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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 computationMon, 14 Nov 2011 14:43:01 -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/14/t1321299891o8p2u6tvy7wbf6c.htm/, Retrieved Fri, 29 Mar 2024 05:18:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=142352, Retrieved Fri, 29 Mar 2024 05:18:53 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
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] [] [2011-11-14 19:43:01] [1da2278adf472a53e0f224e682f25a48] [Current]
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Dataseries X:
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
Dataseries Y:
17298
9858
8467
20510
5114
7721
23654
1271
13872
11235
12777
10681
5603
10843
6589
16859
14431
8857
15750
6978
25068
12029
3960
11590
15216
15173
12205
14522
6008
7005
27658
6268
12588
1308
20414
3963
14294
10474
11082
17263
12043
10149
13551
6083
4922
8065
6773
12649
12030
12688
12485
2694
11347
3597
5011
17745
12888
20771
23195
7483
17441
27499
7763
10506
17544
9863
11786
14626
11114
9482
9432
2970
10957
10467
11882
14093
16933
7947
9348
7854
8475
15615
8312
9400
7905
4525
16732
10784
15550
19591
5537
11778
4695
8911
15746
11288
17295
18560
15476
26914
16766
14341
5373
7409
10791
21000
23021
15492
11181
6006
14271
9346
238
13240
3879
4397
11417
338
7941
3988
13508
11025
1819
5531
1888
12343
16321
14263
19165
9157
25740
12142
4565
12694
13172
304
20477
7945
17712
6397
303
11306
16379
2805
15448
9031
15706
19234
0
2065
0
0
0
0
10853
8571
0
0
556
2089
2658
1419
0
7521




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'AstonUniversity' @ aston.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 & 4 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=142352&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=142352&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=142352&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 time4 seconds
R Server'AstonUniversity' @ aston.wessa.net







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term888.636948412034566.1235898895381.569687192482870.118439052785091
slope0.170310443520520.0087049729783115619.56472971769680

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 888.636948412034 & 566.123589889538 & 1.56968719248287 & 0.118439052785091 \tabularnewline
slope & 0.17031044352052 & 0.00870497297831156 & 19.5647297176968 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=142352&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]888.636948412034[/C][C]566.123589889538[/C][C]1.56968719248287[/C][C]0.118439052785091[/C][/ROW]
[ROW][C]slope[/C][C]0.17031044352052[/C][C]0.00870497297831156[/C][C]19.5647297176968[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=142352&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=142352&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 term888.636948412034566.1235898895381.569687192482870.118439052785091
slope0.170310443520520.0087049729783115619.56472971769680



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')