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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, 16 Nov 2010 23:54:33 +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/t1289951567k4y10k4dvh50nng.htm/, Retrieved Fri, 29 Mar 2024 10:42:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=96489, Retrieved Fri, 29 Mar 2024 10:42:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact187
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] [workshop 6 tutorial] [2010-11-12 10:13:29] [87d60b8864dc39f7ed759c345edfb471]
-    D    [Linear Regression Graphical Model Validation] [workshop 6 mini-t...] [2010-11-12 14:05:27] [87d60b8864dc39f7ed759c345edfb471]
-    D      [Linear Regression Graphical Model Validation] [] [2010-11-15 22:09:40] [1251ac2db27b84d4a3ba43449388906b]
-    D        [Linear Regression Graphical Model Validation] [Happiness - belon...] [2010-11-16 21:50:38] [8ef75e99f9f5061c72c54640f2f1c3e7]
-    D            [Linear Regression Graphical Model Validation] [Happiness - depre...] [2010-11-16 23:54:33] [e26438ba7029caa0090c95690001dbf5] [Current]
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Dataseries X:
14
18
11
12
16
18
14
14
15
15
17
19
10
16
18
14
14
17
14
16
18
11
14
12
17
9
16
14
15
11
16
13
17
15
14
16
9
15
17
13
15
16
16
12
12
11
15
15
17
13
16
14
11
12
12
15
16
15
12
12
8
13
11
14
15
10
11
12
15
15
14
16
15
15
13
12
17
13
15
13
15
16
15
16
15
14
15
14
13
7
17
13
15
14
13
16
12
14
17
15
17
12
16
11
15
9
16
15
10
10
15
11
13
14
18
16
14
14
14
14
12
14
15
15
15
13
17
17
19
15
13
9
15
15
15
16
11
14
11
15
13
15
16
14
15
16
16
11
12
9
16
13
16
12
9
13
13
14
19
13
12
13
Dataseries Y:
12
11
14
12
21
12
22
11
10
13
10
8
15
14
10
14
14
11
10
13
7
14
12
14
11
9
11
15
14
13
9
15
10
11
13
8
20
12
10
10
9
14
8
14
11
13
9
11
15
11
10
14
18
14
11
12
13
9
10
15
20
12
12
14
13
11
17
12
13
14
13
15
13
10
11
19
13
17
13
9
11
10
9
12
12
13
13
12
15
22
13
15
13
15
10
11
16
11
11
10
10
16
12
11
16
19
11
16
15
24
14
15
11
15
12
10
14
13
9
15
15
14
11
8
11
11
8
10
11
13
11
20
10
15
12
14
23
14
16
11
12
10
14
12
12
11
12
13
11
19
12
17
9
12
19
18
15
14
11
9
18
16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96489&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96489&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96489&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'Gwilym Jenkins' @ 72.249.127.135







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term23.26700064550531.2782170390692718.2026994902580
slope-0.7371390081670450.0898307835757035-8.205861942034977.14983627858601e-14

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 23.2670006455053 & 1.27821703906927 & 18.202699490258 & 0 \tabularnewline
slope & -0.737139008167045 & 0.0898307835757035 & -8.20586194203497 & 7.14983627858601e-14 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96489&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]23.2670006455053[/C][C]1.27821703906927[/C][C]18.202699490258[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]-0.737139008167045[/C][C]0.0898307835757035[/C][C]-8.20586194203497[/C][C]7.14983627858601e-14[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96489&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96489&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 term23.26700064550531.2782170390692718.2026994902580
slope-0.7371390081670450.0898307835757035-8.205861942034977.14983627858601e-14



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')