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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_linear_regression.wasp

Title produced by software

Linear Regression Graphical Model Validation

Date of computation

Sat, 06 Nov 2010 16:54:19 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/06/t1289062835r8769pi7qik7u1e.htm/, Retrieved Fri, 09 May 2025 16:43:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=92472, Retrieved Fri, 09 May 2025 16:43:54 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

298

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]
- RM D    [Linear Regression Graphical Model Validation] [Regressiemodel 1] [2010-11-06 16:54:19] [9ea95e194e0eb2a674315798620d5bc6] [Current]
-   PD      [Linear Regression Graphical Model Validation] [Regressiemodel 1] [2010-11-20 19:37:29] [97ad38b1c3b35a5feca8b85f7bc7b3ff] 
-   PD      [Linear Regression Graphical Model Validation] [Regressiemodel 1] [2010-11-20 19:54:37] [97ad38b1c3b35a5feca8b85f7bc7b3ff] 
- RMPD      [Univariate Data Series] [Bestedingen] [2010-12-10 12:35:43] [97ad38b1c3b35a5feca8b85f7bc7b3ff] 
- RMPD      [Mean Plot] [Mean plot Schiphol] [2010-12-10 12:51:13] [97ad38b1c3b35a5feca8b85f7bc7b3ff] 
-    D      [Linear Regression Graphical Model Validation] [Regressiemodel 1] [2010-12-10 12:24:47] [97ad38b1c3b35a5feca8b85f7bc7b3ff] 
- RMPD      [Spectral Analysis] [Spectral Analysis...] [2010-12-10 14:16:07] [97ad38b1c3b35a5feca8b85f7bc7b3ff] 
- R           [Spectral Analysis] [Paper - Spectral ...] [2011-12-20 14:36:26] [69d59b79aaf660457acc70a0ef0bfdab] 
- RMPD      [(Partial) Autocorrelation Function] [Autocorrelatiefun...] [2010-12-10 14:34:45] [97ad38b1c3b35a5feca8b85f7bc7b3ff] 
- R P         [(Partial) Autocorrelation Function] [Paper - autocorre...] [2011-12-20 15:27:59] [69d59b79aaf660457acc70a0ef0bfdab] 
- RMPD      [Multiple Regression] [Schiphol: MR - Mo...] [2010-12-10 14:47:34] [97ad38b1c3b35a5feca8b85f7bc7b3ff] 
- R  D      [Linear Regression Graphical Model Validation] [] [2011-11-15 17:48:12] [06c08141d7d783218a8164fd2ea166f2] 
-             [Linear Regression Graphical Model Validation] [] [2011-12-18 15:51:24] [06c08141d7d783218a8164fd2ea166f2] 
- R P       [Linear Regression Graphical Model Validation] [] [2011-11-15 21:43:38] [ec2187f7727da5d5d939740b21b8b68a] 
- R PD        [Linear Regression Graphical Model Validation] [] [2011-12-19 15:58:44] [ec2187f7727da5d5d939740b21b8b68a] 
- R         [Linear Regression Graphical Model Validation] [Paper - Hypothese...] [2011-12-19 16:27:20] [69d59b79aaf660457acc70a0ef0bfdab] 

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Dataseries Y:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92472&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]3 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=92472&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92472&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Simple Linear Regression
Statistics	Estimate	S.D.	T-STAT (H0: coeff=0)	P-value (two-sided)
constant term	7021.55707756009	521.979470274792	13.4517878143055	0
slope	0.185439762917008	0.0128642866695146	14.4150832207788	0

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 7021.55707756009 & 521.979470274792 & 13.4517878143055 & 0 \tabularnewline
slope & 0.185439762917008 & 0.0128642866695146 & 14.4150832207788 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92472&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]7021.55707756009[/C][C]521.979470274792[/C][C]13.4517878143055[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]0.185439762917008[/C][C]0.0128642866695146[/C][C]14.4150832207788[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92472&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92472&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
Statistics	Estimate	S.D.	T-STAT (H0: coeff=0)	P-value (two-sided)
constant term	7021.55707756009	521.979470274792	13.4517878143055	0
slope	0.185439762917008	0.0128642866695146	14.4150832207788	0

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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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')

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Input Parameters & R Code