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Author's title

Author

*Unverified author*

R Software Module

Ian.Hollidayrwasp_One Factor ANOVA.wasp

Title produced by software

One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)

Date of computation

Mon, 29 Nov 2010 23:49:22 +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/Nov/30/t1291074560zrd0txuz0crjezl.htm/, Retrieved Mon, 29 Apr 2024 09:02:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103195, Retrieved Mon, 29 Apr 2024 09:02:36 +0000

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Original text written by user:

WSCRY7V/MWARM30

IsPrivate?

No (this computation is public)

User-defined keywords

ANOVA TEST

Estimated Impact

168

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

-     [Chi Square Measure of Association- Free Statistics Software (Calculator)] [One Way ANOVA wit...] [2009-11-29 13:09:19] [98fd0e87c3eb04e0cc2efde01dbafab6]
-   PD  [Chi Square Measure of Association- Free Statistics Software (Calculator)] [One Way ANOVA for...] [2009-12-01 13:05:10] [3fdd735c61ad38cbc9b3393dc997cdb7]
- R P     [Chi Square Measure of Association- Free Statistics Software (Calculator)] [CARE date with Tu...] [2009-12-01 18:33:48] [98fd0e87c3eb04e0cc2efde01dbafab6]
-   P       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [CARE Data with Tu...] [2010-11-23 12:09:38] [3fdd735c61ad38cbc9b3393dc997cdb7]
-             [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [compendium 8] [2010-11-29 22:37:06] [b57c483c9c7aeaac92ee0309919c11c0]
-    D          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [compendium 8] [2010-11-29 22:49:28] [b57c483c9c7aeaac92ee0309919c11c0]
-    D            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [compendium 8] [2010-11-29 23:03:13] [b57c483c9c7aeaac92ee0309919c11c0]
-    D              [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [compendium 8] [2010-11-29 23:21:18] [b57c483c9c7aeaac92ee0309919c11c0]
-    D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [compendium 8] [2010-11-29 23:38:11] [b57c483c9c7aeaac92ee0309919c11c0]
-    D                    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [compendium 8] [2010-11-29 23:49:22] [4ce57d25edbdd6fa8e41e5f59df13410] [Current]

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Dataseries X:

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Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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' @ 72.249.127.135 \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103195&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' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103195&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	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

ANOVA Model
WSCRY7V ~ MWARM30
means	108	-16.7	-37	-30.429	-24.182	-19.684	-23.355	-18.905	-16.03

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
WSCRY7V  ~  MWARM30 \tabularnewline
means & 108 & -16.7 & -37 & -30.429 & -24.182 & -19.684 & -23.355 & -18.905 & -16.03 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103195&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]WSCRY7V  ~  MWARM30[/C][/ROW]
[ROW][C]means[/C][C]108[/C][C]-16.7[/C][C]-37[/C][C]-30.429[/C][C]-24.182[/C][C]-19.684[/C][C]-23.355[/C][C]-18.905[/C][C]-16.03[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103195&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103195&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
WSCRY7V ~ MWARM30
means	108	-16.7	-37	-30.429	-24.182	-19.684	-23.355	-18.905	-16.03

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
MWARM30	8	4211.859	526.482	1.141	0.339
Residuals	151	69655.241	461.293

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MWARM30 & 8 & 4211.859 & 526.482 & 1.141 & 0.339 \tabularnewline
Residuals & 151 & 69655.241 & 461.293 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103195&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]MWARM30[/C][C]8[/C][C]4211.859[/C][C]526.482[/C][C]1.141[/C][C]0.339[/C][/ROW]
[ROW][C]Residuals[/C][C]151[/C][C]69655.241[/C][C]461.293[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103195&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103195&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)
MWARM30	8	4211.859	526.482	1.141	0.339
Residuals	151	69655.241	461.293

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
10-0	-16.7	-69.061	35.661	0.985
3-0	-37	-93.556	19.556	0.505
4-0	-30.429	-84.627	23.77	0.704
5-0	-24.182	-76.144	27.781	0.87
6-0	-19.684	-69.935	30.567	0.948
7-0	-23.355	-72.671	25.961	0.858
8-0	-18.905	-67.828	30.019	0.952
9-0	-16.03	-65.256	33.195	0.983
3-10	-20.3	-57.325	16.725	0.73
4-10	-13.729	-47.041	19.584	0.931
5-10	-7.482	-37.017	22.054	0.997
6-10	-2.984	-29.393	23.425	1
7-10	-6.655	-31.238	17.928	0.995
8-10	-2.205	-25.99	21.58	1
9-10	0.67	-23.731	25.071	1
4-3	6.571	-33.009	46.152	1
5-3	12.818	-23.641	49.277	0.972
6-3	17.316	-16.66	51.292	0.801
7-3	13.645	-18.932	46.222	0.924
8-3	18.095	-13.884	50.074	0.695
9-3	20.97	-11.47	53.41	0.522
5-4	6.247	-26.436	38.93	1
6-4	10.744	-19.143	40.632	0.969
7-4	7.074	-21.214	35.361	0.997
8-4	11.524	-16.073	39.12	0.926
9-4	14.398	-13.731	42.527	0.797
6-5	4.498	-21.113	30.108	1
7-5	0.827	-22.896	24.55	1
8-5	5.277	-17.618	28.172	0.998
9-5	8.152	-15.383	31.686	0.975
7-6	-3.671	-23.366	16.024	1
8-6	0.779	-17.91	19.469	1
9-6	3.654	-15.813	23.121	1
8-7	4.45	-11.556	20.456	0.994
9-7	7.325	-9.583	24.232	0.91
9-8	2.874	-12.85	18.599	1

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
10-0 & -16.7 & -69.061 & 35.661 & 0.985 \tabularnewline
3-0 & -37 & -93.556 & 19.556 & 0.505 \tabularnewline
4-0 & -30.429 & -84.627 & 23.77 & 0.704 \tabularnewline
5-0 & -24.182 & -76.144 & 27.781 & 0.87 \tabularnewline
6-0 & -19.684 & -69.935 & 30.567 & 0.948 \tabularnewline
7-0 & -23.355 & -72.671 & 25.961 & 0.858 \tabularnewline
8-0 & -18.905 & -67.828 & 30.019 & 0.952 \tabularnewline
9-0 & -16.03 & -65.256 & 33.195 & 0.983 \tabularnewline
3-10 & -20.3 & -57.325 & 16.725 & 0.73 \tabularnewline
4-10 & -13.729 & -47.041 & 19.584 & 0.931 \tabularnewline
5-10 & -7.482 & -37.017 & 22.054 & 0.997 \tabularnewline
6-10 & -2.984 & -29.393 & 23.425 & 1 \tabularnewline
7-10 & -6.655 & -31.238 & 17.928 & 0.995 \tabularnewline
8-10 & -2.205 & -25.99 & 21.58 & 1 \tabularnewline
9-10 & 0.67 & -23.731 & 25.071 & 1 \tabularnewline
4-3 & 6.571 & -33.009 & 46.152 & 1 \tabularnewline
5-3 & 12.818 & -23.641 & 49.277 & 0.972 \tabularnewline
6-3 & 17.316 & -16.66 & 51.292 & 0.801 \tabularnewline
7-3 & 13.645 & -18.932 & 46.222 & 0.924 \tabularnewline
8-3 & 18.095 & -13.884 & 50.074 & 0.695 \tabularnewline
9-3 & 20.97 & -11.47 & 53.41 & 0.522 \tabularnewline
5-4 & 6.247 & -26.436 & 38.93 & 1 \tabularnewline
6-4 & 10.744 & -19.143 & 40.632 & 0.969 \tabularnewline
7-4 & 7.074 & -21.214 & 35.361 & 0.997 \tabularnewline
8-4 & 11.524 & -16.073 & 39.12 & 0.926 \tabularnewline
9-4 & 14.398 & -13.731 & 42.527 & 0.797 \tabularnewline
6-5 & 4.498 & -21.113 & 30.108 & 1 \tabularnewline
7-5 & 0.827 & -22.896 & 24.55 & 1 \tabularnewline
8-5 & 5.277 & -17.618 & 28.172 & 0.998 \tabularnewline
9-5 & 8.152 & -15.383 & 31.686 & 0.975 \tabularnewline
7-6 & -3.671 & -23.366 & 16.024 & 1 \tabularnewline
8-6 & 0.779 & -17.91 & 19.469 & 1 \tabularnewline
9-6 & 3.654 & -15.813 & 23.121 & 1 \tabularnewline
8-7 & 4.45 & -11.556 & 20.456 & 0.994 \tabularnewline
9-7 & 7.325 & -9.583 & 24.232 & 0.91 \tabularnewline
9-8 & 2.874 & -12.85 & 18.599 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103195&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]10-0[/C][C]-16.7[/C][C]-69.061[/C][C]35.661[/C][C]0.985[/C][/ROW]
[ROW][C]3-0[/C][C]-37[/C][C]-93.556[/C][C]19.556[/C][C]0.505[/C][/ROW]
[ROW][C]4-0[/C][C]-30.429[/C][C]-84.627[/C][C]23.77[/C][C]0.704[/C][/ROW]
[ROW][C]5-0[/C][C]-24.182[/C][C]-76.144[/C][C]27.781[/C][C]0.87[/C][/ROW]
[ROW][C]6-0[/C][C]-19.684[/C][C]-69.935[/C][C]30.567[/C][C]0.948[/C][/ROW]
[ROW][C]7-0[/C][C]-23.355[/C][C]-72.671[/C][C]25.961[/C][C]0.858[/C][/ROW]
[ROW][C]8-0[/C][C]-18.905[/C][C]-67.828[/C][C]30.019[/C][C]0.952[/C][/ROW]
[ROW][C]9-0[/C][C]-16.03[/C][C]-65.256[/C][C]33.195[/C][C]0.983[/C][/ROW]
[ROW][C]3-10[/C][C]-20.3[/C][C]-57.325[/C][C]16.725[/C][C]0.73[/C][/ROW]
[ROW][C]4-10[/C][C]-13.729[/C][C]-47.041[/C][C]19.584[/C][C]0.931[/C][/ROW]
[ROW][C]5-10[/C][C]-7.482[/C][C]-37.017[/C][C]22.054[/C][C]0.997[/C][/ROW]
[ROW][C]6-10[/C][C]-2.984[/C][C]-29.393[/C][C]23.425[/C][C]1[/C][/ROW]
[ROW][C]7-10[/C][C]-6.655[/C][C]-31.238[/C][C]17.928[/C][C]0.995[/C][/ROW]
[ROW][C]8-10[/C][C]-2.205[/C][C]-25.99[/C][C]21.58[/C][C]1[/C][/ROW]
[ROW][C]9-10[/C][C]0.67[/C][C]-23.731[/C][C]25.071[/C][C]1[/C][/ROW]
[ROW][C]4-3[/C][C]6.571[/C][C]-33.009[/C][C]46.152[/C][C]1[/C][/ROW]
[ROW][C]5-3[/C][C]12.818[/C][C]-23.641[/C][C]49.277[/C][C]0.972[/C][/ROW]
[ROW][C]6-3[/C][C]17.316[/C][C]-16.66[/C][C]51.292[/C][C]0.801[/C][/ROW]
[ROW][C]7-3[/C][C]13.645[/C][C]-18.932[/C][C]46.222[/C][C]0.924[/C][/ROW]
[ROW][C]8-3[/C][C]18.095[/C][C]-13.884[/C][C]50.074[/C][C]0.695[/C][/ROW]
[ROW][C]9-3[/C][C]20.97[/C][C]-11.47[/C][C]53.41[/C][C]0.522[/C][/ROW]
[ROW][C]5-4[/C][C]6.247[/C][C]-26.436[/C][C]38.93[/C][C]1[/C][/ROW]
[ROW][C]6-4[/C][C]10.744[/C][C]-19.143[/C][C]40.632[/C][C]0.969[/C][/ROW]
[ROW][C]7-4[/C][C]7.074[/C][C]-21.214[/C][C]35.361[/C][C]0.997[/C][/ROW]
[ROW][C]8-4[/C][C]11.524[/C][C]-16.073[/C][C]39.12[/C][C]0.926[/C][/ROW]
[ROW][C]9-4[/C][C]14.398[/C][C]-13.731[/C][C]42.527[/C][C]0.797[/C][/ROW]
[ROW][C]6-5[/C][C]4.498[/C][C]-21.113[/C][C]30.108[/C][C]1[/C][/ROW]
[ROW][C]7-5[/C][C]0.827[/C][C]-22.896[/C][C]24.55[/C][C]1[/C][/ROW]
[ROW][C]8-5[/C][C]5.277[/C][C]-17.618[/C][C]28.172[/C][C]0.998[/C][/ROW]
[ROW][C]9-5[/C][C]8.152[/C][C]-15.383[/C][C]31.686[/C][C]0.975[/C][/ROW]
[ROW][C]7-6[/C][C]-3.671[/C][C]-23.366[/C][C]16.024[/C][C]1[/C][/ROW]
[ROW][C]8-6[/C][C]0.779[/C][C]-17.91[/C][C]19.469[/C][C]1[/C][/ROW]
[ROW][C]9-6[/C][C]3.654[/C][C]-15.813[/C][C]23.121[/C][C]1[/C][/ROW]
[ROW][C]8-7[/C][C]4.45[/C][C]-11.556[/C][C]20.456[/C][C]0.994[/C][/ROW]
[ROW][C]9-7[/C][C]7.325[/C][C]-9.583[/C][C]24.232[/C][C]0.91[/C][/ROW]
[ROW][C]9-8[/C][C]2.874[/C][C]-12.85[/C][C]18.599[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103195&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103195&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
10-0	-16.7	-69.061	35.661	0.985
3-0	-37	-93.556	19.556	0.505
4-0	-30.429	-84.627	23.77	0.704
5-0	-24.182	-76.144	27.781	0.87
6-0	-19.684	-69.935	30.567	0.948
7-0	-23.355	-72.671	25.961	0.858
8-0	-18.905	-67.828	30.019	0.952
9-0	-16.03	-65.256	33.195	0.983
3-10	-20.3	-57.325	16.725	0.73
4-10	-13.729	-47.041	19.584	0.931
5-10	-7.482	-37.017	22.054	0.997
6-10	-2.984	-29.393	23.425	1
7-10	-6.655	-31.238	17.928	0.995
8-10	-2.205	-25.99	21.58	1
9-10	0.67	-23.731	25.071	1
4-3	6.571	-33.009	46.152	1
5-3	12.818	-23.641	49.277	0.972
6-3	17.316	-16.66	51.292	0.801
7-3	13.645	-18.932	46.222	0.924
8-3	18.095	-13.884	50.074	0.695
9-3	20.97	-11.47	53.41	0.522
5-4	6.247	-26.436	38.93	1
6-4	10.744	-19.143	40.632	0.969
7-4	7.074	-21.214	35.361	0.997
8-4	11.524	-16.073	39.12	0.926
9-4	14.398	-13.731	42.527	0.797
6-5	4.498	-21.113	30.108	1
7-5	0.827	-22.896	24.55	1
8-5	5.277	-17.618	28.172	0.998
9-5	8.152	-15.383	31.686	0.975
7-6	-3.671	-23.366	16.024	1
8-6	0.779	-17.91	19.469	1
9-6	3.654	-15.813	23.121	1
8-7	4.45	-11.556	20.456	0.994
9-7	7.325	-9.583	24.232	0.91
9-8	2.874	-12.85	18.599	1

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	8	0.763	0.636
	151

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 8 & 0.763 & 0.636 \tabularnewline
  & 151 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103195&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]8[/C][C]0.763[/C][C]0.636[/C][/ROW]
[ROW][C] [/C][C]151[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103195&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103195&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	8	0.763	0.636
	151

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = TRUE ;

Parameters (R input):

par1 = 1 ; par2 = 2 ; par3 = TRUE ;

R code (references can be found in the software module):

cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
intercept<-as.logical(par3)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
xdf<-data.frame(x1,f1)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
names(xdf)<-c('Response', 'Treatment')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, 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$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$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)
a<-table.row.start(a)
a<-table.element(a, V2,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$Df[2],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], 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, data=xdf, xlab=V2, ylab=V1)
dev.off()
if(intercept==TRUE){
thsd<-TukeyHSD(aov.xdf)
bitmap(file='TukeyHSDPlot.png')
plot(thsd)
dev.off()
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(i in 1:length(rownames(thsd[[1]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code