R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(8.9634
+ ,8.9522
+ ,8.8682
+ ,8.7331
+ ,8.3188
+ ,8.3462
+ ,8.3087
+ ,8.3836
+ ,8.8412
+ ,9.5001
+ ,10.1883
+ ,10.2931
+ ,10.1945
+ ,10.3014
+ ,10.0675
+ ,9.6715
+ ,9.5019
+ ,9.4597
+ ,9.4362
+ ,9.5919
+ ,10.0167
+ ,10.322
+ ,11.166
+ ,11.5454
+ ,11.3712
+ ,11.0723
+ ,10.813
+ ,10.3016
+ ,10.4227
+ ,10.3162
+ ,10.4519
+ ,10.8567
+ ,11.2716
+ ,11.4341
+ ,12.1273
+ ,11.9814
+ ,11.8352
+ ,11.9847
+ ,11.545
+ ,11.5285
+ ,11.5539
+ ,11.622
+ ,11.6578
+ ,11.6767
+ ,11.8752
+ ,13.2643
+ ,14.2297
+ ,14.308
+ ,13.7915
+ ,13.7633
+ ,13.9775
+ ,13.6478
+ ,13.2247
+ ,13.0971
+ ,13.1039
+ ,13.206
+ ,13.7901
+ ,14.6457
+ ,15.5764
+ ,15.6102
+ ,15.8855
+ ,16.0137
+ ,15.6186
+ ,15.384
+ ,15.2751
+ ,15.0912
+ ,14.9222
+ ,15.6231
+ ,16.6737
+ ,17.6805
+ ,19.1919
+ ,19.1711
+ ,18.5658
+ ,18.1285
+ ,16.791
+ ,16.9468
+ ,17.3164
+ ,17.1816
+ ,16.7627
+ ,17.239
+ ,17.8838
+ ,18.9038
+ ,20.0274
+ ,20.0087
+ ,19.6366
+ ,19.8163
+ ,18.8602
+ ,17.9206
+ ,17.6889
+ ,17.84
+ ,17.678
+ ,17.7258
+ ,18.5865
+ ,19.9804
+ ,21.1584
+ ,21.2921
+ ,20.9445
+ ,20.5731
+ ,19.3274
+ ,17.7866
+ ,17.7483
+ ,17.5648
+ ,17.4763
+ ,17.7264
+ ,18.5736
+ ,19.9236
+ ,21.3286
+ ,20.7249
+ ,20.3334
+ ,19.7658
+ ,18.7569
+ ,17.6963
+ ,17.7978
+ ,18.1771
+ ,18.3738
+ ,18.1996
+ ,18.8443
+ ,20.1001
+ ,21.2458
+ ,20.8381
+ ,20.1967
+ ,19.8159
+ ,18.5784
+ ,19.21
+ ,19.3419
+ ,19.12
+ ,19.1563
+ ,18.9783
+ ,20.2913
+ ,22.5439
+ ,23.2821
+ ,22.6191
+ ,22.1599
+ ,21.2766
+ ,19.0846
+ ,18.9096
+ ,18.8095
+ ,20.1164
+ ,20.7762
+ ,20.9044
+ ,22.0026
+ ,23.6401
+ ,25.04
+ ,24.7185
+ ,24.1752
+ ,24.1382
+ ,22.3949
+ ,21.3743
+ ,21.4911
+ ,21.2187
+ ,21.2137
+ ,21.6735
+ ,22.5096
+ ,24.3097
+ ,25.7989
+ ,25.4376
+ ,23.878
+ ,23.6966
+ ,23.3544
+ ,21.1993
+ ,22.0431
+ ,22.0203
+ ,21.886
+ ,21.9771
+ ,23.0759
+ ,24.9859
+ ,26.2614
+ ,26.1127
+ ,25.6296
+ ,25.2926
+ ,22.8146
+ ,22.2974
+ ,22.8868
+ ,22.4612
+ ,22.3165
+ ,22.7319
+ ,23.2692
+ ,24.9432
+ ,27.8272
+ ,27.4059
+ ,26.6232
+ ,26.8779
+ ,25.105
+ ,23.601
+ ,23.5374
+ ,23.5248
+ ,22.9465
+ ,23.6633
+ ,25.5932
+ ,27.7683
+ ,29.4691
+ ,28.3472
+ ,28.3879
+ ,27.9696
+ ,26.0075
+ ,24.2533
+ ,24.4999
+ ,23.8988
+ ,23.6683
+ ,23.9427
+ ,26.0155
+ ,28.9529
+ ,30.302
+ ,29.874
+ ,28.2257
+ ,28.0811
+ ,26.3398
+ ,25.4847
+ ,25.4823
+ ,24.9697
+ ,25.2282
+ ,25.9257
+ ,28.7818
+ ,27.9552
+ ,33.3475
+ ,32.7834
+ ,31.6586
+ ,31.6613
+ ,29.1839
+ ,28.8825
+ ,27.6334
+ ,27.7511
+ ,27.3792
+ ,27.7748
+ ,31.4329
+ ,33.2735
+ ,35.0962
+ ,34.9537
+ ,31.8307
+ ,30.9984
+ ,28.629
+ ,26.4379
+ ,25.4408
+ ,24.6681
+ ,24.0994
+ ,24.6043
+ ,27.2492
+ ,29.5511
+ ,29.8522
+ ,31.6989
+ ,29.6357
+ ,30.5197
+ ,32.7823
+ ,24.9942
+ ,23.5187
+ ,24.0249
+ ,24.5692
+ ,24.402
+ ,26.7089
+ ,31.6874
+ ,32.8801
+ ,32.7906
+ ,30.8785
+ ,30.3024
+ ,28.3679
+ ,25.6578
+ ,25.1598
+ ,24.6143
+ ,24.528
+ ,25.2905
+ ,30.0016
+ ,34.2728
+ ,34.4408
+ ,34.1907
+ ,33.6636
+ ,33.9073
+ ,30.2175
+ ,28.5274
+ ,25.9505
+ ,26.2398
+ ,26.2819
+ ,26.7362
+ ,28.8395
+ ,31.0951
+ ,33.7015
+ ,33.8091
+ ,32.1126
+ ,32
+ ,29.122
+ ,26.8124
+ ,25.4654
+ ,23.8331
+ ,24.714
+ ,28.3288
+ ,29.6391
+ ,32.4542
+ ,33.5657
+ ,33.1856
+ ,33.297
+ ,33.51
+ ,31.3789
+ ,29.4555
+ ,27.2699
+ ,27.2586
+ ,27.8591
+ ,29.6362
+ ,30.9587
+ ,31.8633
+ ,33.8188
+ ,33.7531
+ ,33.6103
+ ,32.9052
+ ,29.5005
+ ,27.3634
+ ,27.2298
+ ,26.5211
+ ,26.5228
+ ,27.2991
+ ,29.1726
+ ,30.297
+ ,32.5287
+ ,32.487
+ ,32.4197
+ ,30.854
+ ,28.6995
+ ,27.7881
+ ,26.5609
+ ,25.9431
+ ,25.5578
+ ,27.1275
+ ,30.2556
+ ,34.0976
+ ,34.5614
+ ,34.2948
+ ,33.3418
+ ,31.8187
+ ,29.0818
+ ,27.3444
+ ,26.6233
+ ,26.1869
+ ,26.2953
+ ,28.7043
+ ,32.0653
+ ,34.5401
+ ,34.6636
+ ,34.2557
+ ,32.0526
+ ,30.6892
+ ,28.012
+ ,26.1528
+ ,23.2276
+ ,24.244
+ ,24.8141
+ ,27.8632
+ ,29.6233
+ ,32.4245
+ ,33.3417
+ ,33.0442
+ ,32.0526
+ ,30.2182
+ ,28.9292
+ ,26.8221
+ ,26.1032
+ ,25.9792
+ ,27.1443
+ ,29.4993
+ ,31.656
+ ,33.3665
+ ,35.0521
+ ,34.4076
+ ,33.069
+ ,31.5816
+ ,30.0695
+ ,29.0035
+ ,28.6813
+ ,28.359
+ ,30.0447
+ ,31.5073
+ ,34.16
+ ,35.57
+ ,36.42
+ ,35.12
+ ,33.14
+ ,30.29
+ ,28.2
+ ,26.5
+ ,25.47
+ ,24.96
+ ,25.6
+ ,27.76
+ ,30.13
+ ,32.35
+ ,32.8
+ ,32.54
+ ,29.78
+ ,28.79
+ ,26.8
+ ,25.41
+ ,24.34
+ ,24.39
+ ,25
+ ,26.27
+ ,27.88
+ ,29.35
+ ,29.83
+ ,29.46)
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = ''
> par2 = ''
> par1 = '12'
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 396
> (np <- floor(n / par1))
[1] 33
> arr <- array(NA,dim=c(par1,np))
> j <- 0
> k <- 1
> for (i in 1:(np*par1))
+ {
+ j = j + 1
+ arr[j,k] <- x[i]
+ if (j == par1) {
+ j = 0
+ k=k+1
+ }
+ }
> arr
         [,1]    [,2]    [,3]    [,4]    [,5]    [,6]    [,7]    [,8]    [,9]
 [1,]  8.9634 10.1945 11.3712 11.8352 13.7915 15.8855 18.5658 19.6366 20.9445
 [2,]  8.9522 10.3014 11.0723 11.9847 13.7633 16.0137 18.1285 19.8163 20.5731
 [3,]  8.8682 10.0675 10.8130 11.5450 13.9775 15.6186 16.7910 18.8602 19.3274
 [4,]  8.7331  9.6715 10.3016 11.5285 13.6478 15.3840 16.9468 17.9206 17.7866
 [5,]  8.3188  9.5019 10.4227 11.5539 13.2247 15.2751 17.3164 17.6889 17.7483
 [6,]  8.3462  9.4597 10.3162 11.6220 13.0971 15.0912 17.1816 17.8400 17.5648
 [7,]  8.3087  9.4362 10.4519 11.6578 13.1039 14.9222 16.7627 17.6780 17.4763
 [8,]  8.3836  9.5919 10.8567 11.6767 13.2060 15.6231 17.2390 17.7258 17.7264
 [9,]  8.8412 10.0167 11.2716 11.8752 13.7901 16.6737 17.8838 18.5865 18.5736
[10,]  9.5001 10.3220 11.4341 13.2643 14.6457 17.6805 18.9038 19.9804 19.9236
[11,] 10.1883 11.1660 12.1273 14.2297 15.5764 19.1919 20.0274 21.1584 21.3286
[12,] 10.2931 11.5454 11.9814 14.3080 15.6102 19.1711 20.0087 21.2921 20.7249
        [,10]   [,11]   [,12]   [,13]   [,14]   [,15]   [,16]   [,17]   [,18]
 [1,] 20.3334 20.1967 22.1599 24.1752 23.8780 25.6296 26.6232 28.3879 28.2257
 [2,] 19.7658 19.8159 21.2766 24.1382 23.6966 25.2926 26.8779 27.9696 28.0811
 [3,] 18.7569 18.5784 19.0846 22.3949 23.3544 22.8146 25.1050 26.0075 26.3398
 [4,] 17.6963 19.2100 18.9096 21.3743 21.1993 22.2974 23.6010 24.2533 25.4847
 [5,] 17.7978 19.3419 18.8095 21.4911 22.0431 22.8868 23.5374 24.4999 25.4823
 [6,] 18.1771 19.1200 20.1164 21.2187 22.0203 22.4612 23.5248 23.8988 24.9697
 [7,] 18.3738 19.1563 20.7762 21.2137 21.8860 22.3165 22.9465 23.6683 25.2282
 [8,] 18.1996 18.9783 20.9044 21.6735 21.9771 22.7319 23.6633 23.9427 25.9257
 [9,] 18.8443 20.2913 22.0026 22.5096 23.0759 23.2692 25.5932 26.0155 28.7818
[10,] 20.1001 22.5439 23.6401 24.3097 24.9859 24.9432 27.7683 28.9529 27.9552
[11,] 21.2458 23.2821 25.0400 25.7989 26.2614 27.8272 29.4691 30.3020 33.3475
[12,] 20.8381 22.6191 24.7185 25.4376 26.1127 27.4059 28.3472 29.8740 32.7834
        [,19]   [,20]   [,21]   [,22]   [,23]   [,24]   [,25]   [,26]   [,27]
 [1,] 31.6586 31.8307 29.6357 30.8785 33.6636 32.1126 33.2970 33.6103 32.4197
 [2,] 31.6613 30.9984 30.5197 30.3024 33.9073 32.0000 33.5100 32.9052 30.8540
 [3,] 29.1839 28.6290 32.7823 28.3679 30.2175 29.1220 31.3789 29.5005 28.6995
 [4,] 28.8825 26.4379 24.9942 25.6578 28.5274 26.8124 29.4555 27.3634 27.7881
 [5,] 27.6334 25.4408 23.5187 25.1598 25.9505 25.4654 27.2699 27.2298 26.5609
 [6,] 27.7511 24.6681 24.0249 24.6143 26.2398 23.8331 27.2586 26.5211 25.9431
 [7,] 27.3792 24.0994 24.5692 24.5280 26.2819 24.7140 27.8591 26.5228 25.5578
 [8,] 27.7748 24.6043 24.4020 25.2905 26.7362 28.3288 29.6362 27.2991 27.1275
 [9,] 31.4329 27.2492 26.7089 30.0016 28.8395 29.6391 30.9587 29.1726 30.2556
[10,] 33.2735 29.5511 31.6874 34.2728 31.0951 32.4542 31.8633 30.2970 34.0976
[11,] 35.0962 29.8522 32.8801 34.4408 33.7015 33.5657 33.8188 32.5287 34.5614
[12,] 34.9537 31.6989 32.7906 34.1907 33.8091 33.1856 33.7531 32.4870 34.2948
        [,28]   [,29]   [,30]   [,31] [,32] [,33]
 [1,] 33.3418 32.0526 32.0526 33.0690 33.14 29.78
 [2,] 31.8187 30.6892 30.2182 31.5816 30.29 28.79
 [3,] 29.0818 28.0120 28.9292 30.0695 28.20 26.80
 [4,] 27.3444 26.1528 26.8221 29.0035 26.50 25.41
 [5,] 26.6233 23.2276 26.1032 28.6813 25.47 24.34
 [6,] 26.1869 24.2440 25.9792 28.3590 24.96 24.39
 [7,] 26.2953 24.8141 27.1443 30.0447 25.60 25.00
 [8,] 28.7043 27.8632 29.4993 31.5073 27.76 26.27
 [9,] 32.0653 29.6233 31.6560 34.1600 30.13 27.88
[10,] 34.5401 32.4245 33.3665 35.5700 32.35 29.35
[11,] 34.6636 33.3417 35.0521 36.4200 32.80 29.83
[12,] 34.2557 33.0442 34.4076 35.1200 32.54 29.46
> arr.mean <- array(NA,dim=np)
> arr.sd <- array(NA,dim=np)
> arr.range <- array(NA,dim=np)
> for (j in 1:np)
+ {
+ arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
+ arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
+ arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
+ }
> arr.mean
 [1]  8.974742 10.106225 11.035000 12.256750 13.952850 16.377550 17.979625
 [8] 19.015317 19.141508 19.177417 20.261158 21.453200 22.977950 23.374225
[15] 24.156342 25.588075 26.481033 27.717092 30.556758 27.921667 28.209475
[22] 28.975425 29.914117 29.269408 30.838258 29.619792 29.846667 30.410100
[29] 28.790767 30.102525 31.965492 29.145000 27.275000
> arr.sd
 [1] 0.6871145 0.6729825 0.6229705 1.0506125 0.8834792 1.5077029 1.1726146
 [8] 1.3393985 1.4953790 1.2268705 1.6251022 2.1481105 1.7005781 1.6890477
[15] 2.0013131 2.2051704 2.4940890 2.8248425 2.8518542 2.8747406 3.8498125
[22] 3.9298344 3.2555807 3.4553405 2.5172232 2.6876103 3.3745506 3.3903072
[29] 3.6030913 3.2215781 2.8448881 3.1158320 2.1610414
> arr.range
 [1]  1.9844  2.1092  1.8257  2.7795  2.5131  4.2697  3.2647  3.6141  3.8523
[10]  3.5495  4.7037  6.2305  4.5852  5.0621  5.5298  6.5226  6.6337  8.3778
[19]  7.7170  7.7313  9.3614  9.9128  7.9568  9.7326  6.5602  7.0892  9.0036
[28]  8.4767 10.1141  9.0729  8.0610  8.1800  5.4900
> (lm1 <- lm(arr.sd~arr.mean))

Call:
lm(formula = arr.sd ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
    -0.8669       0.1317  

> (lnlm1 <- lm(log(arr.sd)~log(arr.mean)))

Call:
lm(formula = log(arr.sd) ~ log(arr.mean))

Coefficients:
  (Intercept)  log(arr.mean)  
       -3.750          1.427  

> (lm2 <- lm(arr.range~arr.mean))

Call:
lm(formula = arr.range ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
    -1.9555       0.3403  

> postscript(file="/var/fisher/rcomp/tmp/19vjc1386211712.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/2r5nw1386211712.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
> 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,'Standard Deviation-Mean Plot',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Section',header=TRUE)
> a<-table.element(a,'Mean',header=TRUE)
> a<-table.element(a,'Standard Deviation',header=TRUE)
> a<-table.element(a,'Range',header=TRUE)
> a<-table.row.end(a)
> for (j in 1:np) {
+ a<-table.row.start(a)
+ a<-table.element(a,j,header=TRUE)
+ a<-table.element(a,arr.mean[j])
+ a<-table.element(a,arr.sd[j] )
+ a<-table.element(a,arr.range[j] )
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/3m5bt1386211712.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lm1$coefficients[[1]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'beta',header=TRUE)
> a<-table.element(a,lm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/4ywij1386211712.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$coefficients[[1]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'beta',header=TRUE)
> a<-table.element(a,lnlm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Lambda',header=TRUE)
> a<-table.element(a,1-lnlm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/560ld1386211713.tab") 
> 
> try(system("convert tmp/19vjc1386211712.ps tmp/19vjc1386211712.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r5nw1386211712.ps tmp/2r5nw1386211712.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.320   1.081   4.312