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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, 25 Nov 2010 09:02:26 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/25/t1290675672xdvln6hnzm97q2f.htm/, Retrieved Fri, 29 Mar 2024 12:00:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=100636, Retrieved Fri, 29 Mar 2024 12:00:48 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155
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] [parental expectat...] [2010-11-25 09:02:26] [03bcd8c83ef1a42b4029a16ba47a4880] [Current]
-    D      [Linear Regression Graphical Model Validation] [parental expectat...] [2010-11-25 19:52:59] [96348ef82925ade81ab3c243141d80f1]
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Dataseries X:
2.2
1.4
3.4
2
2.4
2.4
2.2
2.2
2.4
2.6
2.8
3.2
2.2
2
2.2
3
1.8
2.2
3.4
3.4
2.2
3.6
2.8
2
2.2
3
3
2.6
3.2
2.6
1.8
3.6
3.6
2.4
3.4
1.8
1.8
2.4
3.6
2.4
3.6
2.8
3
3.2
2
2.2
2.8
1.8
2.4
3.4
1
2.4
2.4
1.2
4.8
2.4
2.4
2.8
1.4
2.6
2.4
2.6
2.8
1.6
2.2
1.8
2.2
2.6
2
2.2
2.4
1.8
3
3.6
3
2.4
2.6
2.8
2
2.6
2.6
2.2
2.6
3.2
1.6
3.2
2.2
1.8
3.2
2.4
2.8
1.6
1.8
3
2.2
4.2
2.8
3.6
2.4
2.6
3
2.4
3.8
3
2.2
2.2
2
2.6
3
2.4
2.4
3.2
1.8
3.6
1.6
2.6
3.4
1.8
3
1.6
1.4
2.4
2.8
1.2
1.6
3.4
2
2.2
2.8
2.2
2.6
2.4
2.2
1.8
2.4
4
2.4
2.6
2.4
2.4
1.8
3
4.8
1.4
3.4
2.2
3.4
2.2
2.4
2.8
2.2
3.2
4.2
2.8
4
2.6
2.2
3
3.8
Dataseries Y:
3.43
3.57
4.29
2.71
3.14
3.14
3.57
3.29
2.43
3.00
2.71
2.71
2.14
2.29
3.29
3.86
3.14
2.00
3.14
3.29
3.29
3.00
2.71
2.57
2.86
3.29
3.57
2.71
3.43
3.14
3.57
3.71
4.14
4.57
3.57
4.14
4.00
2.43
4.00
4.14
3.71
3.57
2.00
3.57
3.71
2.86
2.57
4.57
3.57
3.57
3.29
3.00
2.86
2.14
4.29
3.43
3.71
3.43
3.14
2.00
3.43
3.43
3.43
3.43
2.71
4.43
3.14
3.86
2.71
3.57
2.86
3.00
3.86
3.29
3.57
2.86
3.00
3.14
3.29
3.57
3.57
2.43
2.71
3.57
2.71
2.86
3.71
3.29
3.86
2.43
2.43
2.71
2.43
3.14
3.00
4.57
3.00
3.00
2.57
2.57
3.29
2.71
2.86
3.00
2.86
2.43
2.57
2.71
3.14
2.14
2.00
2.57
3.43
5.00
4.14
3.00
3.57
2.86
3.14
1.86
3.71
2.43
3.57
2.86
2.71
3.00
3.14
3.43
3.00
3.71
3.43
2.29
3.29
2.57
2.29
3.71
2.71
3.00
3.00
3.14
3.29
4.14
3.00
3.00
3.29
3.86
3.57
3.00
1.43
2.86
3.71
3.43
4.14
2.71
3.43
2.71
3.43
3.14
2.43




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100636&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100636&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100636&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'Gwilym Jenkins' @ 72.249.127.135







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term2.615294938168940.18004149782934014.52606743278690
slope0.21310691005660.06765292168002293.150003056254230.00195518251875759

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 2.61529493816894 & 0.180041497829340 & 14.5260674327869 & 0 \tabularnewline
slope & 0.2131069100566 & 0.0676529216800229 & 3.15000305625423 & 0.00195518251875759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100636&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]2.61529493816894[/C][C]0.180041497829340[/C][C]14.5260674327869[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]0.2131069100566[/C][C]0.0676529216800229[/C][C]3.15000305625423[/C][C]0.00195518251875759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100636&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100636&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 term2.615294938168940.18004149782934014.52606743278690
slope0.21310691005660.06765292168002293.150003056254230.00195518251875759



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')