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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 computationThu, 11 Nov 2010 14:37:44 +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/11/t1289486447lkjalhvnozordmi.htm/, Retrieved Fri, 19 Apr 2024 04:36:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=93418, Retrieved Fri, 19 Apr 2024 04:36:23 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact205
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Mean Plot] [Colombia Coffee] [2008-01-07 13:38:24] [74be16979710d4c4e7c6647856088456]
- RMPD      [Linear Regression Graphical Model Validation] [ws 6 - Q4 - linea...] [2010-11-11 14:37:44] [0829c729852d8a4b1b0c41cf0848af95] [Current]
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Dataseries X:
87,28
87,28
87,09
86,92
87,59
90,72
90,69
90,30
89,55
88,94
88,41
87,82
87,07
86,82
86,40
86,02
85,66
85,32
85,00
84,67
83,94
82,83
81,95
81,19
80,48
78,86
69,47
68,77
70,06
73,95
75,80
77,79
81,57
83,07
84,34
85,10
85,25
84,26
83,63
86,44
85,30
84,10
83,36
82,48
81,58
80,47
79,34
82,13
81,69
80,70
79,88
79,16
78,38
77,42
76,47
75,46
74,48
78,27
80,70
79,91
78,75
77,78
81,14
81,08
80,03
78,91
78,01
76,90
75,97
81,93
80,27
78,67
77,42
76,16
74,70
76,39
76,04
74,65
73,29
71,79
74,39
74,91
74,54
73,08
72,75
71,32
70,38
70,35
70,01
69,36
67,77
69,26
69,80
68,38
67,62
68,39
66,95
65,21
66,64
63,45
60,66
62,34
60,32
58,64
60,46
58,59
61,87
61,85
67,44
77,06
91,74
93,15
94,15
93,11
91,51
89,96
88,16
86,98
88,03
86,24
84,65
83,23
81,70
80,25
78,80
77,51
76,20
75,04
74,00
75,49
77,14
76,15
76,27
78,19
76,49
77,31
76,65
74,99
73,51
72,07
70,59
71,96
76,29
74,86
74,93
71,90
71,01
77,47
75,78
76,60
76,07
74,57
73,02
72,65
73,16
71,53
69,78
67,98
69,96
72,16
70,47
68,86
67,37
65,87
72,16
71,34
69,93
68,44
67,16
66,01
67,25
70,91
69,75
68,59
67,48
66,31
64,81
66,58
65,97
64,70
64,70
60,94
59,08
58,42
57,77
57,11
53,31
49,96
49,40
48,84
48,30
47,74
47,24
46,76
46,29
48,90
49,23
48,53
48,03
54,34
53,79
53,24
52,96
52,17
51,70
58,55
78,20
77,03
76,19
77,15
75,87
95,47
109,67
112,28
112,01
107,93
105,96
105,06
102,98
102,20
105,23
101,85
99,89
96,23
94,76
91,51
91,63
91,54
85,23
87,83
87,38
84,44
85,19
84,03
86,73
102,52
104,45
106,98
107,02
99,26
94,45
113,44
157,33
147,38
171,89
171,95
132,71
126,02
121,18
115,45
110,48
117,85
117,63
124,65
109,59
111,27
99,78
98,21
99,20
97,97
89,55
87,91
93,34
94,42
93,20
90,29
91,46
89,98
88,35
88,41
82,44
79,89
75,69
75,66
84,50
96,73
87,48
82,39
83,48
79,31
78,16
72,77
72,45
68,46
67,62
68,76
70,07
68,55
65,30
58,96
59,17
62,37
66,28
55,62
55,23
55,85
56,75
50,89
53,88
52,95
55,08
53,61
58,78
61,85
55,91
53,32
46,41
44,57
50,00
50,00
53,36
46,23
50,45
49,07
45,85
48,45
49,96
46,53
50,51
47,58
48,05
46,84
47,67
49,16
55,54
55,82
58,22
56,19
57,77
63,19
54,76
55,74
62,54
61,39
69,60
79,23
80,00
93,68
107,63
100,18
97,30
90,45
80,64
80,58
75,82
85,59
89,35
89,42
104,73
95,32
89,27
90,44
86,97
79,98
81,22
87,35
83,64
82,22
94,40
102,18
Dataseries Y:
255,00
280,20
299,90
339,20
374,20
393,50
389,20
381,70
375,20
369,00
357,40
352,10
346,50
342,90
340,30
328,30
322,90
314,30
308,90
294,00
285,60
281,20
280,30
278,80
274,50
270,40
263,40
259,90
258,00
262,70
284,70
311,30
322,10
327,00
331,30
333,30
321,40
327,00
320,00
314,70
316,70
314,40
321,30
318,20
307,20
301,30
287,50
277,70
274,40
258,80
253,30
251,00
248,40
249,50
246,10
244,50
243,60
244,00
240,80
249,80
248,00
259,40
260,50
260,80
261,30
259,50
256,60
257,90
256,50
254,20
253,30
253,80
255,50
257,10
257,30
253,20
252,80
252,00
250,70
252,20
250,00
251,00
253,40
251,20
255,60
261,10
258,90
259,90
261,20
264,70
267,10
266,40
267,70
268,60
267,50
268,50
268,50
270,50
270,90
270,10
269,30
269,80
270,10
264,90
263,70
264,80
263,70
255,90
276,20
360,10
380,50
373,70
369,80
366,60
359,30
345,80
326,20
324,50
328,10
327,50
324,40
316,50
310,90
301,50
291,70
290,40
287,40
277,70
281,60
288,00
276,00
272,90
283,00
283,30
276,80
284,50
282,70
281,20
287,40
283,10
284,00
285,50
289,20
292,50
296,40
305,20
303,90
311,50
316,30
316,70
322,50
317,10
309,80
303,80
290,30
293,70
291,70
296,50
289,10
288,50
293,80
297,70
305,40
302,70
302,50
303,00
294,50
294,10
294,50
297,10
289,40
292,40
287,90
286,60
280,50
272,40
269,20
270,60
267,30
262,50
266,80
268,80
263,10
261,20
266,00
262,50
265,20
261,30
253,70
249,20
239,10
236,40
235,20
245,20
246,20
247,70
251,40
253,30
254,80
250,00
249,30
241,50
243,30
248,00
253,00
252,90
251,50
251,60
253,50
259,80
334,10
448,00
445,80
445,00
448,20
438,20
439,80
423,40
410,80
408,40
406,70
405,90
402,70
405,10
399,60
386,50
381,40
375,20
357,70
359,00
355,00
352,70
344,40
343,80
338,00
339,00
333,30
334,40
328,30
330,70
330,00
331,60
351,20
389,40
410,90
442,80
462,80
466,90
461,70
439,20
430,30
416,10
402,50
397,30
403,30
395,90
387,80
378,60
377,10
370,40
362,00
350,30
348,20
344,60
343,50
342,80
347,60
346,60
349,50
342,10
342,00
342,80
339,30
348,20
333,70
334,70
354,00
367,70
363,30
358,40
353,10
343,10
344,60
344,40
333,90
331,70
324,30
321,20
322,40
321,70
320,50
312,80
309,70
315,60
309,70
304,60
302,50
301,50
298,80
291,30
293,60
294,60
285,90
297,60
301,10
293,80
297,70
292,90
292,10
287,20
288,20
283,80
299,90
292,40
293,30
300,80
293,70
293,10
294,40
292,10
291,90
282,50
277,90
287,50
289,20
285,60
293,20
290,80
283,10
275,00
287,80
287,80
287,40
284,00
277,80
277,60
304,90
294,00
300,90
324,00
332,90
341,60
333,40
348,20
344,70
344,70
329,30
323,50
323,20
317,40
330,10
329,20
334,90
315,80
315,40
319,60
317,30
313,80
315,80
311,30




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=93418&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=93418&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93418&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term165.5500976116877.7405351817614621.38742266836560
slope1.841683158895680.09701046945821918.98437528630730

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 165.550097611687 & 7.74053518176146 & 21.3874226683656 & 0 \tabularnewline
slope & 1.84168315889568 & 0.097010469458219 & 18.9843752863073 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=93418&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]165.550097611687[/C][C]7.74053518176146[/C][C]21.3874226683656[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]1.84168315889568[/C][C]0.097010469458219[/C][C]18.9843752863073[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=93418&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93418&T=1

As an alternative you can also use a 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 term165.5500976116877.7405351817614621.38742266836560
slope1.841683158895680.09701046945821918.98437528630730



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')