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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 computationThu, 17 Nov 2011 12:09:51 -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/17/t132154981705crmqpiiclmn7k.htm/, Retrieved Fri, 29 Mar 2024 08:56:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145148, Retrieved Fri, 29 Mar 2024 08:56:35 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact87
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] [paper - conn x happ] [2010-11-16 09:18:56] [9894f466352df31a128e82ec8d720241]
-    D      [Linear Regression Graphical Model Validation] [mini tutorial hyp...] [2011-11-17 17:09:51] [d519577d845e738b812f706f10c86f64] [Current]
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Dataseries X:
41
39
30
31
34
35
39
34
36
37
38
36
38
39
33
32
36
38
39
32
32
31
39
37
39
41
36
33
33
34
31
27
37
34
34
32
29
36
29
35
37
34
38
35
38
37
38
33
36
38
32
32
32
34
32
37
39
29
37
35
30
38
34
31
34
35
36
30
39
35
38
31
34
38
34
39
37
34
28
37
33
37
35
37
32
33
38
33
29
33
31
36
35
32
29
39
37
35
37
32
38
37
36
32
33
40
38
41
36
43
30
31
32
32
37
37
33
34
33
38
33
31
38
37
33
31
39
44
33
35
32
28
40
27
37
32
28
34
30
35
31
32
30
30
31
40
32
36
32
35
38
42
34
35
35
33
36
32
33
34
32
34
Dataseries Y:
12
11
15
6
13
10
12
14
12
6
10
12
12
11
15
12
10
12
11
12
11
12
13
11
9
13
10
14
12
10
12
8
10
12
12
7
6
12
10
10
10
12
15
10
10
12
13
11
11
12
14
10
12
13
5
6
12
12
11
10
7
12
14
11
12
13
14
11
12
12
8
11
14
14
12
9
13
11
12
12
12
12
12
12
11
10
9
12
12
12
9
15
12
12
12
10
13
9
12
10
14
11
15
11
11
12
12
12
11
7
12
14
11
11
10
13
13
8
11
12
11
13
12
14
13
15
10
11
9
11
10
11
8
11
12
12
9
11
10
8
9
8
9
15
11
8
13
12
12
9
7
13
9
6
8
8
15
6
9
11
8
8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145148&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145148&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145148&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'Herman Ole Andreas Wold' @ wold.wessa.net







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term9.688973555515921.741733505523225.56283353612431.09235651191142e-07
slope0.03946983134363010.05006914667058820.7883064515420560.431683869041398

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 9.68897355551592 & 1.74173350552322 & 5.5628335361243 & 1.09235651191142e-07 \tabularnewline
slope & 0.0394698313436301 & 0.0500691466705882 & 0.788306451542056 & 0.431683869041398 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145148&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]9.68897355551592[/C][C]1.74173350552322[/C][C]5.5628335361243[/C][C]1.09235651191142e-07[/C][/ROW]
[ROW][C]slope[/C][C]0.0394698313436301[/C][C]0.0500691466705882[/C][C]0.788306451542056[/C][C]0.431683869041398[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145148&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145148&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 term9.688973555515921.741733505523225.56283353612431.09235651191142e-07
slope0.03946983134363010.05006914667058820.7883064515420560.431683869041398



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')