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(98.6,98,106.8,96.6,100.1,107.7,91.5,97.8,107.4,117.5,105.6,97.4,99.5,98,104.3,100.6,101.1,103.9,96.9,95.5,108.4,117,103.8,100.8,110.6,104,112.6,107.3,98.9,109.8,104.9,102.2,123.9,124.9,112.7,121.9,100.6,104.3,120.4,107.5,102.9,125.6,107.5,108.8,128.4,121.1,119.5,128.7,108.7,105.5,119.8,111.3,110.6,120.1,97.5,107.7,127.3,117.2,119.8,116.2,111,112.4,130.6,109.1,118.8,123.9,101.6,112.8,128,129.6,125.8,119.5,115.7,113.6,129.7,112,116.8,126.3,112.9,115.9)
> x <- c(98.1,101.1,111.1,93.3,100,108,70.4,75.4,105.5,112.3,102.5,93.5,86.7,95.2,103.8,97,95.5,101,67.5,64,106.7,100.6,101.2,93.1,84.2,85.8,91.8,92.4,80.3,79.7,62.5,57.1,100.8,100.7,86.2,83.2,71.7,77.5,89.8,80.3,78.7,93.8,57.6,60.6,91,85.3,77.4,77.3,68.3,69.9,81.7,75.1,69.9,84,54.3,60,89.9,77,85.3,77.6,69.2,75.5,85.7,72.2,79.9,85.3,52.2,61.2,82.4,85.4,78.2,70.2,70.2,69.3,77.5,66.1,69,75.3,58.2,59.7)
> 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 
-19.3157  -7.9489  -0.9406   8.5068  19.3539 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 108.83536    6.41705  16.960   <2e-16 ***
x             0.02813    0.07697   0.365    0.716    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 10.12 on 78 degrees of freedom
Multiple R-Squared: 0.001709,	Adjusted R-squared: -0.01109 
F-statistic: 0.1336 on 1 and 78 DF,  p-value: 0.7158 

> postscript(file="/var/www/html/rcomp/tmp/1qei11194688784.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/2ni4x1194688784.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 
-2.929e-14 -1.578e-14 -7.914e-15 -8.875e-19  6.190e-13 

Coefficients:
             Estimate Std. Error   t value Pr(>|t|)    
(Intercept) 1.088e+02  4.515e-14 2.410e+15   <2e-16 ***
x           2.813e-02  5.416e-16 5.194e+13   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 7.118e-14 on 78 degrees of freedom
Multiple R-Squared:     1,	Adjusted R-squared:     1 
F-statistic: 2.697e+27 on 1 and 78 DF,  p-value: < 2.2e-16 

> z2 <- as.data.frame(cbind(x,m$fitted.values))
> names(z2) <- list('x','Fitted')
> plot(z2,main='Scatterplot, lowess, and regression line')
> lines(lowess(z2),col='red')
> abline(m2)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/300rh1194688784.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m3 <- lm(m$residuals ~ x)
> summary(m3)

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

Residuals:
     Min       1Q   Median       3Q      Max 
-19.3157  -7.9489  -0.9406   8.5068  19.3539 

Coefficients:
              Estimate Std. Error   t value Pr(>|t|)
(Intercept)  4.088e-15  6.417e+00  6.37e-16        1
x           -1.352e-17  7.697e-02 -1.76e-16        1

Residual standard error: 10.12 on 78 degrees of freedom
Multiple R-Squared: 1.204e-31,	Adjusted R-squared: -0.01282 
F-statistic: 9.39e-30 on 1 and 78 DF,  p-value: 1 

> z3 <- as.data.frame(cbind(x,m$residuals))
> names(z3) <- list('x','Residuals')
> plot(z3,main='Scatterplot, lowess, and regression line')
> lines(lowess(z3),col='red')
> abline(m3)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/4dh9n1194688785.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m4 <- lm(m$fitted.values ~ m$residuals)
> summary(m4)

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.84000 -0.33366 -0.02985  0.31755  0.85062 

Coefficients:
              Estimate Std. Error   t value Pr(>|t|)    
(Intercept)  1.111e+02  4.680e-02      2375   <2e-16 ***
m$residuals -1.064e-15  4.685e-03 -2.27e-13        1    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 0.4186 on 78 degrees of freedom
Multiple R-Squared: 1.271e-27,	Adjusted R-squared: -0.01282 
F-statistic: 9.911e-26 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/597ki1194688785.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 
-3.106e-15 -1.889e-16  1.820e-17  2.239e-16  2.343e-15 

Coefficients:
             Estimate Std. Error   t value Pr(>|t|)    
(Intercept) 0.000e+00  7.146e-17 0.000e+00        1    
lag(myr, 1) 1.000e+00  7.154e-18 1.398e+17   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 6.392e-16 on 78 degrees of freedom
Multiple R-Squared:     1,	Adjusted R-squared:     1 
F-statistic: 1.954e+34 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/6iusc1194688785.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(m$residuals,main='Residual Histogram',xlab='Residuals')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/7atqf1194688785.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if (par1 > 0)
+ {
+ densityplot(~x,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
+ } else {
+ densityplot(~x,col='black',main='Density Plot')
+ }
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/80ubv1194688785.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> acf(m$residuals,main='Residual Autocorrelation Function')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/9w1l41194688785.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/10w2051194688785.tab") 
> 
> system("convert tmp/1qei11194688784.ps tmp/1qei11194688784.png")
> system("convert tmp/2ni4x1194688784.ps tmp/2ni4x1194688784.png")
> system("convert tmp/300rh1194688784.ps tmp/300rh1194688784.png")
> system("convert tmp/4dh9n1194688785.ps tmp/4dh9n1194688785.png")
> system("convert tmp/597ki1194688785.ps tmp/597ki1194688785.png")
> system("convert tmp/6iusc1194688785.ps tmp/6iusc1194688785.png")
> system("convert tmp/7atqf1194688785.ps tmp/7atqf1194688785.png")
> system("convert tmp/80ubv1194688785.ps tmp/80ubv1194688785.png")
> system("convert tmp/9w1l41194688785.ps tmp/9w1l41194688785.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.182   1.431   3.184