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(98.1,101.1,111.1,93.3,100,108,70.4,75.4,105.5,112.3,102.5,93.5,86.7,95.2,103.8,97,95.5,101,67.5,64,106.7,100.6,101.2,93.1,84.2,85.8,91.8,92.4,80.3,79.7,62.5,57.1,100.8,100.7,86.2,83.2,71.7,77.5,89.8,80.3,78.7,93.8,57.6,60.6,91,85.3,77.4,77.3,68.3,69.9,81.7,75.1,69.9,84,54.3,60,89.9,77,85.3,77.6,69.2,75.5,85.7,72.2,79.9,85.3,52.2,61.2,82.4,85.4,78.2,70.2,70.2,69.3,77.5,66.1,69,75.3,58.2,59.7)
> x <- 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)
> 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 
-30.8756 -11.6932   0.3213  10.3776  30.5973 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 101.1713    16.2816   6.214 2.36e-08 ***
x            -0.1654     0.1402  -1.180    0.242    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 14.75 on 78 degrees of freedom
Multiple R-Squared: 0.01753,	Adjusted R-squared: 0.004936 
F-statistic: 1.392 on 1 and 78 DF,  p-value: 0.2417 

> postscript(file="/var/www/html/rcomp/tmp/107ga1194691797.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/21zq71194691797.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 
-1.915e-14 -8.131e-15 -2.816e-15  1.409e-15  2.939e-13 

Coefficients:
              Estimate Std. Error    t value Pr(>|t|)    
(Intercept)  1.012e+02  3.785e-14  2.673e+15   <2e-16 ***
x           -1.654e-01  3.259e-16 -5.075e+14   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 3.429e-14 on 78 degrees of freedom
Multiple R-Squared:     1,	Adjusted R-squared:     1 
F-statistic: 2.576e+29 on 1 and 78 DF,  p-value: < 2.2e-16 

> z2 <- as.data.frame(cbind(x,m$fitted.values))
> names(z2) <- list('x','Fitted')
> plot(z2,main='Scatterplot, lowess, and regression line')
> lines(lowess(z2),col='red')
> abline(m2)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/3l4xr1194691797.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m3 <- lm(m$residuals ~ x)
> summary(m3)

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

Residuals:
     Min       1Q   Median       3Q      Max 
-30.8756 -11.6932   0.3213  10.3776  30.5973 

Coefficients:
              Estimate Std. Error   t value Pr(>|t|)
(Intercept) -1.192e-15  1.628e+01 -7.32e-17        1
x            0.000e+00  1.402e-01         0        1

Residual standard error: 14.75 on 78 degrees of freedom
Multiple R-Squared: 9.067e-33,	Adjusted R-squared: -0.01282 
F-statistic: 7.072e-31 on 1 and 78 DF,  p-value: 1 

> z3 <- as.data.frame(cbind(x,m$residuals))
> names(z3) <- list('x','Residuals')
> plot(z3,main='Scatterplot, lowess, and regression line')
> lines(lowess(z3),col='red')
> abline(m3)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/4ztke1194691797.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m4 <- lm(m$fitted.values ~ m$residuals)
> summary(m4)

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

Residuals:
      Min        1Q    Median        3Q       Max 
-3.683226 -1.566002  0.005376  1.527131  3.892468 

Coefficients:
             Estimate Std. Error  t value Pr(>|t|)    
(Intercept) 8.206e+01  2.203e-01    372.5   <2e-16 ***
m$residuals 2.216e-16  1.513e-02 1.46e-14        1    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 1.97 on 78 degrees of freedom
Multiple R-Squared: 9.869e-29,	Adjusted R-squared: -0.01282 
F-statistic: 7.698e-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/52k2i1194691797.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 
-1.432e-14 -2.158e-15 -1.345e-15 -6.302e-16  1.154e-13 

Coefficients:
             Estimate Std. Error   t value Pr(>|t|)    
(Intercept) 1.411e-30  1.493e-15  9.45e-16        1    
lag(myr, 1) 1.000e+00  1.025e-16 9.759e+15   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 1.335e-14 on 78 degrees of freedom
Multiple R-Squared:     1,	Adjusted R-squared:     1 
F-statistic: 9.523e+31 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/6x9w61194691797.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(m$residuals,main='Residual Histogram',xlab='Residuals')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/7hywb1194691797.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if (par1 > 0)
+ {
+ densityplot(~x,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
+ } else {
+ densityplot(~x,col='black',main='Density Plot')
+ }
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/8ja0i1194691797.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> acf(m$residuals,main='Residual Autocorrelation Function')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/9seyp1194691797.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/10ud1p1194691797.tab") 
> 
> system("convert tmp/107ga1194691797.ps tmp/107ga1194691797.png")
> system("convert tmp/21zq71194691797.ps tmp/21zq71194691797.png")
> system("convert tmp/3l4xr1194691797.ps tmp/3l4xr1194691797.png")
> system("convert tmp/4ztke1194691797.ps tmp/4ztke1194691797.png")
> system("convert tmp/52k2i1194691797.ps tmp/52k2i1194691797.png")
> system("convert tmp/6x9w61194691797.ps tmp/6x9w61194691797.png")
> system("convert tmp/7hywb1194691797.ps tmp/7hywb1194691797.png")
> system("convert tmp/8ja0i1194691797.ps tmp/8ja0i1194691797.png")
> system("convert tmp/9seyp1194691797.ps tmp/9seyp1194691797.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.159   1.429   2.563