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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 21:11:15 -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/t1321409520jax00thjsw4g3p2.htm/, Retrieved Thu, 28 Mar 2024 18:04:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=143739, Retrieved Thu, 28 Mar 2024 18:04:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact58
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Linear Regression Graphical Model Validation] [Mini Tutorial - H...] [2011-11-16 01:54:03] [b9f5bf8f9089a40337275cf2fd2f13a1]
- R  D    [Linear Regression Graphical Model Validation] [Mini Tutorial - H...] [2011-11-16 02:11:15] [766d4bb4f790a2464f86fcafbf87a680] [Current]
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Dataseries X:
12
11
14
12
21
12
22
11
10
13
10
8
15
14
10
14
14
11
10
13
7
14
12
14
11
9
11
15
14
13
9
15
10
11
13
8
20
12
10
10
9
14
8
14
11
13
9
11
15
11
10
14
18
14
11
12
13
9
10
15
20
12
12
14
13
11
17
12
13
14
13
15
13
10
11
19
13
17
13
9
11
10
9
12
12
13
13
12
15
22
13
15
13
15
10
11
16
11
11
10
10
16
12
11
16
19
11
16
15
24
14
15
11
15
12
10
14
13
9
15
15
14
11
8
11
11
8
10
11
13
11
20
10
15
12
14
23
14
16
11
12
10
14
12
12
11
12
13
11
19
12
17
9
12
19
18
15
14
11
9
18
16
Dataseries Y:
53
86
66
67
76
78
53
80
74
76
79
54
67
54
87
58
75
88
64
57
66
68
54
56
86
80
76
69
78
67
80
54
71
84
74
71
63
71
76
69
74
75
54
52
69
68
65
75
74
75
72
67
63
62
63
76
74
67
73
70
53
77
77
52
54
80
66
73
63
69
67
54
81
69
84
80
70
69
77
54
79
30
71
73
72
77
75
69
54
70
73
54
77
82
80
80
69
78
81
76
76
73
85
66
79
68
76
71
54
46
82
74
88
38
76
86
54
70
69
90
54
76
89
76
73
79
90
74
81
72
71
66
77
65
74
82
54
63
54
64
69
54
84
86
77
89
76
60
75
73
85
79
71
72
69
78
54
69
81
84
84
69




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=143739&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=143739&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143739&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 term82.69956513590943.4247741188226124.14745097534790
slope-0.92801220832170.257507115295105-3.603831324265310.000418289983261211

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 82.6995651359094 & 3.42477411882261 & 24.1474509753479 & 0 \tabularnewline
slope & -0.9280122083217 & 0.257507115295105 & -3.60383132426531 & 0.000418289983261211 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143739&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]82.6995651359094[/C][C]3.42477411882261[/C][C]24.1474509753479[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]-0.9280122083217[/C][C]0.257507115295105[/C][C]-3.60383132426531[/C][C]0.000418289983261211[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143739&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143739&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 term82.69956513590943.4247741188226124.14745097534790
slope-0.92801220832170.257507115295105-3.603831324265310.000418289983261211



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')