R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> y <- c(98.6,98,106.8,96.7,100.2,107.7,92,98.4,107.4,117.7,105.7,97.5,99.9,98.2,104.5,100.8,101.5,103.9,99.6,98.4,112.7,118.4,108.1,105.4,114.6,106.9,115.9,109.8,101.8,114.2,110.8,108.4,127.5,128.6,116.6,127.4,105,108.3,125,111.6,106.5,130.3,115,116.1,134,126.5,125.8,136.4,114.9,110.9,125.5,116.8,116.8,125.5,104.2,115.1,132.8,123.3,124.8,122,117.4,117.9,137.4,114.6,124.7,129.6,109.4,120.9,134.9,136.3,133.2,127.2,122.7,120.5,137.8,119.1,124.3,134.3,121.7,125)
> x <- c(98.6,98,106.8,96.6,100.1,107.7,91.5,97.8,107.4,117.5,105.6,97.4,99.5,98,104.3,100.6,101.1,103.9,96.9,95.5,108.4,117,103.8,100.8,110.6,104,112.6,107.3,98.9,109.8,104.9,102.2,123.9,124.9,112.7,121.9,100.6,104.3,120.4,107.5,102.9,125.6,107.5,108.8,128.4,121.1,119.5,128.7,108.7,105.5,119.8,111.3,110.6,120.1,97.5,107.7,127.3,117.2,119.8,116.2,111,112.4,130.6,109.1,118.8,123.9,101.6,112.8,128,129.6,125.8,119.5,115.7,113.6,129.7,112,116.8,126.3,112.9,115.9)
> par1 = '0'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: Wessa P., (2007), Linear Regression Graphical Model Validation (v1.0.2) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_linear_regression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description
> par1 <- as.numeric(par1)
> library(lattice)
> z <- as.data.frame(cbind(x,y))
> m <- lm(y~x)
> summary(m)

Call:
lm(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1740 -1.7727  0.2478  1.6879  4.8906 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -12.83956    2.82884  -4.539 2.02e-05 ***
x             1.15501    0.02535  45.563  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 2.267 on 78 degrees of freedom
Multiple R-Squared: 0.9638,	Adjusted R-squared: 0.9633 
F-statistic:  2076 on 1 and 78 DF,  p-value: < 2.2e-16 

> postscript(file="/var/www/html/rcomp/tmp/1mg4p1194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(z,main='Scatterplot, lowess, and regression line')
> lines(lowess(z),col='red')
> abline(m)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2ndr21194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m2 <- lm(m$fitted.values ~ x)
> summary(m2)

Call:
lm(formula = m$fitted.values ~ x)

Residuals:
       Min         1Q     Median         3Q        Max 
-6.220e-13 -4.937e-16  7.501e-15  1.442e-14  6.963e-14 

Coefficients:
              Estimate Std. Error    t value Pr(>|t|)    
(Intercept) -1.284e+01  8.983e-14 -1.429e+14   <2e-16 ***
x            1.155e+00  8.050e-16  1.435e+15   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 7.199e-14 on 78 degrees of freedom
Multiple R-Squared:     1,	Adjusted R-squared:     1 
F-statistic: 2.059e+30 on 1 and 78 DF,  p-value: < 2.2e-16 

> z2 <- as.data.frame(cbind(x,m$fitted.values))
> names(z2) <- list('x','Fitted')
> plot(z2,main='Scatterplot, lowess, and regression line')
> lines(lowess(z2),col='red')
> abline(m2)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/3lzlf1194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m3 <- lm(m$residuals ~ x)
> summary(m3)

Call:
lm(formula = m$residuals ~ x)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1740 -1.7727  0.2478  1.6879  4.8906 

Coefficients:
              Estimate Std. Error   t value Pr(>|t|)
(Intercept)  1.104e-15  2.829e+00  3.90e-16        1
x           -9.932e-18  2.535e-02 -3.92e-16        1

Residual standard error: 2.267 on 78 degrees of freedom
Multiple R-Squared: 1.187e-32,	Adjusted R-squared: -0.01282 
F-statistic: 9.256e-31 on 1 and 78 DF,  p-value: 1 

> z3 <- as.data.frame(cbind(x,m$residuals))
> names(z3) <- list('x','Residuals')
> plot(z3,main='Scatterplot, lowess, and regression line')
> lines(lowess(z3),col='red')
> abline(m3)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/4032i1194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m4 <- lm(m$fitted.values ~ m$residuals)
> summary(m4)

Call:
lm(formula = m$fitted.values ~ m$residuals)

Residuals:
    Min      1Q  Median      3Q     Max 
-22.689  -8.742  -1.090   9.652  22.472 

Coefficients:
             Estimate Std. Error  t value Pr(>|t|)    
(Intercept) 1.155e+02  1.308e+00    88.36   <2e-16 ***
m$residuals 3.460e-15  5.841e-01 5.92e-15        1    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 11.69 on 78 degrees of freedom
Multiple R-Squared: 1.891e-29,	Adjusted R-squared: -0.01282 
F-statistic: 1.475e-27 on 1 and 78 DF,  p-value: 1 

> 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()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/5v2ag1194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> myr <- as.ts(m$residuals)
> m5 <- lm(myr ~ lag(myr,1))
> summary(m5)

Call:
lm(formula = myr ~ lag(myr, 1))

Residuals:
       Min         1Q     Median         3Q        Max 
-5.376e-15 -4.558e-17  7.946e-17  1.588e-16  9.087e-16 

Coefficients:
             Estimate Std. Error   t value Pr(>|t|)    
(Intercept) 0.000e+00  7.276e-17 0.000e+00        1    
lag(myr, 1) 1.000e+00  3.250e-17 3.077e+16   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 6.507e-16 on 78 degrees of freedom
Multiple R-Squared:     1,	Adjusted R-squared:     1 
F-statistic: 9.465e+32 on 1 and 78 DF,  p-value: < 2.2e-16 

> z5 <- as.data.frame(cbind(lag(myr,1),myr))
> names(z5) <- list('Lagged Residuals','Residuals')
> plot(z5,main='Lag plot')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/6p4qy1194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(m$residuals,main='Residual Histogram',xlab='Residuals')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/7ryhp1194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if (par1 > 0)
+ {
+ densityplot(~x,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
+ } else {
+ densityplot(~x,col='black',main='Density Plot')
+ }
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/8p7m11194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> acf(m$residuals,main='Residual Autocorrelation Function')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/9in8j1194690759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(x)
> grid()
> dev.off()
null device 
          1 
> load(file='/var/www/html/rcomp/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="/var/www/html/rcomp/tmp/10wzma1194690759.tab") 
> 
> system("convert tmp/1mg4p1194690759.ps tmp/1mg4p1194690759.png")
> system("convert tmp/2ndr21194690759.ps tmp/2ndr21194690759.png")
> system("convert tmp/3lzlf1194690759.ps tmp/3lzlf1194690759.png")
> system("convert tmp/4032i1194690759.ps tmp/4032i1194690759.png")
> system("convert tmp/5v2ag1194690759.ps tmp/5v2ag1194690759.png")
> system("convert tmp/6p4qy1194690759.ps tmp/6p4qy1194690759.png")
> system("convert tmp/7ryhp1194690759.ps tmp/7ryhp1194690759.png")
> system("convert tmp/8p7m11194690759.ps tmp/8p7m11194690759.png")
> system("convert tmp/9in8j1194690759.ps tmp/9in8j1194690759.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.175   1.406   2.793