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mean error component

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

Title produced by software: Central Tendency

Date of computation: Fri, 19 Oct 2007 02:51:33 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Oct/19/hak98mz9wvu5dt41192787441.htm/, Retrieved Fri, 19 Oct 2007 11:50:43 +0200

User-defined keywords:

mean, error compenent, zero, robust result

Dataseries X:

» Textbox « » Textfile « » CSV «

2.57421141308358 2.37438865362012 2.17333105724820 1.98457618749651 1.81397104103089 1.66191004679337 1.52661766991268 1.40653860167760 1.30113057719606 1.21102623834896 1.13721456461338 1.08062522747986 1.04128637742589 1.01844844158507 1.01032053581325 1.01437215382075 1.02789157893426 1.04813366384893 1.07250036229251 1.09918338206534 1.12620725670397 1.15252948038855 1.17719921937284 1.19960528665096 1.21959389078944 1.23689811123881 1.25168613913275 1.26422557085352 1.27493538369740 1.28419313922586 1.29239105902577 1.30007239951788 1.30778269183781 1.31581911140617 1.32461110013878 1.33427302049863 1.34513946520297 1.35710263251695 1.36998176015130 1.3837402197024 1.39786055262194 1.41214924809857 1.42606216361429 1.43905152014551 1.45078552817955 1.46067076382584 1.46832983222105 1.47332530003405 1.47527302918506 1.47394821462 1.46919688433279 1.46100108052782 1.44930765571025 1.43432692671794 1.41637987902313 1.39572955399521 1.37289027255654 1.34828610104814 1.32262833233890 1.29658022814158 1.27062376297728 1.24562234036689 1.22226068622882 1.20101369474792 1.18265772483492 1.16777155507847 1.15657605725328 1.14955918248913 1.14686696809245 1.1487028289674 1.15481178659679 1.16533465232674 1.17984501469895 1.19805046035913 1.21962157148744 1.24408655589057 1.27090227729259 1.29954948724120 1.32953732876533 1.36038511655776 1.391697870781 1.42290970051674 1.45357964609664 1.48342562268815 1.51216511449393 1.53942157316611 1.56520734241193 1.58917572428048 1.61139843487111 1.63195481005366 1.65057646107131 1.66755779705487 1.68299504380833 1.69707249696932 1.70963412061507 1.72113577494283 1.73179259602584 1.74160095950941 1.75095822790409 1.75969181019091 1.76811912874389 1.77645812700617 1.78454607285558 1.79259347030708 1.80051058099801 1.80844482573184 1.81626223975829 1.82373708503119 1.83091133628721 1.83775856778032 1.84389496214624 1.84925463423884 1.85350714886811 1.85644670627810 1.85791579931575 1.85738713526759 1.85487224106694 1.84984585824875 1.84203480212952 1.83133817515461 1.81749445139327 1.80027923807550 1.77944358664246 1.75505023379921 1.72706152944447 1.69545484105505 1.66030329149397 1.6219199425272 1.58042337265847 1.53613311336789 1.48951425425951 1.44092540997448 1.39088865268471 1.33998320566317 1.28872566072988 1.23770855742888 1.18766567973502 1.13927045442951 1.09325612440417 1.05043480531320 1.01176342940769 0.977971838296494 0.95013992568343 0.929349046585566 0.916721786917897 0.913325147278157 0.920308195956507 0.938663107959344 0.969202901137482 1.01226015315935 1.06814266940975 1.13674792359946 1.21733333878304 1.30897791986825 1.4103897458368 1.51975869556741 1.63510096079528 1.75388348295212 1.87332950179234 1.99048808596635 2.10219325152500 2.20516817761169 2.29617433229331 2.37239062748756 2.43110281103951 2.4701738204676 2.48828621794700 2.48470382166261 2.45968250902683 2.41450734562590 2.3513268396712 2.27344979633146 2.18493963953756 2.09084988790547 1.99675465366941 1.90850637290152 1.83197136576008 1.77219437836728 1.73261948284206 1.71437060052883 1.71567013900682 1.73184932634623 1.755961943303 1.77981520103902 1.79529913867175 1.79564874890001 1.77687215007853 1.73924988429807 1.68889173806828 1.6393756439734 1.61293967388695 1.63847502162992 1.74402993949359 1.94444642046778 2.2332400331236 2.58339268606338 2.95252091255679

Text written by user:

Output produced by software:

Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 1.55075053630121 0.028079605765887 55.2269340684679 Geometric Mean 1.50362637107984 Harmonic Mean 1.45899231467572 Quadratic Mean 1.59980193173785 Winsorized Mean ( 1 / 65 ) 1.54889403078419 0.0276597541702777 55.9981126820201 Winsorized Mean ( 2 / 65 ) 1.54883723013504 0.0276378038128081 56.0405320417415 Winsorized Mean ( 3 / 65 ) 1.54766640255371 0.0273833397429671 56.5185407288092 Winsorized Mean ( 4 / 65 ) 1.54778278154029 0.0273489701533683 56.5938231992134 Winsorized Mean ( 5 / 65 ) 1.54770528957403 0.0272520515080199 56.7922488007392 Winsorized Mean ( 6 / 65 ) 1.54796635548306 0.0271329972028274 57.0510638360938 Winsorized Mean ( 7 / 65 ) 1.54726241981911 0.0269271469302224 57.4610605359938 Winsorized Mean ( 8 / 65 ) 1.54790214498066 0.0266764671466917 58.025005202858 Winsorized Mean ( 9 / 65 ) 1.54613523033242 0.0263709011986172 58.6303523981767 Winsorized Mean ( 10 / 65 ) 1.54605902208974 0.0263520555104571 58.6693900017107 Winsorized Mean ( 11 / 65 ) 1.54500080047195 0.0261532633968025 59.0748763177617 Winsorized Mean ( 12 / 65 ) 1.54188955866808 0.0256107037811233 60.204888231324 Winsorized Mean ( 13 / 65 ) 1.54101312119618 0.0253226718557732 60.8550760351477 Winsorized Mean ( 14 / 65 ) 1.53910748918589 0.0248098659607120 62.0361065885305 Winsorized Mean ( 15 / 65 ) 1.53749140524511 0.0244551118307176 62.8699396628354 Winsorized Mean ( 16 / 65 ) 1.53603537303314 0.0242092312363882 63.4483333251974 Winsorized Mean ( 17 / 65 ) 1.53656170699620 0.0239181501012580 64.2424978725835 Winsorized Mean ( 18 / 65 ) 1.53045997079758 0.0230243072910660 66.4714882167784 Winsorized Mean ( 19 / 65 ) 1.53014955724321 0.0228072261499575 67.0905592456738 Winsorized Mean ( 20 / 65 ) 1.52187906614557 0.0215395340146471 70.6551527582107 Winsorized Mean ( 21 / 65 ) 1.52184289603958 0.0214007371511030 71.1117044844945 Winsorized Mean ( 22 / 65 ) 1.52420057865741 0.0210291766996881 72.4802782545462 Winsorized Mean ( 23 / 65 ) 1.52074601366724 0.0204005712756910 74.5442857023978 Winsorized Mean ( 24 / 65 ) 1.51642438038167 0.0199433702090939 76.0365156181178 Winsorized Mean ( 25 / 65 ) 1.51222121016682 0.0194828703424112 77.6179887044132 Winsorized Mean ( 26 / 65 ) 1.51118949996806 0.0191954227578472 78.726554712127 Winsorized Mean ( 27 / 65 ) 1.51136865892402 0.0191644173082317 78.8632722099428 Winsorized Mean ( 28 / 65 ) 1.51135670911134 0.0191403680863544 78.9617369056147 Winsorized Mean ( 29 / 65 ) 1.51156218701977 0.0190769903088497 79.2348353984623 Winsorized Mean ( 30 / 65 ) 1.51170186428008 0.0190245996480355 79.4603772088408 Winsorized Mean ( 31 / 65 ) 1.51140334844836 0.0189453868499188 79.7768533533451 Winsorized Mean ( 32 / 65 ) 1.51273002801198 0.0188015876791424 80.4575684685465 Winsorized Mean ( 33 / 65 ) 1.51224042705641 0.0186814889405743 80.9486027514636 Winsorized Mean ( 34 / 65 ) 1.51354649378453 0.0185004022411619 81.8115451791102 Winsorized Mean ( 35 / 65 ) 1.51325682187687 0.0183882429616489 82.2948024470293 Winsorized Mean ( 36 / 65 ) 1.51271325990817 0.018245931984905 82.906878155616 Winsorized Mean ( 37 / 65 ) 1.51353491614625 0.0181488233637723 83.3957599238906 Winsorized Mean ( 38 / 65 ) 1.51545573739882 0.0179593817181712 84.3824003064361 Winsorized Mean ( 39 / 65 ) 1.51434326240590 0.0178050208897075 85.0514735021345 Winsorized Mean ( 40 / 65 ) 1.51336169376815 0.0176673746369517 85.658550003401 Winsorized Mean ( 41 / 65 ) 1.5151890725022 0.0174582260287817 86.7894063236581 Winsorized Mean ( 42 / 65 ) 1.51604525459195 0.0172962647941133 87.6515983443966 Winsorized Mean ( 43 / 65 ) 1.51533244483772 0.0171472123203741 88.3719415451116 Winsorized Mean ( 44 / 65 ) 1.51356651175357 0.0169930504594775 89.0697356170921 Winsorized Mean ( 45 / 65 ) 1.5141165103924 0.0169353913073759 89.4054635592007 Winsorized Mean ( 46 / 65 ) 1.51645315530807 0.0165446167755142 91.6584032065584 Winsorized Mean ( 47 / 65 ) 1.51656310094363 0.0165207759213519 91.7973289004898 Winsorized Mean ( 48 / 65 ) 1.51745788188097 0.0163307879153415 92.920065446165 Winsorized Mean ( 49 / 65 ) 1.51583824210542 0.0161270343449774 93.9936140569773 Winsorized Mean ( 50 / 65 ) 1.51617654843773 0.0158940668496496 95.3926117701687 Winsorized Mean ( 51 / 65 ) 1.51932659251659 0.0156127688311853 97.3130780929676 Winsorized Mean ( 52 / 65 ) 1.52033670058314 0.0154149135988320 98.6276498298591 Winsorized Mean ( 53 / 65 ) 1.52030024391246 0.0153992658473991 98.7255015257262 Winsorized Mean ( 54 / 65 ) 1.52023702218332 0.0152091156248195 99.9556489466403 Winsorized Mean ( 55 / 65 ) 1.52168391469489 0.0149015937072650 102.115514929992 Winsorized Mean ( 56 / 65 ) 1.52057676426473 0.0145958090451560 104.178998201499 Winsorized Mean ( 57 / 65 ) 1.52055811091577 0.0144199773242674 105.448023718929 Winsorized Mean ( 58 / 65 ) 1.52152304826347 0.0142983314717810 106.412629422274 Winsorized Mean ( 59 / 65 ) 1.52206288576042 0.0141979752010944 107.202813373212 Winsorized Mean ( 60 / 65 ) 1.52133120775900 0.0141111425088763 107.810633107988 Winsorized Mean ( 61 / 65 ) 1.51951355925812 0.013907500785462 109.258563612416 Winsorized Mean ( 62 / 65 ) 1.52084266762751 0.0136774802965659 111.193190167443 Winsorized Mean ( 63 / 65 ) 1.52047303121941 0.0135852654906783 111.920744740889 Winsorized Mean ( 64 / 65 ) 1.52054151124602 0.0132327131088465 114.907766739799 Winsorized Mean ( 65 / 65 ) 1.5225340959587 0.0130353448424113 116.800446352984 Trimmed Mean ( 1 / 65 ) 1.55075053630121 0.0272397034726369 56.9297877217712 Trimmed Mean ( 2 / 65 ) 1.54683081841797 0.0267911672954134 57.7365965940119 Trimmed Mean ( 3 / 65 ) 1.54260406025907 0.0263238427127209 58.6010210247006 Trimmed Mean ( 4 / 65 ) 1.54260406025907 0.025922136500046 59.5091403926652 Trimmed Mean ( 5 / 65 ) 1.5390180391194 0.0255027450532451 60.3471522734596 Trimmed Mean ( 6 / 65 ) 1.53716788956312 0.0250776721586569 61.2962750225795 Trimmed Mean ( 7 / 65 ) 1.53523045988531 0.0246473348054387 62.2878892182106 Trimmed Mean ( 8 / 65 ) 1.53523045988531 0.0242248401832909 63.3742244848426 Trimmed Mean ( 9 / 65 ) 1.53135905937563 0.0238136593974112 64.305910898436 Trimmed Mean ( 10 / 65 ) 1.52953174884299 0.0234204140089648 65.3076306959186 Trimmed Mean ( 11 / 65 ) 1.52767125008322 0.023000519174251 66.4189898719085 Trimmed Mean ( 12 / 65 ) 1.52587728191370 0.0225763152558881 67.587525449519 Trimmed Mean ( 13 / 65 ) 1.52434004091925 0.0221908802533745 68.6921845151886 Trimmed Mean ( 14 / 65 ) 1.52284500368004 0.0218113647319611 69.8188775619597 Trimmed Mean ( 15 / 65 ) 1.52147472581663 0.0214628201004934 70.888854246216 Trimmed Mean ( 16 / 65 ) 1.52147472581663 0.0211271937318423 72.0149938097792 Trimmed Mean ( 17 / 65 ) 1.51900369892678 0.0207921447889457 73.0566141370067 Trimmed Mean ( 18 / 65 ) 1.51773993290937 0.0204605243197387 74.17893643347 Trimmed Mean ( 19 / 65 ) 1.51686437474585 0.0201933588183410 75.1169920958436 Trimmed Mean ( 20 / 65 ) 1.51598700935799 0.0199257121519738 76.0819486799532 Trimmed Mean ( 21 / 65 ) 1.51561257865246 0.0197537163118155 76.725440151528 Trimmed Mean ( 22 / 65 ) 1.51523057662157 0.0195795083711422 77.3885915774491 Trimmed Mean ( 23 / 65 ) 1.51469864091986 0.0194217586532021 77.989777752188 Trimmed Mean ( 24 / 65 ) 1.51435100962974 0.0193023368664486 78.4542835464652 Trimmed Mean ( 25 / 65 ) 1.51423523467563 0.0192082428886072 78.8325742993257 Trimmed Mean ( 26 / 65 ) 1.51434468649031 0.0191397830238456 79.1202640387115 Trimmed Mean ( 27 / 65 ) 1.51451186555026 0.0190846275861382 79.3576850643028 Trimmed Mean ( 28 / 65 ) 1.51467451637909 0.0190244989643842 79.6170516350898 Trimmed Mean ( 29 / 65 ) 1.51484245266680 0.0189583936260573 79.9035236078613 Trimmed Mean ( 30 / 65 ) 1.51500510364401 0.0188886040389421 80.2073620962442 Trimmed Mean ( 31 / 65 ) 1.51516577973159 0.0188138986550941 80.534386174199 Trimmed Mean ( 32 / 65 ) 1.51516577973159 0.0187356462792688 80.870750714809 Trimmed Mean ( 33 / 65 ) 1.51546846611982 0.0186581261914507 81.222972262575 Trimmed Mean ( 34 / 65 ) 1.51561784918195 0.0185793595408715 81.5753549441704 Trimmed Mean ( 35 / 65 ) 1.51571235057456 0.0185034779486324 81.9149975362652 Trimmed Mean ( 36 / 65 ) 1.51582291952392 0.0184254762883413 82.2677740212913 Trimmed Mean ( 37 / 65 ) 1.51596126707232 0.0183471174728839 82.6266725175131 Trimmed Mean ( 38 / 65 ) 1.51606803301658 0.0182650151969123 83.0039294614365 Trimmed Mean ( 39 / 65 ) 1.51609470754924 0.0181853106515172 83.369194873927 Trimmed Mean ( 40 / 65 ) 1.51617032330493 0.0181053603941708 83.7415157884998 Trimmed Mean ( 41 / 65 ) 1.51629060591771 0.0180237052809545 84.1275743406633 Trimmed Mean ( 42 / 65 ) 1.51633744426928 0.0179453133756152 84.4976854140505 Trimmed Mean ( 43 / 65 ) 1.51634979119473 0.0178667295853259 84.8700252585745 Trimmed Mean ( 44 / 65 ) 1.51639255143205 0.0177867572859086 85.2540194402606 Trimmed Mean ( 45 / 65 ) 1.51651080330475 0.0177053789171176 85.6525471950554 Trimmed Mean ( 46 / 65 ) 1.51661062885051 0.0176151015319811 86.0971834932106 Trimmed Mean ( 47 / 65 ) 1.51661717639966 0.0175402642730099 86.4648988632033 Trimmed Mean ( 48 / 65 ) 1.51661942052539 0.0174538470721385 86.8931310247563 Trimmed Mean ( 49 / 65 ) 1.51658466107946 0.0173674427004057 87.3234296632536 Trimmed Mean ( 50 / 65 ) 1.51661559828499 0.0172819363762214 87.7572724067968 Trimmed Mean ( 51 / 65 ) 1.51663380729971 0.0171995397763333 88.178743560721 Trimmed Mean ( 52 / 65 ) 1.51652196275244 0.0171241437085660 88.5604552590752 Trimmed Mean ( 53 / 65 ) 1.51652196275244 0.0170493764139821 88.9488228735889 Trimmed Mean ( 54 / 65 ) 1.51619872155755 0.0169598775472506 89.399154995865 Trimmed Mean ( 55 / 65 ) 1.51602938455813 0.0168687252280699 89.8721962721537 Trimmed Mean ( 56 / 65 ) 1.51579110810103 0.0167855567725345 90.3032963780659 Trimmed Mean ( 57 / 65 ) 1.51579110810103 0.0167112362000942 90.704905965753 Trimmed Mean ( 58 / 65 ) 1.51537621870788 0.0166346000628333 91.0978450328774 Trimmed Mean ( 59 / 65 ) 1.51511193991642 0.0165498638718915 91.5483022485585 Trimmed Mean ( 60 / 65 ) 1.51481052315849 0.0164536748131885 92.0651793813434 Trimmed Mean ( 61 / 65 ) 1.51452506207709 0.0163431067208058 92.6705728568125 Trimmed Mean ( 62 / 65 ) 1.51430437176839 0.0162281105444330 93.3136588897505 Trimmed Mean ( 63 / 65 ) 1.51401176743303 0.0161098769939312 93.9803431151819 Trimmed Mean ( 64 / 65 ) 1.51401176743303 0.0159728419050673 94.7866244736772 Trimmed Mean ( 65 / 65 ) 1.51340550879598 0.0158434609981537 95.522405677165 Median 1.47394821462 Midrange 1.93292302991747 Midmean - Weighted Average at Xnp 1.51385036095930 Midmean - Weighted Average at X(n+1)p 1.51658466107946 Midmean - Empirical Distribution Function 1.51658466107946 Midmean - Empirical Distribution Function - Averaging 1.51658466107946 Midmean - Empirical Distribution Function - Interpolation 1.51658466107946 Midmean - Closest Observation 1.51385968002757 Midmean - True Basic - Statistics Graphics Toolkit 1.51658466107946 Midmean - MS Excel (old versions) 1.51658466107946 Number of observations 197

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/19/hak98mz9wvu5dt41192787441/1zupd1192787490.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/19/hak98mz9wvu5dt41192787441/1zupd1192787490.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/19/hak98mz9wvu5dt41192787441/2e14b1192787490.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/19/hak98mz9wvu5dt41192787441/2e14b1192787490.ps (open in new window)

Parameters:

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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Software written by Ed van Stee & Patrick Wessa

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