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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 computationFri, 26 Nov 2010 14:52:17 +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/26/t1290784527qeefoql9fojvgwv.htm/, Retrieved Thu, 31 Oct 2024 23:36:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=102000, Retrieved Thu, 31 Oct 2024 23:36:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162
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] [] [2010-11-25 11:12:40] [e71d94d32f847f62b540eebe6fadd003]
-    D      [Linear Regression Graphical Model Validation] [] [2010-11-26 14:52:17] [c8b0d20ebafa6d61ca10522fa626ae82] [Current]
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Dataseries X:
10926126
204514194
20655116
7365385
3918657
38494822
90838469
8652220
47482511
5438191
5986958
59447386
37113055
23669000
5572851
7784176
5270786
43583746
19903814
7799064
35989363
29552113
21506498
7859684
13006528
89426565
40100910
38650820
17818636
51985948
24571878
29266469
45758250
22712439
86459749
49703065
65187751
39091325
34955804
33411147
15575837
36116313
27405375
55382004
7478767
72881801
37479848
27030251
109851111
28356456
48560722
23550558
52319234
105318425
17345510
71519979
59398481
36287185
44559709
114256076
23174724
38795625
94992548
48389051
46785833
56063795
27622103
11848002
55125816
42484133
49639784
30006747
19776143
41176416
34933018
39696259
38610419
44404281
44628459
20428394
33494104
15246622
10078039
36293305
11594321
34818494
36718379
19690322
44432615
36047082
40379563
23458900
24477773
21472076
27623825
42834376
21692408
2490957
5804447
4149313
22772412
8743945
6818743
30796414
17697315
62514975
17618542
37171201
29747133
39734547
18636718
30751381
58198919
35172258
30568494
32732200
33931175
38495426
39639276
18386312
49064295
34517203
54053990
56632146
46131933
31138610
37573144
32922160
71766977
21574297
46733768
36375026
50499581
44157910
26780541
71675809
17411180
138402202
89520475
53764838
79645460
45232765
60335913
48862499
56443228
149401818
55893936
39185258
54999796
62938759
130610415
3578335
119330610
43612479
67573324
94235304
52529046
38148241
41788652
21748127
16815679
29140007
80020386
10008346
13150180
33070836
44343752
38761463
63242438
10776650
13079087
42254065
45334695
75653353
37723883
35383003
42169175
37836247
16157265
25483137
24758200
25931415
35341058
59793935
46240034
25935511
29348057
16621841
34421545
30201523
68504357
21760376
68420735
34576094
80009790
23896896
43959624
31004430
96339703
78115427
13773684
79714473
30300608
47400000
29685182
72574056
26667187
56654962
34032684
37819725
25253403
55674674
40214078
32066001
26916456
31925699
36677072
22865662
33057141
87224667
30046064
33705054
94643577
74500253
13705457
81920371
75534971
17984250
26564680
75039636
31345912
156181130
37274013
60344095
19480849
15213215
36646854
37800225
35192619
51894705
43116866
14343902
21908691
34532932
38781483
32523543
46755676
68059999
34481666
23934252
56464201
30381482
41957576
11196762
26125759
45626240
33816982
150181287
33424206
67497380
54983068
83797137
44798290
39918703
22068324
78296142
41187726
87854574
56492262
36294788
102238796
42832378
22490585
53384345
22461067
24979034
106897458
51351879
39339354
32067212
32690247
53227096
59342371
64960155
54764596
102374548
76913992
42777761
40847167
86951363
43659732
76698629
78268805
91261974
65606487
28139063
75895280
51122832
53066011
1354614
30021100
33899609
58995770
57876331
87561067
26724300
76278583
50626962
Dataseries Y:
14.267
472.071
12.335
16.434
10.132
37.190
27.169
21.389
17.416
9.862
8.326
17.578
26.242
20.290
8.089
24.421
9.155
18.061
14.693
8.034
19.516
33.256
17.737
8.886
12.187
18.837
12.341
21.003
18.421
14.225
10.730
14.558
20.347
16.235
39.199
33.492
79.503
21.210
15.907
16.399
7.887
19.976
23.664
12.518
2.385
20.643
16.756
8.932
36.014
10.815
26.268
8.534
14.107
19.193
9.594
18.053
15.805
9.578
8.321
33.400
11.547
12.677
14.138
10.545
10.982
40.070
7.399
10.250
23.433
15.080
29.558
23.587
2.093
39.654
8.285
8.931
34.526
35.350
6.566
10.244
11.090
8.969
12.206
17.525
12.953
18.466
15.110
12.575
24.394
4.401
31.119
11.201
14.781
38.557
29.500
21.700
10.934
4.893
13.080
4.721
18.021
14.916
13.534
8.747
12.229
28.129
9.647
5.915
9.435
9.280
11.673
7.538
23.025
5.837
13.894
19.563
6.356
9.460
9.300
12.637
7.587
18.662
15.058
92.704
13.784
10.937
15.290
20.969
32.207
14.050
8.048
7.049
22.317
10.219
5.246
14.806
18.450
117.073
10.875
13.675
22.111
19.755
22.060
2.768
34.026
15.998
9.307
8.342
12.019
3.346
34.812
997
19.837
17.775
12.092
8.167
7.792
3.672
14.116
9.597
11.283
26.328
73.941
12.649
5.467
32.450
35.963
30.930
23.898
2.050
15.578
11.384
13.574
18.378
69.175
8.955
12.243
9.928
10.620
26.781
9.371
8.489
10.840
56.547
11.040
8.223
11.031
7.502
6.586
5.128
19.474
9.093
13.461
9.086
4.963
10.285
21.515
11.062
11.748
78.271
17.937
31.667
17.528
16.999
17.473
36.219
9.534
24.660
19.201
14.452
13.973
43.618
23.547
11.851
17.651
10.312
23.414
11.063
20.457
13.637
19.825
6.171
22.397
6.592
12.242
19.028
28.799
10.251
17.451
32.545
12.226
237.250
7.999
20.734
9.418
10.710
22.705
5.942
11.080
11.431
13.241
8.251
7.776
7.026
8.033
14.713
8.148
29.050
24.639
6.953
13.881
6.382
6.135
2.073
6.475
6.446
7.919
45.887
15.489
38.489
18.387
70.450
17.096
27.285
7.605
41.984
18.001
64.294
7.966
8.812
71.543
11.958
14.636
14.007
6.707
9.807
38.828
17.228
20.306
6.985
9.974
31.278
12.561
14.774
12.118
32.424
24.139
16.247
13.719
15.996
13.898
11.612
29.993
12.664
19.296
11.153
30.384
10.339
6.898
84
9.412
8.151
24.935
16.145
29.859
7.102
36.661
4.207




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=102000&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=102000&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102000&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term3.919475802193216.660866503941280.58843332168180.556675542360027
slope4.46251178710518e-071.27643757639314e-073.496067390710180.000542035179284861

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 3.91947580219321 & 6.66086650394128 & 0.5884333216818 & 0.556675542360027 \tabularnewline
slope & 4.46251178710518e-07 & 1.27643757639314e-07 & 3.49606739071018 & 0.000542035179284861 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102000&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]3.91947580219321[/C][C]6.66086650394128[/C][C]0.5884333216818[/C][C]0.556675542360027[/C][/ROW]
[ROW][C]slope[/C][C]4.46251178710518e-07[/C][C]1.27643757639314e-07[/C][C]3.49606739071018[/C][C]0.000542035179284861[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102000&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102000&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 term3.919475802193216.660866503941280.58843332168180.556675542360027
slope4.46251178710518e-071.27643757639314e-073.496067390710180.000542035179284861



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')