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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 computationWed, 21 Dec 2016 09:12:15 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/21/t1482308175jxmstfenq0bbmjn.htm/, Retrieved Mon, 06 May 2024 16:40:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301912, Retrieved Mon, 06 May 2024 16:40:14 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [eZGQZEF] [2016-12-21 08:12:15] [2a4be59ea15844c348dc523b08af79fc] [Current]
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Dataseries X:
0 12 21
1 8 22
0 11 22
1 13 18
1 11 23
1 10 12
0 7 20
1 10 22
1 15 21
1 12 19
1 12 22
1 10 15
1 10 20
0 14 19
0 6 18
0 12 15
1 14 20
0 11 21
1 8 21
0 12 15
1 15 16
1 13 23
0 11 21
1 12 18
1 7 25
1 11 9
1 7 30
0 12 20
1 12 23
0 13 16
0 9 16
0 11 19
1 12 25
1 15 18
1 12 23
1 6 21
0 5 10
1 13 14
1 11 22
0 6 26
1 12 23
1 10 23
1 6 24
1 12 24
1 11 18
0 6 23
1 12 15
1 12 19
0 8 16
1 10 25
1 11 23
1 7 17
1 12 19
1 13 21
1 14 18
1 12 27
0 6 21
1 14 13
0 10 8
1 12 29
1 11 28
0 10 23
0 7 21
1 12 19
0 7 19
1 12 20
0 12 18
1 10 19
1 10 17
0 12 19
0 12 25
0 12 19
0 8 22
1 10 23
0 5 14
0 10 16
1 12 24
0 11 20
0 9 12
1 12 24
0 11 22
0 10 12
0 12 22
1 10 20
0 9 10
1 11 23
1 12 17
0 7 22
0 11 24
0 12 18
1 6 21
1 9 20
1 15 20
0 10 22
1 11 19
0 12 20
1 12 26
1 12 23
1 11 24
1 9 21
1 11 21
0 12 19
1 12 8
1 14 17
1 8 20
0 10 11
0 9 8
0 10 15
0 9 18
0 10 18
0 12 19
1 11 19
1 9 23
1 11 22
1 12 21
1 12 25
0 7 30
1 12 17
1 12 27
0 12 23
1 10 23
0 15 18
0 10 18
1 15 23
1 10 19
1 15 15
1 9 20
1 15 16
1 12 24
1 13 25
1 12 25
0 12 19
1 8 19
1 9 16
1 15 19
1 12 19
1 12 23
1 15 21
0 11 22
1 12 19
1 6 20
1 14 20
1 12 3
1 12 23
0 12 23
0 11 20
1 12 15
0 12 16
0 12 7
1 12 24
0 8 17
1 8 24
1 12 24
0 12 19
1 11 25
1 10 20
1 11 28
0 12 23
0 13 27
0 12 18
0 12 28
1 10 21
0 10 19
1 11 23
0 8 27
1 12 22
0 9 28
1 12 25
0 9 21
0 11 22
1 15 28
0 8 20
1 8 29
1 11 25
1 11 25
1 11 20
1 13 20
0 7 16
1 12 20
0 8 20
0 8 23
0 4 18
1 11 25
0 10 18
1 7 19
0 12 25
0 11 25
0 9 25
0 10 24
1 8 19
1 8 26
1 11 10
1 12 17
0 10 13
0 10 17
1 12 30
0 8 25
0 11 4
0 8 16
0 10 21
1 14 23
1 9 22
0 9 17
0 10 20
1 13 20
0 12 22
1 13 16
1 8 23
0 3 0
1 8 18
1 12 25
1 11 23
0 9 12
0 12 18
0 12 24
1 12 11
1 10 18
1 13 23
1 9 24
0 12 29
0 11 18
0 14 15
1 11 29
1 9 16
0 12 19
0 8 22
0 15 16
1 12 23
1 14 23
0 12 19
0 9 4
0 9 20
1 13 24
1 13 20
1 15 4
1 11 24
0 7 22
1 10 16
1 11 3
1 14 15
0 14 24
0 13 17
1 12 20
0 8 27
1 13 26
1 9 23
0 12 17
1 13 20
0 11 22
1 11 19
1 13 24
0 12 19
1 12 23
0 10 15
1 9 27
0 10 26
1 13 22
0 13 22
0 9 18
1 11 15
1 12 22
0 8 27
1 12 10
1 12 20
0 12 17
1 9 23
0 12 19
0 12 13
1 11 27
1 12 23
0 6 16
1 7 25
0 10 2
0 12 26
1 10 20
0 12 23
0 9 22
1 3 24




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301912&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301912&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301912&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 Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center







ANOVA Model
Response ~ Treatment_A * Treatment_B - 1
means-1-10-2-100-1.25-3-1-0.50.5-1111.5131.3331.51.51.251.66711.81.51.51.75121210.511101210NANANANANA2NANANANANA0NANANANANANANANANANANANANANANANANANANANANA-0.5NA-1NA1NANANANANANANANANANANANANANANANANANANANA0.667-0.833NA0.167-0.333NANANANANANANANA-1.51NA-0.833NANANA-0.5-1-20.167NA-0.90.50.5NANANANANA0-2-0.50.25-1.051.750.75-0.75NANANA-0.25NA-0.75-0.250.083-1.292NA-0.667-0.667NANANANA-0.667-1.167NANANANANANANANANANANANANA-0.467-1.1331.20.2-0.8NANANA0.2-1.3-1.967-0.133-0.167-11.5NA-0.5NANANA0.167-1-10-0.214-0.91NANANANANANA-1-1.66700.25-1.0581.250.25-0.75NANANA-0.75NA-1.750.250.667-0.1432NANANANANA1NA-0.51-0.2-1.2861NANANANANANA-0.5-2.5-1NA-0.52NANANANANANANA-0.5NA0-1NANANANANANANANA-2.5NA1-1NANA0NANANANANANANA1.5NANANANANANANANANANANA1NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B - 1 \tabularnewline
means & -1 & -1 & 0 & -2 & -1 & 0 & 0 & -1.25 & -3 & -1 & -0.5 & 0.5 & -1 & 1 & 1 & 1.5 & 1 & 3 & 1.333 & 1.5 & 1.5 & 1.25 & 1.667 & 1 & 1.8 & 1.5 & 1.5 & 1.75 & 1 & 2 & 1 & 2 & 1 & 0.5 & 1 & 1 & 1 & 0 & 1 & 2 & 1 & 0 & NA & NA & NA & NA & NA & 2 & NA & NA & NA & NA & NA & 0 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -0.5 & NA & -1 & NA & 1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 0.667 & -0.833 & NA & 0.167 & -0.333 & NA & NA & NA & NA & NA & NA & NA & NA & -1.5 & 1 & NA & -0.833 & NA & NA & NA & -0.5 & -1 & -2 & 0.167 & NA & -0.9 & 0.5 & 0.5 & NA & NA & NA & NA & NA & 0 & -2 & -0.5 & 0.25 & -1.05 & 1.75 & 0.75 & -0.75 & NA & NA & NA & -0.25 & NA & -0.75 & -0.25 & 0.083 & -1.292 & NA & -0.667 & -0.667 & NA & NA & NA & NA & -0.667 & -1.167 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -0.467 & -1.133 & 1.2 & 0.2 & -0.8 & NA & NA & NA & 0.2 & -1.3 & -1.967 & -0.133 & -0.167 & -1 & 1.5 & NA & -0.5 & NA & NA & NA & 0.167 & -1 & -1 & 0 & -0.214 & -0.9 & 1 & NA & NA & NA & NA & NA & NA & -1 & -1.667 & 0 & 0.25 & -1.058 & 1.25 & 0.25 & -0.75 & NA & NA & NA & -0.75 & NA & -1.75 & 0.25 & 0.667 & -0.143 & 2 & NA & NA & NA & NA & NA & 1 & NA & -0.5 & 1 & -0.2 & -1.286 & 1 & NA & NA & NA & NA & NA & NA & -0.5 & -2.5 & -1 & NA & -0.5 & 2 & NA & NA & NA & NA & NA & NA & NA & -0.5 & NA & 0 & -1 & NA & NA & NA & NA & NA & NA & NA & NA & -2.5 & NA & 1 & -1 & NA & NA & 0 & NA & NA & NA & NA & NA & NA & NA & 1.5 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301912&T=1

[TABLE]
[ROW]ANOVA Model[/C][/ROW]
[ROW]Response ~ Treatment_A * Treatment_B - 1[/C][/ROW]
[ROW][C]means[/C][C]-1[/C][C]-1[/C][C]0[/C][C]-2[/C][C]-1[/C][C]0[/C][C]0[/C][C]-1.25[/C][C]-3[/C][C]-1[/C][C]-0.5[/C][C]0.5[/C][C]-1[/C][C]1[/C][C]1[/C][C]1.5[/C][C]1[/C][C]3[/C][C]1.333[/C][C]1.5[/C][C]1.5[/C][C]1.25[/C][C]1.667[/C][C]1[/C][C]1.8[/C][C]1.5[/C][C]1.5[/C][C]1.75[/C][C]1[/C][C]2[/C][C]1[/C][C]2[/C][C]1[/C][C]0.5[/C][C]1[/C][C]1[/C][C]1[/C][C]0[/C][C]1[/C][C]2[/C][C]1[/C][C]0[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]2[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.5[/C][C]NA[/C][C]-1[/C][C]NA[/C][C]1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.667[/C][C]-0.833[/C][C]NA[/C][C]0.167[/C][C]-0.333[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-1.5[/C][C]1[/C][C]NA[/C][C]-0.833[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.5[/C][C]-1[/C][C]-2[/C][C]0.167[/C][C]NA[/C][C]-0.9[/C][C]0.5[/C][C]0.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0[/C][C]-2[/C][C]-0.5[/C][C]0.25[/C][C]-1.05[/C][C]1.75[/C][C]0.75[/C][C]-0.75[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.25[/C][C]NA[/C][C]-0.75[/C][C]-0.25[/C][C]0.083[/C][C]-1.292[/C][C]NA[/C][C]-0.667[/C][C]-0.667[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.667[/C][C]-1.167[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.467[/C][C]-1.133[/C][C]1.2[/C][C]0.2[/C][C]-0.8[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.2[/C][C]-1.3[/C][C]-1.967[/C][C]-0.133[/C][C]-0.167[/C][C]-1[/C][C]1.5[/C][C]NA[/C][C]-0.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.167[/C][C]-1[/C][C]-1[/C][C]0[/C][C]-0.214[/C][C]-0.9[/C][C]1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-1[/C][C]-1.667[/C][C]0[/C][C]0.25[/C][C]-1.058[/C][C]1.25[/C][C]0.25[/C][C]-0.75[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.75[/C][C]NA[/C][C]-1.75[/C][C]0.25[/C][C]0.667[/C][C]-0.143[/C][C]2[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]1[/C][C]NA[/C][C]-0.5[/C][C]1[/C][C]-0.2[/C][C]-1.286[/C][C]1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.5[/C][C]-2.5[/C][C]-1[/C][C]NA[/C][C]-0.5[/C][C]2[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.5[/C][C]NA[/C][C]0[/C][C]-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-2.5[/C][C]NA[/C][C]1[/C][C]-1[/C][C]NA[/C][C]NA[/C][C]0[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]1.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301912&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301912&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 - 1
means-1-10-2-100-1.25-3-1-0.50.5-1111.5131.3331.51.51.251.66711.81.51.51.75121210.511101210NANANANANA2NANANANANA0NANANANANANANANANANANANANANANANANANANANANA-0.5NA-1NA1NANANANANANANANANANANANANANANANANANANANA0.667-0.833NA0.167-0.333NANANANANANANANA-1.51NA-0.833NANANA-0.5-1-20.167NA-0.90.50.5NANANANANA0-2-0.50.25-1.051.750.75-0.75NANANA-0.25NA-0.75-0.250.083-1.292NA-0.667-0.667NANANANA-0.667-1.167NANANANANANANANANANANANANA-0.467-1.1331.20.2-0.8NANANA0.2-1.3-1.967-0.133-0.167-11.5NA-0.5NANANA0.167-1-10-0.214-0.91NANANANANANA-1-1.66700.25-1.0581.250.25-0.75NANANA-0.75NA-1.750.250.667-0.1432NANANANANA1NA-0.51-0.2-1.2861NANANANANANA-0.5-2.5-1NA-0.52NANANANANANANA-0.5NA0-1NANANANANANANANA-2.5NA1-1NANA0NANANANANANANA1.5NANANANANANANANANANANA1NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
13
Treatment_A1394.777.2929.6060
Treatment_B137.8410.291.1790.263
Treatment_A:Treatment_B1319.4390.2110.8580.786
Residuals14635.950.246

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 13 &  &  &  &  \tabularnewline
Treatment_A & 13 & 94.77 & 7.29 & 29.606 & 0 \tabularnewline
Treatment_B & 13 & 7.841 & 0.29 & 1.179 & 0.263 \tabularnewline
Treatment_A:Treatment_B & 13 & 19.439 & 0.211 & 0.858 & 0.786 \tabularnewline
Residuals & 146 & 35.95 & 0.246 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301912&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]13[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]13[/C][C]94.77[/C][C]7.29[/C][C]29.606[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]13[/C][C]7.841[/C][C]0.29[/C][C]1.179[/C][C]0.263[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]13[/C][C]19.439[/C][C]0.211[/C][C]0.858[/C][C]0.786[/C][/ROW]
[ROW][C]Residuals[/C][C]146[/C][C]35.95[/C][C]0.246[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301912&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301912&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)
13
Treatment_A1394.777.2929.6060
Treatment_B137.8410.291.1790.263
Treatment_A:Treatment_B1319.4390.2110.8580.786
Residuals14635.950.246







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=301912&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301912&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301912&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
DfF valuePr(>F)
Group1310.4271
146

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 131 & 0.427 & 1 \tabularnewline
  & 146 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301912&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]131[/C][C]0.427[/C][C]1[/C][/ROW]
[ROW][C] [/C][C]146[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301912&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301912&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)
Group1310.4271
146



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = FALSE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = FALSE ;
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')