Workshop 3 Simple Linear Regression Mini-tutorial

*The author of this computation has been verified*

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

Title produced by software: Central Tendency

Date of computation: Sat, 27 Nov 2010 11:00:34 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/27/t12908555586b1gnhjb4e3sgni.htm/, Retrieved Sat, 27 Nov 2010 11:59:20 +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/2010/Nov/27/t12908555586b1gnhjb4e3sgni.htm/}, year = {2010}, } @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 = {2010}, 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:

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38 32 35 33 37 29 31 36 35 38 31 34 35 38 37 33 32 38 38 32 33 31 38 39 32 32 35 37 33 33 28 32 31 37 30 33 31 33 31 33 32 33 32 33 28 35 39 34 38 32 38 30 33 38 32 32 34 34 36 34 28 34 35 35 31 37 35 27 40 37 36 38 39 41 27 30 37 31 31 27 36 38 37 33 34 31 39 34 32 33 36 32 41 28 30 36 35 31 34 36 36 35 37 28 39 32 35 39 35 42 34 33 41 33 34 32 40 40 35 36 37 27 39 38 31 33 32 39 36 33 33 32 37 30 38 29 22 35 35 34 35 34 34 35 23 31 27 36 31 32 39 37 38 39 34 31 32 37 36 32 35 36

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 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 34.0925925925926 0.279093578641282 122.154700794502 Geometric Mean 33.9004082539014 Harmonic Mean 33.6981656985924 Quadratic Mean 34.2760219251352 Winsorized Mean ( 1 / 54 ) 34.0925925925926 0.276471123542529 123.313394020147 Winsorized Mean ( 2 / 54 ) 34.141975308642 0.266164019595235 128.274194838817 Winsorized Mean ( 3 / 54 ) 34.141975308642 0.266164019595235 128.274194838817 Winsorized Mean ( 4 / 54 ) 34.1172839506173 0.262467776744870 129.986562060076 Winsorized Mean ( 5 / 54 ) 34.1172839506173 0.262467776744870 129.986562060076 Winsorized Mean ( 6 / 54 ) 34.1172839506173 0.262467776744870 129.986562060076 Winsorized Mean ( 7 / 54 ) 34.1172839506173 0.249896198444207 136.525822173459 Winsorized Mean ( 8 / 54 ) 34.1172839506173 0.249896198444207 136.525822173459 Winsorized Mean ( 9 / 54 ) 34.1172839506173 0.249896198444207 136.525822173459 Winsorized Mean ( 10 / 54 ) 34.1172839506173 0.249896198444207 136.525822173459 Winsorized Mean ( 11 / 54 ) 34.1172839506173 0.249896198444207 136.525822173459 Winsorized Mean ( 12 / 54 ) 34.1913580246914 0.239259578962032 142.904865807346 Winsorized Mean ( 13 / 54 ) 34.1913580246914 0.239259578962032 142.904865807346 Winsorized Mean ( 14 / 54 ) 34.2777777777778 0.228390916759102 150.083804838582 Winsorized Mean ( 15 / 54 ) 34.2777777777778 0.228390916759102 150.083804838582 Winsorized Mean ( 16 / 54 ) 34.2777777777778 0.228390916759102 150.083804838582 Winsorized Mean ( 17 / 54 ) 34.1728395061728 0.215847178263242 158.319602698241 Winsorized Mean ( 18 / 54 ) 34.1728395061728 0.215847178263242 158.319602698241 Winsorized Mean ( 19 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 20 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 21 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 22 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 23 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 24 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 25 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 26 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 27 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 28 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 29 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 30 / 54 ) 34.2901234567901 0.202863123132063 169.030836789727 Winsorized Mean ( 31 / 54 ) 34.0987654320988 0.18247123077577 186.872008738742 Winsorized Mean ( 32 / 54 ) 34.0987654320988 0.18247123077577 186.872008738742 Winsorized Mean ( 33 / 54 ) 34.0987654320988 0.18247123077577 186.872008738742 Winsorized Mean ( 34 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 35 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 36 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 37 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 38 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 39 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 40 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 41 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 42 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 43 / 54 ) 34.3086419753086 0.162008507279856 211.770619650507 Winsorized Mean ( 44 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 45 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 46 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 47 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 48 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 49 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 50 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 51 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 52 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 53 / 54 ) 34.037037037037 0.135628081631429 250.958626175463 Winsorized Mean ( 54 / 54 ) 34.3703703703704 0.106490857850246 322.754188145467 Trimmed Mean ( 1 / 54 ) 34.11875 0.267672808315153 127.464385399316 Trimmed Mean ( 2 / 54 ) 34.1455696202532 0.25803635044821 132.328524880088 Trimmed Mean ( 3 / 54 ) 34.1474358974359 0.253479424461371 134.714823382597 Trimmed Mean ( 4 / 54 ) 34.1493506493507 0.248547681641655 137.395571038099 Trimmed Mean ( 5 / 54 ) 34.1578947368421 0.244333705908472 139.800174559779 Trimmed Mean ( 6 / 54 ) 34.1666666666667 0.239764363568245 142.501021245143 Trimmed Mean ( 7 / 54 ) 34.1756756756757 0.23480014938329 145.552188810097 Trimmed Mean ( 8 / 54 ) 34.1849315068493 0.231852620564222 147.442506466647 Trimmed Mean ( 9 / 54 ) 34.1944444444444 0.228648816850095 149.550060724184 Trimmed Mean ( 10 / 54 ) 34.2042253521127 0.225162397667951 151.909136278402 Trimmed Mean ( 11 / 54 ) 34.2142857142857 0.22136308417282 154.561840526103 Trimmed Mean ( 12 / 54 ) 34.2246376811594 0.217215804190152 157.560532065148 Trimmed Mean ( 13 / 54 ) 34.2279411764706 0.21414238736381 159.837300769045 Trimmed Mean ( 14 / 54 ) 34.2313432835821 0.210783837039715 162.400228425163 Trimmed Mean ( 15 / 54 ) 34.2272727272727 0.208425052886413 164.218611214295 Trimmed Mean ( 16 / 54 ) 34.2230769230769 0.205837696688682 166.262436247707 Trimmed Mean ( 17 / 54 ) 34.21875 0.202997196317088 168.567599064517 Trimmed Mean ( 18 / 54 ) 34.2222222222222 0.201230780847498 170.064550155264 Trimmed Mean ( 19 / 54 ) 34.2258064516129 0.199277053480127 171.749862083474 Trimmed Mean ( 20 / 54 ) 34.2213114754098 0.198392228601128 172.49320558928 Trimmed Mean ( 21 / 54 ) 34.2166666666667 0.197386046029433 173.348964402299 Trimmed Mean ( 22 / 54 ) 34.2118644067797 0.196245984895876 174.331538170994 Trimmed Mean ( 23 / 54 ) 34.2068965517241 0.194957897803445 175.457865196163 Trimmed Mean ( 24 / 54 ) 34.2017543859649 0.193505733657828 176.748015366011 Trimmed Mean ( 25 / 54 ) 34.1964285714286 0.191871198734575 178.225959898933 Trimmed Mean ( 26 / 54 ) 34.1909090909091 0.190033338256607 179.920583433315 Trimmed Mean ( 27 / 54 ) 34.1851851851852 0.187968014330206 181.867033638668 Trimmed Mean ( 28 / 54 ) 34.1792452830189 0.185647246776345 184.108549286463 Trimmed Mean ( 29 / 54 ) 34.1730769230769 0.183038369617169 186.698980080249 Trimmed Mean ( 30 / 54 ) 34.1666666666667 0.180102935112494 189.706329024154 Trimmed Mean ( 31 / 54 ) 34.16 0.176795264802442 193.217844596526 Trimmed Mean ( 32 / 54 ) 34.1632653061224 0.175080082996406 195.129364353933 Trimmed Mean ( 33 / 54 ) 34.1666666666667 0.173120652245531 197.357543559905 Trimmed Mean ( 34 / 54 ) 34.1702127659574 0.170883487482816 199.962051742381 Trimmed Mean ( 35 / 54 ) 34.1630434782609 0.170211900981677 200.708900383754 Trimmed Mean ( 36 / 54 ) 34.1555555555556 0.169386873419015 201.642281164635 Trimmed Mean ( 37 / 54 ) 34.1477272727273 0.168386972785251 202.793165693863 Trimmed Mean ( 38 / 54 ) 34.1395348837209 0.167187068646939 204.199614001343 Trimmed Mean ( 39 / 54 ) 34.1309523809524 0.165757498932364 205.908948921094 Trimmed Mean ( 40 / 54 ) 34.1219512195122 0.164062990435396 207.980795235770 Trimmed Mean ( 41 / 54 ) 34.1125 0.16206123823021 210.491419000161 Trimmed Mean ( 42 / 54 ) 34.1025641025641 0.159701002250439 213.540075653911 Trimmed Mean ( 43 / 54 ) 34.0921052631579 0.156919502862385 217.258560225340 Trimmed Mean ( 44 / 54 ) 34.0810810810811 0.153638768117859 221.826050147296 Trimmed Mean ( 45 / 54 ) 34.0833333333333 0.15289332662701 222.922308548372 Trimmed Mean ( 46 / 54 ) 34.0857142857143 0.151927378781915 224.355310800451 Trimmed Mean ( 47 / 54 ) 34.0882352941176 0.150701162113489 226.197560895030 Trimmed Mean ( 48 / 54 ) 34.0909090909091 0.149165937116794 228.543525082517 Trimmed Mean ( 49 / 54 ) 34.09375 0.147261291237125 231.518749520545 Trimmed Mean ( 50 / 54 ) 34.0967741935484 0.144911354254724 235.294013839753 Trimmed Mean ( 51 / 54 ) 34.1 0.142019335040774 240.108151402129 Trimmed Mean ( 52 / 54 ) 34.1034482758621 0.138459363224804 246.306551478873 Trimmed Mean ( 53 / 54 ) 34.1071428571429 0.134063784558848 254.409816710578 Trimmed Mean ( 54 / 54 ) 34.1111111111111 0.128602298306625 265.244957207378 Median 34 Midrange 32 Midmean - Weighted Average at Xnp 34.2577319587629 Midmean - Weighted Average at X(n+1)p 34.2577319587629 Midmean - Empirical Distribution Function 34.2577319587629 Midmean - Empirical Distribution Function - Averaging 34.2577319587629 Midmean - Empirical Distribution Function - Interpolation 34.2577319587629 Midmean - Closest Observation 34.2577319587629 Midmean - True Basic - Statistics Graphics Toolkit 34.2577319587629 Midmean - MS Excel (old versions) 34.2577319587629 Number of observations 162

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/27/t12908555586b1gnhjb4e3sgni/1f3ik1290855630.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908555586b1gnhjb4e3sgni/1f3ik1290855630.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908555586b1gnhjb4e3sgni/28vz51290855630.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908555586b1gnhjb4e3sgni/28vz51290855630.ps (open in new window)

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

geomean <- function(x) { return(exp(mean(log(x)))) } harmean <- function(x) { return(1/mean(1/x)) } quamean <- function(x) { return(sqrt(mean(x*x))) } winmean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) win <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn } return(win) } trimean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) tri <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { tri[j,1] <- mean(x,trim=j/n) tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2) } return(tri) } midrange <- function(x) { return((max(x)+min(x))/2) } q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } midmean <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } midm <- 0 myn <- 0 roundno4 <- round(n/4) round3no4 <- round(3*n/4) for (i in 1:n) { if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){ midm = midm + x[i] myn = myn + 1 } } midm = midm / myn return(midm) } (arm <- mean(x)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') lb <- win[,1] - 2*win[,2] ub <- win[,1] + 2*win[,2] if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() bitmap(file='test2.png') lb <- tri[,1] - 2*tri[,2] ub <- tri[,1] + 2*tri[,2] if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Measure',header=TRUE) a<-table.element(a,'Value',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'Value/S.E.',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE) a<-table.element(a,arm) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean')) a<-table.element(a,armose) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE) a<-table.element(a,geo) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE) a<-table.element(a,har) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE) a<-table.element(a,qua) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) for (j in 1:length(win[,1])) { a<-table.row.start(a) mylabel <- paste('Winsorized Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(win[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE) a<-table.element(a,win[j,1]) a<-table.element(a,win[j,2]) a<-table.element(a,win[j,1]/win[j,2]) a<-table.row.end(a) } for (j in 1:length(tri[,1])) { a<-table.row.start(a) mylabel <- paste('Trimmed Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(tri[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE) a<-table.element(a,tri[j,1]) a<-table.element(a,tri[j,2]) a<-table.element(a,tri[j,1]/tri[j,2]) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE) a<-table.element(a,median(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE) a<-table.element(a,midr) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[2]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[3]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[4]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[5]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[6]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[7]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[8]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of observations',header=TRUE) a<-table.element(a,length(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')

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