SMP s=4

*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, 23 Dec 2009 04:23:21 -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/23/t1261567529un4yn6gvtmbv7el.htm/, Retrieved Wed, 23 Dec 2009 12:25:31 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/23/t1261567529un4yn6gvtmbv7el.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

83.87 84.23 84.61 84.82 85.04 85.06 84.93 84.98 85.23 85.30 85.33 85.55 85.70 85.88 86.04 86.07 86.31 86.38 86.35 86.55 86.70 86.74 86.85 86.95 86.80 87.01 87.17 87.43 87.66 87.68 87.59 87.65 87.72 87.70 87.71 87.80 87.62 87.84 88.17 88.47 88.58 88.57 88.55 88.68 88.79 88.85 88.95 89.27 89.09 89.42 89.72 89.85 89.96 90.25 90.20 90.27 90.78 90.79 90.98 91.25 90.75 91.01 91.50 92.09 92.56 92.66 92.38 92.38 92.66 92.69 92.59 92.98 92.98 93.15 93.65 94.06 94.24 94.24 94.11 94.16 94.43 94.67 94.60 95.00 94.84 95.26 95.81 95.92 95.85 95.90 95.80 96.00 96.34 96.43 96.48 96.75 96.51 96.69 97.28 97.69 98.08 98.09 97.92 98.06 98.23 98.57 98.53 98.92 98.42 98.73 99.32 99.73 100.00 100.08 100.02 100.26 100.71 100.95 100.75 101.03 100.64 100.93 101.41 102.07 102.42 102.53 102.43 102.60 102.65 102.74 102.82 103.21 102.75 103.09 103.71 104.30 104.58 104.71 104.44 104.57 104.95 105.49 etc...

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Standard Deviation-Mean Plot Section Mean Standard Deviation Range 1 84.3825 0.419950393895909 0.949999999999989 2 85.0025 0.0590903263374516 0.129999999999995 3 85.3525 0.138172597379749 0.319999999999993 4 85.9225 0.170171482138850 0.36999999999999 5 86.3975 0.105633012516605 0.239999999999995 6 86.81 0.112842072532072 0.25 7 87.1025 0.265753645318370 0.63000000000001 8 87.645 0.0387298334620744 0.0900000000000034 9 87.7325 0.0457347424467065 0.0999999999999943 10 88.025 0.372961123979428 0.849999999999994 11 88.595 0.0580229839517692 0.130000000000010 12 88.965 0.213775583264317 0.47999999999999 13 89.52 0.338526217596211 0.759999999999991 14 90.17 0.143061758225835 0.310000000000002 15 90.95 0.220151463012777 0.469999999999999 16 91.3375 0.59021888595108 1.34000000000000 17 92.495 0.138924439894500 0.280000000000001 18 92.73 0.171852650062005 0.390000000000001 19 93.46 0.490781689416655 1.08000000000000 20 94.1875 0.0639661368746496 0.129999999999995 21 94.675 0.238956062906969 0.569999999999993 22 95.4575 0.502817064149576 1.08000000000000 23 95.8875 0.0853912563829987 0.200000000000003 24 96.5 0.176446403571545 0.409999999999997 25 97.0425 0.542670864029629 1.17999999999999 26 98.0375 0.0793200268952718 0.170000000000002 27 98.5625 0.282533183891732 0.689999999999998 28 99.05 0.58725349438438 1.31000000000000 29 100.09 0.118321595661996 0.260000000000005 30 100.86 0.154488403016756 0.320000000000007 31 101.2625 0.624999999999995 1.42999999999999 32 102.495 0.0858292879305538 0.179999999999993 33 102.855 0.246644143115809 0.559999999999988 34 103.4625 0.685340547951647 1.55000000000000 35 104.575 0.110302614051827 0.269999999999996 36 105.7375 0.662891393819532 1.53 37 107.15 0.821583836257747 1.80000000000000 38 109.0025 0.196022957839128 0.450000000000003 39 109.11 0.293598365118069 0.600000000000009 40 108.815 0.563175520301326 1.32000000000001 41 109.615 0.209841209807159 0.509999999999991 Regression: S.E.(k) = alpha + beta * Mean(k) alpha -0.712602397891834 beta 0.0103761011438288 S.D. 0.00406920741544725 T-STAT 2.54990716483011 p-value 0.0148177709545112 Regression: ln S.E.(k) = alpha + beta * ln Mean(k) alpha -19.0581659628586 beta 3.83513868217663 S.D. 1.52698413406944 T-STAT 2.51157729580066 p-value 0.0162701948539795 Lambda -2.83513868217663

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/23/t1261567529un4yn6gvtmbv7el/19ea41261567397.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/23/t1261567529un4yn6gvtmbv7el/19ea41261567397.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/23/t1261567529un4yn6gvtmbv7el/29gq21261567397.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/23/t1261567529un4yn6gvtmbv7el/29gq21261567397.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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