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(103.1,100.6,103.1,95.5,90.5,90.9,88.8,90.7,94.3,104.6,111.1,110.8,107.2,99,99,91,96.2,96.9,96.2,100.1,99,115.4,106.9,107.1,99.3,99.2,108.3,105.6,99.5,107.4,93.1,88.1,110.7,113.1,99.6,93.6,98.6,99.6,114.3,107.8,101.2,112.5,100.5,93.9,116.2,112,106.4,95.7,96,95.8,103,102.2,98.4,111.4,86.6,91.3,107.9,101.8,104.4,93.4,100.1,98.5,112.9,101.4,107.1,110.8,90.3,95.5,111.4,113,107.5,95.9,106.3,105.2,117.2,106.9,108.2,110,96.1,100.6)
> 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 
-14.2662  -3.7667   0.3639   4.0772  15.0073 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 51.68737    7.53669   6.858 1.47e-09 ***
x            0.45283    0.06754   6.705 2.86e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 6.039 on 78 degrees of freedom
Multiple R-Squared: 0.3656,	Adjusted R-squared: 0.3575 
F-statistic: 44.95 on 1 and 78 DF,  p-value: 2.855e-09 

> postscript(file="/var/www/html/rcomp/tmp/1gy9y1194691469.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/2tkky1194691469.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 
-4.172e-14 -2.992e-15  2.089e-16  3.777e-15  8.293e-15 

Coefficients:
             Estimate Std. Error   t value Pr(>|t|)    
(Intercept) 5.169e+01  7.948e-15 6.504e+15   <2e-16 ***
x           4.528e-01  7.122e-17 6.358e+15   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 6.369e-15 on 78 degrees of freedom
Multiple R-Squared:     1,	Adjusted R-squared:     1 
F-statistic: 4.043e+31 on 1 and 78 DF,  p-value: < 2.2e-16 

> z2 <- as.data.frame(cbind(x,m$fitted.values))
> names(z2) <- list('x','Fitted')
> plot(z2,main='Scatterplot, lowess, and regression line')
> lines(lowess(z2),col='red')
> abline(m2)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/3dhws1194691469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m3 <- lm(m$residuals ~ x)
> summary(m3)

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

Residuals:
     Min       1Q   Median       3Q      Max 
-14.2662  -3.7667   0.3639   4.0772  15.0073 

Coefficients:
              Estimate Std. Error   t value Pr(>|t|)
(Intercept)  4.769e-15  7.537e+00  6.33e-16        1
x           -4.470e-17  6.754e-02 -6.62e-16        1

Residual standard error: 6.039 on 78 degrees of freedom
Multiple R-Squared: 1.187e-32,	Adjusted R-squared: -0.01282 
F-statistic: 9.26e-31 on 1 and 78 DF,  p-value: 1 

> z3 <- as.data.frame(cbind(x,m$residuals))
> names(z3) <- list('x','Residuals')
> plot(z3,main='Scatterplot, lowess, and regression line')
> lines(lowess(z3),col='red')
> abline(m3)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/4u7ph1194691469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m4 <- lm(m$fitted.values ~ m$residuals)
> summary(m4)

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

Residuals:
    Min      1Q  Median      3Q     Max 
-8.8952 -3.4273 -0.4274  3.7839  8.8103 

Coefficients:
              Estimate Std. Error   t value Pr(>|t|)    
(Intercept)  1.020e+02  5.126e-01       199   <2e-16 ***
m$residuals -4.995e-17  8.596e-02 -5.81e-16        1    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 4.585 on 78 degrees of freedom
Multiple R-Squared: 1.11e-28,	Adjusted R-squared: -0.01282 
F-statistic: 8.655e-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/5v9ic1194691469.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 
-1.253e-14 -5.800e-17  1.879e-16  3.345e-16  2.123e-15 

Coefficients:
              Estimate Std. Error   t value Pr(>|t|)    
(Intercept) -4.410e-32  1.687e-16 -2.61e-16        1    
lag(myr, 1)  1.000e-00  2.829e-17 3.535e+16   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 1.509e-15 on 78 degrees of freedom
Multiple R-Squared:     1,	Adjusted R-squared:     1 
F-statistic: 1.249e+33 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/6tfoc1194691469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(m$residuals,main='Residual Histogram',xlab='Residuals')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/7p7cn1194691469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if (par1 > 0)
+ {
+ densityplot(~x,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
+ } else {
+ densityplot(~x,col='black',main='Density Plot')
+ }
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/8txj61194691469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> acf(m$residuals,main='Residual Autocorrelation Function')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/98ypy1194691469.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/100yue1194691469.tab") 
> 
> system("convert tmp/1gy9y1194691469.ps tmp/1gy9y1194691469.png")
> system("convert tmp/2tkky1194691469.ps tmp/2tkky1194691469.png")
> system("convert tmp/3dhws1194691469.ps tmp/3dhws1194691469.png")
> system("convert tmp/4u7ph1194691469.ps tmp/4u7ph1194691469.png")
> system("convert tmp/5v9ic1194691469.ps tmp/5v9ic1194691469.png")
> system("convert tmp/6tfoc1194691469.ps tmp/6tfoc1194691469.png")
> system("convert tmp/7p7cn1194691469.ps tmp/7p7cn1194691469.png")
> system("convert tmp/8txj61194691469.ps tmp/8txj61194691469.png")
> system("convert tmp/98ypy1194691469.ps tmp/98ypy1194691469.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.110   1.379   2.574