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Author*The author of this computation has been verified*
R Software Modulerwasp_One Factor ANOVA.wasp
Title produced by softwareOne-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)
Date of computationThu, 24 Nov 2011 07:44:41 -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/24/t1322138697czugi555kfol94b.htm/, Retrieved Fri, 29 Mar 2024 07:46:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146691, Retrieved Fri, 29 Mar 2024 07:46:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105
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]
- RM          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [IQ and Mothers Age] [2011-11-21 16:34:08] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R PD          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2011-11-24 12:30:07] [483074838c7eb9e0ff7f7d3e3c3f8586]
-   P               [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2011-11-24 12:44:41] [7cb3389c165bc116919f8c93cbf97eb6] [Current]
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Dataseries X:
2	1	86	6	36	88
3	3	86	8	56	94
4	3	103	8	48	90
5	3	74	7	32	73
6	1	63	5	44	68
7	2	82	7	39	80
8	3	93	8	34	86
9	3	77	9	41	86
10	2	111	9	50	91
11	1	71	3	39	79
12	1	103	9	62	96
13	3	89	7	52	92
14	3	75	9	37	72
15	3	88	8	50	96
16	1	84	6	41	70
17	3	85	7	55	86
18	3	70	8	41	87
19	3	104	9	56	88
20	2	88	7	39	79
21	2	77	6	52	90
22	1	77	8	46	95
23	1	72	7	44	85
25	3	83	8	41	90
26	1	110	9	50	115
27	1	91	9	50	84
28	3	80	7	44	79
29	1	91	4	52	94
30	2	86	7	54	97
31	3	85	7	44	86
32	2	107	9	52	111
33	2	93	7	37	87
34	3	87	9	52	98
35	1	84	10	50	87
36	3	73	5	36	68
37	3	84	6	50	88
38	1	86	9	52	82
39	2	99	9	55	111
40	1	75	8	31	75
41	1	87	6	36	94
42	2	79	6	49	95
43	1	82	5	42	80
44	1	95	8	37	95
45	3	84	8	41	68
46	2	85	5	30	94
47	2	95	6	52	88
48	1	63	9	30	84
50	1	85	4	44	101
51	1	86	8	66	98
52	2	75	9	48	78
53	3	98	7	43	109
54	3	71	7	57	102
55	3	63	6	46	81
56	3	71	9	54	97
57	2	84	9	48	75
58	2	81	8	48	97
60	3	79	6	62	101
61	3	63	10	58	101
62	2	93	8	58	95
63	2	92	7	62	95
65	3	83	8	46	95
66	2	80	3	34	90
67	3	111	8	66	107
68	3	92	10	52	92
69	2	79	7	55	86
70	2	69	5	55	70
71	3	83	10	57	95
72	3	80	5	56	96
73	2	91	8	55	91
74	1	97	9	56	87
75	2	85	6	54	92
76	2	85	9	55	97
77	2	99	8	46	102
78	2	67	5	52	91
79	3	87	8	32	68
80	2	68	3	44	88
81	3	81	7	46	97
82	1	80	8	59	90
83	3	93	10	46	101
84	3	93	9	46	94
85	1	102	10	54	101
86	1	104	9	66	109
87	2	90	8	56	100
88	1	85	8	59	103
89	3	92	8	57	94
90	1	82	9	52	97
91	3	85	4	48	85
92	2	89	6	44	75
93	3	77	7	41	77
94	1	79	4	50	87
95	1	76	9	48	78
96	2	101	7	48	108
97	3	81	8	59	97
99	3	89	8	46	106
100	1	81	7	54	107
101	2	77	7	55	95
102	2	95	9	54	107
103	3	85	8	59	115
104	3	81	8	44	101
105	1	76	9	54	85
106	3	93	9	52	90
107	3	104	10	66	115
108	3	89	7	44	95
109	2	76	8	57	97
110	3	77	5	39	112
111	3	71	9	60	97
112	3	79	8	45	77
113	3	89	7	41	90
114	1	81	8	50	94
115	3	99	8	39	103
116	1	81	7	43	77
117	3	84	6	48	98
118	3	85	7	37	90
119	3	111	7	58	111
120	2	78	6	46	77
121	3	111	6	43	88
122	2	78	7	44	75
123	3	87	9	34	92
124	2	92	6	30	78
125	2	93	10	50	106
126	2	70	4	39	80
127	3	84	8	37	87
128	3	75	7	55	92
131	3	85	5	39	86
132	3	87	9	36	85
133	2	75	8	43	90
134	1	103	9	50	101
135	3	86	8	55	94
136	3	77	8	43	86
137	3	74	9	60	86
138	2	74	8	48	90
139	1	76	9	30	75
140	3	83	7	43	86
141	3	101	6	39	91
142	3	83	8	52	97
143	2	92	6	39	91
144	3	74	5	39	70
145	2	87	3	56	98
146	3	71	6	59	96
147	3	79	8	46	95
148	2	83	7	57	100
149	3	80	8	50	95
150	3	90	6	54	97
151	3	80	9	50	97
152	3	96	9	60	92
153	1	109	10	59	115
154	3	98	7	41	88
155	2	85	5	48	87
156	3	83	8	59	100
157	3	86	9	60	98
158	1	72	8	56	102
160	3	75	4	51	96




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146691&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146691&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146691&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 time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ANOVA Model
WISCRY7V ~ MOMAGE
means90.80.2481.227

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
WISCRY7V  ~  MOMAGE \tabularnewline
means & 90.8 & 0.248 & 1.227 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146691&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]WISCRY7V  ~  MOMAGE[/C][/ROW]
[ROW][C]means[/C][C]90.8[/C][C]0.248[/C][C]1.227[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146691&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146691&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
WISCRY7V ~ MOMAGE
means90.80.2481.227







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MOMAGE246.16523.0830.2010.818
Residuals14816993.451114.821

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MOMAGE & 2 & 46.165 & 23.083 & 0.201 & 0.818 \tabularnewline
Residuals & 148 & 16993.451 & 114.821 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146691&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]MOMAGE[/C][C]2[/C][C]46.165[/C][C]23.083[/C][C]0.201[/C][C]0.818[/C][/ROW]
[ROW][C]Residuals[/C][C]148[/C][C]16993.451[/C][C]114.821[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146691&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146691&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)
MOMAGE246.16523.0830.2010.818
Residuals14816993.451114.821







Tukey Honest Significant Difference Comparisons
difflwruprp adj
2-10.248-5.5596.0540.994
3-11.227-3.9776.4310.842
3-20.979-3.9225.8810.884

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
2-1 & 0.248 & -5.559 & 6.054 & 0.994 \tabularnewline
3-1 & 1.227 & -3.977 & 6.431 & 0.842 \tabularnewline
3-2 & 0.979 & -3.922 & 5.881 & 0.884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146691&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]0.248[/C][C]-5.559[/C][C]6.054[/C][C]0.994[/C][/ROW]
[ROW][C]3-1[/C][C]1.227[/C][C]-3.977[/C][C]6.431[/C][C]0.842[/C][/ROW]
[ROW][C]3-2[/C][C]0.979[/C][C]-3.922[/C][C]5.881[/C][C]0.884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146691&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146691&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
2-10.248-5.5596.0540.994
3-11.227-3.9776.4310.842
3-20.979-3.9225.8810.884







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group21.2970.276
148

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 2 & 1.297 & 0.276 \tabularnewline
  & 148 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146691&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]2[/C][C]1.297[/C][C]0.276[/C][/ROW]
[ROW][C] [/C][C]148[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146691&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146691&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)
Group21.2970.276
148



Parameters (Session):
par1 = 6 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 6 ; 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()
}
if(intercept==TRUE){
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')