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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, 11 Nov 2010 13:04:30 +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/11/t1289480683i683cj0wd7gy1lr.htm/, Retrieved Fri, 29 Mar 2024 08:28:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=93278, Retrieved Fri, 29 Mar 2024 08:28:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact225
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Linear Regression Graphical Model Validation] [Mini tutorial] [2010-11-11 13:04:30] [380f6bceef280be3d93cc6fafd18141e] [Current]
-    D    [Linear Regression Graphical Model Validation] [ws6 mini-tutorial...] [2010-11-15 17:25:55] [e4076051fbfb461c886b1e223cd7862f]
-    D    [Linear Regression Graphical Model Validation] [ws6 mini-tutorial...] [2010-11-15 17:25:55] [e4076051fbfb461c886b1e223cd7862f]
-    D    [Linear Regression Graphical Model Validation] [ws6 mini tutorial...] [2010-11-15 17:31:10] [e4076051fbfb461c886b1e223cd7862f]
-    D    [Linear Regression Graphical Model Validation] [ws6 mini tutorial...] [2010-11-15 17:35:11] [e4076051fbfb461c886b1e223cd7862f]
-    D    [Linear Regression Graphical Model Validation] [ws6.mini hypothese 1] [2010-11-15 18:16:27] [e4076051fbfb461c886b1e223cd7862f]
-   PD      [Linear Regression Graphical Model Validation] [] [2010-11-16 10:25:31] [717f3d787904f94c39256c5c1fc72d4c]
-    D      [Linear Regression Graphical Model Validation] [] [2010-11-16 13:40:13] [8d09066a9d3795298da6860e7d4a4400]
-             [Linear Regression Graphical Model Validation] [] [2010-11-17 06:02:33] [6e5489189f7de5cfbcc25dd35ae15009]
F    D      [Linear Regression Graphical Model Validation] [] [2010-11-16 14:33:13] [cbb1f7583f1ea41fcafd5f9709bd0e0a]
-    D      [Linear Regression Graphical Model Validation] [PAPER BAEYENS (Li...] [2010-12-20 10:12:09] [e4076051fbfb461c886b1e223cd7862f]
- RMPD        [Univariate Explorative Data Analysis] [PAPER BAEYENS (Un...] [2010-12-20 10:23:43] [e4076051fbfb461c886b1e223cd7862f]
-    D        [Linear Regression Graphical Model Validation] [PAPER BAEYENS (Li...] [2010-12-20 10:23:31] [e4076051fbfb461c886b1e223cd7862f]
- RMPD      [Univariate Explorative Data Analysis] [PAPER BAEYENS (Un...] [2010-12-20 10:18:50] [e4076051fbfb461c886b1e223cd7862f]
- RM      [Linear Regression Graphical Model Validation] [mini tutorial] [2011-11-15 15:18:50] [d31984dff2665bea309b726bae3d5241]
- R  D    [Linear Regression Graphical Model Validation] [] [2011-11-15 17:03:12] [ad2d4c5ace9fa07b356a7b5098237581]
- R  D    [Linear Regression Graphical Model Validation] [] [2011-11-15 17:03:12] [ad2d4c5ace9fa07b356a7b5098237581]
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Dataseries X:
5,11
3,53
4,52
3,72
5,99
3,15
3,17
3,50
3,39
4,15
4,50
3,31
3,09
5,31
4,24
5,06
4,72
4,58
5,30
5,11
4,05
4,62
4,66
4,66
2,76
5,10
4,97
2,87
5,14
4,98
4,55
5,45
4,36
4,78
4,74
5,44
5,78
2,92
4,22
3,93
3,01
3,22
5,12
3,04
5,82
3,11
3,87
3,75
4,82
2,83
Dataseries Y:
71,91
6,06
8,10
79,38
65,34
34,62
26,26
60,92
39,56
65,61
56,49
56,19
80,30
61,20
58,20
75,91
73,66
73,87
87,21
64,29
71,82
89,31
1,41
35,17
34,68
41,08
30,57
68,84
7,17
71,05
23,32
61,39
8,41
65,88
64,06
26,80
12,78
23,84
42,69
54,94
89,99
5,68
72,64
45,92
24,96
18,17
29,12
40,08
1,08
57,52




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=93278&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=93278&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93278&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 time6 seconds
R Server'George Udny Yule' @ 72.249.76.132







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term41.645356622211117.97977843940322.316233026039080.0248614916275738
slope1.323157503455024.110091947254920.3219289301638960.748904402540612

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 41.6453566222111 & 17.9797784394032 & 2.31623302603908 & 0.0248614916275738 \tabularnewline
slope & 1.32315750345502 & 4.11009194725492 & 0.321928930163896 & 0.748904402540612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=93278&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]41.6453566222111[/C][C]17.9797784394032[/C][C]2.31623302603908[/C][C]0.0248614916275738[/C][/ROW]
[ROW][C]slope[/C][C]1.32315750345502[/C][C]4.11009194725492[/C][C]0.321928930163896[/C][C]0.748904402540612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=93278&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93278&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 term41.645356622211117.97977843940322.316233026039080.0248614916275738
slope1.323157503455024.110091947254920.3219289301638960.748904402540612



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')