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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 computationSun, 14 Nov 2010 15:45:40 +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/14/t1289749724mc60qer961ww1eh.htm/, Retrieved Fri, 29 Mar 2024 05:59:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94571, Retrieved Fri, 29 Mar 2024 05:59:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Paired and Unpaired Two Samples Tests about the Mean] [WS 5 Question 3] [2010-11-01 07:38:26] [00b18f0d8e13a2047ccd266ce7bab24a]
- RMPD    [Linear Regression Graphical Model Validation] [Mini-tutorial, Hy...] [2010-11-14 15:45:40] [99c051a77087383325372ff23bc64341] [Current]
-    D      [Linear Regression Graphical Model Validation] [Mini-tutorial, Hy...] [2010-11-16 19:48:44] [d946de7cca328fbcf207448a112523ab]
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Dataseries X:
8,7
7,7
8,3
7,7
6,3
9,7
8,3
7,0
7,3
8,3
8,0
6,0
7,3
5,0
7,3
9,3
6,7
4,0
8,0
6,7
7,0
6,7
7,0
7,7
9,3
8,0
8,0
8,0
7,7
7,7
9,7
8,0
6,0
8,3
7,0
8,7
7,3
7,3
7,3
7,7
10,0
7,7
5,7
7,7
7,7
8,3
8,0
8,0
7,7
7,0
8,0
8,0
9,3
5,3
6,7
9,7
9,0
7,3
9,3
5,3
8,3
8,0
9,3
8,0
7,7
10,0
8,0
7,0
8,3
8,3
7,3
7,7
8,7
7,7
8,3
7,0
8,3
8,0
9,7
7,3
9,0
8,7
7,3
8,0
9,0
8,0
8,0
9,7
7,3
7,0
8,0
8,0
7,7
6,7
9,0
8,7
8,3
7,0
7,0
6,3
7,0
7,0
5,3
7,3
9,7
5,0
5,7
5,0
7,0
7,0
6,3
8,0
6,7
5,7
7,7
8,0
4,7
6,3
8,0
4,3
7,3
5,3
6,3
8,3
8,3
7,7
8,0
8,7
8,7
8,3
6,0
7,0
8,7
7,7
7,7
7,3
6,7
4,3
8,0
5,0
4,7
7,3
3,3
8,0
7,3
8,0
6,3
6,7
4,3
6,7
7,3
8,0
9,7
4,0
6,7
7,0
8,0
7,3
6,7
Dataseries Y:
7
5,5
3
6
4
5
5
5,5
8
5,5
6,5
6
4
6
5,5
2
4,5
4
4
7
7,5
8
4,5
7
5,5
4
4,5
4,5
4,5
4,5
5
8
5,5
4
4,5
8
5,5
8
6
6
7
4,5
5
4,5
5
6
7
7
5
7
8
4,5
5
3
4
6,5
5
4
3,5
7,5
4,5
5
6
6,5
5
5,5
4
4,5
6,5
5,5
4
4,5
4,5
7,5
4,5
5
7
6
6
5,5
7
3
6
4
7
5,5
5
7
6
5
7
2,5
5,5
5
4,5
5
8
6,5
4,5
5
5
3,5
4,5
4
7
7
4
4,5
7
7
4
4
4
3,5
3
4
3
5,5
7
5,5
5,5
5,5
7
4
10
5,5
4
5,5
5
7
5,5
4,5
4,5
4
5
6,5
6,5
6
4
6,5
7
6
7
7,5
6,5
8
4,5
4,5
4,5
4
3,5
8
5,5
4,5
5,5
4,5
7
6,5
8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94571&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94571&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94571&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term4.895285189732220.6456136894387697.582375141375452.78177481050079e-12
slope0.07405344274465830.08543447429370770.8667864273393490.387381822248436

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 4.89528518973222 & 0.645613689438769 & 7.58237514137545 & 2.78177481050079e-12 \tabularnewline
slope & 0.0740534427446583 & 0.0854344742937077 & 0.866786427339349 & 0.387381822248436 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94571&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]4.89528518973222[/C][C]0.645613689438769[/C][C]7.58237514137545[/C][C]2.78177481050079e-12[/C][/ROW]
[ROW][C]slope[/C][C]0.0740534427446583[/C][C]0.0854344742937077[/C][C]0.866786427339349[/C][C]0.387381822248436[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94571&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94571&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 term4.895285189732220.6456136894387697.582375141375452.78177481050079e-12
slope0.07405344274465830.08543447429370770.8667864273393490.387381822248436



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')