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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, 07 Dec 2016 13:46:02 +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/07/t14811155648ox72tmxjtqma5k.htm/, Retrieved Fri, 01 Nov 2024 03:39:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298079, Retrieved Fri, 01 Nov 2024 03:39:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [2-way ANOVA-EP3 e...] [2016-12-07 12:46:02] [55eb8f21ed24cda91766c505eb72bb6f] [Current]
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Dataseries X:
13	4	0
16	2	1
17	3	1
NA	2	1
NA	2	0
16	3	1
NA	3	1
NA	2	1
NA	2	0
17	4	0
17	2	0
15	2	0
16	3	0
14	2	0
16	3	0
17	2	1
NA	2	0
NA	NA	1
NA	3	1
NA	2	1
16	2	1
NA	3	0
16	1	1
NA	2	1
NA	3	0
NA	2	0
16	2	0
15	3	0
16	5	0
16	2	0
13	5	0
15	2	1
17	2	0
NA	4	0
13	1	1
17	2	0
NA	2	0
14	3	1
14	2	0
18	3	0
NA	2	1
17	3	1
13	4	1
16	4	1
15	3	1
15	2	1
NA	2	1
15	1	1
13	4	1
NA	4	0
17	3	1
NA	2	0
NA	2	1
11	2	1
14	2	0
13	3	1
NA	2	0
17	2	0
16	3	1
NA	3	0
17	2	1
16	2	1
16	4	1
16	4	1
15	3	0
12	4	1
17	4	0
14	4	1
14	4	0
16	5	1
NA	3	1
NA	4	1
NA	4	0
NA	2	1
NA	2	0
15	3	1
16	4	0
14	2	1
15	5	0
17	1	1
NA	3	1
10	3	0
NA	2	0
17	2	0
NA	1	1
20	2	1
17	1	1
18	2	1
NA	2	1
17	2	0
14	2	1
NA	3	0
17	2	0
NA	1	0
17	1	0
NA	3	1
16	2	1
18	3	1
18	1	0
16	2	1
NA	2	1
NA	3	0
15	2	0
13	2	1
NA	3	1
NA	1	1
NA	4	1
NA	3	0
NA	2	0
16	3	0
NA	3	1
NA	3	1
NA	4	1
12	3	0
NA	2	0
16	3	1
16	2	0
NA	1	0
16	1	1
14	2	0
15	4	0
14	3	0
NA	2	1
15	2	1
NA	3	1
15	3	1
16	3	1
NA	2	0
NA	2	0
NA	2	1
11	3	1
NA	4	1
18	2	0
NA	2	1
11	1	0
NA	3	0
18	3	0
NA	4	0
15	3	1
19	1	0
17	1	0
NA	2	0
14	1	0
NA	5	1
13	3	0
17	2	1
14	2	1
19	4	1
14	2	1
NA	4	0
NA	4	0
16	3	0
16	4	0
15	3	1
12	4	1
NA	3	1
17	4	1
NA	2	0
NA	4	1
18	1	0
15	4	1
18	3	0
15	2	0
NA	2	0
NA	2	0
NA	4	0
16	3	1
NA	3	1
16	2	0




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=298079&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=298079&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298079&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
means16.286-0.391-1.132-0.857-1.619-0.6190.1980.8770.0091.952

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 16.286 & -0.391 & -1.132 & -0.857 & -1.619 & -0.619 & 0.198 & 0.877 & 0.009 & 1.952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298079&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]16.286[/C][C]-0.391[/C][C]-1.132[/C][C]-0.857[/C][C]-1.619[/C][C]-0.619[/C][C]0.198[/C][C]0.877[/C][C]0.009[/C][C]1.952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298079&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298079&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
means16.286-0.391-1.132-0.857-1.619-0.6190.1980.8770.0091.952







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
4
Treatment_A410.2342.5590.6980.596
Treatment_B41.2981.2980.3540.553
Treatment_A:Treatment_B45.0411.260.3440.848
Residuals93341.1153.668

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 4 &  &  &  &  \tabularnewline
Treatment_A & 4 & 10.234 & 2.559 & 0.698 & 0.596 \tabularnewline
Treatment_B & 4 & 1.298 & 1.298 & 0.354 & 0.553 \tabularnewline
Treatment_A:Treatment_B & 4 & 5.041 & 1.26 & 0.344 & 0.848 \tabularnewline
Residuals & 93 & 341.115 & 3.668 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298079&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]4[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]4[/C][C]10.234[/C][C]2.559[/C][C]0.698[/C][C]0.596[/C][/ROW]
[ROW][C]Treatment_B[/C][C]4[/C][C]1.298[/C][C]1.298[/C][C]0.354[/C][C]0.553[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]4[/C][C]5.041[/C][C]1.26[/C][C]0.344[/C][C]0.848[/C][/ROW]
[ROW][C]Residuals[/C][C]93[/C][C]341.115[/C][C]3.668[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298079&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298079&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)
4
Treatment_A410.2342.5590.6980.596
Treatment_B41.2981.2980.3540.553
Treatment_A:Treatment_B45.0411.260.3440.848
Residuals93341.1153.668







Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-1-0.316-2.0281.3960.986
3-1-0.7-2.4691.0690.806
4-1-0.944-2.8840.9950.658
5-1-1-4.0462.0460.891
3-2-0.384-1.6850.9170.923
4-2-0.629-2.1530.8960.781
5-2-0.684-3.4852.1170.96
4-3-0.244-1.8331.3440.993
5-3-0.3-3.1362.5360.998
5-4-0.056-3.0012.891
1-0-0.222-0.9730.5280.558
2:0-1:0-0.391-3.1362.3541
3:0-1:0-1.132-4.0431.7790.96
4:0-1:0-0.857-4.1762.4620.998
5:0-1:0-1.619-5.9032.6650.966
1:1-1:0-0.619-4.0732.8351
2:1-1:0-0.812-3.5571.9330.994
3:1-1:0-0.874-3.6621.9140.991
4:1-1:0-1.468-4.4691.5340.852
5:1-1:0-0.286-6.9236.3521
3:0-2:0-0.741-2.9761.4940.986
4:0-2:0-0.466-3.2112.2791
5:0-2:0-1.228-5.0852.6290.989
1:1-2:0-0.228-3.1362.6791
2:1-2:0-0.421-2.4351.5931
3:1-2:0-0.483-2.5561.590.999
4:1-2:0-1.077-3.4291.2760.895
5:1-2:00.105-6.2656.4751
4:0-3:00.275-2.6363.1851
5:0-3:0-0.487-4.4643.491
1:1-3:00.513-2.5513.5771
2:1-3:00.32-1.9152.5551
3:1-3:00.258-2.032.5451
4:1-3:0-0.336-2.8792.2081
5:1-3:00.846-5.5977.2891
5:0-4:0-0.762-5.0463.5221
1:1-4:00.238-3.2163.6921
2:1-4:00.045-2.72.791
3:1-4:0-0.017-2.8052.7711
4:1-4:0-0.61-3.6122.3911
5:1-4:00.571-6.0667.2091
1:1-5:01-3.395.390.999
2:1-5:00.807-3.054.6641
3:1-5:00.745-3.1434.6331
4:1-5:00.152-3.8924.1951
5:1-5:01.333-5.8368.5021
2:1-1:1-0.193-3.12.7141
3:1-1:1-0.255-3.2032.6931
4:1-1:1-0.848-3.9992.3030.997
5:1-1:10.333-6.3737.0391
3:1-2:1-0.062-2.1352.0111
4:1-2:1-0.656-3.0081.6970.996
5:1-2:10.526-5.8446.8961
4:1-3:1-0.594-2.9961.8090.998
5:1-3:10.588-5.86.9771
5:1-4:11.182-5.3037.6671

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2-1 & -0.316 & -2.028 & 1.396 & 0.986 \tabularnewline
3-1 & -0.7 & -2.469 & 1.069 & 0.806 \tabularnewline
4-1 & -0.944 & -2.884 & 0.995 & 0.658 \tabularnewline
5-1 & -1 & -4.046 & 2.046 & 0.891 \tabularnewline
3-2 & -0.384 & -1.685 & 0.917 & 0.923 \tabularnewline
4-2 & -0.629 & -2.153 & 0.896 & 0.781 \tabularnewline
5-2 & -0.684 & -3.485 & 2.117 & 0.96 \tabularnewline
4-3 & -0.244 & -1.833 & 1.344 & 0.993 \tabularnewline
5-3 & -0.3 & -3.136 & 2.536 & 0.998 \tabularnewline
5-4 & -0.056 & -3.001 & 2.89 & 1 \tabularnewline
1-0 & -0.222 & -0.973 & 0.528 & 0.558 \tabularnewline
2:0-1:0 & -0.391 & -3.136 & 2.354 & 1 \tabularnewline
3:0-1:0 & -1.132 & -4.043 & 1.779 & 0.96 \tabularnewline
4:0-1:0 & -0.857 & -4.176 & 2.462 & 0.998 \tabularnewline
5:0-1:0 & -1.619 & -5.903 & 2.665 & 0.966 \tabularnewline
1:1-1:0 & -0.619 & -4.073 & 2.835 & 1 \tabularnewline
2:1-1:0 & -0.812 & -3.557 & 1.933 & 0.994 \tabularnewline
3:1-1:0 & -0.874 & -3.662 & 1.914 & 0.991 \tabularnewline
4:1-1:0 & -1.468 & -4.469 & 1.534 & 0.852 \tabularnewline
5:1-1:0 & -0.286 & -6.923 & 6.352 & 1 \tabularnewline
3:0-2:0 & -0.741 & -2.976 & 1.494 & 0.986 \tabularnewline
4:0-2:0 & -0.466 & -3.211 & 2.279 & 1 \tabularnewline
5:0-2:0 & -1.228 & -5.085 & 2.629 & 0.989 \tabularnewline
1:1-2:0 & -0.228 & -3.136 & 2.679 & 1 \tabularnewline
2:1-2:0 & -0.421 & -2.435 & 1.593 & 1 \tabularnewline
3:1-2:0 & -0.483 & -2.556 & 1.59 & 0.999 \tabularnewline
4:1-2:0 & -1.077 & -3.429 & 1.276 & 0.895 \tabularnewline
5:1-2:0 & 0.105 & -6.265 & 6.475 & 1 \tabularnewline
4:0-3:0 & 0.275 & -2.636 & 3.185 & 1 \tabularnewline
5:0-3:0 & -0.487 & -4.464 & 3.49 & 1 \tabularnewline
1:1-3:0 & 0.513 & -2.551 & 3.577 & 1 \tabularnewline
2:1-3:0 & 0.32 & -1.915 & 2.555 & 1 \tabularnewline
3:1-3:0 & 0.258 & -2.03 & 2.545 & 1 \tabularnewline
4:1-3:0 & -0.336 & -2.879 & 2.208 & 1 \tabularnewline
5:1-3:0 & 0.846 & -5.597 & 7.289 & 1 \tabularnewline
5:0-4:0 & -0.762 & -5.046 & 3.522 & 1 \tabularnewline
1:1-4:0 & 0.238 & -3.216 & 3.692 & 1 \tabularnewline
2:1-4:0 & 0.045 & -2.7 & 2.79 & 1 \tabularnewline
3:1-4:0 & -0.017 & -2.805 & 2.771 & 1 \tabularnewline
4:1-4:0 & -0.61 & -3.612 & 2.391 & 1 \tabularnewline
5:1-4:0 & 0.571 & -6.066 & 7.209 & 1 \tabularnewline
1:1-5:0 & 1 & -3.39 & 5.39 & 0.999 \tabularnewline
2:1-5:0 & 0.807 & -3.05 & 4.664 & 1 \tabularnewline
3:1-5:0 & 0.745 & -3.143 & 4.633 & 1 \tabularnewline
4:1-5:0 & 0.152 & -3.892 & 4.195 & 1 \tabularnewline
5:1-5:0 & 1.333 & -5.836 & 8.502 & 1 \tabularnewline
2:1-1:1 & -0.193 & -3.1 & 2.714 & 1 \tabularnewline
3:1-1:1 & -0.255 & -3.203 & 2.693 & 1 \tabularnewline
4:1-1:1 & -0.848 & -3.999 & 2.303 & 0.997 \tabularnewline
5:1-1:1 & 0.333 & -6.373 & 7.039 & 1 \tabularnewline
3:1-2:1 & -0.062 & -2.135 & 2.011 & 1 \tabularnewline
4:1-2:1 & -0.656 & -3.008 & 1.697 & 0.996 \tabularnewline
5:1-2:1 & 0.526 & -5.844 & 6.896 & 1 \tabularnewline
4:1-3:1 & -0.594 & -2.996 & 1.809 & 0.998 \tabularnewline
5:1-3:1 & 0.588 & -5.8 & 6.977 & 1 \tabularnewline
5:1-4:1 & 1.182 & -5.303 & 7.667 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298079&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]2-1[/C][C]-0.316[/C][C]-2.028[/C][C]1.396[/C][C]0.986[/C][/ROW]
[ROW][C]3-1[/C][C]-0.7[/C][C]-2.469[/C][C]1.069[/C][C]0.806[/C][/ROW]
[ROW][C]4-1[/C][C]-0.944[/C][C]-2.884[/C][C]0.995[/C][C]0.658[/C][/ROW]
[ROW][C]5-1[/C][C]-1[/C][C]-4.046[/C][C]2.046[/C][C]0.891[/C][/ROW]
[ROW][C]3-2[/C][C]-0.384[/C][C]-1.685[/C][C]0.917[/C][C]0.923[/C][/ROW]
[ROW][C]4-2[/C][C]-0.629[/C][C]-2.153[/C][C]0.896[/C][C]0.781[/C][/ROW]
[ROW][C]5-2[/C][C]-0.684[/C][C]-3.485[/C][C]2.117[/C][C]0.96[/C][/ROW]
[ROW][C]4-3[/C][C]-0.244[/C][C]-1.833[/C][C]1.344[/C][C]0.993[/C][/ROW]
[ROW][C]5-3[/C][C]-0.3[/C][C]-3.136[/C][C]2.536[/C][C]0.998[/C][/ROW]
[ROW][C]5-4[/C][C]-0.056[/C][C]-3.001[/C][C]2.89[/C][C]1[/C][/ROW]
[ROW][C]1-0[/C][C]-0.222[/C][C]-0.973[/C][C]0.528[/C][C]0.558[/C][/ROW]
[ROW][C]2:0-1:0[/C][C]-0.391[/C][C]-3.136[/C][C]2.354[/C][C]1[/C][/ROW]
[ROW][C]3:0-1:0[/C][C]-1.132[/C][C]-4.043[/C][C]1.779[/C][C]0.96[/C][/ROW]
[ROW][C]4:0-1:0[/C][C]-0.857[/C][C]-4.176[/C][C]2.462[/C][C]0.998[/C][/ROW]
[ROW][C]5:0-1:0[/C][C]-1.619[/C][C]-5.903[/C][C]2.665[/C][C]0.966[/C][/ROW]
[ROW][C]1:1-1:0[/C][C]-0.619[/C][C]-4.073[/C][C]2.835[/C][C]1[/C][/ROW]
[ROW][C]2:1-1:0[/C][C]-0.812[/C][C]-3.557[/C][C]1.933[/C][C]0.994[/C][/ROW]
[ROW][C]3:1-1:0[/C][C]-0.874[/C][C]-3.662[/C][C]1.914[/C][C]0.991[/C][/ROW]
[ROW][C]4:1-1:0[/C][C]-1.468[/C][C]-4.469[/C][C]1.534[/C][C]0.852[/C][/ROW]
[ROW][C]5:1-1:0[/C][C]-0.286[/C][C]-6.923[/C][C]6.352[/C][C]1[/C][/ROW]
[ROW][C]3:0-2:0[/C][C]-0.741[/C][C]-2.976[/C][C]1.494[/C][C]0.986[/C][/ROW]
[ROW][C]4:0-2:0[/C][C]-0.466[/C][C]-3.211[/C][C]2.279[/C][C]1[/C][/ROW]
[ROW][C]5:0-2:0[/C][C]-1.228[/C][C]-5.085[/C][C]2.629[/C][C]0.989[/C][/ROW]
[ROW][C]1:1-2:0[/C][C]-0.228[/C][C]-3.136[/C][C]2.679[/C][C]1[/C][/ROW]
[ROW][C]2:1-2:0[/C][C]-0.421[/C][C]-2.435[/C][C]1.593[/C][C]1[/C][/ROW]
[ROW][C]3:1-2:0[/C][C]-0.483[/C][C]-2.556[/C][C]1.59[/C][C]0.999[/C][/ROW]
[ROW][C]4:1-2:0[/C][C]-1.077[/C][C]-3.429[/C][C]1.276[/C][C]0.895[/C][/ROW]
[ROW][C]5:1-2:0[/C][C]0.105[/C][C]-6.265[/C][C]6.475[/C][C]1[/C][/ROW]
[ROW][C]4:0-3:0[/C][C]0.275[/C][C]-2.636[/C][C]3.185[/C][C]1[/C][/ROW]
[ROW][C]5:0-3:0[/C][C]-0.487[/C][C]-4.464[/C][C]3.49[/C][C]1[/C][/ROW]
[ROW][C]1:1-3:0[/C][C]0.513[/C][C]-2.551[/C][C]3.577[/C][C]1[/C][/ROW]
[ROW][C]2:1-3:0[/C][C]0.32[/C][C]-1.915[/C][C]2.555[/C][C]1[/C][/ROW]
[ROW][C]3:1-3:0[/C][C]0.258[/C][C]-2.03[/C][C]2.545[/C][C]1[/C][/ROW]
[ROW][C]4:1-3:0[/C][C]-0.336[/C][C]-2.879[/C][C]2.208[/C][C]1[/C][/ROW]
[ROW][C]5:1-3:0[/C][C]0.846[/C][C]-5.597[/C][C]7.289[/C][C]1[/C][/ROW]
[ROW][C]5:0-4:0[/C][C]-0.762[/C][C]-5.046[/C][C]3.522[/C][C]1[/C][/ROW]
[ROW][C]1:1-4:0[/C][C]0.238[/C][C]-3.216[/C][C]3.692[/C][C]1[/C][/ROW]
[ROW][C]2:1-4:0[/C][C]0.045[/C][C]-2.7[/C][C]2.79[/C][C]1[/C][/ROW]
[ROW][C]3:1-4:0[/C][C]-0.017[/C][C]-2.805[/C][C]2.771[/C][C]1[/C][/ROW]
[ROW][C]4:1-4:0[/C][C]-0.61[/C][C]-3.612[/C][C]2.391[/C][C]1[/C][/ROW]
[ROW][C]5:1-4:0[/C][C]0.571[/C][C]-6.066[/C][C]7.209[/C][C]1[/C][/ROW]
[ROW][C]1:1-5:0[/C][C]1[/C][C]-3.39[/C][C]5.39[/C][C]0.999[/C][/ROW]
[ROW][C]2:1-5:0[/C][C]0.807[/C][C]-3.05[/C][C]4.664[/C][C]1[/C][/ROW]
[ROW][C]3:1-5:0[/C][C]0.745[/C][C]-3.143[/C][C]4.633[/C][C]1[/C][/ROW]
[ROW][C]4:1-5:0[/C][C]0.152[/C][C]-3.892[/C][C]4.195[/C][C]1[/C][/ROW]
[ROW][C]5:1-5:0[/C][C]1.333[/C][C]-5.836[/C][C]8.502[/C][C]1[/C][/ROW]
[ROW][C]2:1-1:1[/C][C]-0.193[/C][C]-3.1[/C][C]2.714[/C][C]1[/C][/ROW]
[ROW][C]3:1-1:1[/C][C]-0.255[/C][C]-3.203[/C][C]2.693[/C][C]1[/C][/ROW]
[ROW][C]4:1-1:1[/C][C]-0.848[/C][C]-3.999[/C][C]2.303[/C][C]0.997[/C][/ROW]
[ROW][C]5:1-1:1[/C][C]0.333[/C][C]-6.373[/C][C]7.039[/C][C]1[/C][/ROW]
[ROW][C]3:1-2:1[/C][C]-0.062[/C][C]-2.135[/C][C]2.011[/C][C]1[/C][/ROW]
[ROW][C]4:1-2:1[/C][C]-0.656[/C][C]-3.008[/C][C]1.697[/C][C]0.996[/C][/ROW]
[ROW][C]5:1-2:1[/C][C]0.526[/C][C]-5.844[/C][C]6.896[/C][C]1[/C][/ROW]
[ROW][C]4:1-3:1[/C][C]-0.594[/C][C]-2.996[/C][C]1.809[/C][C]0.998[/C][/ROW]
[ROW][C]5:1-3:1[/C][C]0.588[/C][C]-5.8[/C][C]6.977[/C][C]1[/C][/ROW]
[ROW][C]5:1-4:1[/C][C]1.182[/C][C]-5.303[/C][C]7.667[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298079&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298079&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
2-1-0.316-2.0281.3960.986
3-1-0.7-2.4691.0690.806
4-1-0.944-2.8840.9950.658
5-1-1-4.0462.0460.891
3-2-0.384-1.6850.9170.923
4-2-0.629-2.1530.8960.781
5-2-0.684-3.4852.1170.96
4-3-0.244-1.8331.3440.993
5-3-0.3-3.1362.5360.998
5-4-0.056-3.0012.891
1-0-0.222-0.9730.5280.558
2:0-1:0-0.391-3.1362.3541
3:0-1:0-1.132-4.0431.7790.96
4:0-1:0-0.857-4.1762.4620.998
5:0-1:0-1.619-5.9032.6650.966
1:1-1:0-0.619-4.0732.8351
2:1-1:0-0.812-3.5571.9330.994
3:1-1:0-0.874-3.6621.9140.991
4:1-1:0-1.468-4.4691.5340.852
5:1-1:0-0.286-6.9236.3521
3:0-2:0-0.741-2.9761.4940.986
4:0-2:0-0.466-3.2112.2791
5:0-2:0-1.228-5.0852.6290.989
1:1-2:0-0.228-3.1362.6791
2:1-2:0-0.421-2.4351.5931
3:1-2:0-0.483-2.5561.590.999
4:1-2:0-1.077-3.4291.2760.895
5:1-2:00.105-6.2656.4751
4:0-3:00.275-2.6363.1851
5:0-3:0-0.487-4.4643.491
1:1-3:00.513-2.5513.5771
2:1-3:00.32-1.9152.5551
3:1-3:00.258-2.032.5451
4:1-3:0-0.336-2.8792.2081
5:1-3:00.846-5.5977.2891
5:0-4:0-0.762-5.0463.5221
1:1-4:00.238-3.2163.6921
2:1-4:00.045-2.72.791
3:1-4:0-0.017-2.8052.7711
4:1-4:0-0.61-3.6122.3911
5:1-4:00.571-6.0667.2091
1:1-5:01-3.395.390.999
2:1-5:00.807-3.054.6641
3:1-5:00.745-3.1434.6331
4:1-5:00.152-3.8924.1951
5:1-5:01.333-5.8368.5021
2:1-1:1-0.193-3.12.7141
3:1-1:1-0.255-3.2032.6931
4:1-1:1-0.848-3.9992.3030.997
5:1-1:10.333-6.3737.0391
3:1-2:1-0.062-2.1352.0111
4:1-2:1-0.656-3.0081.6970.996
5:1-2:10.526-5.8446.8961
4:1-3:1-0.594-2.9961.8090.998
5:1-3:10.588-5.86.9771
5:1-4:11.182-5.3037.6671







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group90.7680.646
93

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 9 & 0.768 & 0.646 \tabularnewline
  & 93 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298079&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]9[/C][C]0.768[/C][C]0.646[/C][/ROW]
[ROW][C] [/C][C]93[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298079&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298079&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)
Group90.7680.646
93



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):
par4 <- 'FALSE'
par3 <- '3'
par2 <- '2'
par1 <- '1'
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')