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Univariate summary analysis: kijkcijfers

*The author of this computation has been verified*
R Software Module: /rwasp_summary1.wasp (opens new window with default values)
Title produced by software: Univariate Summary Statistics
Date of computation: Tue, 21 Dec 2010 09:08:53 +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/Dec/21/t129292248459la5lir2kj1fyq.htm/, Retrieved Tue, 21 Dec 2010 10:08:13 +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/Dec/21/t129292248459la5lir2kj1fyq.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:
» Textbox « » Textfile « » CSV «
15561600 14917500 14805920 16958000 17605000 17131200 18474600 17286700 18574400 18056000 19701600 19061700 19681900 34521200 19922700 20177900 19759900 23076700 22532000 22029400 22587000 23256600 22680300 21916400 19640200 18813100 18730000 18154700 17848800 18077500 17133100 16602600 15878900 15789100 15422000 14661400 15879200 14339300 13169600 14528900 13375800 12309900 11933900 10061900 12609600 11156500 12187200 11284300 10177000 10970720 10820680 11492390 14573750 13992820 14727070 15685360 16736210 17950180 17002730 17415160 17929810 17865790 19202360 19085000 18188880 18466410 18520400 20025500 20636100 20672000 22589100 21864800 22750100 22548746 21325495 21556563 21415269 20401054 19062253 19085706 19279967 18552045 17800733 17142490 17593173 17633859 17336613 17008347 17951965 14520929 16941217 15436824 14744261 14248004 11540953 12881661 15185757 13554339 13575106 12238400 13303614 14151478 1417 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time107 seconds
R Server'George Udny Yule' @ 72.249.76.132


Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean17167453.75368154.04448744446.6311697699833
Geometric Mean16771879.6454805
Harmonic Mean16378027.4789571
Quadratic Mean17569343.2007874
Winsorized Mean ( 1 / 34 )17060247.0192308332599.02744880751.2937369363074
Winsorized Mean ( 2 / 34 )17069165.8653846329596.78571912251.7880228356678
Winsorized Mean ( 3 / 34 )17064072.7884615327178.62719483952.1552184956742
Winsorized Mean ( 4 / 34 )17068533.5576923325451.28694643552.4457399380374
Winsorized Mean ( 5 / 34 )17070293.1730769323646.26170126552.7436747866202
Winsorized Mean ( 6 / 34 )17082177.2115385321571.18816813453.12098172989
Winsorized Mean ( 7 / 34 )17082871.0865385320594.01736503253.2850588633653
Winsorized Mean ( 8 / 34 )17111809.625315433.83760340654.2484907612056
Winsorized Mean ( 9 / 34 )17090235.5865385304923.95491218856.0475335283516
Winsorized Mean ( 10 / 34 )17084293.2788462302459.72751236956.4845224829065
Winsorized Mean ( 11 / 34 )17086398.0865385300443.26158109956.8706317346589
Winsorized Mean ( 12 / 34 )17085413.0480769289768.80613899758.9622232832107
Winsorized Mean ( 13 / 34 )17101758.9230769282114.31676502060.6199611532705
Winsorized Mean ( 14 / 34 )17128434.9807692274794.5489859362.3317858522959
Winsorized Mean ( 15 / 34 )17053509.8365385258676.45554320565.9260225316104
Winsorized Mean ( 16 / 34 )17059092.2980769256371.25208529666.540581907371
Winsorized Mean ( 17 / 34 )17049855.5769231247199.92793135468.9719277817011
Winsorized Mean ( 18 / 34 )17014827.0576923241708.66591197770.3939471656702
Winsorized Mean ( 19 / 34 )17063297.8846154227912.04388680474.8679077841543
Winsorized Mean ( 20 / 34 )17049201.7307692224695.34015464475.876970652953
Winsorized Mean ( 21 / 34 )17042408.6346154217267.54370409678.4397353790955
Winsorized Mean ( 22 / 34 )17034419.0384615215215.83865966179.1503968506681
Winsorized Mean ( 23 / 34 )17046868.8942308212531.94563063980.2085015673674
Winsorized Mean ( 24 / 34 )17058314.125208695.37210228481.7378648753147
Winsorized Mean ( 25 / 34 )17015380.4711538193052.51624643988.1386101667435
Winsorized Mean ( 26 / 34 )16997971.4711538190608.07722946189.177603164692
Winsorized Mean ( 27 / 34 )16979330.0480769185776.42779514891.3965794777779
Winsorized Mean ( 28 / 34 )17002738.0480769182804.80475352493.0103454939367
Winsorized Mean ( 29 / 34 )17014706.9615385179834.13204862494.6133348976156
Winsorized Mean ( 30 / 34 )17019506.3846154179204.63401174694.972468086399
Winsorized Mean ( 31 / 34 )16963783.5865385168889.230860071100.443252066163
Winsorized Mean ( 32 / 34 )16972546.6634615161938.100600853104.808853509377
Winsorized Mean ( 33 / 34 )17008293.5961538146377.368165416116.194831272915
Winsorized Mean ( 34 / 34 )17078218.5192308136389.638705397125.216392398545
Trimmed Mean ( 1 / 34 )17066981.2745098326647.01013186152.2490050272317
Trimmed Mean ( 2 / 34 )17073984.9319945.93018270053.365219836521
Trimmed Mean ( 3 / 34 )17076541.9387755314171.95542422554.3541256434463
Trimmed Mean ( 4 / 34 )17081044.6875308648.7461058255.3413707426622
Trimmed Mean ( 5 / 34 )17084505.2127660302954.84995415656.3929087629765
Trimmed Mean ( 6 / 34 )17087718.3695652296985.01148304757.5373089848362
Trimmed Mean ( 7 / 34 )17088785.5555556290692.33985163058.786501096925
Trimmed Mean ( 8 / 34 )17089784.1022727283716.84277938360.235352737101
Trimmed Mean ( 9 / 34 )17086454.6627907276797.64446160561.7290464881857
Trimmed Mean ( 10 / 34 )17085934.5357143270895.11173981363.0721404531095
Trimmed Mean ( 11 / 34 )17086142.6951220264535.97263653564.5891087130061
Trimmed Mean ( 12 / 34 )17086112.5125257530.36050743966.3460124811438
Trimmed Mean ( 13 / 34 )17086190.2307692251247.38121979568.0054460580505
Trimmed Mean ( 14 / 34 )17084551.4210526245203.47359616369.6749975458747
Trimmed Mean ( 15 / 34 )17084551.4210526239321.03243978471.3875886581399
Trimmed Mean ( 16 / 34 )17082711.0972222234967.89634213572.7023196068802
Trimmed Mean ( 17 / 34 )17084904.2714286230161.06182532774.2302113829949
Trimmed Mean ( 18 / 34 )17088057.4411765225854.41917132475.6596107522437
Trimmed Mean ( 19 / 34 )17094468.1818182221518.32965653877.1695426212493
Trimmed Mean ( 20 / 34 )17097134.0625218388.4569509878.2877185964901
Trimmed Mean ( 21 / 34 )17101154.1935484215023.37057786679.5316069485367
Trimmed Mean ( 22 / 34 )17106003.0333333211992.18426902780.6916683854019
Trimmed Mean ( 23 / 34 )17111837.4655172208516.93796503082.0644962107923
Trimmed Mean ( 24 / 34 )17117083.375204601.53348679783.6605820264027
Trimmed Mean ( 25 / 34 )17121799.4259259200323.73040808785.4706498877915
Trimmed Mean ( 26 / 34 )17130312.9423077197481.23925681586.7440016417484
Trimmed Mean ( 27 / 34 )17140900.26194158.14134517788.2831909145992
Trimmed Mean ( 28 / 34 )17153865.7708333190624.66323145889.987651545409
Trimmed Mean ( 29 / 34 )17166068.6304348186539.13437929992.0239535127798
Trimmed Mean ( 30 / 34 )17166068.6304348181720.91184231494.4639142320087
Trimmed Mean ( 31 / 34 )17191520.7857143175436.18882261397.9930133063766
Trimmed Mean ( 32 / 34 )17210621.325169206.794188412101.713535839677
Trimmed Mean ( 33 / 34 )17230982.9736842162400.190391754106.101987516876
Trimmed Mean ( 34 / 34 )17250477.6666667157056.741758431109.835957842546
Median17375886.5
Midrange22291550
Midmean - Weighted Average at Xnp17081079.2830189
Midmean - Weighted Average at X(n+1)p17130312.9423077
Midmean - Empirical Distribution Function17081079.2830189
Midmean - Empirical Distribution Function - Averaging17130312.9423077
Midmean - Empirical Distribution Function - Interpolation17130312.9423077
Midmean - Closest Observation17081079.2830189
Midmean - True Basic - Statistics Graphics Toolkit17130312.9423077
Midmean - MS Excel (old versions)17121799.4259259
Number of observations104


Variability - Ungrouped Data
Absolute range24459300
Relative range (unbiased)6.51475035519945
Relative range (biased)6.54629896758497
Variance (unbiased)14095889649136.2
Variance (biased)13960352248663.7
Standard Deviation (unbiased)3754449.31369917
Standard Deviation (biased)3736355.47675321
Coefficient of Variation (unbiased)0.21869575816968
Coefficient of Variation (biased)0.217641796574126
Mean Squared Error (MSE versus 0)308681820507053
Mean Squared Error (MSE versus Mean)13960352248663.7
Mean Absolute Deviation from Mean (MAD Mean)2881323.625
Mean Absolute Deviation from Median (MAD Median)2875777.36538462
Median Absolute Deviation from Mean2510250
Median Absolute Deviation from Median2558390
Mean Squared Deviation from Mean13960352248663.7
Mean Squared Deviation from Median14003796459936.3
Interquartile Difference (Weighted Average at Xnp)4681431
Interquartile Difference (Weighted Average at X(n+1)p)4737643.5
Interquartile Difference (Empirical Distribution Function)4681431
Interquartile Difference (Empirical Distribution Function - Averaging)4716249
Interquartile Difference (Empirical Distribution Function - Interpolation)4694854.5
Interquartile Difference (Closest Observation)4681431
Interquartile Difference (True Basic - Statistics Graphics Toolkit)4694854.5
Interquartile Difference (MS Excel (old versions))4759038
Semi Interquartile Difference (Weighted Average at Xnp)2340715.5
Semi Interquartile Difference (Weighted Average at X(n+1)p)2368821.75
Semi Interquartile Difference (Empirical Distribution Function)2340715.5
Semi Interquartile Difference (Empirical Distribution Function - Averaging)2358124.5
Semi Interquartile Difference (Empirical Distribution Function - Interpolation)2347427.25
Semi Interquartile Difference (Closest Observation)2340715.5
Semi Interquartile Difference (True Basic - Statistics Graphics Toolkit)2347427.25
Semi Interquartile Difference (MS Excel (old versions))2379519
Coefficient of Quartile Variation (Weighted Average at Xnp)0.138818933111773
Coefficient of Quartile Variation (Weighted Average at X(n+1)p)0.140235479540641
Coefficient of Quartile Variation (Empirical Distribution Function)0.138818933111773
Coefficient of Quartile Variation (Empirical Distribution Function - Averaging)0.139674172404625
Coefficient of Quartile Variation (Empirical Distribution Function - Interpolation)0.139112286176382
Coefficient of Quartile Variation (Closest Observation)0.138818933111773
Coefficient of Quartile Variation (True Basic - Statistics Graphics Toolkit)0.139112286176382
Coefficient of Quartile Variation (MS Excel (old versions))0.140796208479207
Number of all Pairs of Observations5356
Squared Differences between all Pairs of Observations28191779298272.1
Mean Absolute Differences between all Pairs of Observations4118118.88125467
Gini Mean Difference4118118.88125467
Leik Measure of Dispersion0.492026927252605
Index of Diversity0.989929154311385
Index of Qualitative Variation0.999540116974602
Coefficient of Dispersion0.165823114981788
Observations104


Percentiles - Ungrouped Data
pWeighted Average at XnpWeighted Average at X(n+1)pEmpirical Distribution FunctionEmpirical Distribution Function - AveragingEmpirical Distribution Function - InterpolationClosest ObservationTrue Basic - Statistics Graphics ToolkitMS Excel (old versions)
0.011006650410067655101770001017700010196310.4100619001017124510061900
0.0210228494.410241368108206801082068010829682.4101770001075631210177000
0.0310838684.810843186109707201097072010987440.2108206801094821410820680
0.0411000444.811007876111565001115650011171836109707201111934410970720
0.051118206011188450112843001128430011315513.5111565001125235011156500
0.0611334241.611346727114923901149239011501131.34112843001142996311284300
0.0711505987.6411509387.05115409531154095311623471.871149239011523955.9511492390
0.0811666696.0411698131.81193390011933900119946921154095311776721.211540953
0.091202508812047885121872001218720012201024119339001207321511933900
0.11220768012212800122384001223840012259850121872001221280012212800
0.111226986012277725123099001230990012408801122384001227057512309900
0.121245375612489720126096001260960012707541.96123099001242978012609600
0.1312751071.7212786439.65128816611288166112993957.211288166112704821.3512881661
0.1413042906.8413083218.3131696001316960013225885.881316960012968042.713169600
0.1513250008.413270110.5133036141330361413336097.71330361413203103.513303614
0.1613349813.0413361362.8133758001337580013461498.721337580013318051.213375800
0.1713497206.5213527558.15135543391355433913564930.171355433913402580.8513554339
0.1813569291.2413573029.3135751061357510613800671.561357510613556415.713575106
0.1913892568.6413971934.31399282013992820140096351399282013595991.713992820
0.21401642014022320140223201402232014099814.8140223201402232014022320
0.2114130812.7214152504.55141514781415147814164412.531415147814170982.4514151478
0.2214169545.2814179608.5141720091417200914222165.71417200914240404.514172009
0.2314241924.414261698.4142480041424800414310998.241424800414325605.614248004
0.2414335648.1614375625.8143393001433930014470072.881433930014484603.214339300
0.251452092914522921.751452092914524914.514526907.251452092914526907.2514520929
0.261453069414542355145737501457375014563883145289001456029514528900
0.271458076214604427.5146614001466140014644746.51457375014630722.514573750
0.2814669280.414687668147270701472707014716562.8146614001470080214661400
0.2914729820.5614734805.95147442611474426114742026.171472707014736525.0514727070
0.314756592.814775090.5148059201480592014799754.11474426114775090.514775090.5
0.3114832699.214867289149175001491750014909689.4148059201485613114917500
0.3214992611.9615078454.2151857571518575715175026.721491750015024802.815185757
0.3315261354.7615339314.95154220001542200015419637.571518575715268442.0515422000
0.3415427336.6415432376.8154368241543682415439319.521542200015426447.215436824
0.3515486734.415530406155616001556160015567788154368241546801815561600
0.3615616054.415660608156853601568536015693659.2155616001558635215685360
0.3715735155.215773539157891001578910015798978156853601570092115789100
0.381583579615869920158789001587890015878942158789001579808015878900
0.391587906815879185158792001587920016002178158792001587891515879200
0.41631324016602600166026001660260016629322166026001660260016602600
0.4116688110.416746460.35167362101673621016783361.611673621016930966.6516736210
0.4216875614.7616942895.3169412171694121716945580.581694121716956321.716941217
0.4316953300.7616964709.5169580001695800016970971.71695800016996020.516958000
0.4416991994.817003853.4170027301700273017004527.441700273017007223.617002730
0.4517007223.617039060.25170083471700834717051345.551700834717100486.7517008347
0.4617111543.5217131770171312001713120017131922171312001713253017131200
0.471713287217136386.5171331001713310017136949.91713310017139203.517133100
0.4817141738.817200174171424901714249017205942.4171424901722901617142490
0.4917280931.617309160.85172867001728670017310159.111728670017314152.1517286700
0.51733661317375886.51733661317375886.517375886.51733661317375886.517375886.5
0.5117422280.5217513067.15175931731759317317509506.891741516017495265.8517593173
0.5217594119.1617600269.2176050001760500017599796.121759317317597903.817605000
0.5317608463.0817623758.35176338591763385917622026.811760500017615100.6517633859
0.5417660558.8417750670.8178007331780073317737320.881763385917683921.217800733
0.5517810346.417836783.25178488001784880017831976.551780073317812749.7517848800
0.5617852877.617862392178657901786579017860353.2178488001785219817865790
0.5717883715.617920207179298101792981017911244.2178657901787539317929810
0.5817936328.417948143179501801795018017944883.8179298101793184717950180
0.5917950822.617951875.75179519651795196517951554.451795018017950269.2517951965
0.61799357918056000180560001805600018035193179519651805600018056000
0.611806546018081360180775001807750018073845180560001815084018077500
0.621811455618158118181547001815470018143892180775001818546218154700
0.6318172473.618230509.5181888801818888018185120.21818888018424780.518188880
0.6418344296.818468048184664101846641018444207.6184664101847296218466410
0.651847132418486050184746001847460018474190.5184746001850895018474600
0.661850391218529893.51852040018520400185194841852040018542551.518520400
0.6718541918.618559869.25185520451855204518552268.551855204518566575.7518552045
0.6818568140.618636640185744001857440018580624185744001866776018574400
0.691869265618767395187300001873000018735817187300001877570518730000
0.71879648018937400188131001881310018837960188131001893740018937400
0.711902192419062004.15190617001906170019061771.891906170019061948.8519062253
0.7219062186.6419075901.2190622531906225319065892.521906225319071351.819085000
0.7319083180.2419085458.9190850001908500019085134.141908500019085247.119085706
0.7419085677.7619167363.8190857061908570619111369.881908570619120702.219202360
0.751920236019260565.251920236019241163.519221761.751920236019221761.7519279967
0.7619294376.3219568153.4196402001964020019380832.241927996719352013.619640200
0.771964353619675645196819001968190019653127196402001964645519681900
0.781968426419699630197016001970160019688598196819001968387019701600
0.791971092819756985197599001975990019723171197016001970451519759900
0.81979246019922700199227001992270019825020197599001992270019922700
0.811994737220033120200255002002550019966904199227002017028020025500
0.822006817220200215.42017790020177900200956042002550020378738.620177900
0.8320249309.2820436310.9204010542040105420287245.462017790020600843.120401054
0.8420485670.5620643280206361002063610020523277.92204010542066482020636100
0.852065046020835373.752067200020672000206558452063610021162121.2520672000
0.8620959537.821352427.2213254952132549521051027.12067200021388336.821325495
0.8721368586.5221464721.9214152692141526921380257.142132549521507110.121415269
0.8821488741.8821679857.8215565632155656321505697.162155656321741505.221556563
0.8921729175.7221888020218648002186480021763081.79218648002189318021864800
0.92189576021972900219164002191640021900920219164002197290021972900
0.912198872022305830220294002202940021998890220294002225557022532000
0.922237116822542047.62253200022532000224113762253200022538698.422548746
0.9322544057.1222573611.1225487462254874622545229.342254874622562134.922587000
0.9422577819.0422588470225870002258700022580114.28225870002258763022589100
0.952258868022657500225891002258910022588785225891002261190022680300
0.962266570822736140226803002268030022669356226803002269426022750100
0.972274172423027710227501002275010022743818227501002279909023076700
0.982305057223238610230767002307670023057104230767002309469023256600
0.992324940433957970232566002325660023251203232566002381983034521200


Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[1e+07,1.5e+07[12500000330.3173080.3173080
[1.5e+07,2e+07[17500000510.4903850.8076920
[2e+07,2.5e+07[22500000190.1826920.9903850
[2.5e+07,3e+07[27500000000.9903850
[3e+07,3.5e+07]3250000010.00961510


Properties of Density Trace
Bandwidth1245527.09813467
#Observations104
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/1qkns1292922423.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/1qkns1292922423.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/2qkns1292922423.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/2qkns1292922423.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/5rhvy1292922424.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/5rhvy1292922424.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/7vhu41292922424.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/7vhu41292922424.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/9r9sv1292922424.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t129292248459la5lir2kj1fyq/9r9sv1292922424.ps (open in new window)


 
Parameters (Session):
 
Parameters (R input):
 
R code (references can be found in the software module):
load(file='createtable')
x <-sort(x[!is.na(x)])
num <- 50
res <- array(NA,dim=c(num,3))
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]
}
}
}
}
iqd <- 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)
}
iqdiff <- qvalue3 - qvalue1
return(c(iqdiff,iqdiff/2,iqdiff/(qvalue3 + qvalue1)))
}
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)
}
range <- max(x) - min(x)
lx <- length(x)
biasf <- (lx-1)/lx
varx <- var(x)
bvarx <- varx*biasf
sdx <- sqrt(varx)
mx <- mean(x)
bsdx <- sqrt(bvarx)
x2 <- x*x
mse0 <- sum(x2)/lx
xmm <- x-mx
xmm2 <- xmm*xmm
msem <- sum(xmm2)/lx
axmm <- abs(x - mx)
medx <- median(x)
axmmed <- abs(x - medx)
xmmed <- x - medx
xmmed2 <- xmmed*xmmed
msemed <- sum(xmmed2)/lx
qarr <- array(NA,dim=c(8,3))
for (j in 1:8) {
qarr[j,] <- iqd(x,j)
}
sdpo <- 0
adpo <- 0
for (i in 1:(lx-1)) {
for (j in (i+1):lx) {
ldi <- x[i]-x[j]
aldi <- abs(ldi)
sdpo = sdpo + ldi * ldi
adpo = adpo + aldi
}
}
denom <- (lx*(lx-1)/2)
sdpo = sdpo / denom
adpo = adpo / denom
gmd <- 0
for (i in 1:lx) {
for (j in 1:lx) {
ldi <- abs(x[i]-x[j])
gmd = gmd + ldi
}
}
gmd <- gmd / (lx*(lx-1))
sumx <- sum(x)
pk <- x / sumx
ck <- cumsum(pk)
dk <- array(NA,dim=lx)
for (i in 1:lx) {
if (ck[i] <= 0.5) dk[i] <- ck[i] else dk[i] <- 1 - ck[i]
}
bigd <- sum(dk) * 2 / (lx-1)
iod <- 1 - sum(pk*pk)
res[1,] <- c('Absolute range','http://www.xycoon.com/absolute.htm', range)
res[2,] <- c('Relative range (unbiased)','http://www.xycoon.com/relative.htm', range/sd(x))
res[3,] <- c('Relative range (biased)','http://www.xycoon.com/relative.htm', range/sqrt(varx*biasf))
res[4,] <- c('Variance (unbiased)','http://www.xycoon.com/unbiased.htm', varx)
res[5,] <- c('Variance (biased)','http://www.xycoon.com/biased.htm', bvarx)
res[6,] <- c('Standard Deviation (unbiased)','http://www.xycoon.com/unbiased1.htm', sdx)
res[7,] <- c('Standard Deviation (biased)','http://www.xycoon.com/biased1.htm', bsdx)
res[8,] <- c('Coefficient of Variation (unbiased)','http://www.xycoon.com/variation.htm', sdx/mx)
res[9,] <- c('Coefficient of Variation (biased)','http://www.xycoon.com/variation.htm', bsdx/mx)
res[10,] <- c('Mean Squared Error (MSE versus 0)','http://www.xycoon.com/mse.htm', mse0)
res[11,] <- c('Mean Squared Error (MSE versus Mean)','http://www.xycoon.com/mse.htm', msem)
res[12,] <- c('Mean Absolute Deviation from Mean (MAD Mean)', 'http://www.xycoon.com/mean2.htm', sum(axmm)/lx)
res[13,] <- c('Mean Absolute Deviation from Median (MAD Median)', 'http://www.xycoon.com/median1.htm', sum(axmmed)/lx)
res[14,] <- c('Median Absolute Deviation from Mean', 'http://www.xycoon.com/mean3.htm', median(axmm))
res[15,] <- c('Median Absolute Deviation from Median', 'http://www.xycoon.com/median2.htm', median(axmmed))
res[16,] <- c('Mean Squared Deviation from Mean', 'http://www.xycoon.com/mean1.htm', msem)
res[17,] <- c('Mean Squared Deviation from Median', 'http://www.xycoon.com/median.htm', msemed)
mylink1 <- hyperlink('http://www.xycoon.com/difference.htm','Interquartile Difference','')
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[18,] <- c('', mylink2, qarr[1,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[19,] <- c('', mylink2, qarr[2,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[20,] <- c('', mylink2, qarr[3,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[21,] <- c('', mylink2, qarr[4,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[22,] <- c('', mylink2, qarr[5,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ')
res[23,] <- c('', mylink2, qarr[6,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[24,] <- c('', mylink2, qarr[7,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[25,] <- c('', mylink2, qarr[8,1])
mylink1 <- hyperlink('http://www.xycoon.com/deviation.htm','Semi Interquartile Difference','')
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[26,] <- c('', mylink2, qarr[1,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[27,] <- c('', mylink2, qarr[2,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[28,] <- c('', mylink2, qarr[3,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[29,] <- c('', mylink2, qarr[4,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[30,] <- c('', mylink2, qarr[5,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ')
res[31,] <- c('', mylink2, qarr[6,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[32,] <- c('', mylink2, qarr[7,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[33,] <- c('', mylink2, qarr[8,2])
mylink1 <- hyperlink('http://www.xycoon.com/variation1.htm','Coefficient of Quartile Variation','')
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[34,] <- c('', mylink2, qarr[1,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[35,] <- c('', mylink2, qarr[2,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[36,] <- c('', mylink2, qarr[3,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[37,] <- c('', mylink2, qarr[4,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[38,] <- c('', mylink2, qarr[5,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ')
res[39,] <- c('', mylink2, qarr[6,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[40,] <- c('', mylink2, qarr[7,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[41,] <- c('', mylink2, qarr[8,3])
res[42,] <- c('Number of all Pairs of Observations', 'http://www.xycoon.com/pair_numbers.htm', lx*(lx-1)/2)
res[43,] <- c('Squared Differences between all Pairs of Observations', 'http://www.xycoon.com/squared_differences.htm', sdpo)
res[44,] <- c('Mean Absolute Differences between all Pairs of Observations', 'http://www.xycoon.com/mean_abs_differences.htm', adpo)
res[45,] <- c('Gini Mean Difference', 'http://www.xycoon.com/gini_mean_difference.htm', gmd)
res[46,] <- c('Leik Measure of Dispersion', 'http://www.xycoon.com/leiks_d.htm', bigd)
res[47,] <- c('Index of Diversity', 'http://www.xycoon.com/diversity.htm', iod)
res[48,] <- c('Index of Qualitative Variation', 'http://www.xycoon.com/qualitative_variation.htm', iod*lx/(lx-1))
res[49,] <- c('Coefficient of Dispersion', 'http://www.xycoon.com/dispersion.htm', sum(axmm)/lx/medx)
res[50,] <- c('Observations', '', lx)
res
(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='Robustness of Central Tendency', xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main='Robustness of Central Tendency', 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='Robustness of Central Tendency', xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main='Robustness of Central Tendency', xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax))
lines(ub,lty=3)
lines(lb,lty=3)
grid()
dev.off()
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')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variability - Ungrouped Data',2,TRUE)
a<-table.row.end(a)
for (i in 1:num) {
a<-table.row.start(a)
if (res[i,1] != '') {
a<-table.element(a,hyperlink(res[i,2],res[i,1],''),header=TRUE)
} else {
a<-table.element(a,res[i,2],header=TRUE)
}
a<-table.element(a,res[i,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
lx <- length(x)
qval <- array(NA,dim=c(99,8))
mystep <- 25
mystart <- 25
if (lx>10){
mystep=10
mystart=10
}
if (lx>20){
mystep=5
mystart=5
}
if (lx>50){
mystep=2
mystart=2
}
if (lx>=100){
mystep=1
mystart=1
}
for (perc in seq(mystart,99,mystep)) {
qval[perc,1] <- q1(x,lx,perc/100,i,f)
qval[perc,2] <- q2(x,lx,perc/100,i,f)
qval[perc,3] <- q3(x,lx,perc/100,i,f)
qval[perc,4] <- q4(x,lx,perc/100,i,f)
qval[perc,5] <- q5(x,lx,perc/100,i,f)
qval[perc,6] <- q6(x,lx,perc/100,i,f)
qval[perc,7] <- q7(x,lx,perc/100,i,f)
qval[perc,8] <- q8(x,lx,perc/100,i,f)
}
bitmap(file='test3.png')
myqqnorm <- qqnorm(x,col=2)
qqline(x)
grid()
dev.off()
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Percentiles - Ungrouped Data',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p',1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_1.htm', 'Weighted Average at Xnp',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),1,TRUE)
a<-table.row.end(a)
for (perc in seq(mystart,99,mystep)) {
a<-table.row.start(a)
a<-table.element(a,round(perc/100,2),1,TRUE)
for (j in 1:8) {
a<-table.element(a,round(qval[perc,j],6))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
bitmap(file='histogram1.png')
myhist<-hist(x)
dev.off()
myhist
n <- length(x)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/histogram.htm','Frequency Table (Histogram)',''),6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bins',header=TRUE)
a<-table.element(a,'Midpoint',header=TRUE)
a<-table.element(a,'Abs. Frequency',header=TRUE)
a<-table.element(a,'Rel. Frequency',header=TRUE)
a<-table.element(a,'Cumul. Rel. Freq.',header=TRUE)
a<-table.element(a,'Density',header=TRUE)
a<-table.row.end(a)
crf <- 0
mybracket <- '['
mynumrows <- (length(myhist$breaks)-1)
for (i in 1:mynumrows) {
a<-table.row.start(a)
if (i == 1)
dum <- paste('[',myhist$breaks[i],sep='')
else
dum <- paste(mybracket,myhist$breaks[i],sep='')
dum <- paste(dum,myhist$breaks[i+1],sep=',')
if (i==mynumrows)
dum <- paste(dum,']',sep='')
else
dum <- paste(dum,mybracket,sep='')
a<-table.element(a,dum,header=TRUE)
a<-table.element(a,myhist$mids[i])
a<-table.element(a,myhist$counts[i])
rf <- myhist$counts[i]/n
crf <- crf + rf
a<-table.element(a,round(rf,6))
a<-table.element(a,round(crf,6))
a<-table.element(a,round(myhist$density[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
bitmap(file='density1.png')
mydensity1<-density(x,kernel='gaussian',na.rm=TRUE)
plot(mydensity1,main='Gaussian Kernel')
grid()
dev.off()
mydensity1
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Properties of Density Trace',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bandwidth',header=TRUE)
a<-table.element(a,mydensity1$bw)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Observations',header=TRUE)
a<-table.element(a,mydensity1$n)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable4.tab')
 





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Software written by Ed van Stee & Patrick Wessa


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