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Analyse zonneschijnduur tijdreeks

*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: Fri, 17 Dec 2010 14:31:21 +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/17/t1292596224hejy2bocg2rhbfc.htm/, Retrieved Fri, 17 Dec 2010 15:30:25 +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/17/t1292596224hejy2bocg2rhbfc.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 «
142,86 380,71 460,00 361,43 140,00 275,00 274,29 212,86 172,86 186,43 77,14 17,86 37,14 42,86 85,00 45,00 206,43 178,57 285,71 58,57 88,57 309,29 58,57 132,14 3,57 102,86 185,71 177,14 530,00 162,86 553,57 258,57 326,43 580,00 286,43 310,71 148,57 627,14 477,86 385,71 327,86 402,14 567,86 678,57 253,57 459,29 331,43 421,43 595,00 425,71 603,57 420,00 308,57 325,00 319,29 452,86 83,57 99,43 312,71 128,00 152,67 135,00 57,71 190,43 12,86 32,43 38,29 210,14 109,14 71,43 102,29 48,43 70,43 139,86 83,14 27,71 96,14 40,57 364,71 207,43 156,29 229,00 160,43 357,43 542,00 578,43 427,43 130,29 174,29 679,14 389,43 532,57 253,71 414,14 719,71 639,86 619,71 507,14 463,86 254,14 226,29 299,57 274,00 253,29
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean272.45423076923118.327616389747214.8657755037718
Geometric Mean194.569624447562
Harmonic Mean94.4263927222034
Quadratic Mean329.89265630598
Winsorized Mean ( 1 / 34 )272.15346153846218.226578185903814.931681567577
Winsorized Mean ( 2 / 34 )272.23865384615418.211042454275614.9490977537224
Winsorized Mean ( 3 / 34 )271.40615384615417.940695365354515.1279617829234
Winsorized Mean ( 4 / 34 )271.09846153846217.820694718385115.2125641464907
Winsorized Mean ( 5 / 34 )270.96769230769217.722701547309415.2892995226695
Winsorized Mean ( 6 / 34 )270.10288461538517.539293493639715.3998725611914
Winsorized Mean ( 7 / 34 )269.67951923076917.414094779836215.486278364755
Winsorized Mean ( 8 / 34 )268.70182692307717.185487322995415.6353917624166
Winsorized Mean ( 9 / 34 )268.75115384615417.138071492962815.6815283421188
Winsorized Mean ( 10 / 34 )268.06461538461516.919805452765415.8432445416092
Winsorized Mean ( 11 / 34 )267.53471153846216.540577053524116.1744484894776
Winsorized Mean ( 12 / 34 )266.29894230769216.306675267189616.3306705962011
Winsorized Mean ( 13 / 34 )265.12019230769216.114954075222916.4518118432197
Winsorized Mean ( 14 / 34 )266.37076923076915.86408427481716.7908064919708
Winsorized Mean ( 15 / 34 )263.21788461538515.325765710008717.1748602709936
Winsorized Mean ( 16 / 34 )259.59173076923114.542138876001517.8510006665957
Winsorized Mean ( 17 / 34 )258.28403846153814.094387635429918.3253111197448
Winsorized Mean ( 18 / 34 )257.69038461538513.991203649896418.4180282886021
Winsorized Mean ( 19 / 34 )257.82192307692313.941495647818.4931322714724
Winsorized Mean ( 20 / 34 )257.27192307692313.686568261825318.79740181434
Winsorized Mean ( 21 / 34 )253.66557692307712.808876777686419.8038892344544
Winsorized Mean ( 22 / 34 )253.99769230769212.678254876688420.0341210030979
Winsorized Mean ( 23 / 34 )253.68365384615412.479574725364120.3279085568962
Winsorized Mean ( 24 / 34 )253.48519230769212.42107690929620.4076662723168
Winsorized Mean ( 25 / 34 )253.58615384615412.061731932391921.0240250129541
Winsorized Mean ( 26 / 34 )255.30115384615411.13968120189422.9181741576902
Winsorized Mean ( 27 / 34 )252.59596153846210.656641258090923.7031495591242
Winsorized Mean ( 28 / 34 )252.092510.476771037905524.0620415477171
Winsorized Mean ( 29 / 34 )251.49576923076910.216370715447324.6169384643123
Winsorized Mean ( 30 / 34 )248.2823076923089.502935650768726.1269061284472
Winsorized Mean ( 31 / 34 )247.3463461538469.3824472527118226.362668447974
Winsorized Mean ( 32 / 34 )246.9955769230779.140058295370627.0234137399516
Winsorized Mean ( 33 / 34 )240.5574038461547.9979152354957230.0775135473468
Winsorized Mean ( 34 / 34 )240.7306730769237.7205326420384831.1805783665934
Trimmed Mean ( 1 / 34 )270.70549019607817.966752257293615.0670241521355
Trimmed Mean ( 2 / 34 )269.199617.672665976746615.2325404867725
Trimmed Mean ( 3 / 34 )267.58704081632717.34838855713515.4243167851042
Trimmed Mean ( 4 / 34 )266.20791666666717.093260767986415.5738521912239
Trimmed Mean ( 5 / 34 )264.85521276595716.841617351399315.7262338432094
Trimmed Mean ( 6 / 34 )263.47326086956516.580709729069315.8903487953621
Trimmed Mean ( 7 / 34 )262.19644444444416.326527973307716.0595348180036
Trimmed Mean ( 8 / 34 )260.93306818181816.061747095461516.2456217639953
Trimmed Mean ( 9 / 34 )259.75872093023315.803229866755716.4370652784512
Trimmed Mean ( 10 / 34 )258.52166666666715.512904908449716.6649423942419
Trimmed Mean ( 11 / 34 )257.31134146341515.216318812321216.9102228099388
Trimmed Mean ( 12 / 34 )256.10312514.937304735860317.1452032029023
Trimmed Mean ( 13 / 34 )254.97025641025614.651469570384617.4023673997613
Trimmed Mean ( 14 / 34 )253.90184210526314.348787611490617.6950031584506
Trimmed Mean ( 15 / 34 )253.90184210526314.031433698527818.0952173213706
Trimmed Mean ( 16 / 34 )251.632513.745617330770918.3063804225581
Trimmed Mean ( 17 / 34 )250.89342857142913.530172802352518.5432538250956
Trimmed Mean ( 18 / 34 )250.22852941176513.339300566662318.758744370536
Trimmed Mean ( 19 / 34 )249.57530303030313.123702280557319.0171414814894
Trimmed Mean ( 20 / 34 )248.8712.869309090555519.338256486717
Trimmed Mean ( 21 / 34 )248.16532258064512.600075127401819.6955430877513
Trimmed Mean ( 22 / 34 )247.71133333333312.40987385374719.9608260529208
Trimmed Mean ( 23 / 34 )247.19896551724112.192887719117820.2740295171952
Trimmed Mean ( 24 / 34 )246.67535714285711.953930372949220.6355022529715
Trimmed Mean ( 25 / 34 )246.12888888888911.664130534966321.1013489733377
Trimmed Mean ( 26 / 34 )245.53230769230811.362139426138821.6096897321516
Trimmed Mean ( 27 / 34 )244.750811.139158887529721.9721078109406
Trimmed Mean ( 28 / 34 )244.1212510.938775034597622.3170555412177
Trimmed Mean ( 29 / 34 )243.47760869565210.707790022577622.7383622747808
Trimmed Mean ( 30 / 34 )243.47760869565210.452767911437623.2931230042175
Trimmed Mean ( 31 / 34 )242.37357142857110.25953260488923.624231313722
Trimmed Mean ( 32 / 34 )241.956510.018974073265624.1498279395323
Trimmed Mean ( 33 / 34 )241.5255263157899.7417306048481524.7928767600687
Trimmed Mean ( 34 / 34 )241.6102777777789.6241860631820625.1044894801103
Median253.43
Midrange361.64
Midmean - Weighted Average at Xnp242.958867924528
Midmean - Weighted Average at X(n+1)p245.532307692308
Midmean - Empirical Distribution Function242.958867924528
Midmean - Empirical Distribution Function - Averaging245.532307692308
Midmean - Empirical Distribution Function - Interpolation245.532307692308
Midmean - Closest Observation242.958867924528
Midmean - True Basic - Statistics Graphics Toolkit245.532307692308
Midmean - MS Excel (old versions)246.128888888889
Number of observations104


Variability - Ungrouped Data
Absolute range716.14
Relative range (unbiased)3.83155687109502
Relative range (biased)3.85011173443876
Variance (unbiased)34933.7583430919
Variance (biased)34597.8568205621
Standard Deviation (unbiased)186.905747217928
Standard Deviation (biased)186.004991386151
Coefficient of Variation (unbiased)0.686007872552499
Coefficient of Variation (biased)0.68270179127333
Mean Squared Error (MSE versus 0)108829.164684615
Mean Squared Error (MSE versus Mean)34597.8568205621
Mean Absolute Deviation from Mean (MAD Mean)155.901863905325
Mean Absolute Deviation from Median (MAD Median)154.559038461538
Median Absolute Deviation from Mean143.309230769231
Median Absolute Deviation from Median146.5
Mean Squared Deviation from Mean34597.8568205621
Mean Squared Deviation from Median34959.7781769231
Interquartile Difference (Weighted Average at Xnp)293
Interquartile Difference (Weighted Average at X(n+1)p)297.285
Interquartile Difference (Empirical Distribution Function)293
Interquartile Difference (Empirical Distribution Function - Averaging)289.57
Interquartile Difference (Empirical Distribution Function - Interpolation)281.855
Interquartile Difference (Closest Observation)293
Interquartile Difference (True Basic - Statistics Graphics Toolkit)281.855
Interquartile Difference (MS Excel (old versions))305
Semi Interquartile Difference (Weighted Average at Xnp)146.5
Semi Interquartile Difference (Weighted Average at X(n+1)p)148.6425
Semi Interquartile Difference (Empirical Distribution Function)146.5
Semi Interquartile Difference (Empirical Distribution Function - Averaging)144.785
Semi Interquartile Difference (Empirical Distribution Function - Interpolation)140.9275
Semi Interquartile Difference (Closest Observation)146.5
Semi Interquartile Difference (True Basic - Statistics Graphics Toolkit)140.9275
Semi Interquartile Difference (MS Excel (old versions))152.5
Coefficient of Quartile Variation (Weighted Average at Xnp)0.573071506806447
Coefficient of Quartile Variation (Weighted Average at X(n+1)p)0.566262535833675
Coefficient of Quartile Variation (Empirical Distribution Function)0.573071506806447
Coefficient of Quartile Variation (Empirical Distribution Function - Averaging)0.549771221355205
Coefficient of Quartile Variation (Empirical Distribution Function - Interpolation)0.533386951790699
Coefficient of Quartile Variation (Closest Observation)0.573071506806447
Coefficient of Quartile Variation (True Basic - Statistics Graphics Toolkit)0.533386951790699
Coefficient of Quartile Variation (MS Excel (old versions))0.582861947714417
Number of all Pairs of Observations5356
Squared Differences between all Pairs of Observations69867.5166861843
Mean Absolute Differences between all Pairs of Observations212.472378640776
Gini Mean Difference212.472378640775
Leik Measure of Dispersion0.387443202051751
Index of Diversity0.985903060232617
Index of Qualitative Variation0.995474934603808
Coefficient of Dispersion0.615167359449653
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)
0.013.94164.034512.8612.8613.013.5712.39553.57
0.0213.2613.3617.8617.8618.45112.8617.3612.86
0.0319.04219.337527.7127.7128.134817.8626.232517.86
0.0428.465228.65432.4332.4332.995227.7131.48627.71
0.0533.37233.607537.1437.1437.312532.4335.962532.43
0.0637.41637.48538.2938.2938.700437.1437.94537.14
0.0738.928439.08840.5740.5741.050938.2939.77238.29
0.0841.302841.48642.8642.8643.373640.5741.94440.57
0.0943.630443.823454545.926142.8644.03742.86
0.146.37246.71548.4348.4351.2144546.71546.715
0.1152.513253.53457.7157.7157.993848.4352.60657.71
0.1258.122858.22658.5758.5758.5757.7158.05458.57
0.1358.5758.5758.5758.5763.195458.5758.5758.57
0.1465.211666.87270.4370.4370.8570.4362.12870.43
0.1571.0371.1871.4371.4373.999571.4370.6871.43
0.1675.084475.99877.1477.1480.0277.1472.57277.14
0.1781.2282.2483.1483.1483.359383.1478.0483.14
0.1883.449683.52783.5783.5784.342283.5783.18383.57
0.1984.656884.9285858587.03498583.641585
0.287.85688.5788.5788.5793.11288.5788.5788.57
0.2194.928896.304596.1496.1498.212796.1499.265596.14
0.2299.035299.71699.4399.43101.317699.43102.00499.43
0.23102.0612102.3755102.29102.29102.6833102.29102.7745102.29
0.24102.8372104.116102.86102.86107.3816102.86107.884102.86
0.25109.14113.855109.14118.57123.285109.14123.285109.14
0.26128.0916128.687130.29130.29129.7862128129.603128
0.27130.438130.9375132.14132.14131.7885130.29131.4925130.29
0.28132.4832133.284135135134.5424132.14133.856132.14
0.29135.7776137.187139.86139.86139.2282135137.673135
0.3139.888139.93140140139.986139.86139.93139.93
0.31140.6864141.573142.86142.86142.6598140141.287142.86
0.32144.4588146.286148.57148.57148.3416142.86145.144148.57
0.33149.882151.235152.67152.67152.629148.57150.005152.67
0.34153.9732155.204156.29156.29156.3728152.67153.756156.29
0.35157.946159.395160.43160.43160.5515156.29157.325160.43
0.36161.4992162.374162.86162.86163.66160.43160.916162.86
0.37167.66171.36172.86172.86173.0173162.86164.36172.86
0.38173.6036174.147174.29174.29174.689174.29173.003174.29
0.39175.886176.9975177.14177.14177.3831177.14174.4325177.14
0.4177.998178.57178.57178.57179.998178.57178.57178.57
0.41183.1396185.746185.71185.71185.8756185.71186.394185.71
0.42186.1996186.83186.43186.43187.47186.43190.03186.43
0.43189.31192.83190.43190.43195.07190.43204.03190.43
0.44202.59206.63206.43206.43206.75206.43207.23206.43
0.45207.23208.1075207.43207.43208.3785207.43209.4625207.43
0.46209.7064210.956210.14210.14211.1736210.14212.044210.14
0.47212.5336217.5605212.86212.86218.3663212.86221.5895212.86
0.48225.2156227.374226.29226.29227.4824226.29227.916226.29
0.49228.8916239.9305229229240.4163229242.3595229
0.5253.29253.43253.29253.43253.43253.29253.43253.43
0.51253.5756253.647253.71253.71253.6442253.57253.633253.71
0.52253.7444253.968254.14254.14253.9508253.71253.882254.14
0.53254.6716257.0195258.57258.57256.7537254.14255.6905258.57
0.54261.0388269.371274274268.1366258.57263.199274
0.55274.058274.2175274.29274.29274.1885274274.0725274.29
0.56274.4604274.858275275274.7728274.29274.432275
0.57277.9988284.1035285.71285.71282.6041275276.6065285.71
0.58285.9404286.358286.43286.43286.2428285.71285.782286.43
0.59291.1604298.913299.57299.57296.5478286.43287.087299.57
0.6303.17308.57308.57308.57306.77299.57308.57308.57
0.61308.8868309.361309.29309.29309.1676308.57310.639309.29
0.62309.9716310.91310.71310.71310.5112309.29312.51310.71
0.63311.75313.697312.71312.71312.49312.71318.303312.71
0.64316.3948320.432319.29319.29318.7636319.29323.858319.29
0.65322.716325.3575325325324.7145325326.0725325
0.66325.9152326.859326.43326.43326.4014326.43327.431326.43
0.67327.4024329.1095327.86327.86327.8957327.86330.1805327.86
0.68330.4304341.83331.43331.43332.47331.43347.03331.43
0.69351.19359.23357.43357.43357.71357.43359.63357.43
0.7360.63363.07361.43361.43361.758361.43363.07363.07
0.71364.1852373.51364.71364.71366.79364.71371.91380.71
0.72378.79383.71380.71380.71381.51380.71382.71385.71
0.73385.31388.128385.71385.71386.4168385.71387.012389.43
0.74389.2812398.327389.43389.43392.2262389.43393.243402.14
0.75402.14411.14402.14408.14405.14402.14405.14414.14
0.76414.3744418.828420420415.7808414.14415.312420
0.77420.1144421.2155421.43421.43420.4433420420.2145421.43
0.78421.9436425.282425.71425.71422.8852421.43421.858425.71
0.79425.9852427.344427.43427.43426.3464425.71425.796427.43
0.8432.516452.86452.86452.86437.602427.43452.86452.86
0.81454.4032459.3255459.29459.29455.6249452.86459.9645459.29
0.82459.4888460.386460460459.6166459.29463.474460
0.83461.2352465.96463.86463.86461.8914460475.76463.86
0.84468.9483.716477.86477.86471.14463.86501.284477.86
0.85489.572512.855507.14507.14493.964477.86524.285507.14
0.86517.1984530.771530530520.3988507.14531.799530
0.87531.2336535.8705532.57532.57531.5677530538.6995532.57
0.88537.4736546.628542542538.6052542548.942542
0.89548.4792560.0005553.57553.57549.7519553.57561.4295553.57
0.9562.144573.145567.86567.86563.573567.86573.145573.145
0.91574.6248579.2935578.43578.43575.5761578.43579.1365580
0.92579.4976589580580579.6232580586595
0.93590.8600.5705595595591.85595597.9995603.57
0.94601.5132614.868603.57603.57602.0274603.57608.412619.71
0.95616.482625.2825619.71619.71617.289619.71621.5675627.14
0.96625.9512637.316627.14627.14626.2484627.14629.684639.86
0.97638.3336672.7635639.86639.86638.7152639.86645.6665678.57
0.98675.4732679.083678.57678.57676.2474678.57678.627679.14
0.99679.1172717.6815679.14679.14679.1229679.14681.1685719.71


Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[0,100[50230.2211540.2211540.002212
[100,200[150220.2115380.4326920.002115
[200,300[250170.1634620.5961540.001635
[300,400[350150.1442310.7403850.001442
[400,500[450110.1057690.8461540.001058
[500,600[55090.0865380.9326920.000865
[600,700[65060.0576920.9903850.000577
[700,800]75010.00961519.6e-05


Properties of Density Trace
Bandwidth66.444417342951
#Observations104
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/1suu81292596273.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/1suu81292596273.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/2llbt1292596273.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/2llbt1292596273.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/5jy0j1292596273.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/5jy0j1292596273.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/7ngh71292596273.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/7ngh71292596273.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/9jqef1292596273.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596224hejy2bocg2rhbfc/9jqef1292596273.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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