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Analyse temperatuur 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 14:20:07 +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/t12925955504vj3ufr31w1fw73.htm/, Retrieved Fri, 17 Dec 2010 15:19:10 +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/t12925955504vj3ufr31w1fw73.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 «
15,73 16,17 12,00 12,86 10,30 12,97 12,06 10,49 5,97 9,26 9,74 5,46 2,71 3,90 1,51 5,01 2,96 -1,97 -4,61 4,27 4,01 0,04 3,04 2,29 4,37 6,39 5,74 7,64 7,07 6,23 10,20 14,07 12,83 12,04 11,97 12,63 13,56 15,66 16,34 14,09 15,03 16,09 19,27 22,50 16,07 19,11 18,66 18,29 20,26 19,20 20,10 17,93 16,11 16,90 16,14 15,04 13,41 14,14 9,59 10,74 11,67 8,09 10,07 11,80 12,01 6,61 6,47 -3,11 1,94 1,10 -3,40 1,64 3,11 -0,16 3,80 -2,39 1,51 7,24 2,00 2,11 10,54 11,10 7,34 9,53 9,71 10,14 13,93 8,33 8,31 13,83 14,50 16,71 16,49 14,57 19,04 22,84 22,23 19,56 19,76 18,36 16,99 16,87 18,50 16,51
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean10.57096153846150.64745687252358416.3268967974025
Geometric MeanNaN
Harmonic Mean3.32858959136114
Quadratic Mean12.4468015662968
Winsorized Mean ( 1 / 34 )10.57932692307690.64431261553314216.4195557684727
Winsorized Mean ( 2 / 34 )10.57971153846150.64222455550996916.4735394305510
Winsorized Mean ( 3 / 34 )10.54365384615380.62872538796716716.7698872161728
Winsorized Mean ( 4 / 34 )10.55365384615380.62462163390581716.8960747967708
Winsorized Mean ( 5 / 34 )10.62432692307690.60621420637501317.5256977011597
Winsorized Mean ( 6 / 34 )10.62432692307690.60255907715073217.6320087539221
Winsorized Mean ( 7 / 34 )10.67615384615380.58803970026741918.1554984149858
Winsorized Mean ( 8 / 34 )10.70230769230770.58236612456341218.3772840501858
Winsorized Mean ( 9 / 34 )10.69451923076920.58126705938022318.398632879992
Winsorized Mean ( 10 / 34 )10.70028846153850.57841361713365518.4993716340290
Winsorized Mean ( 11 / 34 )10.69182692307690.56807693437475218.8210896730824
Winsorized Mean ( 12 / 34 )10.68028846153850.56454399531167818.9184342588606
Winsorized Mean ( 13 / 34 )10.67653846153850.56015490778003519.0599748627588
Winsorized Mean ( 14 / 34 )10.69134615384620.55532307737980919.2524794832791
Winsorized Mean ( 15 / 34 )10.70.53969279771680619.8260937430828
Winsorized Mean ( 16 / 34 )10.59384615384620.516045153998320.5289131615042
Winsorized Mean ( 17 / 34 )10.59221153846150.51240615011174920.6715152348416
Winsorized Mean ( 18 / 34 )10.59913461538460.51005560824134620.7803510913841
Winsorized Mean ( 19 / 34 )10.69596153846150.48894272011705621.8756944287888
Winsorized Mean ( 20 / 34 )10.67673076923080.48175539895208922.1621403568174
Winsorized Mean ( 21 / 34 )10.69490384615380.47825661261808622.3622707224214
Winsorized Mean ( 22 / 34 )10.71817307692310.46711820432464822.9453122950308
Winsorized Mean ( 23 / 34 )10.70269230769230.45979076464312123.2773103130931
Winsorized Mean ( 24 / 34 )10.84346153846150.43958951610194624.6672432832698
Winsorized Mean ( 25 / 34 )10.94442307692310.42499426317182225.7519313207725
Winsorized Mean ( 26 / 34 )11.00942307692310.41570939186677226.4834600620508
Winsorized Mean ( 27 / 34 )11.06394230769230.40779643863971827.1310420086016
Winsorized Mean ( 28 / 34 )11.04240384615380.38852348022025028.421458182898
Winsorized Mean ( 29 / 34 )11.06750.38089527781097629.0565429521875
Winsorized Mean ( 30 / 34 )10.91173076923080.3576249198730230.5116622552621
Winsorized Mean ( 31 / 34 )10.95048076923080.3522781766417131.0847548764513
Winsorized Mean ( 32 / 34 )10.95048076923080.3197237704511234.2498174401609
Winsorized Mean ( 33 / 34 )10.98221153846150.31095347588666135.3178606772173
Winsorized Mean ( 34 / 34 )10.89721153846150.29436421077842737.0194851800922
Trimmed Mean ( 1 / 34 )10.59950980392160.63158014877169816.7825252654561
Trimmed Mean ( 2 / 34 )10.62050.61723959257849317.2064464556353
Trimmed Mean ( 3 / 34 )10.64214285714290.60229552901460817.6693040948752
Trimmed Mean ( 4 / 34 )10.67770833333330.59104559964432418.0657944831311
Trimmed Mean ( 5 / 34 )10.71202127659570.57963212995890418.4807237606984
Trimmed Mean ( 6 / 34 )10.73184782608700.57176942923192418.769537644752
Trimmed Mean ( 7 / 34 )10.75255555555560.56363876127468719.0770335440350
Trimmed Mean ( 8 / 34 )10.76545454545450.55743206728880719.3125856533778
Trimmed Mean ( 9 / 34 )10.7750.551349538196519.5429564251486
Trimmed Mean ( 10 / 34 )10.78607142857140.54446798072830519.8102952062369
Trimmed Mean ( 11 / 34 )10.79695121951220.53695550949934820.1077203390262
Trimmed Mean ( 12 / 34 )10.8093750.52991880031642320.3981723115797
Trimmed Mean ( 13 / 34 )10.82371794871790.52222933674040820.7259860510254
Trimmed Mean ( 14 / 34 )10.83921052631580.51388348611702421.0927395394983
Trimmed Mean ( 15 / 34 )10.83921052631580.50481117026082221.4718119662754
Trimmed Mean ( 16 / 34 )10.86888888888890.49650667601285621.8907205360668
Trimmed Mean ( 17 / 34 )10.89442857142860.49009660752617622.2291450381988
Trimmed Mean ( 18 / 34 )10.92161764705880.48287875090193522.6177226201383
Trimmed Mean ( 19 / 34 )10.94984848484850.4744949218719323.0768507314056
Trimmed Mean ( 20 / 34 )10.97156250.46758626542339723.4642531470964
Trimmed Mean ( 21 / 34 )10.99629032258060.46015909199491323.8967142318297
Trimmed Mean ( 22 / 34 )11.02116666666670.45155056724959124.4073808472813
Trimmed Mean ( 23 / 34 )11.04586206896550.44269120296051824.9516186341536
Trimmed Mean ( 24 / 34 )11.07357142857140.43283574375976325.5837730321958
Trimmed Mean ( 25 / 34 )11.09203703703700.42402272888047126.1590624312118
Trimmed Mean ( 26 / 34 )11.10384615384620.41542062438190526.7291643749448
Trimmed Mean ( 27 / 34 )11.11140.40609217914062827.361768019059
Trimmed Mean ( 28 / 34 )11.11520833333330.39557010541292828.0992122034358
Trimmed Mean ( 29 / 34 )11.12108695652170.38563959398682828.8380320120905
Trimmed Mean ( 30 / 34 )11.12108695652170.37415181802701929.7234609607554
Trimmed Mean ( 31 / 34 )11.14309523809520.36375437592432230.6335702760413
Trimmed Mean ( 32 / 34 )11.159250.35099024522569031.7936186312657
Trimmed Mean ( 33 / 34 )11.17710526315790.34133823905569332.7449549575201
Trimmed Mean ( 34 / 34 )11.19416666666670.33005852655609633.9157021134073
Median11.385
Midrange9.115
Midmean - Weighted Average at Xnp10.9973584905660
Midmean - Weighted Average at X(n+1)p11.1038461538462
Midmean - Empirical Distribution Function10.9973584905660
Midmean - Empirical Distribution Function - Averaging11.1038461538462
Midmean - Empirical Distribution Function - Interpolation11.1038461538462
Midmean - Closest Observation10.9973584905660
Midmean - True Basic - Statistics Graphics Toolkit11.1038461538462
Midmean - MS Excel (old versions)11.0920370370370
Number of observations104


Variability - Ungrouped Data
Absolute range27.45
Relative range (unbiased)4.1573332047279
Relative range (biased)4.17746568666234
Variance (unbiased)43.5968417849141
Variance (biased)43.1776413831361
Standard Deviation (unbiased)6.60279045441502
Standard Deviation (biased)6.57096959231559
Coefficient of Variation (unbiased)0.624615881004896
Coefficient of Variation (biased)0.621605666467301
Mean Squared Error (MSE versus 0)154.922869230769
Mean Squared Error (MSE versus Mean)43.1776413831361
Mean Absolute Deviation from Mean (MAD Mean)5.5078476331361
Mean Absolute Deviation from Median (MAD Median)5.49442307692308
Median Absolute Deviation from Mean5.50903846153846
Median Absolute Deviation from Median4.975
Mean Squared Deviation from Mean43.1776413831361
Mean Squared Deviation from Median43.8403
Interquartile Difference (Weighted Average at Xnp)10.63
Interquartile Difference (Weighted Average at X(n+1)p)10.575
Interquartile Difference (Empirical Distribution Function)10.63
Interquartile Difference (Empirical Distribution Function - Averaging)10.5
Interquartile Difference (Empirical Distribution Function - Interpolation)10.425
Interquartile Difference (Closest Observation)10.63
Interquartile Difference (True Basic - Statistics Graphics Toolkit)10.425
Interquartile Difference (MS Excel (old versions))10.65
Semi Interquartile Difference (Weighted Average at Xnp)5.315
Semi Interquartile Difference (Weighted Average at X(n+1)p)5.2875
Semi Interquartile Difference (Empirical Distribution Function)5.315
Semi Interquartile Difference (Empirical Distribution Function - Averaging)5.25
Semi Interquartile Difference (Empirical Distribution Function - Interpolation)5.2125
Semi Interquartile Difference (Closest Observation)5.315
Semi Interquartile Difference (True Basic - Statistics Graphics Toolkit)5.2125
Semi Interquartile Difference (MS Excel (old versions))5.325
Coefficient of Quartile Variation (Weighted Average at Xnp)0.493271461716937
Coefficient of Quartile Variation (Weighted Average at X(n+1)p)0.488791310376705
Coefficient of Quartile Variation (Empirical Distribution Function)0.493271461716937
Coefficient of Quartile Variation (Empirical Distribution Function - Averaging)0.483870967741935
Coefficient of Quartile Variation (Empirical Distribution Function - Interpolation)0.478980013783597
Coefficient of Quartile Variation (Closest Observation)0.493271461716937
Coefficient of Quartile Variation (True Basic - Statistics Graphics Toolkit)0.478980013783597
Coefficient of Quartile Variation (MS Excel (old versions))0.493741307371349
Number of all Pairs of Observations5356
Squared Differences between all Pairs of Observations87.1936835698284
Mean Absolute Differences between all Pairs of Observations7.58608663181479
Gini Mean Difference7.58608663181478
Leik Measure of Dispersion0.374475675345345
Index of Diversity0.986669292263613
Index of Qualitative Variation0.996248605780735
Coefficient of Dispersion0.483781083279411
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)
0.01-4.5616-4.5495-3.4-3.4-3.3913-4.61-3.4605-4.61
0.02-3.3768-3.371-3.11-3.11-3.0668-3.4-3.139-3.4
0.03-3.0236-3.002-2.39-2.39-2.3522-3.11-2.498-3.11
0.04-2.3228-2.306-1.97-1.97-1.7528-2.39-2.054-2.39
0.05-1.608-1.5175-0.16-0.16-0.13-1.97-0.6125-1.97
0.06-0.112-0.10.040.040.2308-0.16-0.02-0.16
0.070.33680.4111.11.11.18610.040.7290.04
0.081.23121.2641.511.511.511.11.3461.1
0.091.511.511.511.511.54511.511.511.51
0.11.5621.5751.641.641.731.511.5751.575
0.111.7721.8051.941.941.95981.641.7751.94
0.121.96881.976222.03961.941.9642
0.132.05722.07152.112.112.18022.112.03852.11
0.142.21082.2362.292.292.46642.292.1642.29
0.152.5422.6052.712.712.82252.712.3952.71
0.162.872.912.962.962.99842.962.762.96
0.173.01443.0283.043.043.07573.042.9723.04
0.183.09043.1033.113.113.48263.113.0473.11
0.193.63443.76553.83.83.8573.83.14453.8
0.23.883.93.93.93.9663.93.93.9
0.213.99244.0234.014.014.17384.014.2574.01
0.224.23884.284.274.274.3364.274.364.27
0.234.3624.4664.374.374.81164.374.9144.37
0.244.98445.15.015.015.3345.015.375.01
0.255.465.535.465.65.675.465.675.46
0.265.74925.8095.975.975.91945.745.9015.74
0.275.99086.0616.236.236.18065.976.1395.97
0.286.24926.2946.396.396.36446.236.3266.23
0.296.40286.4266.476.476.45966.396.4346.39
0.36.4986.546.616.616.5966.476.546.54
0.316.72046.8637.077.077.03786.616.8177.07
0.327.11767.1727.247.247.23327.077.1387.24
0.337.2727.3057.347.347.3397.247.2757.34
0.347.4487.557.647.647.6497.347.437.64
0.357.827.97758.098.098.1017.647.75258.09
0.368.18688.2668.318.318.31168.098.1348.31
0.378.31968.3278.338.338.43238.318.3138.33
0.388.81369.1679.269.269.29789.268.4239.26
0.399.41129.51659.539.539.54029.539.27359.53
0.49.5669.599.599.599.6149.599.599.59
0.419.66689.71159.719.719.71699.719.73859.71
0.429.73049.7739.749.749.82589.7410.0379.74
0.439.977610.080510.0710.0710.090310.0710.129510.07
0.4410.123210.15210.1410.1410.159210.1410.18810.14
0.4510.18810.22510.210.210.23510.210.27510.2
0.4610.28410.35710.310.310.372210.310.43310.3
0.4710.467210.507510.4910.4910.510510.4910.522510.49
0.4810.53610.6210.5410.5410.62810.5410.6610.54
0.4910.73210.90210.7410.7410.909210.7410.93810.74
0.511.111.38511.111.38511.38511.111.38511.385
0.5111.675211.741511.811.811.738911.6711.728511.8
0.5211.813611.90211.9711.9711.895211.811.86811.97
0.5311.973611.9895121211.987711.9711.980512
0.5412.001612.00712.0112.0112.00621212.00312.01
0.5512.01612.032512.0412.0412.029512.0112.017512.04
0.5612.044812.05612.0612.0612.053612.0412.04412.06
0.5712.219612.544512.6312.6312.464712.0612.145512.63
0.5812.69412.8112.8312.8312.77812.6312.6512.83
0.5912.840812.858512.8612.8612.853112.8312.831512.86
0.612.90412.9712.9712.9712.94812.8612.9712.97
0.6113.163613.417513.4113.4113.335212.9713.552513.41
0.6213.48213.58713.5613.5613.53913.4113.80313.56
0.6313.700413.84513.8313.8313.800313.8313.91513.83
0.6413.88613.95813.9313.9313.92213.9314.04213.93
0.6514.01414.07514.0714.0714.06314.0714.08514.07
0.6614.082814.10514.0914.0914.089614.0914.12514.09
0.6714.12414.26614.1414.1414.143614.1414.37414.14
0.6814.399214.52814.514.514.502814.514.54214.5
0.6914.553214.77714.5714.5714.602214.5714.82314.57
0.714.93815.03515.0315.0315.03115.0315.03515.035
0.7115.038415.38115.0415.0415.120615.0415.31915.66
0.7215.585615.70215.6615.6615.671215.6615.68815.73
0.7315.724415.95115.7315.7315.794615.7315.84916.07
0.7416.056416.08416.0716.0716.074416.0716.07616.09
0.7516.0916.10516.0916.116.09516.0916.09516.11
0.7616.111216.13416.1416.1416.118416.1116.11616.14
0.7716.142416.165516.1716.1716.149316.1416.144516.17
0.7816.190416.32316.3416.3416.227816.1716.18716.34
0.7916.36416.482516.4916.4916.395516.3416.347516.49
0.816.49416.5116.5116.5116.49816.4916.5116.51
0.8116.55816.71816.7116.7116.59616.5116.86216.71
0.8216.754816.87316.8716.8716.783616.7116.89716.87
0.8316.879616.913516.916.916.884716.8716.976516.9
0.8416.932417.17816.9916.9916.946816.917.74216.99
0.8517.36618.0217.9317.9317.50716.9918.217.93
0.8618.088418.31118.2918.2918.138817.9318.33918.29
0.8718.323618.40918.3618.3618.332718.2918.45118.36
0.8818.432818.56418.518.518.449618.518.59618.5
0.8918.589618.83118.6618.6618.607218.6618.86918.66
0.918.88819.07519.0419.0418.92619.0419.07519.075
0.9119.084819.159519.1119.1119.091119.1119.150519.2
0.9219.171219.24219.219.219.178419.219.22819.27
0.9319.250419.458519.2719.2719.255319.2719.371519.56
0.9419.490419.719.5619.5619.507819.5619.6219.76
0.9519.7220.01519.7619.7619.7319.7619.84520.1
0.9620.045620.22820.120.120.059220.120.13220.26
0.9720.240821.934520.2620.2620.245620.2620.555522.23
0.9822.072422.47322.2322.2322.111822.2322.25722.5
0.9922.489222.82322.522.522.491922.522.51722.84


Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[-5,0[-2.560.0576920.0576920.011538
[0,5[2.5180.1730770.2307690.034615
[5,10[7.5200.1923080.4230770.038462
[10,15[12.5280.2692310.6923080.053846
[15,20[17.5270.2596150.9519230.051923
[20,25]22.550.04807710.009615


Properties of Density Trace
Bandwidth2.3472716655934
#Observations104
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/164sz1292595599.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/164sz1292595599.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/2zd9k1292595599.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/2zd9k1292595599.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/5fqga1292595599.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/5fqga1292595599.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/7jqef1292595599.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/7jqef1292595599.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/98rc91292595599.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/17/t12925955504vj3ufr31w1fw73/98rc91292595599.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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