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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 computationTue, 15 Nov 2011 12:43:21 -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/15/t1321379123pwtdfuq9fey9lvr.htm/, Retrieved Thu, 28 Mar 2024 10:20:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=143272, Retrieved Thu, 28 Mar 2024 10:20:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Linear Regression Graphical Model Validation] [tryout] [2011-11-15 17:43:21] [2adf2d2c11e011c12275478b9efd18e5] [Current]
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Dataseries X:
145766
78642
113289
127858
59528
25417
285071
32750
92936
108063
149277
112619
92159
167645
122490
218894
154534
166221
99745
132469
151199
251194
90298
98318
130114
133160
150852
133986
90545
155231
177702
106521
178549
59102
216420
153601
203589
107673
78776
174599
192038
102531
108418
62398
79864
260371
78800
139046
202021
119127
90761
24019
215627
64672
90402
149157
144731
176629
170460
131729
230652
178429
127465
157162
121578
101823
104349
132045
137862
74427
118803
63131
119127
128736
94763
178575
189257
90583
77597
107325
95780
252436
128784
110865
80953
90557
79146
190898
138401
99946
110959
133606
93456
148443
163576
107135
135420
160134
167432
204800
135069
136833
88506
43287
110313
107627
127431
166298
59464
85837
150763
134062
20764
115199
61675
75795
186239
21054
152149
31249
174869
126627
57260
55352
38214
87280
172148
110569
221936
158395
225323
114015
51227
157216
229805
46069
223607
83448
143703
102524
74792
223851
135508
173260
173505
57817
119515
161915
0
14688
98
455
0
0
118444
165367
0
203
7199
46660
17547
73567
969
91234
Dataseries Y:
68
56
37
70
30
43
74
22
52
34
87
107
61
89
41
122
75
45
40
86
82
76
48
104
83
78
43
83
56
81
93
72
107
75
72
62
90
40
18
75
59
63
55
47
23
69
66
102
73
87
42
7
95
61
32
56
108
71
86
69
85
47
50
76
56
27
68
68
66
52
81
51
91
50
75
81
75
61
62
61
55
60
79
32
27
59
82
71
36
80
36
76
57
73
71
60
67
56
123
65
87
62
37
64
17
28
48
64
43
60
87
75
0
54
30
47
56
0
29
9
78
90
56
24
21
71
102
83
89
83
104
60
48
71
81
58
82
49
75
59
17
57
62
78
73
89
51
84
0
0
0
0
0
0
34
55
0
0
0
13
4
31
0
22




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=143272&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=143272&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143272&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 term19.54225841252433.517073555611455.556397414931741.10929098795509e-07
slope0.000321726082950082.65099526065566e-0512.13604896715320

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 19.5422584125243 & 3.51707355561145 & 5.55639741493174 & 1.10929098795509e-07 \tabularnewline
slope & 0.00032172608295008 & 2.65099526065566e-05 & 12.1360489671532 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143272&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]19.5422584125243[/C][C]3.51707355561145[/C][C]5.55639741493174[/C][C]1.10929098795509e-07[/C][/ROW]
[ROW][C]slope[/C][C]0.00032172608295008[/C][C]2.65099526065566e-05[/C][C]12.1360489671532[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143272&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143272&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 term19.54225841252433.517073555611455.556397414931741.10929098795509e-07
slope0.000321726082950082.65099526065566e-0512.13604896715320



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')