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Author's title

Author*Unverified author*
R Software Modulerwasp_linear_regression.wasp
Title produced by softwareLinear Regression Graphical Model Validation
Date of computationTue, 15 Nov 2011 11:49:20 -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/t13213757835ishinx82t1yinj.htm/, Retrieved Thu, 28 Mar 2024 12:46:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=143191, Retrieved Thu, 28 Mar 2024 12:46:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Linear Regression Graphical Model Validation] [Workshop 6 - Mini...] [2010-11-15 18:34:24] [74be16979710d4c4e7c6647856088456]
-    D  [Linear Regression Graphical Model Validation] [Workshop 6 - mini...] [2010-11-15 18:55:36] [74be16979710d4c4e7c6647856088456]
-    D    [Linear Regression Graphical Model Validation] [Workshop 6 - Mini...] [2010-11-15 19:17:39] [74be16979710d4c4e7c6647856088456]
-    D      [Linear Regression Graphical Model Validation] [Mini-tutorial Hyp...] [2010-11-16 18:00:55] [a9e130f95bad0a0597234e75c6380c5a]
- R P           [Linear Regression Graphical Model Validation] [WS 6 - Mini-tutorial] [2011-11-15 16:49:20] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-  M              [Linear Regression Graphical Model Validation] [] [2011-11-15 19:17:30] [97a82ed57455ec27012f2e899dc4f1a4]
-  M              [Linear Regression Graphical Model Validation] [] [2011-11-15 19:18:44] [97a82ed57455ec27012f2e899dc4f1a4]
-  M              [Linear Regression Graphical Model Validation] [] [2011-11-15 19:36:09] [97a82ed57455ec27012f2e899dc4f1a4]
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Dataseries X:
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
Dataseries Y:
24
25
17
18
18
16
20
16
18
17
23
30
23
18
15
12
21
15
20
31
27
34
21
31
19
16
20
21
22
17
24
25
26
25
17
32
33
13
32
25
29
22
18
17
20
15
20
33
29
23
26
18
20
11
28
26
22
17
12
14
17
21
19
18
10
29
31
19
9
20
28
19
30
29
26
23
13
21
19
28
23
18
21
20
23
21
21
15
28
19
26
10
16
22
19
31
31
29
19
22
23
15
20
18
23
25
21
24
25
17
13
28
21
25
9
16
19
17
25
20
29
14
22
15
19
20
15
20
18
33
22
16
17
16
21
26
18
18
17
22
30
30
24
21
21
29
31
20
16
22
20
28
38
22
20
17
28
22
31




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' @ 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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143191&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' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143191&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143191&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' @ yule.wessa.net







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term16.17847016263081.3883383028342911.65311806899110
slope0.6572043150930480.1603585708910784.098342305254456.65330421218968e-05

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 16.1784701626308 & 1.38833830283429 & 11.6531180689911 & 0 \tabularnewline
slope & 0.657204315093048 & 0.160358570891078 & 4.09834230525445 & 6.65330421218968e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143191&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]16.1784701626308[/C][C]1.38833830283429[/C][C]11.6531180689911[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]0.657204315093048[/C][C]0.160358570891078[/C][C]4.09834230525445[/C][C]6.65330421218968e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143191&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143191&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 term16.17847016263081.3883383028342911.65311806899110
slope0.6572043150930480.1603585708910784.098342305254456.65330421218968e-05



Parameters (Session):
par1 = 0 ; par2 = 2 ; 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')