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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationMon, 07 Nov 2011 09:07:09 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/07/t1320674921x0nv78h7d3qar3e.htm/, Retrieved Fri, 29 Mar 2024 11:10:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=140184, Retrieved Fri, 29 Mar 2024 11:10:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Paired and Unpaired Two Samples Tests about the Mean] [Workshop 5 Questi...] [2011-11-03 11:21:01] [8501ca4b76170905b8a207a77f626994]
- RMPD  [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [WS5 Question 6] [2011-11-04 10:09:19] [8501ca4b76170905b8a207a77f626994]
- RMPD      [Two-Way ANOVA] [ws5 Q8] [2011-11-07 14:07:09] [5ecdd7f9023ba8f0fbc3191d3a9c3da8] [Current]
Feedback Forum

Post a new message
Dataseries X:
'E'	0	1
'F'	1	0
'F'	0	1
'H'	0	1
'H'	0	1
'H'	0	1
'E'	1	1
'F'	1	1
'E'	0	1
'F'	1	0
'H'	0	0
'E'	0	0
'F'	1	1
'H'	0	0
'E'	1	0
'H'	0	0
'E'	0	1
'F'	0	1
'H'	0	0
'F'	1	0
'H'	0	0
'H'	0	1
'H'	0	0
'E'	0	0
'F'	1	0
'E'	1	0
'E'	1	0
'F'	0	1
'F'	0	0
'H'	0	0
'E'	0	1
'E'	1	1
'H'	0	1
'E'	1	1
'F'	1	1
'E'	0	1
'F'	1	0
'H'	0	0
'E'	1	0
'F'	1	0
'F'	1	0
'F'	0	0
'F'	1	0
'H'	1	1
'E'	1	0
'E'	0	0
'H'	0	0
'E'	1	1
'F'	0	1
'F'	0	0
'H'	0	0
'E'	0	1
'F'	1	1
'E'	1	1
'H'	0	1
'H'	0	1
'H'	0	1
'E'	0	1
'H'	0	0
'E'	1	0
'H'	0	1
'F'	0	1
'H'	0	1
'F'	1	0
'E'	0	1
'E'	1	1
'F'	0	0
'H'	0	1
'F'	0	0
'E'	0	1
'E'	-1	1
'H'	0	0
'H'	0	1
'F'	0	1
'H'	0	1
'E'	1	0
'F'	0	1
'E'	1	0
'E'	0	0
'E'	0	0
'F'	0	1
'E'	0	1
'F'	1	1
'H'	0	1
'H'	0	1
'H'	0	1
'F'	0	0
'H'	0	1
'H'	0	1
'F'	1	1
'F'	1	1
'H'	0	0
'F'	0	1
'H'	0	1
'E'	0	0
'F'	1	1
'E'	0	0
'H'	0	1
'F'	1	1
'F'	0	1
'H'	0	1
'E'	1	1
'F'	0	0
'H'	0	1
'E'	0	1
'F'	0	0
'H'	0	0
'H'	0	1
'F'	1	1
'F'	1	1
'H'	0	1
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'H'	0	1
'E'	0	1
'E'	0	0
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'F'	0	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140184&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140184&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140184&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ANOVA Model
Response ~ Treatment_A * Treatment_B
means10.0830.533-0.429-0.533-0.055-0.464NANA

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 1 & 0.083 & 0.533 & -0.429 & -0.533 & -0.055 & -0.464 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140184&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]1[/C][C]0.083[/C][C]0.533[/C][C]-0.429[/C][C]-0.533[/C][C]-0.055[/C][C]-0.464[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140184&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140184&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
Response ~ Treatment_A * Treatment_B
means10.0830.533-0.429-0.533-0.055-0.464NANA







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A20.2440.1220.4850.617
Treatment_B20.2750.1370.5470.58
Treatment_A:Treatment_B20.1890.0940.3770.687
Residuals11027.6010.251

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment_A & 2 & 0.244 & 0.122 & 0.485 & 0.617 \tabularnewline
Treatment_B & 2 & 0.275 & 0.137 & 0.547 & 0.58 \tabularnewline
Treatment_A:Treatment_B & 2 & 0.189 & 0.094 & 0.377 & 0.687 \tabularnewline
Residuals & 110 & 27.601 & 0.251 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140184&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]2[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]2[/C][C]0.244[/C][C]0.122[/C][C]0.485[/C][C]0.617[/C][/ROW]
[ROW][C]Treatment_B[/C][C]2[/C][C]0.275[/C][C]0.137[/C][C]0.547[/C][C]0.58[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]2[/C][C]0.189[/C][C]0.094[/C][C]0.377[/C][C]0.687[/C][/ROW]
[ROW][C]Residuals[/C][C]110[/C][C]27.601[/C][C]0.251[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140184&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140184&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
DfSum SqMean SqF valuePr(>F)
2
Treatment_A20.2440.1220.4850.617
Treatment_B20.2750.1370.5470.58
Treatment_A:Treatment_B20.1890.0940.3770.687
Residuals11027.6010.251







Tukey Honest Significant Difference Comparisons
difflwruprp adj
F-E0.034-0.2370.3060.951
H-E0.109-0.1620.3810.605
H-F0.075-0.1910.3410.782
0--1-0.449-1.6470.7480.647
1--1-0.494-1.7010.7120.595
1-0-0.045-0.2840.1940.896
F:-1-E:-1NANANANA
H:-1-E:-1NANANANA
E:0-E:-1-0.429-2.0511.1940.995
F:0-E:-1-0.4-2.0241.2240.997
H:0-E:-1-0.359-1.9641.2460.999
E:1-E:-1-0.533-2.171.1040.982
F:1-E:-1-0.45-2.0741.1740.994
H:1-E:-10-2.2422.2421
H:-1-F:-1NANANANA
E:0-F:-1NANANANA
F:0-F:-1NANANANA
H:0-F:-1NANANANA
E:1-F:-1NANANANA
F:1-F:-1NANANANA
H:1-F:-1NANANANA
E:0-H:-1NANANANA
F:0-H:-1NANANANA
H:0-H:-1NANANANA
E:1-H:-1NANANANA
F:1-H:-1NANANANA
H:1-H:-1NANANANA
F:0-E:00.029-0.4670.5241
H:0-E:00.07-0.3590.4991
E:1-E:0-0.105-0.6410.4310.999
F:1-E:0-0.021-0.5170.4741
H:1-E:00.429-1.1942.0510.995
H:0-F:00.041-0.3950.4771
E:1-F:0-0.133-0.6750.4080.997
F:1-F:0-0.05-0.5510.4511
H:1-F:00.4-1.2242.0240.997
E:1-H:0-0.174-0.6560.3070.966
F:1-H:0-0.091-0.5270.3450.999
H:1-H:00.359-1.2461.9640.999
F:1-E:10.083-0.4580.6251
H:1-E:10.533-1.1042.170.982
H:1-F:10.45-1.1742.0740.994

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
F-E & 0.034 & -0.237 & 0.306 & 0.951 \tabularnewline
H-E & 0.109 & -0.162 & 0.381 & 0.605 \tabularnewline
H-F & 0.075 & -0.191 & 0.341 & 0.782 \tabularnewline
0--1 & -0.449 & -1.647 & 0.748 & 0.647 \tabularnewline
1--1 & -0.494 & -1.701 & 0.712 & 0.595 \tabularnewline
1-0 & -0.045 & -0.284 & 0.194 & 0.896 \tabularnewline
F:-1-E:-1 & NA & NA & NA & NA \tabularnewline
H:-1-E:-1 & NA & NA & NA & NA \tabularnewline
E:0-E:-1 & -0.429 & -2.051 & 1.194 & 0.995 \tabularnewline
F:0-E:-1 & -0.4 & -2.024 & 1.224 & 0.997 \tabularnewline
H:0-E:-1 & -0.359 & -1.964 & 1.246 & 0.999 \tabularnewline
E:1-E:-1 & -0.533 & -2.17 & 1.104 & 0.982 \tabularnewline
F:1-E:-1 & -0.45 & -2.074 & 1.174 & 0.994 \tabularnewline
H:1-E:-1 & 0 & -2.242 & 2.242 & 1 \tabularnewline
H:-1-F:-1 & NA & NA & NA & NA \tabularnewline
E:0-F:-1 & NA & NA & NA & NA \tabularnewline
F:0-F:-1 & NA & NA & NA & NA \tabularnewline
H:0-F:-1 & NA & NA & NA & NA \tabularnewline
E:1-F:-1 & NA & NA & NA & NA \tabularnewline
F:1-F:-1 & NA & NA & NA & NA \tabularnewline
H:1-F:-1 & NA & NA & NA & NA \tabularnewline
E:0-H:-1 & NA & NA & NA & NA \tabularnewline
F:0-H:-1 & NA & NA & NA & NA \tabularnewline
H:0-H:-1 & NA & NA & NA & NA \tabularnewline
E:1-H:-1 & NA & NA & NA & NA \tabularnewline
F:1-H:-1 & NA & NA & NA & NA \tabularnewline
H:1-H:-1 & NA & NA & NA & NA \tabularnewline
F:0-E:0 & 0.029 & -0.467 & 0.524 & 1 \tabularnewline
H:0-E:0 & 0.07 & -0.359 & 0.499 & 1 \tabularnewline
E:1-E:0 & -0.105 & -0.641 & 0.431 & 0.999 \tabularnewline
F:1-E:0 & -0.021 & -0.517 & 0.474 & 1 \tabularnewline
H:1-E:0 & 0.429 & -1.194 & 2.051 & 0.995 \tabularnewline
H:0-F:0 & 0.041 & -0.395 & 0.477 & 1 \tabularnewline
E:1-F:0 & -0.133 & -0.675 & 0.408 & 0.997 \tabularnewline
F:1-F:0 & -0.05 & -0.551 & 0.451 & 1 \tabularnewline
H:1-F:0 & 0.4 & -1.224 & 2.024 & 0.997 \tabularnewline
E:1-H:0 & -0.174 & -0.656 & 0.307 & 0.966 \tabularnewline
F:1-H:0 & -0.091 & -0.527 & 0.345 & 0.999 \tabularnewline
H:1-H:0 & 0.359 & -1.246 & 1.964 & 0.999 \tabularnewline
F:1-E:1 & 0.083 & -0.458 & 0.625 & 1 \tabularnewline
H:1-E:1 & 0.533 & -1.104 & 2.17 & 0.982 \tabularnewline
H:1-F:1 & 0.45 & -1.174 & 2.074 & 0.994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140184&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]F-E[/C][C]0.034[/C][C]-0.237[/C][C]0.306[/C][C]0.951[/C][/ROW]
[ROW][C]H-E[/C][C]0.109[/C][C]-0.162[/C][C]0.381[/C][C]0.605[/C][/ROW]
[ROW][C]H-F[/C][C]0.075[/C][C]-0.191[/C][C]0.341[/C][C]0.782[/C][/ROW]
[ROW][C]0--1[/C][C]-0.449[/C][C]-1.647[/C][C]0.748[/C][C]0.647[/C][/ROW]
[ROW][C]1--1[/C][C]-0.494[/C][C]-1.701[/C][C]0.712[/C][C]0.595[/C][/ROW]
[ROW][C]1-0[/C][C]-0.045[/C][C]-0.284[/C][C]0.194[/C][C]0.896[/C][/ROW]
[ROW][C]F:-1-E:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]H:-1-E:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]E:0-E:-1[/C][C]-0.429[/C][C]-2.051[/C][C]1.194[/C][C]0.995[/C][/ROW]
[ROW][C]F:0-E:-1[/C][C]-0.4[/C][C]-2.024[/C][C]1.224[/C][C]0.997[/C][/ROW]
[ROW][C]H:0-E:-1[/C][C]-0.359[/C][C]-1.964[/C][C]1.246[/C][C]0.999[/C][/ROW]
[ROW][C]E:1-E:-1[/C][C]-0.533[/C][C]-2.17[/C][C]1.104[/C][C]0.982[/C][/ROW]
[ROW][C]F:1-E:-1[/C][C]-0.45[/C][C]-2.074[/C][C]1.174[/C][C]0.994[/C][/ROW]
[ROW][C]H:1-E:-1[/C][C]0[/C][C]-2.242[/C][C]2.242[/C][C]1[/C][/ROW]
[ROW][C]H:-1-F:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]E:0-F:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]F:0-F:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]H:0-F:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]E:1-F:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]F:1-F:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]H:1-F:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]E:0-H:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]F:0-H:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]H:0-H:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]E:1-H:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]F:1-H:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]H:1-H:-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]F:0-E:0[/C][C]0.029[/C][C]-0.467[/C][C]0.524[/C][C]1[/C][/ROW]
[ROW][C]H:0-E:0[/C][C]0.07[/C][C]-0.359[/C][C]0.499[/C][C]1[/C][/ROW]
[ROW][C]E:1-E:0[/C][C]-0.105[/C][C]-0.641[/C][C]0.431[/C][C]0.999[/C][/ROW]
[ROW][C]F:1-E:0[/C][C]-0.021[/C][C]-0.517[/C][C]0.474[/C][C]1[/C][/ROW]
[ROW][C]H:1-E:0[/C][C]0.429[/C][C]-1.194[/C][C]2.051[/C][C]0.995[/C][/ROW]
[ROW][C]H:0-F:0[/C][C]0.041[/C][C]-0.395[/C][C]0.477[/C][C]1[/C][/ROW]
[ROW][C]E:1-F:0[/C][C]-0.133[/C][C]-0.675[/C][C]0.408[/C][C]0.997[/C][/ROW]
[ROW][C]F:1-F:0[/C][C]-0.05[/C][C]-0.551[/C][C]0.451[/C][C]1[/C][/ROW]
[ROW][C]H:1-F:0[/C][C]0.4[/C][C]-1.224[/C][C]2.024[/C][C]0.997[/C][/ROW]
[ROW][C]E:1-H:0[/C][C]-0.174[/C][C]-0.656[/C][C]0.307[/C][C]0.966[/C][/ROW]
[ROW][C]F:1-H:0[/C][C]-0.091[/C][C]-0.527[/C][C]0.345[/C][C]0.999[/C][/ROW]
[ROW][C]H:1-H:0[/C][C]0.359[/C][C]-1.246[/C][C]1.964[/C][C]0.999[/C][/ROW]
[ROW][C]F:1-E:1[/C][C]0.083[/C][C]-0.458[/C][C]0.625[/C][C]1[/C][/ROW]
[ROW][C]H:1-E:1[/C][C]0.533[/C][C]-1.104[/C][C]2.17[/C][C]0.982[/C][/ROW]
[ROW][C]H:1-F:1[/C][C]0.45[/C][C]-1.174[/C][C]2.074[/C][C]0.994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140184&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140184&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
difflwruprp adj
F-E0.034-0.2370.3060.951
H-E0.109-0.1620.3810.605
H-F0.075-0.1910.3410.782
0--1-0.449-1.6470.7480.647
1--1-0.494-1.7010.7120.595
1-0-0.045-0.2840.1940.896
F:-1-E:-1NANANANA
H:-1-E:-1NANANANA
E:0-E:-1-0.429-2.0511.1940.995
F:0-E:-1-0.4-2.0241.2240.997
H:0-E:-1-0.359-1.9641.2460.999
E:1-E:-1-0.533-2.171.1040.982
F:1-E:-1-0.45-2.0741.1740.994
H:1-E:-10-2.2422.2421
H:-1-F:-1NANANANA
E:0-F:-1NANANANA
F:0-F:-1NANANANA
H:0-F:-1NANANANA
E:1-F:-1NANANANA
F:1-F:-1NANANANA
H:1-F:-1NANANANA
E:0-H:-1NANANANA
F:0-H:-1NANANANA
H:0-H:-1NANANANA
E:1-H:-1NANANANA
F:1-H:-1NANANANA
H:1-H:-1NANANANA
F:0-E:00.029-0.4670.5241
H:0-E:00.07-0.3590.4991
E:1-E:0-0.105-0.6410.4310.999
F:1-E:0-0.021-0.5170.4741
H:1-E:00.429-1.1942.0510.995
H:0-F:00.041-0.3950.4771
E:1-F:0-0.133-0.6750.4080.997
F:1-F:0-0.05-0.5510.4511
H:1-F:00.4-1.2242.0240.997
E:1-H:0-0.174-0.6560.3070.966
F:1-H:0-0.091-0.5270.3450.999
H:1-H:00.359-1.2461.9640.999
F:1-E:10.083-0.4580.6251
H:1-E:10.533-1.1042.170.982
H:1-F:10.45-1.1742.0740.994







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group60.3450.912
110

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 6 & 0.345 & 0.912 \tabularnewline
  & 110 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=140184&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]6[/C][C]0.345[/C][C]0.912[/C][/ROW]
[ROW][C] [/C][C]110[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=140184&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=140184&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
DfF valuePr(>F)
Group60.3450.912
110



Parameters (Session):
par1 = 3 ; par2 = 1 ; par3 = 2 ; par4 = TRUE ;
Parameters (R input):
par1 = 3 ; par2 = 1 ; par3 = 2 ; 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])
names(xdf)<-c('Response', 'Treatment_A', 'Treatment_B')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment_A * Treatment_B- 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment_A * Treatment_B, 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$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],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$coefficients[i], digits=3),,FALSE)
}
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',,TRUE)
a<-table.element(a, 'Df',,FALSE)
a<-table.element(a, 'Sum Sq',,FALSE)
a<-table.element(a, 'Mean Sq',,FALSE)
a<-table.element(a, 'F value',,FALSE)
a<-table.element(a, 'Pr(>F)',,FALSE)
a<-table.row.end(a)
for(i in 1 : length(rownames(anova.xdf))-1){
a<-table.row.start(a)
a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE)
a<-table.element(a, anova.xdf$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$'Mean Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(Response ~ Treatment_A + Treatment_B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups')
dev.off()
bitmap(file='designplot.png')
xdf2 <- xdf # to preserve xdf make copy for function
names(xdf2) <- c(V1, V2, V3)
plot.design(xdf2, main='Design Plot of Group Means')
dev.off()
bitmap(file='interactionplot.png')
interaction.plot(xdf$Treatment_A, xdf$Treatment_B, xdf$Response, 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(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')