Michiel van Schaik - Standard Deviation-Mean Plot - Gemiddelde consumptieprijs hotelkamer

R Software Module: rwasp_smp.wasp (opens new window with default values)

Title produced by software: Standard Deviation-Mean Plot

Date of computation: Mon, 12 May 2008 16:13:06 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/13/t1210630431wp4b3cay1j5o80d.htm/, Retrieved Tue, 13 May 2008 00:13:53 +0200

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Dataseries X:

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65,05 65,84 66,6 67,55 68,07 69,06 69,06 69,11 69,29 69,38 69,28 69,75 69,9 70,21 70,48 71,55 72,18 72,64 72,77 72,74 73,13 73,44 73,34 73,34 73,81 74,26 74,72 75,11 75,26 75,89 75,91 76,43 76,56 76,76 76,76 76,56 76,82 77,09 77,51 77,76 77,86 77,89 77,94 77,99 78,17 78,91 78,87 78,88 79,08 79,41 79,51 79,73 80,38 80,56 80,46 80,45 80,58 80,68 80,52 81,49 81,66 81,95 82,3 82,4 83,14 83,17 83,11 83,21 83,33 83,88 83,8 83,73

Text written by user:

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 66.26 1.06773904427377 2.5 2 68.825 0.503884907493769 1.04000000000001 3 69.425 0.221284131077369 0.469999999999999 4 70.535 0.716961179795204 1.64999999999999 5 72.5825 0.274028587316477 0.589999999999989 6 73.3125 0.130479883507001 0.310000000000002 7 74.475 0.563234705370089 1.30000000000000 8 75.8725 0.478774477181063 1.17000000000000 9 76.66 0.115470053837927 0.200000000000003 10 77.295 0.420356991139679 0.940000000000012 11 77.92 0.0571547606649386 0.129999999999995 12 78.7075 0.358736207632664 0.739999999999995 13 79.4325 0.270354705279320 0.650000000000006 14 80.4625 0.0741057802513883 0.180000000000007 15 80.8175 0.453164797102921 0.969999999999999 16 82.0775 0.338661581326651 0.740000000000009 17 83.1575 0.0427200187265854 0.0999999999999943 18 83.685 0.244472220644118 0.549999999999997 Regression: S.E.(k) = alpha + beta * Mean(k) alpha 2.60132512777320 beta -0.0295240207489937 S.D. 0.0101723626125703 T-STAT -2.90237596450896 p-value 0.0103896413775685 Regression: ln S.E.(k) = alpha + beta * ln Mean(k) alpha 26.7654833077433 beta -6.4923659769773 S.D. 2.78865606378659 T-STAT -2.32813435162801 p-value 0.0333456163930565 Lambda 7.4923659769773

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/13/t1210630431wp4b3cay1j5o80d/100sp1210630384.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/13/t1210630431wp4b3cay1j5o80d/100sp1210630384.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/13/t1210630431wp4b3cay1j5o80d/2htuj1210630384.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/13/t1210630431wp4b3cay1j5o80d/2htuj1210630384.ps (open in new window)

Parameters (Session):

par1 = 4 ;

Parameters (R input):

par1 = 4 ;

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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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