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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 computationWed, 17 Nov 2010 00:22:02 +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/17/t12899532302v6w84njqxggb38.htm/, Retrieved Thu, 28 Mar 2024 11:18:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=96499, Retrieved Thu, 28 Mar 2024 11:18:49 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160
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] [ws6vb] [2010-11-13 11:31:05] [c7506ced21a6c0dca45d37c8a93c80e0]
-    D    [Linear Regression Graphical Model Validation] [tt] [2010-11-13 12:51:28] [4a7069087cf9e0eda253aeed7d8c30d6]
-    D      [Linear Regression Graphical Model Validation] [W6tutorial] [2010-11-13 14:33:02] [c7506ced21a6c0dca45d37c8a93c80e0]
-    D        [Linear Regression Graphical Model Validation] [Workshop 6, Mini ...] [2010-11-14 15:39:04] [3635fb7041b1998c5a1332cf9de22bce]
-    D          [Linear Regression Graphical Model Validation] [Workshop 6, Simpl...] [2010-11-16 19:47:28] [3635fb7041b1998c5a1332cf9de22bce]
- R  D              [Linear Regression Graphical Model Validation] [Mini Toturial - H...] [2010-11-17 00:22:02] [694ff701b218c88f710fbbc10aa38a8e] [Current]
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Dataseries X:
5.5
3.5
8.5
5
6
6
5.5
5.5
6
6.5
7
8
5.5
5
5.5
7.5
4.5
5.5
8.5
8.5
5.5
9
7
5
5.5
7.5
7.5
6.5
8
6.5
4.5
9
9
6
8.5
4.5
4.5
6
9
6
9
7
7.5
8
5
5.5
7
4.5
6
8.5
2.5
6
6
3
12
6
6
7
3.5
6.5
6
6.5
7
4
5.5
4.5
5.5
6.5
5
5.5
6
4.5
7.5
9
7.5
6
6.5
7
5
6.5
6.5
5.5
6.5
8
4
8
5.5
4.5
8
6
7
4
4.5
7.5
5.5
10.5
7
9
6
6.5
7.5
6
9.5
7.5
5.5
5.5
5
6.5
7.5
6
6
8
4.5
9
4
6.5
8.5
4.5
7.5
4
3.5
6
7
3
4
8.5
5
5.5
7
5.5
6.5
6
5.5
4.5
6
10
6
6.5
6
6
4.5
7.5
12
3.5
8.5
5.5
8.5
5.5
6
7
5.5
8
10.5
7
10
6.5
5.5
7.5
9.5
Dataseries Y:
6
4
4
4
4.5
3.5
2
5.5
3.5
3.5
6
5
5
4
4
2
4.5
4
3.5
5.5
4.5
5.5
6.5
4
4
4.5
3
4.5
4.5
3
3
8
2.5
3.5
4.5
3
3
2.5
6
3.5
5
4.5
4
2.5
4
4
5
3
4
3.5
2
4
4
2
10
4
4
3
2
4
4.5
3
3.5
4.5
2.5
2.5
4
4
3
4
3.5
3.5
4.5
5.5
3
4
3
4.5
4
3
5
4
4
5
2.5
3.5
2.5
4
7
3.5
4
3
2.5
3
5
6
4.5
6
3.5
4
5
3
5
5
5
2.5
3.5
5
5.5
3
3.5
6
5.5
5.5
5.5
2.5
4
3
4.5
2
2
3.5
5.5
3
3.5
4
2
4
4.5
4
5.5
4
2.5
2
4
5
3
4.5
4.5
6.5
4.5
5
10
2.5
5.5
3
4.5
3.5
4.5
5
4.5
4
3.5
3
6.5
3
4
5
8




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=96499&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=96499&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96499&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 term1.117418653933850.3358493084551703.327142935245930.00109286665592379
slope0.4660767456208590.05047988877040959.23291942541022.22044604925031e-16

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 1.11741865393385 & 0.335849308455170 & 3.32714293524593 & 0.00109286665592379 \tabularnewline
slope & 0.466076745620859 & 0.0504798887704095 & 9.2329194254102 & 2.22044604925031e-16 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96499&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]1.11741865393385[/C][C]0.335849308455170[/C][C]3.32714293524593[/C][C]0.00109286665592379[/C][/ROW]
[ROW][C]slope[/C][C]0.466076745620859[/C][C]0.0504798887704095[/C][C]9.2329194254102[/C][C]2.22044604925031e-16[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96499&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96499&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 term1.117418653933850.3358493084551703.327142935245930.00109286665592379
slope0.4660767456208590.05047988877040959.23291942541022.22044604925031e-16



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')