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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 computationSat, 12 Nov 2011 10:43:54 -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/12/t1321112651vhtbpmb53x9ikou.htm/, Retrieved Mon, 30 Jan 2023 23:55:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=141491, Retrieved Mon, 30 Jan 2023 23:55:59 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
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]
- RM D    [Linear Regression Graphical Model Validation] [Mini Tutorial (WS6)] [2011-11-12 15:43:54] [e1aba6efa0fba8dc2a9839c208d0186e] [Current]
-    D      [Linear Regression Graphical Model Validation] [Mini tutorial (WS6)] [2011-11-12 15:57:53] [c26e829f03d42da3805b9c4b60f90f25]
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Dataseries X:
278
201
331
650
126
74
962
96
463
371
438
299
352
603
350
944
390
583
288
373
389
689
218
265
675
611
625
293
361
466
514
284
430
131
605
552
616
323
209
595
848
409
363
226
791
1203
310
381
915
525
267
216
460
253
390
585
398
619
561
358
690
696
607
489
296
238
366
541
492
284
414
299
392
411
299
518
542
240
260
339
394
735
497
232
437
548
201
721
405
584
351
356
310
625
465
320
301
419
518
744
334
475
284
206
425
346
490
547
169
291
329
335
135
395
185
252
563
146
517
181
657
277
173
289
276
275
476
215
853
460
668
287
252
520
822
191
970
405
309
396
177
890
366
716
442
290
596
436
0
85
0
0
0
0
389
443
0
0
74
259
69
187
0
295
Dataseries Y:
93809
75589
61071
64672
39014
32213
131696
6853
43253
57555
85473
44501
42453
67285
57056
94982
89324
60461
64491
60717
83022
91708
28378
59360
54436
70629
95409
68631
42319
91440
100462
41411
94894
16617
114686
64881
106679
54866
45988
82618
79199
46657
71070
29970
42948
61186
47824
62913
101444
61145
58388
15049
81451
25109
44688
77378
80180
96279
115202
31648
119420
121683
46568
57903
71075
63394
58118
62099
63739
35768
53318
22330
57871
52090
50767
68496
101760
58441
38972
43530
38996
98432
49867
56434
55792
25155
43461
65474
82234
49606
47992
63723
39066
54501
69924
58204
80248
106887
82830
125642
62525
93166
30028
19630
56584
63525
68879
80800
56474
25551
77587
60521
5841
54108
24587
23872
116211
6622
83478
13155
87473
43580
10439
33067
13983
52276
81079
63507
106262
92878
120184
63110
29996
55746
105611
6783
69866
39663
93382
42310
1472
83141
70434
10901
66220
25867
71760
94809
0
7953
0
0
0
0
63404
89657
0
0
4245
21509
7670
10641
0
31359




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=141491&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=141491&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=141491&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term17068.52869774663675.032013370864.644457146399317.015268857824e-06
slope99.57246481623868.0403950229547812.38402647282450

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 17068.5286977466 & 3675.03201337086 & 4.64445714639931 & 7.015268857824e-06 \tabularnewline
slope & 99.5724648162386 & 8.04039502295478 & 12.3840264728245 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=141491&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]17068.5286977466[/C][C]3675.03201337086[/C][C]4.64445714639931[/C][C]7.015268857824e-06[/C][/ROW]
[ROW][C]slope[/C][C]99.5724648162386[/C][C]8.04039502295478[/C][C]12.3840264728245[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=141491&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=141491&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 term17068.52869774663675.032013370864.644457146399317.015268857824e-06
slope99.57246481623868.0403950229547812.38402647282450



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')