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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 10:57:53 -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/t1321113514irxdmkn1qkfkg3b.htm/, Retrieved Mon, 30 Jan 2023 23:42:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=141495, Retrieved Mon, 30 Jan 2023 23:42:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164
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] [c26e829f03d42da3805b9c4b60f90f25]
-    D      [Linear Regression Graphical Model Validation] [Mini tutorial (WS6)] [2011-11-12 15:57:53] [e1aba6efa0fba8dc2a9839c208d0186e] [Current]
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Dataseries X:
78973
46146
46492
60656
21898
36555
74680
22807
61282
37981
41553
45081
38557
51641
30658
52924
79256
53462
68950
53639
67819
48333
28001
51665
39019
46221
65792
39858
19574
41829
78688
36781
44314
24874
56911
37048
48426
33388
26998
46502
41507
40001
33144
29501
43059
43249
29272
49821
98341
44372
42448
5950
64839
32551
30767
62046
71930
67328
67253
35373
85544
88087
30621
50580
49670
25456
69245
43787
53638
35683
38008
18801
44324
51408
53880
55708
63858
183643
35660
41664
29883
62047
33321
46553
56622
15430
49379
58215
38253
77786
21331
55292
30105
37651
59370
46216
73122
93927
55935
93308
74344
78094
25625
43750
28995
47336
57582
60875
165877
32984
61638
36367
1168
40530
21427
15024
39088
855
80455
14116
43915
76705
40112
41821
8773
52045
51491
53470
53211
63091
131634
41745
23656
51442
54574
35708
66627
39585
50029
25266
34860
62759
62307
37238
42452
59820
75075
97567
0
6023
0
0
0
0
42420
31116
0
0
1644
6179
3926
23238
0
38818
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=141495&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=141495&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=141495&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 term21403.78323295553615.258993490125.920401075413041.86116049238905e-08
slope0.775506627899570.067912266842918911.41924226580920

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 21403.7832329555 & 3615.25899349012 & 5.92040107541304 & 1.86116049238905e-08 \tabularnewline
slope & 0.77550662789957 & 0.0679122668429189 & 11.4192422658092 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=141495&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]21403.7832329555[/C][C]3615.25899349012[/C][C]5.92040107541304[/C][C]1.86116049238905e-08[/C][/ROW]
[ROW][C]slope[/C][C]0.77550662789957[/C][C]0.0679122668429189[/C][C]11.4192422658092[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=141495&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=141495&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 term21403.78323295553615.258993490125.920401075413041.86116049238905e-08
slope0.775506627899570.067912266842918911.41924226580920



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')