## Free Statistics

of Irreproducible Research!

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 computationSun, 14 Nov 2010 14:41:18 +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/14/t1289746062yd74uwmbdswup4m.htm/, Retrieved Wed, 27 Sep 2023 08:39:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94543, Retrieved Wed, 27 Sep 2023 08:39:28 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
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 Tutori...] [2010-11-14 14:41:18] [c9b1b69acb8f4b2b921fdfd5091a94b7] [Current]
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Dataseries X:
25
25
19
18
18
22
29
26
25
23
23
23
24
30
19
24
32
30
29
17
25
26
26
25
23
21
19
35
19
20
21
21
24
23
19
17
24
15
25
27
29
27
18
25
22
26
23
16
27
25
14
19
20
16
18
22
21
22
22
32
23
31
18
23
26
24
19
14
20
22
24
25
21
28
24
20
21
23
13
24
21
21
17
14
29
25
16
25
25
21
23
22
19
24
26
25
20
22
14
20
32
21
22
28
25
17
21
23
27
22
19
20
17
24
21
21
23
24
19
22
26
17
17
19
15
17
27
19
21
25
19
22
18
20
15
20
29
19
29
24
23
22
23
22
29
26
26
21
18
10

Dataseries Y:
14
18
11
12
16
18
14
14
15
15
17
19
10
18
14
14
17
14
16
18
14
12
17
9
16
14
11
16
13
17
15
14
16
9
15
17
13
15
16
16
12
12
11
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
17
13
15
13
15
16
15
16
15
14
15
7
17
13
15
14
13
16
12
14
17
15
17
12
16
11
15
9
16
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
16
11
14
11
15
13
15
16
14
15
16
16
11
13
16
12
9
13
13
14
19
13
12
13


 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=94543&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=94543&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94543&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 13.6064024067897 1.01469796225126 13.4093128329556 0 slope 0.0209825161718201 0.0447691848906079 0.468682113000052 0.639986467491451

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 13.6064024067897 & 1.01469796225126 & 13.4093128329556 & 0 \tabularnewline
slope & 0.0209825161718201 & 0.0447691848906079 & 0.468682113000052 & 0.639986467491451 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94543&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]13.6064024067897[/C][C]1.01469796225126[/C][C]13.4093128329556[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]0.0209825161718201[/C][C]0.0447691848906079[/C][C]0.468682113000052[/C][C]0.639986467491451[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94543&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94543&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 13.6064024067897 1.01469796225126 13.4093128329556 0 slope 0.0209825161718201 0.0447691848906079 0.468682113000052 0.639986467491451

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$coefficients[[1]]/sda<-table.element(a,tstat)pval <- 2*(1-pt(abs(tstat),length(x)-2))a<-table.element(a,pval)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'slope',header=TRUE)a<-table.element(a,m$coefficients[[2]])sd <- sqrt(vcov(m)[2,2])a<-table.element(a,sd)tstat <- m\$coefficients[[2]]/sda<-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')