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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_linear_regression.wasp
Title produced by softwareLinear Regression Graphical Model Validation
Date of computationTue, 09 Nov 2010 18:52:38 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/09/t1289329172qcsi3h1wr5w9ly0.htm/, Retrieved Sat, 27 Apr 2024 20:35:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=93018, Retrieved Sat, 27 Apr 2024 20:35:54 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Linear Regression Graphical Model Validation] [Colombia Coffee -...] [2008-02-26 10:22:06] [74be16979710d4c4e7c6647856088456]
-  M D    [Linear Regression Graphical Model Validation] [Test] [2010-11-09 18:52:38] [934c3727858e074bf543f25f5906ed72] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
59
2.110
59
65
36
134
109
92
88
33
21
61
101
75
37
83
46
64
61
21
49
158
93
47
44
82
52
69
84
59
42
37
79
76
144
178
380
87
56
54
36
75
89
51
7
78
79
31
158
30
115
31
57
62
47
41
69
47
37
154
49
48
44
45
37
150
27
35
100
63
398
127
88
797
212
147
206
109
386
219
86
534
204
133
676
303
95
226
124
96
67
7
122
34
26
99
118
25
34
45
39
37
55
43
48
59
44
57
17
102
31
47
144
72
69
32
22
39
13
23
52
39
27
48
117
40
30
28
42
47
34
99
26
45
80
23
37
31
41
17
74
68
569
52
39
55
49
145
62
43
31
97
35
19
15
130
38
48
40
71
49
19
28
50
20
32
119
29
68
94
25
87
135
17
13
49
37
140
16
38
23
63
75
474
43
52
97
102
89
8
116
60
44
36
53
17
149
10
89
57
51
40
28
10
45
35
41
109
299
44
18
138
152
142
94
9
86
42
55
48
297
42
40
40
30
126
35
44
36
253
36
18
47
26
38
28
69
44
58
37
24
34
66
48
50
355
81
106
64
70
68
137
29
76
74
57
40
181
85
49
84
46
100
40
86
57
86
21
75
30
64
85
110
35
47
157
50
1.105
22
86
29
38
79
24
34
55
36
39
31
30
40
57
31
139
104
28
44
23
17
6
20
24
27
181
65
155
73
338
77
110
115
55
45
7
19
21
12
56
57
774
126
140
43
104
61
50
76
30
68
35
51
23
67
48
72
19
35
16
384
68
23
69
63
14
20
180
60
62
48
263
63
34
23
81
33
48
27
105
27
41
9
21
17
17
55
31
52
61
18
37
18
38
13
55
19
223
20
51
12
62
10
19
19
11
14
98
21
15
21
9
9
15
42
17
18
5
129
31
39
29
47
72
23
23
44
55
128
23
804
88
87
245
59
50
64
70
52
105
34
26
16
13
16
21
11
55
73
50
45
45
13
24
26
36
11
40
43
27
42
31
20
37
209
26
35
11
20
31
21
11
22
5
9
13
8
55
15
20
19
27
59
13
7
40
178
73
255
37
34
342
51
68
51
23
59
170
92
86
27
48
121
53
77
44
128
102
62
64
72
51
58
111
50
68
42
149
28
39
1
40
30
115
68
114
37
157
11
144
19
81
11
38
59
11
16
21
20
16
17
13
35
7
20
16
57
19
9
17
12
42
22
5
5
23
12
41
27
19
9
10
49
30
22
18
8
14
6
22
45
36
10
24
35
15
59
57
14
26
23
15
12
43
14
40
20
7
110
23
55
19
34
21
56
457
14
36
25
110
32
83
34
113
12
39
7
43
25
61
18
Dataseries Y:
Download CSV
Histogram 
32
1.106
15
51
30
94
46
62
33
19
15
33
57
50
16
58
19
38
28
14
45
84
42
18
35
42
25
48
42
18
34
24
51
45
101
84
206
45
34
35
14
45
65
28
2
49
39
22
72
21
76
20
45
34
27
37
35
26
13
59
25
22
33
29
30
117
17
25
47
47
230
69
32
4.600
122
105
113
67
270
126
43
254
144
112
412
179
75
119
101
71
30
3
72
22
24
76
98
6
20
23
23
21
36
29
35
40
30
29
3
62
29
30
96
37
40
27
13
24
11
20
39
26
27
23
74
27
14
16
15
24
14
73
12
25
40
10
18
16
27
14
36
29
255
29
15
36
28
95
25
21
10
55
26
12
15
89
26
18
20
40
27
7
20
33
12
24
86
21
62
53
22
52
67
18
7
37
21
71
20
28
16
37
45
360
35
26
54
54
55
7
87
28
21
21
31
1
86
6
68
47
33
21
16
8
19
19
33
72
217
31
10
91
87
73
57
4
43
32
39
48
239
24
23
23
25
75
25
19
28
127
35
17
25
18
22
15
51
30
31
27
14
24
62
28
25
210
36
81
39
36
38
88
19
71
47
38
28
130
73
22
52
31
58
37
56
33
67
14
59
11
34
44
79
18
47
75
23
664
19
35
20
39
57
21
23
20
37
18
16
16
26
30
11
63
68
14
26
16
8
5
14
15
14
100
35
86
39
217
35
62
69
39
33
14
19
19
9
23
49
782
117
97
31
58
49
31
68
28
29
34
28
13
61
57
65
15
35
8
293
52
18
65
68
14
22
154
44
56
37
234
53
17
8
52
37
29
18
107
23
27
7
16
13
11
42
25
43
47
13
26
13
28
12
57
10
206
22
29
8
41
3
7
13
12
8
76
15
9
12
4
10
23
38
5
21
6
90
28
42
29
31
72
6
19
44
55
146
19
812
73
71
255
64
29
25
58
35
95
26
32
7
11
7
6
11
45
49
43
19
27
17
8
13
36
16
26
21
17
39
20
5
29
200
14
30
10
6
24
14
9
16
9
6
4
5
36
1
10
11
19
44
7
11
24
101
55
179
15
28
239
28
45
33
14
20
110
50
52
14
18
65
26
26
27
71
53
27
41
37
28
31
72
34
58
24
63
22
13
0
29
13
69
30
81
13
108
7
99
5
42
5
15
40
4
5
14
29
8
19
0
32
10
16
13
63
13
9
4
13
33
19
0
6
12
15
22
14
15
10
7
25
18
10
14
11
14
6
10
30
18
6
21
24
12
41
48
14
19
13
11
5
40
14
18
9
10
96
14
28
21
25
22
30
343
15
24
25
109
17
72
23
53
24
26
5
45
27
46
15

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=93018&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=93018&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93018&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term2.449204779942082.115903490712961.157521971437750.247529945690319
slope0.6351297755534250.018797251278776833.7884388591710

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 2.44920477994208 & 2.11590349071296 & 1.15752197143775 & 0.247529945690319 \tabularnewline
slope & 0.635129775553425 & 0.0187972512787768 & 33.788438859171 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=93018&T=1

[TABLE]
[ROW][C]Simple Linear Regression[/C][/ROW]
[ROW][C]Statistics[/C][C]Estimate[/C][C]S.D.[/C][C]T-STAT (H0: coeff=0)[/C][C]P-value (two-sided)[/C][/ROW]
[ROW][C]constant term[/C][C]2.44920477994208[/C][C]2.11590349071296[/C][C]1.15752197143775[/C][C]0.247529945690319[/C][/ROW]
[ROW][C]slope[/C][C]0.635129775553425[/C][C]0.0187972512787768[/C][C]33.788438859171[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=93018&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93018&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:

Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term2.449204779942082.115903490712961.157521971437750.247529945690319
slope0.6351297755534250.018797251278776833.7884388591710


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Input Parameters & R Code

Parameters (Session):
par1 = 0 ;
Parameters (R input):
par1 = 0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
library(lattice)
z <- as.data.frame(cbind(x,y))
m <- lm(y~x)
summary(m)
bitmap(file='test1.png')
plot(z,main='Scatterplot, lowess, and regression line')
lines(lowess(z),col='red')
abline(m)
grid()
dev.off()
bitmap(file='test2.png')
m2 <- lm(m$fitted.values ~ x)
summary(m2)
z2 <- as.data.frame(cbind(x,m$fitted.values))
names(z2) <- list('x','Fitted')
plot(z2,main='Scatterplot, lowess, and regression line')
lines(lowess(z2),col='red')
abline(m2)
grid()
dev.off()
bitmap(file='test3.png')
m3 <- lm(m$residuals ~ x)
summary(m3)
z3 <- as.data.frame(cbind(x,m$residuals))
names(z3) <- list('x','Residuals')
plot(z3,main='Scatterplot, lowess, and regression line')
lines(lowess(z3),col='red')
abline(m3)
grid()
dev.off()
bitmap(file='test4.png')
m4 <- lm(m$fitted.values ~ m$residuals)
summary(m4)
z4 <- as.data.frame(cbind(m$residuals,m$fitted.values))
names(z4) <- list('Residuals','Fitted')
plot(z4,main='Scatterplot, lowess, and regression line')
lines(lowess(z4),col='red')
abline(m4)
grid()
dev.off()
bitmap(file='test5.png')
myr <- as.ts(m$residuals)
z5 <- as.data.frame(cbind(lag(myr,1),myr))
names(z5) <- list('Lagged Residuals','Residuals')
plot(z5,main='Lag plot')
m5 <- lm(z5)
summary(m5)
abline(m5)
grid()
dev.off()
bitmap(file='test6.png')
hist(m$residuals,main='Residual Histogram',xlab='Residuals')
dev.off()
bitmap(file='test7.png')
if (par1 > 0)
{
densityplot(~m$residuals,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
} else {
densityplot(~m$residuals,col='black',main='Density Plot')
}
dev.off()
bitmap(file='test8.png')
acf(m$residuals,main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test9.png')
qqnorm(x)
qqline(x)
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Simple Linear Regression',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Statistics',1,TRUE)
a<-table.element(a,'Estimate',1,TRUE)
a<-table.element(a,'S.D.',1,TRUE)
a<-table.element(a,'T-STAT (H0: coeff=0)',1,TRUE)
a<-table.element(a,'P-value (two-sided)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'constant term',header=TRUE)
a<-table.element(a,m$coefficients[[1]])
sd <- sqrt(vcov(m)[1,1])
a<-table.element(a,sd)
tstat <- m$coefficients[[1]]/sd
a<-table.element(a,tstat)
pval <- 2*(1-pt(abs(tstat),length(x)-2))
a<-table.element(a,pval)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'slope',header=TRUE)
a<-table.element(a,m$coefficients[[2]])
sd <- sqrt(vcov(m)[2,2])
a<-table.element(a,sd)
tstat <- m$coefficients[[2]]/sd
a<-table.element(a,tstat)
pval <- 2*(1-pt(abs(tstat),length(x)-2))
a<-table.element(a,pval)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')