CHE central tendency

*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: Tue, 23 Nov 2010 15:40:47 +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/23/t1290526779hn4iafu4u5orvqw.htm/, Retrieved Tue, 23 Nov 2010 16:39:40 +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/23/t1290526779hn4iafu4u5orvqw.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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2.5313084961454E-14 0.0070484873310255 -3.5971225997855E-14 -3.5971225997855E-14 -3.5971225997855E-14 -0.0070484873310361 2.5313084961454E-14 2.5313084961454E-14 2.5313084961454E-14 2.5313084961454E-14 2.5313084961454E-14 2.5313084961454E-14 2.5313084961454E-14 0.0070484873310255 -0.0070484873310361 0.0070484873310255 -3.5971225997855E-14 -3.5971225997855E-14 -0.22336306618724 -1.2434497875802E-14 -1.2434497875802E-14 0.029207051746288 2.1316282072803E-14 2.1316282072803E-14 -0.029207051746279 -1.2434497875802E-14 -1.2434497875802E-14 0.029207051746288 2.1316282072803E-14 2.1316282072803E-14 2.1316282072803E-14 2.1316282072803E-14 -0.25344890080958 -3.9968028886506E-14 -3.9968028886506E-14 -3.9968028886506E-14 -3.9968028886506E-14 0.0027434859457602 -0.0027434859457909 -3.9968028886506E-14 -3.9968028886506E-14 -3.9968028886506E-14 -3.9968028886506E-14 0.0027434859457602 -0.041964199098991 0.039220713153242 -3.9968028886506E-14 -3.9968028886506E-14 0.002743485945 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 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.00383253642898714 0.00217441206561657 -1.76256216086641 Geometric Mean NaN Harmonic Mean -3.32847602006107e-13 Quadratic Mean 0.0309123573004097 Winsorized Mean ( 1 / 66 ) -0.00368210725587544 0.00209124700133668 -1.76072326871092 Winsorized Mean ( 2 / 66 ) -0.00264878565219908 0.00143865061302598 -1.84115978418677 Winsorized Mean ( 3 / 66 ) -0.00277668789563627 0.00141308426743727 -1.96498394301141 Winsorized Mean ( 4 / 66 ) -0.00298711547046851 0.00137718584913445 -2.16899953796786 Winsorized Mean ( 5 / 66 ) -0.00256728759570176 0.00114546761136232 -2.24125725619466 Winsorized Mean ( 6 / 66 ) -0.00247090937528078 0.00112058238529435 -2.20502250232295 Winsorized Mean ( 7 / 66 ) -0.00256828368321514 0.00109560408479663 -2.3441713287258 Winsorized Mean ( 8 / 66 ) -0.00267115623517714 0.00100996346047838 -2.64480482681218 Winsorized Mean ( 9 / 66 ) -0.00251123424647562 0.000970250828854633 -2.58823200330588 Winsorized Mean ( 10 / 66 ) -0.00212233960668222 0.000878194340219346 -2.4167083633813 Winsorized Mean ( 11 / 66 ) -0.00142069650228306 0.000729916922397728 -1.94638109994239 Winsorized Mean ( 12 / 66 ) -0.00131503199403144 0.00071003510080651 -1.85206617607739 Winsorized Mean ( 13 / 66 ) -0.00124920522389582 0.000697979562826844 -1.78974470088564 Winsorized Mean ( 14 / 66 ) -0.000780682856138106 0.000619067726435584 -1.26106211453318 Winsorized Mean ( 15 / 66 ) -0.000934050949545756 0.000558225126137274 -1.67325135650749 Winsorized Mean ( 16 / 66 ) -0.000934050949545756 0.000558225126137274 -1.67325135650749 Winsorized Mean ( 17 / 66 ) -0.000934050949545756 0.000558225126137274 -1.67325135650749 Winsorized Mean ( 18 / 66 ) -0.000990138198187146 0.00055064950295699 -1.79812783425773 Winsorized Mean ( 19 / 66 ) -0.00138439647279033 0.000502722375356291 -2.75379919544894 Winsorized Mean ( 20 / 66 ) -0.00162742213514456 0.000478401531316053 -3.40179123312509 Winsorized Mean ( 21 / 66 ) -0.00162742213514456 0.000478401531316053 -3.40179123312509 Winsorized Mean ( 22 / 66 ) -0.00121310902764254 0.000409384287512796 -2.9632525347095 Winsorized Mean ( 23 / 66 ) -0.00147475801415852 0.000385380270828437 -3.82676054222572 Winsorized Mean ( 24 / 66 ) -0.00171833315098237 0.000367979803822951 -4.66963983656322 Winsorized Mean ( 25 / 66 ) -0.00171833315098237 0.000367979803822951 -4.66963983656322 Winsorized Mean ( 26 / 66 ) -0.00168236995493138 0.000361832094828746 -4.64958741630602 Winsorized Mean ( 27 / 66 ) -0.00200595608054578 0.000336644125077705 -5.95868435275014 Winsorized Mean ( 28 / 66 ) -0.00108470795909955 0.000178644418770049 -6.07188271857396 Winsorized Mean ( 29 / 66 ) -0.00108470795909968 0.000178644418770045 -6.07188271857482 Winsorized Mean ( 30 / 66 ) -0.000438957751312902 7.12977518418489e-05 -6.15668432696992 Winsorized Mean ( 31 / 66 ) -0.000438957751312902 7.12977518418489e-05 -6.15668432696992 Winsorized Mean ( 32 / 66 ) 5.82645043323286e-15 2.73327565587133e-15 2.13167318880446 Winsorized Mean ( 33 / 66 ) 5.75317571360761e-15 2.7276185151659e-15 2.10923033467445 Winsorized Mean ( 34 / 66 ) 5.75317571360761e-15 2.7276185151659e-15 2.10923033467445 Winsorized Mean ( 35 / 66 ) 5.75317571360761e-15 2.7276185151659e-15 2.10923033467445 Winsorized Mean ( 36 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 37 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 38 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 39 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 40 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 41 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 42 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 43 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 44 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 45 / 66 ) 4.47419878923943e-15 2.63448450749747e-15 1.69832040253276 Winsorized Mean ( 46 / 66 ) 5.18918241709816e-15 2.56240802116522e-15 2.02511948691858 Winsorized Mean ( 47 / 66 ) 5.18918241709816e-15 2.56240802116522e-15 2.02511948691858 Winsorized Mean ( 48 / 66 ) 5.18918241709816e-15 2.56240802116522e-15 2.02511948691858 Winsorized Mean ( 49 / 66 ) 6.60360655047041e-15 2.4234116152388e-15 2.72492155643139 Winsorized Mean ( 50 / 66 ) 6.60360655047041e-15 2.4234116152388e-15 2.72492155643139 Winsorized Mean ( 51 / 66 ) 6.6036065504704e-15 2.4234116152388e-15 2.72492155643139 Winsorized Mean ( 52 / 66 ) 6.6036065504704e-15 2.4234116152388e-15 2.72492155643139 Winsorized Mean ( 53 / 66 ) 6.6036065504704e-15 2.4234116152388e-15 2.72492155643139 Winsorized Mean ( 54 / 66 ) 6.6036065504704e-15 2.4234116152388e-15 2.72492155643139 Winsorized Mean ( 55 / 66 ) 6.6036065504704e-15 2.4234116152388e-15 2.72492155643139 Winsorized Mean ( 56 / 66 ) 6.6036065504704e-15 2.4234116152388e-15 2.72492155643139 Winsorized Mean ( 57 / 66 ) 5.21138687759037e-15 2.32901384139622e-15 2.23759377680048 Winsorized Mean ( 58 / 66 ) 3.40838468559908e-15 2.21697081428603e-15 1.5374062047347 Winsorized Mean ( 59 / 66 ) 3.40838468559908e-15 2.21697081428603e-15 1.5374062047347 Winsorized Mean ( 60 / 66 ) 3.40838468559908e-15 2.21697081428603e-15 1.5374062047347 Winsorized Mean ( 61 / 66 ) 4.62740956663764e-15 2.09774859500814e-15 2.20589329800966 Winsorized Mean ( 62 / 66 ) 4.62740956663764e-15 2.09774859500814e-15 2.20589329800966 Winsorized Mean ( 63 / 66 ) 4.62740956663764e-15 2.09774859500814e-15 2.20589329800966 Winsorized Mean ( 64 / 66 ) 4.62740956663764e-15 2.09774859500814e-15 2.20589329800966 Winsorized Mean ( 65 / 66 ) 4.62740956663764e-15 2.09774859500814e-15 2.20589329800966 Winsorized Mean ( 66 / 66 ) 1.23945298469151e-14 1.35899156004533e-15 9.12038765457948 Trimmed Mean ( 1 / 66 ) -0.00304145601999842 0.00173265109619857 -1.75537707890028 Trimmed Mean ( 2 / 66 ) -0.00238773026910351 0.0012526737928191 -1.90610698714308 Trimmed Mean ( 3 / 66 ) -0.00225316563864187 0.00114122304917018 -1.97434291243962 Trimmed Mean ( 4 / 66 ) -0.00207138707718549 0.00102466305402595 -2.02152997421631 Trimmed Mean ( 5 / 66 ) -0.00183040592105837 0.000901813722760708 -2.02969402090598 Trimmed Mean ( 6 / 66 ) -0.00167362258602787 0.00083607367571985 -2.00176447917332 Trimmed Mean ( 7 / 66 ) -0.00153073964888577 0.000768381450107059 -1.99216111824731 Trimmed Mean ( 8 / 66 ) -0.00136963032678494 0.000696995011498425 -1.96505040092103 Trimmed Mean ( 9 / 66 ) -0.00119084929541238 0.00063492428637726 -1.87557685374914 Trimmed Mean ( 10 / 66 ) -0.0010278388076268 0.000572189784944343 -1.79632498634483 Trimmed Mean ( 11 / 66 ) -0.0009048611897554 0.000519645912841012 -1.7413033902419 Trimmed Mean ( 12 / 66 ) -0.000851572417800064 0.000488438296560316 -1.74345956039281 Trimmed Mean ( 13 / 66 ) -0.000807179738084415 0.000456911269899293 -1.76660063180828 Trimmed Mean ( 14 / 66 ) -0.000767642574773914 0.000423413770567631 -1.81298443304007 Trimmed Mean ( 15 / 66 ) -0.000766546752810536 0.000398399490746904 -1.92406559399322 Trimmed Mean ( 16 / 66 ) -0.000753252768942662 0.000379323300932466 -1.9857803807227 Trimmed Mean ( 17 / 66 ) -0.000739638448114116 0.00035816213145419 -2.06509394254238 Trimmed Mean ( 18 / 66 ) -0.000725692070679995 0.000334455323212102 -2.16977282260143 Trimmed Mean ( 19 / 66 ) -0.000707554476200767 0.000308675284343464 -2.29222912260597 Trimmed Mean ( 20 / 66 ) -0.000663025397477769 0.000286032091418742 -2.31801052178834 Trimmed Mean ( 21 / 66 ) -0.00060198762927101 0.000263327990314851 -2.28607535625528 Trimmed Mean ( 22 / 66 ) -0.00053938479008459 0.000236951643027109 -2.27634965174257 Trimmed Mean ( 23 / 66 ) -0.000499613583617179 0.00021721946780816 -2.30004054727921 Trimmed Mean ( 24 / 66 ) -0.000443827288963784 0.000197398470864779 -2.24838260914297 Trimmed Mean ( 25 / 66 ) -0.000373021407740529 0.000176058508185515 -2.11873547938661 Trimmed Mean ( 26 / 66 ) -0.00030030185405178 0.000150021759958007 -2.00172197777069 Trimmed Mean ( 27 / 66 ) -0.000227484778136101 0.000117565730954669 -1.93495822540171 Trimmed Mean ( 28 / 66 ) -0.000135999628835191 7.36680378709113e-05 -1.84611444482211 Trimmed Mean ( 29 / 66 ) -0.000135999628835191 5.63729040596933e-05 -2.41249996081772 Trimmed Mean ( 30 / 66 ) -3.91926563592298e-05 2.76135246829148e-05 -1.41932827515783 Trimmed Mean ( 31 / 66 ) -1.98803329315161e-05 1.98803329405794e-05 -0.999999999544107 Trimmed Mean ( 32 / 66 ) 8.77729261821302e-15 2.79742427492746e-15 3.13763367855261 Trimmed Mean ( 33 / 66 ) 8.91492518281098e-15 2.79091120879341e-15 3.19427044282972 Trimmed Mean ( 34 / 66 ) 9.06009274338046e-15 2.78363447838818e-15 3.25477098869193 Trimmed Mean ( 35 / 66 ) 9.20972699812132e-15 2.775152863125e-15 3.31863773001339 Trimmed Mean ( 36 / 66 ) 9.36403732332282e-15 2.7653502116035e-15 3.38620305089425 Trimmed Mean ( 37 / 66 ) 9.5796386696228e-15 2.75972818340175e-15 3.47122543706988 Trimmed Mean ( 38 / 66 ) 9.80219489806148e-15 2.75285400064803e-15 3.56073910776017 Trimmed Mean ( 39 / 66 ) 1.00320480520227e-14 2.74459617273287e-15 3.65520004425043 Trimmed Mean ( 40 / 66 ) 1.02695629777827e-14 2.73480679655973e-15 3.75513290032092 Trimmed Mean ( 41 / 66 ) 1.05151292569583e-14 2.72331896455121e-15 3.86114494623333 Trimmed Mean ( 42 / 66 ) 1.0769163338864e-14 2.70994365212025e-15 3.97394363917429 Trimmed Mean ( 43 / 66 ) 1.10321108973279e-14 2.694465952704e-15 4.09435899023209 Trimmed Mean ( 44 / 66 ) 1.13044494400226e-14 2.67664048682886e-15 4.2233723563733 Trimmed Mean ( 45 / 66 ) 1.15866912024516e-14 2.65618575392736e-15 4.36215395904442 Trimmed Mean ( 46 / 66 ) 1.18793863634892e-14 2.63277711421166e-15 4.51211243798975 Trimmed Mean ( 47 / 66 ) 1.21537999790097e-14 2.6124032311757e-15 4.65234456686075 Trimmed Mean ( 48 / 66 ) 1.2438767964358e-14 2.58896149279617e-15 4.80453958043373 Trimmed Mean ( 49 / 66 ) 1.2734911164818e-14 2.56204682632405e-15 4.97060047223635 Trimmed Mean ( 50 / 66 ) 1.29851684960158e-14 2.54402254861776e-15 5.10418765866325 Trimmed Mean ( 51 / 66 ) 1.32456404121605e-14 2.52299282809797e-15 5.24997148808629 Trimmed Mean ( 52 / 66 ) 1.35169653248113e-14 2.4985433321128e-15 5.40993832329541 Trimmed Mean ( 53 / 66 ) 1.37998359784259e-14 2.47018552209674e-15 5.58655852160946 Trimmed Mean ( 54 / 66 ) 1.40950053561107e-14 2.43733863845493e-15 5.78294912890961 Trimmed Mean ( 55 / 66 ) 1.44032933728037e-14 2.39930576163648e-15 6.00310873382796 Trimmed Mean ( 56 / 66 ) 1.47255944811646e-14 2.35524135007763e-15 6.25226560355659 Trimmed Mean ( 57 / 66 ) 1.47255944811646e-14 2.30410616278985e-15 6.39102256613669 Trimmed Mean ( 58 / 66 ) 1.50628863387515e-14 2.25162290701284e-15 6.68979085789058 Trimmed Mean ( 59 / 66 ) 1.59817958325309e-14 2.19702143153379e-15 7.27430128952984 Trimmed Mean ( 60 / 66 ) 1.65145674912992e-14 2.13209212213321e-15 7.74571010317132 Trimmed Mean ( 61 / 66 ) 1.70746607735941e-14 2.05449765372469e-15 8.31086895749852 Trimmed Mean ( 62 / 66 ) 1.76116431380012e-14 1.98085609390332e-15 8.89092508648472 Trimmed Mean ( 63 / 66 ) 1.817765157616e-14 1.89183283759129e-15 9.608487185001 Trimmed Mean ( 64 / 66 ) 1.87751049275499e-14 1.78303823743001e-15 10.5298386391373 Trimmed Mean ( 65 / 66 ) 1.94066984704477e-14 1.6478943142932e-15 11.7766644997325 Trimmed Mean ( 66 / 66 ) 2.00754445746925e-14 1.47565368924975e-15 13.6044416931586 Median 2.5313084961454e-14 Midrange -0.08214949691887 Midmean - Weighted Average at Xnp 1.58636389049049e-14 Midmean - Weighted Average at X(n+1)p 1.58636389049049e-14 Midmean - Empirical Distribution Function 1.58636389049049e-14 Midmean - Empirical Distribution Function - Averaging 1.58636389049049e-14 Midmean - Empirical Distribution Function - Interpolation 1.58636389049049e-14 Midmean - Closest Observation 1.58636389049049e-14 Midmean - True Basic - Statistics Graphics Toolkit 1.58636389049049e-14 Midmean - MS Excel (old versions) 1.58636389049049e-14 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526779hn4iafu4u5orvqw/1am3i1290526842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526779hn4iafu4u5orvqw/1am3i1290526842.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526779hn4iafu4u5orvqw/23v2k1290526842.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290526779hn4iafu4u5orvqw/23v2k1290526842.ps (open in new window)

Parameters (Session):

par1 = Apollo Oil Corp. ; par3 = Time series of Xycoon Stock Exchange ; par4 = No season ;

Parameters (R input):

par1 = Apollo Oil Corp. ; par3 = Time series of Xycoon Stock Exchange ; par4 = No season ;

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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