-2.144	-16.068	11.78	0.998
HIGH:NONE-LOW:GOOD	8.059	-4.74	20.858	0.45
LOW:NONE-LOW:GOOD	14.559	2.132	26.986	0.012
HIGH:SCIENTIFIC-LOW:GOOD	11.114	-1.313	23.542	0.107
LOW:SCIENTIFIC-LOW:GOOD	19.982	6.444	33.52	0.001
LOW:NONE-HIGH:NONE	6.5	-6.125	19.125	0.666
HIGH:SCIENTIFIC-HIGH:NONE	3.056	-9.57	15.681	0.981
LOW:SCIENTIFIC-HIGH:NONE	11.923	-1.797	25.643	0.126
HIGH:SCIENTIFIC-LOW:NONE	-3.444	-15.693	8.804	0.963
LOW:SCIENTIFIC-LOW:NONE	5.423	-7.951	18.797	0.845
LOW:SCIENTIFIC-HIGH:SCIENTIFIC	8.868	-4.507	22.242	0.391

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	5	1.731	0.135
	91

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 1.731 & 0.135 \tabularnewline
  & 91 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76643&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]1.731[/C][C]0.135[/C][/ROW]
[ROW][C] [/C][C]91[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76643&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76643&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	1.731	0.135
	91

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

Parameters (R input):

par1 = 3 ; par2 = 2 ; par3 = 1 ; 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$coefficients)){
a<-table.element(a, round(lmout$coefficients[i], digits=3),,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$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[i], digits=3),,FALSE)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$'Df'[i+1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i+1], 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(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(1,2,3,3), 2,2))
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,round(thsd[[nt]][i,j], digits=3), 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,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