R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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> y <- c(9.769,9.321,9.939,9.336,10.195,9.464,10.010,10.213,9.563,9.890,9.305,9.391,9.928,8.686,9.843,9.627,10.074,9.503,10.119,10.000,9.313,9.866,9.172,9.241,9.659,8.904,9.755,9.080,9.435,8.971,10.063,9.793,9.454,9.759,8.820,9.403,9.676,8.642,9.402,9.610,9.294,9.448,10.319,9.548,9.801,9.596,8.923,9.746,9.829,9.125,9.782,9.441,9.162,9.915,10.444,10.209,9.985,9.842,9.429,10.132,9.849,9.172,10.313,9.819,9.955,10.048,10.082,10.541,10.208,10.233,9.439,9.963,10.158,9.225,10.474,9.757,10.490,10.281,10.444,10.640,10.695,10.786,9.832,9.747,10.411,9.511,10.402,9.701,10.540,10.112,10.915,11.183,10.384,10.834,9.886,10.216,10.943,9.867,10.203,10.837,10.573,10.647,11.502,10.656,10.866,10.835,9.945,10.331,10.718,9.462,10.579,10.633,10.346,10.757,11.207,11.013,11.015,10.765,10.042,10.661)
> x <- c(1579,2146,2462,3695,4831,5134,6250,5760,6249,2917,1741,2359,1511,2059,2635,2867,4403,5720,4502,5749,5627,2846,1762,2429,1169,2154,2249,2687,4359,5382,4459,6398,4596,3024,1887,2070,1351,2218,2461,3028,4784,4975,4607,6249,4809,3157,1910,2228,1594,2467,2222,3607,4685,4962,5770,5480,5000,3228,1993,2288,1580,2111,2192,3601,4665,4876,5813,5589,5331,3075,2002,2306,1507,1992,2487,3490,4647,5594,5611,5788,6204,3013,1931,2549,1504,2090,2702,2939,4500,6208,6415,5657,5964,3163,1997,2422,1.376,2.202,2.683,3.303,5.202,5.231,4.880,7.998,4.977,3.531,2.025,2.205,1.442,2.238,2.179,3.218,5.139,4.990,4.914,6.084,5.672,3.548,1.793,2.086)
> par1 = '0'
> par1 <- '0'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., (2012), Linear Regression Graphical Model Validation (v1.0.7) 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
> #
> 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 
-1.36472 -0.39812 -0.06532  0.44582  1.38508 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.012e+01  9.293e-02 108.870   <2e-16 ***
x           -4.980e-05  2.636e-05  -1.889   0.0613 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 0.5845 on 118 degrees of freedom
Multiple R-squared: 0.02935,	Adjusted R-squared: 0.02113 
F-statistic: 3.568 on 1 and 118 DF,  p-value: 0.06134 

> postscript(file="/var/fisher/rcomp/tmp/12up21353803533.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/2g85g1353803533.ps",horizontal=F,onefile=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 
-1.097e-15 -5.497e-16  7.458e-17  4.015e-16  1.029e-15 

Coefficients:
              Estimate Std. Error    t value Pr(>|t|)    
(Intercept)  1.012e+01  8.691e-17  1.164e+17   <2e-16 ***
x           -4.980e-05  2.465e-20 -2.020e+15   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 5.467e-16 on 118 degrees of freedom
Multiple R-squared:     1,	Adjusted R-squared:     1 
F-statistic: 4.08e+30 on 1 and 118 DF,  p-value: < 2.2e-16 

> z2 <- as.data.frame(cbind(x,m$fitted.values))
> names(z2) <- list('x','Fitted')
> plot(z2,main='Scatterplot, lowess, and regression line')
> lines(lowess(z2),col='red')
> abline(m2)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/3f5hy1353803533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m3 <- lm(m$residuals ~ x)
> summary(m3)

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

Residuals:
     Min       1Q   Median       3Q      Max 
-1.36472 -0.39812 -0.06532  0.44582  1.38508 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.884e-18  9.293e-02       0        1
x           -4.743e-22  2.636e-05       0        1

Residual standard error: 0.5845 on 118 degrees of freedom
Multiple R-squared: 1.299e-34,	Adjusted R-squared: -0.008475 
F-statistic: 1.533e-32 on 1 and 118 DF,  p-value: 1 

> z3 <- as.data.frame(cbind(x,m$residuals))
> names(z3) <- list('x','Residuals')
> plot(z3,main='Scatterplot, lowess, and regression line')
> lines(lowess(z3),col='red')
> abline(m3)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/4sg6b1353803533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> m4 <- lm(m$fitted.values ~ m$residuals)
> summary(m4)

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.17572 -0.09080  0.02038  0.06506  0.14365 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 9.973e+00  9.279e-03    1075   <2e-16 ***
m$residuals 7.645e-17  1.601e-02       0        1    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 0.1017 on 118 degrees of freedom
Multiple R-squared: 3.131e-28,	Adjusted R-squared: -0.008475 
F-statistic: 3.695e-26 on 1 and 118 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/fisher/rcomp/tmp/5s50n1353803533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> 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)

Call:
lm(formula = z5)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2810 -0.4181  0.0368  0.3936  1.1386 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.004353   0.047865   0.091    0.928    
Residuals   0.456774   0.082534   5.534 1.94e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 0.5221 on 117 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared: 0.2075,	Adjusted R-squared: 0.2007 
F-statistic: 30.63 on 1 and 117 DF,  p-value: 1.94e-07 

> abline(m5)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/64c5j1353803533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(m$residuals,main='Residual Histogram',xlab='Residuals')
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/7w7o11353803533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if (par1 > 0)
+ {
+ densityplot(~m$residuals,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
+ } else {
+ densityplot(~m$residuals,col='black',main='Density Plot')
+ }
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/82zgz1353803533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> acf(m$residuals,main='Residual Autocorrelation Function')
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/907nb1353803533.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(x)
> qqline(x)
> grid()
> dev.off()
null device 
          1 
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/107ajc1353803533.tab") 
> 
> try(system("convert tmp/12up21353803533.ps tmp/12up21353803533.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g85g1353803533.ps tmp/2g85g1353803533.png",intern=TRUE))
character(0)
> try(system("convert tmp/3f5hy1353803533.ps tmp/3f5hy1353803533.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sg6b1353803533.ps tmp/4sg6b1353803533.png",intern=TRUE))
character(0)
> try(system("convert tmp/5s50n1353803533.ps tmp/5s50n1353803533.png",intern=TRUE))
character(0)
> try(system("convert tmp/64c5j1353803533.ps tmp/64c5j1353803533.png",intern=TRUE))
character(0)
> try(system("convert tmp/7w7o11353803533.ps tmp/7w7o11353803533.png",intern=TRUE))
character(0)
> try(system("convert tmp/82zgz1353803533.ps tmp/82zgz1353803533.png",intern=TRUE))
character(0)
> try(system("convert tmp/907nb1353803533.ps tmp/907nb1353803533.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.878   1.115   5.990