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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, 15 Nov 2011 18:31:53 -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/15/t13213999586rj8lkrnvpfkgra.htm/, Retrieved Thu, 28 Mar 2024 12:53:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=143674, Retrieved Thu, 28 Mar 2024 12:53:52 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79
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] [Regression Model 1] [2010-11-16 09:46:35] [1429a1a14191a86916b95357f6de790b]
- R P       [Linear Regression Graphical Model Validation] [Workshop 6 - Mini...] [2011-11-15 23:31:53] [c18e83883fa784c15a15b4fbc0636edd] [Current]
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Dataseries X:
14
11
6
12
8
10
10
11
16
11
13
12
8
12
11
4
9
8
8
14
15
16
9
14
11
8
9
9
9
9
10
16
11
8
9
16
11
16
12
12
14
9
10
9
10
12
14
14
10
14
16
9
10
6
8
13
10
8
7
15
9
10
12
13
10
11
8
9
13
11
8
9
9
15
9
10
14
12
12
11
14
6
12
8
14
11
10
14
12
10
14
5
11
10
9
10
16
13
9
10
10
7
9
8
14
14
8
9
14
14
8
8
8
7
6
8
6
11
14
11
11
11
14
8
20
11
8
11
10
14
11
9
9
8
10
13
13
12
8
13
14
12
14
15
13
16
9
9
9
8
7
16
11
9
11
9
14
13
16
Dataseries Y:
12
8
8
8
9
7
4
11
7
7
12
10
10
8
8
4
9
8
7
11
9
11
13
8
8
9
6
9
9
6
6
16
5
7
9
6
6
5
12
7
10
9
8
5
8
8
10
6
8
7
4
8
8
4
20
8
8
6
4
8
9
6
7
9
5
5
8
8
6
8
7
7
9
11
6
8
6
9
8
6
10
8
8
10
5
7
5
8
14
7
8
6
5
6
10
12
9
12
7
8
10
6
10
10
10
5
7
10
11
6
7
12
11
11
11
5
8
6
9
4
4
7
11
6
7
8
4
8
9
8
11
8
5
4
8
10
6
9
9
13
9
10
20
5
11
6
9
7
9
10
9
8
7
6
13
6
8
10
16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143674&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143674&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143674&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'George Udny Yule' @ yule.wessa.net







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term6.601492158554540.8571146385428827.701994414396251.41331391034782e-12
slope0.1491701771303860.07622099880064181.95707455265110.0521123179537182

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 6.60149215855454 & 0.857114638542882 & 7.70199441439625 & 1.41331391034782e-12 \tabularnewline
slope & 0.149170177130386 & 0.0762209988006418 & 1.9570745526511 & 0.0521123179537182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143674&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]6.60149215855454[/C][C]0.857114638542882[/C][C]7.70199441439625[/C][C]1.41331391034782e-12[/C][/ROW]
[ROW][C]slope[/C][C]0.149170177130386[/C][C]0.0762209988006418[/C][C]1.9570745526511[/C][C]0.0521123179537182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143674&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143674&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 term6.601492158554540.8571146385428827.701994414396251.41331391034782e-12
slope0.1491701771303860.07622099880064181.95707455265110.0521123179537182



Parameters (Session):
par1 = 1 ; par2 = 4 ; par3 = Pearson Chi-Squared ;
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')