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*The author of this computation has been verified* R Software Module: /rwasp_smp.wasp (opens new window with default values) Title produced by software: Standard Deviation-Mean Plot Date of computation: Wed, 30 Dec 2009 04:32:10 -0700   Cite this page as follows: Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/30/t1262173244xikwnxlu4cbsfvy.htm/, Retrieved Thu, 23 May 2013 10:52:30 +0000   Original text written by user:   IsPrivate? No (this computation is public)   User-defined keywords:   System-generated keywords (parent): t1261336026d73sooy5j8mi3iw (pk = 69989) Estimated Impact 56   Dataseries X: » Textfile « » CSV « » Stem and Leaf « » Histogram « » Kernel Density « » Harrell-Davis Quantiles « » Central Tendency « » Variability « 228 136 174 69 108 149 134 131 180 127 59 59 202 173 296 154 117 86 38 17 52 12 61 65 70 91 111 90 110 100 99 137 139 124 103 75 55 75 65 17 27 17 20 131 26 66 59 35 57 6 24 57 42 55 30 35 22 18 22 82 90 66 64 50 56 99 97 41 59 92 91 47   Output produced by software: Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Standard Deviation-Mean Plot Section Mean Standard Deviation Range 1 129.5 51.1210684196216 169 2 106.083333333333 85.6169676612945 284 3 104.083333333333 21.7483890033819 69 4 49.4166666666667 33.2714956718574 114 5 37.5 21.6689975436210 76 6 71 21.3669243117828 58 Regression: S.E.(k) = alpha + beta * Mean(k) alpha 7.54771276008735 beta 0.380855935791731 S.D. 0.299234692112343 T-STAT 1.27276664715315 p-value 0.272047373025901 Regression: ln S.E.(k) = alpha + beta * ln Mean(k) alpha 0.797234900314882 beta 0.628751262734051 S.D. 0.497245607415463 T-STAT 1.26446820918563 p-value 0.274719884565775 Lambda 0.371248737265949   Charts produced by software: http://www.freestatistics.org/blog/date/2009/Dec/30/t1262173244xikwnxlu4cbsfvy/1k5w61262172728.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262173244xikwnxlu4cbsfvy/1k5w61262172728.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/30/t1262173244xikwnxlu4cbsfvy/27pb61262172728.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262173244xikwnxlu4cbsfvy/27pb61262172728.ps (opens in new window) Click here to open pdf file.   Parameters (Session): par1 = 12 ;   Parameters (R input): par1 = 12 ;   R code (references can be found in the software module): par1 <- as.numeric(par1) (n <- length(x)) (np <- floor(n / par1)) 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 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 arr.sd arr.range (lm1 <- lm(arr.sd~arr.mean)) (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) (lm2 <- lm(arr.range~arr.mean)) bitmap(file='test1.png') plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation') dev.off() bitmap(file='test2.png') plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range') dev.off() load(file='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='mytable.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)$coefficients[2,4]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'alpha',header=TRUE) a<-table.element(a,lnlm1$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='mytable2.tab')