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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, 29 Nov 2011 10:54:53 -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/29/t1322582107yzuk3gqozfppire.htm/, Retrieved Fri, 29 Mar 2024 07:46:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148553, Retrieved Fri, 29 Mar 2024 07:46:05 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact52
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]
- RMPD    [Linear Regression Graphical Model Validation] [] [2011-11-29 15:54:53] [f04206f511735117c791d4a2bb2fa643] [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:
38
32
35
33
37
29
31
36
35
38
31
34
35
38
37
33
32
38
38
32
33
31
38
39
32
32
35
37
33
33
28
32
31
37
30
33
31
33
31
33
32
33
32
33
28
35
39
34
38
32
38
30
33
38
32
32
34
34
36
34
28
34
35
35
31
37
35
27
40
37
36
38
39
41
27
30
37
31
31
27
36
38
37
33
34
31
39
34
32
33
36
32
41
28
30
36
35
31
34
36
36
35
37
28
39
32
35
39
35
42
34
33
41
33
34
32
40
40
35
36
37
27
39
38
31
33
32
39
36
33
33
32
37
30
38
29
22
35
35
34
35
34
34
35
23
31
27
36
31
32
39
37
38
39
34
31
32
37
36
32
35
36




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148553&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148553&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148553&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 time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term20.6767357873942.691171488294357.683172877436671.46571643711013e-12
slope0.3874788380178580.07736238611465265.008620564567481.43595671975305e-06

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 20.676735787394 & 2.69117148829435 & 7.68317287743667 & 1.46571643711013e-12 \tabularnewline
slope & 0.387478838017858 & 0.0773623861146526 & 5.00862056456748 & 1.43595671975305e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148553&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]20.676735787394[/C][C]2.69117148829435[/C][C]7.68317287743667[/C][C]1.46571643711013e-12[/C][/ROW]
[ROW][C]slope[/C][C]0.387478838017858[/C][C]0.0773623861146526[/C][C]5.00862056456748[/C][C]1.43595671975305e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148553&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148553&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 term20.6767357873942.691171488294357.683172877436671.46571643711013e-12
slope0.3874788380178580.07736238611465265.008620564567481.43595671975305e-06



Parameters (Session):
par1 = two.sided ; par2 = 0.95 ; par3 = 35 ;
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')