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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationFri, 21 Dec 2012 21:55:00 -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/2012/Dec/21/t1356144946yu6nht2sah01o6w.htm/, Retrieved Fri, 19 Apr 2024 21:49:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204453, Retrieved Fri, 19 Apr 2024 21:49:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95

Tree of Dependent Computations

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] [test] [2012-10-26 10:44:09] [f8950b13b9b6c1e097d81f3c7491f9a1]
- R PD  [Paired and Unpaired Two Samples Tests about the Mean] [Two-Sample T-test...] [2012-10-26 11:02:58] [f8950b13b9b6c1e097d81f3c7491f9a1]
-    D    [Paired and Unpaired Two Samples Tests about the Mean] [Two-Sample T-test...] [2012-10-26 11:35:15] [f8950b13b9b6c1e097d81f3c7491f9a1]
-    D      [Paired and Unpaired Two Samples Tests about the Mean] [Two-Sample T-test...] [2012-10-26 11:53:05] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RMP         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [test] [2012-10-26 12:37:49] [f8950b13b9b6c1e097d81f3c7491f9a1]
- R  D          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [1-way ANOVA Quest...] [2012-10-26 12:49:47] [f8950b13b9b6c1e097d81f3c7491f9a1]
-    D            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [1-way ANOVA Quest...] [2012-10-26 12:59:00] [f8950b13b9b6c1e097d81f3c7491f9a1]
-                   [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [1-way ANOVA Quest...] [2012-10-26 13:23:33] [f8950b13b9b6c1e097d81f3c7491f9a1]
-    D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [1-way ANOVA Quest...] [2012-10-26 13:48:59] [f8950b13b9b6c1e097d81f3c7491f9a1]
-                       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [1-way ANOVA Quest...] [2012-10-26 13:59:27] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RM D                    [Two-Way ANOVA] [2-Way Anova - Exp...] [2012-10-26 14:07:12] [f8950b13b9b6c1e097d81f3c7491f9a1]
- R PD                        [Two-Way ANOVA] [Anova deel 2] [2012-12-22 02:55:00] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	'Yes'	'Treatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'Treatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'Yes'	'Treatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'Yes'	'Bad'	0	0
4	'Yes'	'Treatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'Treatment'	NA	'UsedStats'	'No'	'Yes'	'Good'	0	1
4	'Yes'	'Treatment'	NA	'UsedStats'	'Yes'	'Yes'	'Bad'	1	0
4	'Yes'	'Treatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'Treatment'	NA	'UsedStats'	'Yes'	'Yes'	'Good'	1	1
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Bad'	0	0
4	'Yes'	'NoTreatment'	NA	'UsedStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'Treatment'	NA	'UsedStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'Yes'	'Bad'	0	0
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Bad'	0	0
4	'No'	'Treatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'Yes'	'Treatment'	NA	'UsedStats'	'No'	'Yes'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'Treatment'	NA	'NoStats'	'No'	'Yes'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'UsedStats'	'Yes'	'Yes'	'Good'	1	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'No'	'Good'	0	1
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'Yes'	'Treatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'Treatment'	NA	'UsedStats'	'No'	'No'	'Bad'	0	0
4	'Yes'	'Treatment'	NA	'UsedStats'	'Yes'	'Yes'	'Bad'	1	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'Yes'	'No'	'Bad'	1	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'Treatment'	NA	'UsedStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'Yes'	'Treatment'	NA	'UsedStats'	'Yes'	'Yes'	'Good'	1	1
4	'Yes'	'Treatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'Yes'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'Yes'	'Treatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'Treatment'	NA	'UsedStats'	'Yes'	'Yes'	'Bad'	1	0
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'No'	'Good'	0	1
4	'Yes'	'NoTreatment'	NA	'UsedStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'Treatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'UsedStats'	'No'	'Yes'	'Good'	0	1
4	'No'	'Treatment'	NA	'UsedStats'	'Yes'	'No'	'Good'	1	1
4	'No'	'Treatment'	NA	'NoStats'	'No'	'Yes'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'Yes'	'NoTreatment'	NA	'UsedStats'	'No'	'No'	'Good'	0	1
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
4	'No'	'NoTreatment'	NA	'UsedStats'	'Yes'	'No'	'Bad'	1	0
4	'No'	'NoTreatment'	NA	'NoStats'	'No'	'Yes'	'Good'	0	1
4	'Yes'	'NoTreatment'	NA	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'Treatment'	'UsedStats'	'No'	'No'	'Good'	0	1
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'Yes'	'Bad'	0	0
2	'Yes'	NA	'Treatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'Yes'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'Treatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'Treatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'Treatment'	'UsedStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'Treatment'	'UsedStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'Treatment'	'UsedStats'	'No'	'Yes'	'Bad'	0	0
2	'No'	NA	'Treatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'UsedStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'Treatment'	'UsedStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'Treatment'	'UsedStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'UsedStats'	'No'	'Yes'	'Good'	0	1
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'No'	NA	'Treatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'Yes'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'NoTreatment'	'UsedStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'UsedStats'	'No'	'Yes'	'Good'	0	1
2	'Yes'	NA	'Treatment'	'UsedStats'	'No'	'Yes'	'Good'	0	1
2	'No'	NA	'Treatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'UsedStats'	'Yes'	'No'	'Good'	1	1
2	'No'	NA	'Treatment'	'UsedStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'Yes'	'Good'	0	1
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'Yes'	'Bad'	0	0
2	'No'	NA	'Treatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'No'	NA	'Treatment'	'UsedStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'Treatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'Yes'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Bad'	0	0
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'Yes'	'Good'	0	1
2	'No'	NA	'NoTreatment'	'NoStats'	'No'	'No'	'Good'	0	1
2	'Yes'	NA	'NoTreatment'	'UsedStats'	'Yes'	'No'	'Bad'	1	0
2	'Yes'	NA	'NoTreatment'	'UsedStats'	'Yes'	'Yes'	'Bad'	1	0
2	'Yes'	NA	'NoTreatment'	'UsedStats'	'No'	'No'	'Bad'	0	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204453&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204453&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204453&T=0

As an alternative you can also use a QR Code:  
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 time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.3080.160.026-0.049

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.308 & 0.16 & 0.026 & -0.049 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204453&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.308[/C][C]0.16[/C][C]0.026[/C][C]-0.049[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204453&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204453&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.3080.160.026-0.049






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A10.9280.9283.8750.051
Treatment_B10.0010.0010.0050.945
Treatment_A:Treatment_B10.0050.0050.0210.885
Residuals15035.9040.239 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 0.928 & 0.928 & 3.875 & 0.051 \tabularnewline
Treatment_B & 1 & 0.001 & 0.001 & 0.005 & 0.945 \tabularnewline
Treatment_A:Treatment_B & 1 & 0.005 & 0.005 & 0.021 & 0.885 \tabularnewline
Residuals & 150 & 35.904 & 0.239 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204453&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]Treatment_A[/C][C]1[/C][C]0.928[/C][C]0.928[/C][C]3.875[/C][C]0.051[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]0.001[/C][C]0.001[/C][C]0.005[/C][C]0.945[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]0.005[/C][C]0.005[/C][C]0.021[/C][C]0.885[/C][/ROW]
[ROW][C]Residuals[/C][C]150[/C][C]35.904[/C][C]0.239[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204453&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204453&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A10.9280.9283.8750.051
Treatment_B10.0010.0010.0050.945
Treatment_A:Treatment_B10.0050.0050.0210.885
Residuals15035.9040.239






Tukey Honest Significant Difference Comparisons
difflwruprp adj
4-20.156-0.0010.3130.051
1-0-0.01-0.3010.280.945
4:0-2:00.16-0.0540.3740.216
2:1-2:00.026-0.7250.7761
4:1-2:00.137-0.3150.5890.861
2:1-4:0-0.134-0.8820.6140.966
4:1-4:0-0.023-0.4710.4250.999
4:1-2:10.111-0.7360.9590.986

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
4-2 & 0.156 & -0.001 & 0.313 & 0.051 \tabularnewline
1-0 & -0.01 & -0.301 & 0.28 & 0.945 \tabularnewline
4:0-2:0 & 0.16 & -0.054 & 0.374 & 0.216 \tabularnewline
2:1-2:0 & 0.026 & -0.725 & 0.776 & 1 \tabularnewline
4:1-2:0 & 0.137 & -0.315 & 0.589 & 0.861 \tabularnewline
2:1-4:0 & -0.134 & -0.882 & 0.614 & 0.966 \tabularnewline
4:1-4:0 & -0.023 & -0.471 & 0.425 & 0.999 \tabularnewline
4:1-2:1 & 0.111 & -0.736 & 0.959 & 0.986 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204453&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]4-2[/C][C]0.156[/C][C]-0.001[/C][C]0.313[/C][C]0.051[/C][/ROW]
[ROW][C]1-0[/C][C]-0.01[/C][C]-0.301[/C][C]0.28[/C][C]0.945[/C][/ROW]
[ROW][C]4:0-2:0[/C][C]0.16[/C][C]-0.054[/C][C]0.374[/C][C]0.216[/C][/ROW]
[ROW][C]2:1-2:0[/C][C]0.026[/C][C]-0.725[/C][C]0.776[/C][C]1[/C][/ROW]
[ROW][C]4:1-2:0[/C][C]0.137[/C][C]-0.315[/C][C]0.589[/C][C]0.861[/C][/ROW]
[ROW][C]2:1-4:0[/C][C]-0.134[/C][C]-0.882[/C][C]0.614[/C][C]0.966[/C][/ROW]
[ROW][C]4:1-4:0[/C][C]-0.023[/C][C]-0.471[/C][C]0.425[/C][C]0.999[/C][/ROW]
[ROW][C]4:1-2:1[/C][C]0.111[/C][C]-0.736[/C][C]0.959[/C][C]0.986[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204453&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204453&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
4-20.156-0.0010.3130.051
1-0-0.01-0.3010.280.945
4:0-2:00.16-0.0540.3740.216
2:1-2:00.026-0.7250.7761
4:1-2:00.137-0.3150.5890.861
2:1-4:0-0.134-0.8820.6140.966
4:1-4:0-0.023-0.4710.4250.999
4:1-2:10.111-0.7360.9590.986






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group31.30.277
150 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 1.3 & 0.277 \tabularnewline
  & 150 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204453&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]3[/C][C]1.3[/C][C]0.277[/C][/ROW]
[ROW][C] [/C][C]150[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204453&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204453&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group31.30.277
150


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 10 ; par2 = 1 ; par3 = 9 ; par4 = TRUE ;
Parameters (R input):
par1 = 10 ; par2 = 1 ; par3 = 9 ; 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')