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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Simple Regression Y ~ X.wasp
Title produced by softwareSimple Linear Regression
Date of computationSun, 09 Dec 2012 08:05:55 -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/09/t1355058436qpzo2y508bykovr.htm/, Retrieved Thu, 25 Apr 2024 03:45:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197851, Retrieved Thu, 25 Apr 2024 03:45:00 +0000
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Estimated Impact92
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Dataseries X:
58,58527778	79
33,60611111	58
49,03	60
49,81138889	108
34,21805556	49
14,65166667	0
107,0927778	121
9,213888889	1
28,23472222	20
41,40583333	43
45,95722222	69
65,8925	78
48,14611111	86
36,98083333	44
71,90916667	104
50,02305556	63
90,22194444	158
64,15666667	102
65,77361111	77
37,63138889	82
56,36805556	115
59,76305556	101
95,63805556	80
42,75972222	50
36,92861111	83
48,53444444	123
48,44861111	73
62,65222222	81
62,12	105
34,67138889	47
61,58277778	105
58,54638889	94
47,29611111	44
72,37805556	114
23,57027778	38
81,78444444	107
28,05861111	30
59,90027778	71
90,3075	84
1,993333333	0
46,53944444	59
29,55777778	33
26,82222222	42
73,82472222	96
74,90305556	106
41,42	56
48,84	57
42,46416667	59
31,01805556	39
32,33555556	34
100,6391667	76
21,88888889	20
50,87972222	91
77,2125	115
41,84138889	85
46,89138889	76
6,718888889	8
91,46305556	79
18,06361111	21
28,0825	30
60,81833333	76
67,79222222	101
94,88055556	94
28,77694444	27
64,81333333	92
71,23944444	123
57,26694444	75
86,52027778	128
65,5	105
49,4275	55
57,54888889	56
54,59805556	41
48,38444444	72
39,79055556	67
52,09972222	75
52,13361111	114
33,06	118
50,60888889	77
20,435	22
54,16083333	66
46,52444444	69
39,93222222	105
76,53916667	116
67,55527778	88
50,83305556	73
37,68027778	99
42,30527778	62
33,39472222	53
96,24583333	118
40,49722222	30
53,70527778	100
22,48694444	49
34,10388889	24
36,27361111	67
31,28083333	46
79,57444444	57
66,96277778	75
41,235	135
56,86472222	68
50,5775	124
38,98444444	33
61,25444444	98
67,51666667	58
45,2125	68
50,72583333	81
64,48277778	131
73,69944444	110
23,77055556	37
86,34416667	130
62,51666667	93
64,5325	118
40,26833333	39
12,02416667	13
43,265	74
45,7525	81
56,09444444	109
65,40388889	151
61,33361111	51
27,62944444	28
25,73916667	40
37,03555556	56
17,04472222	27
34,98055556	37
27,98611111	83
62,37472222	54
22,86555556	27
28,33611111	28
28,20083333	59
67,64194444	133
6,371666667	12
11,54611111	0
42,35388889	106
17,1825	23
27,75638889	44
36,80194444	71
88,165	116
5,848333333	4
58,23361111	62
6,291111111	12
8,726111111	18
12,97166667	14
36,58277778	60
25,48194444	7
67,98583333	98
51,25277778	64
22,18416667	29
35,67305556	32
27,1775	25
10,615	16
41,9725	48
75,68277778	100
47,915	46
30,01194444	45
91,14083333	129
69,60527778	130
97,51861111	136
43,89305556	59
27,46277778	25
23,73305556	32
63,67833333	63
97,67194444	95
23,39083333	14
33,45694444	36
90,16611111	113
36,40805556	47
56,74194444	92
45,98416667	70
39,36722222	19
32,23555556	50
69,4575	41
83,27083333	91
54,39944444	111
48,12777778	41
70,69111111	120
28,99694444	135
37,80111111	27
55,41	87
25,69416667	25
62,31388889	131
37,71694444	45
20,66888889	29
22,56666667	58
4,08	4
50,45361111	47
75,51555556	109
1,999722222	7
12,96111111	12
4,874166667	0
37,04666667	37
26,45194444	37
42,38916667	46
27,26277778	15
22,11638889	42
16,44277778	7
38,87277778	54
32,94777778	54
20,24444444	14
18,1875	16
27,67861111	33
19,99027778	32
21,46444444	21
13,69138889	15
37,53638889	38
30,12388889	22
24,92944444	28
12,30444444	10
21,56888889	31
50,42444444	32
37,2275	32
34,46222222	43
25,73055556	27
33,84666667	37
14,69861111	20
22,74222222	32
16,38361111	0
14,86527778	5
16,89222222	26
15,65972222	10
18,19166667	27
22,48583333	11
21,195	29
28,89194444	25
27,25111111	55
18,88583333	23
8,608055556	5
37,62722222	43
20,41777778	23
17,53416667	34
17,015	36
20,80944444	35
8,826111111	0
22,62138889	37
24,21833333	28
13,91388889	16
18,2625	26
15,73694444	38
43,99972222	23
12,90416667	22
20,45111111	30
10,66527778	16
25,5275	18
38,75722222	28
14,49	32
14,32416667	21
19,5975	23
23,57111111	29
28,48277778	50
24,07722222	12
23,80805556	21
9,628333333	18
41,82777778	27
27,66972222	41
5,374722222	13
27,60361111	12
23,95277778	21
8,565833333	8
8,807222222	26
24,94611111	27
17,24666667	13
11,15305556	16
7,676111111	2
21,38611111	42
10,40555556	5
15,04361111	37
13,85055556	17
23,42694444	38
17,82638889	37
16,495	29
33,14111111	32
21,30611111	35
28,72916667	17
19,54	20
12,05833333	7
29,12166667	46
17,28194444	24
19,25111111	40
14,75472222	3
5,49	10
24,07777778	37
23,3625	17
21,65138889	28
24,75361111	19
25,27916667	29
11,18	8
17,82972222	10
14,12694444	15
15,72583333	15
17,44222222	28
20,14861111	17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=197851&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=197851&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197851&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 time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)11.4171.3038.7640
X0.5150.0225.2950
- - -
Residual Std. Err. 12.75 on 287 df
Multiple R-sq. 0.69
Adjusted R-sq. 0.689

\begin{tabular}{lllllllll}
\hline
Linear Regression Model \tabularnewline
Y ~ X \tabularnewline
coefficients: &   \tabularnewline
  & Estimate & Std. Error & t value & Pr(>|t|) \tabularnewline
(Intercept) & 11.417 & 1.303 & 8.764 & 0 \tabularnewline
X & 0.515 & 0.02 & 25.295 & 0 \tabularnewline
- - -  &   \tabularnewline
Residual Std. Err.  & 12.75  on  287 df \tabularnewline
Multiple R-sq.  & 0.69 \tabularnewline
Adjusted R-sq.  & 0.689 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197851&T=1

[TABLE]
[ROW][C]Linear Regression Model[/C][/ROW]
[ROW][C]Y ~ X[/C][/ROW]
[ROW][C]coefficients:[/C][C] [/C][/ROW]
[ROW][C] [/C][C]Estimate[/C][C]Std. Error[/C][C]t value[/C][C]Pr(>|t|)[/C][/ROW]
[C](Intercept)[/C][C]11.417[/C][C]1.303[/C][C]8.764[/C][C]0[/C][/ROW]
[C]X[/C][C]0.515[/C][C]0.02[/C][C]25.295[/C][C]0[/C][/ROW]
[ROW][C]- - - [/C][C] [/C][/ROW]
[ROW][C]Residual Std. Err. [/C][C]12.75  on  287 df[/C][/ROW]
[ROW][C]Multiple R-sq. [/C][C]0.69[/C][/ROW]
[ROW][C]Adjusted R-sq. [/C][C]0.689[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197851&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197851&T=1

As an alternative you can also use a QR Code:  

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

Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)11.4171.3038.7640
X0.5150.0225.2950
- - -
Residual Std. Err. 12.75 on 287 df
Multiple R-sq. 0.69
Adjusted R-sq. 0.689







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
blogged_computations 1104008.831104008.831639.8430
Residuals28746652.897162.554

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
blogged_computations
 & 1 & 104008.831 & 104008.831 & 639.843 & 0 \tabularnewline
Residuals & 287 & 46652.897 & 162.554 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197851&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]blogged_computations
[/C][C]1[/C][C]104008.831[/C][C]104008.831[/C][C]639.843[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]287[/C][C]46652.897[/C][C]162.554[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197851&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197851&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)
blogged_computations 1104008.831104008.831639.8430
Residuals28746652.897162.554



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)
xdf<-data.frame(t(y))
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
xdf <- data.frame(xdf[[cat1]], xdf[[cat2]])
names(xdf)<-c('Y', 'X')
if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) )
sumlmxdf<-summary(lmxdf)
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
nc <- ncol(sumlmxdf$'coefficients')
nr <- nrow(sumlmxdf$'coefficients')
a<-table.row.start(a)
a<-table.element(a,'Linear Regression Model', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],nc+1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'coefficients:',1,TRUE)
a<-table.element(a, ' ',nc,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
for(i in 1 : nc){
a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE)
}#end header
a<-table.row.end(a)
for(i in 1: nr){
a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE)
for(j in 1 : nc){
a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE)
}
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, '- - - ',1,TRUE)
a<-table.element(a, ' ',nc,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Std. Err. ',1,TRUE)
a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, 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, ' ',1,TRUE)
a<-table.element(a, 'Df',1,TRUE)
a<-table.element(a, 'Sum Sq',1,TRUE)
a<-table.element(a, 'Mean Sq',1,TRUE)
a<-table.element(a, 'F value',1,TRUE)
a<-table.element(a, 'Pr(>F)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, V2,1,TRUE)
a<-table.element(a, anova.xdf$Df[1])
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',1,TRUE)
a<-table.element(a, anova.xdf$Df[2])
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3))
a<-table.element(a, ' ')
a<-table.element(a, ' ')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='regressionplot.png')
plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution')
if(intercept == TRUE) abline(coef(lmxdf), col='red')
if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red')
dev.off()
library(car)
bitmap(file='residualsQQplot.png')
qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit')
dev.off()
bitmap(file='residualsplot.png')
plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit')
dev.off()
bitmap(file='cooksDistanceLmplot.png')
plot.lm(lmxdf, which=4)
dev.off()