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*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: Wed, 28 Oct 2009 13:03:12 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4.htm/, Retrieved Wed, 28 Oct 2009 20:04:24 +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/2009/Oct/28/t1256756659ds5omql85vnlas4.htm/},
    year = {2009},
}
@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 = {2009},
    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:
Rob_WS4_P3
 
Dataseries X:
» Textbox « » Textfile « » CSV «
-3,239330138 -3,223530746 -3,221630593 -3,210969321 -3,206067341 -3,182271127 -3,168313052 -3,148127454 -3,131799718 -3,103579844 -3,094097623 -3,074490772 -3,056816215 -3,011372708 -2,994879467 -2,979407923 -2,973954219 -2,952778068 -2,948345387 -2,93872903 -2,933487872 -2,912442759 -2,913216091 -2,908926547 -2,91532832 -2,899600446 -2,896830574 -2,893319019 -2,891843495 -2,87440184 -2,873769788 -2,867582591 -2,858611262 -2,840404629 -2,837788179 -2,836480175 -2,831100673 -2,818610694 -2,816702252 -2,810869877 -2,804972562 -2,792174149 -2,782703876 -2,77455987 -2,77137068 -2,752842706 -2,749832542 -2,745593395 -2,749809303 -2,738906093 -2,738333888 -2,731152116 -2,726919567 -2,70860183 -2,706872587 -2,700479487 -2,701595932 -2,68946872 -2,688926737 -2,690108611 -2,689479855 -2,677507331 -2,674127578 -2,665138738 -2,659483429 -2,647867128 -2,645137451 -2,639138743 -2,634170789 -2,620694256 -2,614828735 -2,605302179 -2,602601643 -2,591099 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean-2.793106507933330.0216991644209116-128.719542087142
Geometric MeanNaN
Harmonic Mean-2.77848896533309
Quadratic Mean2.80059814097166
Winsorized Mean ( 1 / 30 )-2.793028678822220.0216429374814470-129.050351007875
Winsorized Mean ( 2 / 30 )-2.79300633980.0216302404665911-129.125071175881
Winsorized Mean ( 3 / 30 )-2.793073799466670.0214833840419954-130.010886274099
Winsorized Mean ( 4 / 30 )-2.793167120088890.0213868713369609-130.601950892262
Winsorized Mean ( 5 / 30 )-2.791896171977780.0210980179986514-132.329784350181
Winsorized Mean ( 6 / 30 )-2.791319807111110.0208513093555508-133.867842997976
Winsorized Mean ( 7 / 30 )-2.79075484730.0203823658244666-136.920064693866
Winsorized Mean ( 8 / 30 )-2.789645450588890.0200482640008274-139.146484227949
Winsorized Mean ( 9 / 30 )-2.788433713388890.0192770113848489-144.650727113255
Winsorized Mean ( 10 / 30 )-2.788265334388890.0189549257570577-147.099776075393
Winsorized Mean ( 11 / 30 )-2.787145659788890.0183423613574126-151.951300352205
Winsorized Mean ( 12 / 30 )-2.785929244988890.0177705996085335-156.771820105107
Winsorized Mean ( 13 / 30 )-2.781868645877780.0163365797117410-170.284643111586
Winsorized Mean ( 14 / 30 )-2.779714170788890.0158812703791778-175.030970723440
Winsorized Mean ( 15 / 30 )-2.778078356788890.0153633428011450-180.825123330701
Winsorized Mean ( 16 / 30 )-2.777753139366670.0151317702780187-183.570929794103
Winsorized Mean ( 17 / 30 )-2.775925857344440.0142624171677747-194.632215892304
Winsorized Mean ( 18 / 30 )-2.775579428344440.0140658729486522-197.327207381779
Winsorized Mean ( 19 / 30 )-2.775560470355560.0135168815079101-205.340297518425
Winsorized Mean ( 20 / 30 )-2.775699217688890.0131864228235330-210.496755248533
Winsorized Mean ( 21 / 30 )-2.774606513255560.0122160319758276-227.128294911621
Winsorized Mean ( 22 / 30 )-2.775304579366670.0119933649245999-231.403329825659
Winsorized Mean ( 23 / 30 )-2.776639953233330.0117738049673788-235.831998315451
Winsorized Mean ( 24 / 30 )-2.776430210566670.0115615224078627-240.143997703839
Winsorized Mean ( 25 / 30 )-2.777066377233330.0108314947940709-256.388100629794
Winsorized Mean ( 26 / 30 )-2.777899947922220.0105319477867227-263.759373306450
Winsorized Mean ( 27 / 30 )-2.779543133422220.0100775397876285-275.815644690829
Winsorized Mean ( 28 / 30 )-2.780135560222220.00989625598142964-280.928016154711
Winsorized Mean ( 29 / 30 )-2.778195057766670.00876772325603745-316.86618939
Winsorized Mean ( 30 / 30 )-2.778165034766670.00872110415700903-318.556570905519
Trimmed Mean ( 1 / 30 )-2.791748359397730.0212756802978529-131.217818669679
Trimmed Mean ( 2 / 30 )-2.790408490232560.0208529696778841-133.813482364191
Trimmed Mean ( 3 / 30 )-2.789016785107140.0203730808918677-136.897153646528
Trimmed Mean ( 4 / 30 )-2.787532511560980.0198816329873822-140.206416310475
Trimmed Mean ( 5 / 30 )-2.78594777791250.0193442798669584-144.019203458234
Trimmed Mean ( 6 / 30 )-2.784575071589740.0188057764558855-148.070199500764
Trimmed Mean ( 7 / 30 )-2.783243873789470.0182421899456987-152.57180645933
Trimmed Mean ( 8 / 30 )-2.781938878391890.0176957152642375-157.209744667067
Trimmed Mean ( 9 / 30 )-2.780734726486110.0171286448954280-162.344116739110
Trimmed Mean ( 10 / 30 )-2.779634871214290.0166255413663696-167.19063818499
Trimmed Mean ( 11 / 30 )-2.778492604029410.0160935129599886-172.646743500767
Trimmed Mean ( 12 / 30 )-2.777419911166670.0155819750087721-178.245691550852
Trimmed Mean ( 13 / 30 )-2.777419911166670.0150808346760776-184.168845479914
Trimmed Mean ( 14 / 30 )-2.775814618145160.0147504638886727-188.184903138998
Trimmed Mean ( 15 / 30 )-2.775396808933330.0144311993955099-192.319205969593
Trimmed Mean ( 16 / 30 )-2.775119407431030.0141324975895690-196.364399841066
Trimmed Mean ( 17 / 30 )-2.774854858464290.0138040141942150-201.01796618169
Trimmed Mean ( 18 / 30 )-2.774749858574070.0135512595974915-204.759553059386
Trimmed Mean ( 19 / 30 )-2.774670092250.0132660415389064-209.155842314567
Trimmed Mean ( 20 / 30 )-2.774585740640.0130032154486545-213.376895245329
Trimmed Mean ( 21 / 30 )-2.774481352166670.0127253072313246-218.028633944256
Trimmed Mean ( 22 / 30 )-2.774469691195650.0125455098348139-221.152406536438
Trimmed Mean ( 23 / 30 )-2.774392067295450.0123427022010241-224.779956780069
Trimmed Mean ( 24 / 30 )-2.774182636928570.0121083243657154-229.113670326147
Trimmed Mean ( 25 / 30 )-2.77397192690.0118308519584545-234.469329566556
Trimmed Mean ( 26 / 30 )-2.77397192690.0116081813690621-238.966969821228
Trimmed Mean ( 27 / 30 )-2.773272885805560.0113565968430411-244.199289992839
Trimmed Mean ( 28 / 30 )-2.772658155647060.0111003915563925-249.780211946699
Trimmed Mean ( 29 / 30 )-2.77190707706250.0107657656737750-257.474216052721
Trimmed Mean ( 30 / 30 )-2.77125659630.0105901108627974-261.683435820799
Median-2.762106693
Midrange-2.8528650435
Midmean - Weighted Average at Xnp-2.7774770456
Midmean - Weighted Average at X(n+1)p-2.77446969119565
Midmean - Empirical Distribution Function-2.77446969119565
Midmean - Empirical Distribution Function - Averaging-2.77446969119565
Midmean - Empirical Distribution Function - Interpolation-2.77439206729545
Midmean - Closest Observation-2.77446969119565
Midmean - True Basic - Statistics Graphics Toolkit-2.77446969119565
Midmean - MS Excel (old versions)-2.77446969119565
Number of observations90


Variability - Ungrouped Data
Absolute range0.772930189
Relative range (unbiased)3.7547065901286
Relative range (biased)3.77574152552781
Variance (unbiased)0.042376836290918
Variance (biased)0.0419059825543522
Standard Deviation (unbiased)0.205856348677708
Standard Deviation (biased)0.204709507728274
Coefficient of Variation (unbiased)-0.0737015749642946
Coefficient of Variation (biased)-0.0732909780371182
Mean Squared Error (MSE versus 0)7.8433499472139
Mean Squared Error (MSE versus Mean)0.0419059825543522
Mean Absolute Deviation from Mean (MAD Mean)0.168431412571852
Mean Absolute Deviation from Median (MAD Median)0.167284357977778
Median Absolute Deviation from Mean0.142810372
Median Absolute Deviation from Median0.1441161455
Mean Squared Deviation from Mean0.0419059825543522
Mean Squared Deviation from Median0.0428669710802531
Interquartile Difference (Weighted Average at Xnp)0.272134108500000
Interquartile Difference (Weighted Average at X(n+1)p)0.27584739375
Interquartile Difference (Empirical Distribution Function)0.274077348000000
Interquartile Difference (Empirical Distribution Function - Averaging)0.274077348000000
Interquartile Difference (Empirical Distribution Function - Interpolation)0.272384338
Interquartile Difference (Closest Observation)0.274077348000000
Interquartile Difference (True Basic - Statistics Graphics Toolkit)0.27938748525
Interquartile Difference (MS Excel (old versions))0.274077348000000
Semi Interquartile Difference (Weighted Average at Xnp)0.136067054250000
Semi Interquartile Difference (Weighted Average at X(n+1)p)0.137923696875
Semi Interquartile Difference (Empirical Distribution Function)0.137038674000000
Semi Interquartile Difference (Empirical Distribution Function - Averaging)0.137038674000000
Semi Interquartile Difference (Empirical Distribution Function - Interpolation)0.136192169
Semi Interquartile Difference (Closest Observation)0.137038674000000
Semi Interquartile Difference (True Basic - Statistics Graphics Toolkit)0.139693742625
Semi Interquartile Difference (MS Excel (old versions))0.137038674000000
Coefficient of Quartile Variation (Weighted Average at Xnp)-0.0489766042614884
Coefficient of Quartile Variation (Weighted Average at X(n+1)p)-0.0496875425810339
Coefficient of Quartile Variation (Empirical Distribution Function)-0.0493623617715639
Coefficient of Quartile Variation (Empirical Distribution Function - Averaging)-0.0493623617715639
Coefficient of Quartile Variation (Empirical Distribution Function - Interpolation)-0.0490459049030592
Coefficient of Quartile Variation (Closest Observation)-0.0493623617715639
Coefficient of Quartile Variation (True Basic - Statistics Graphics Toolkit)-0.0503381551706014
Coefficient of Quartile Variation (MS Excel (old versions))-0.0493623617715639
Number of all Pairs of Observations4005
Squared Differences between all Pairs of Observations0.0847536725818355
Mean Absolute Differences between all Pairs of Observations0.234767861759301
Gini Mean Difference0.234767861759301
Leik Measure of Dispersion0.506306551494301
Index of Diversity0.988829204805982
Index of Qualitative Variation0.99993964530942
Coefficient of Dispersion-0.0609793289298734
Observations90


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)
0.02-3.226691-3.226375-3.223531-3.223531-3.222049-3.223531-3.236486-3.223531
0.04-3.215234-3.214807-3.210969-3.210969-3.208224-3.210969-3.217793-3.210969
0.06-3.196549-3.195121-3.182271-3.182271-3.177525-3.206067-3.193217-3.206067
0.08-3.164276-3.162661-3.148127-3.148127-3.146168-3.168313-3.153779-3.168313
0.1-3.1318-3.128978-3.1318-3.11769-3.106402-3.1318-3.106402-3.1318
0.12-3.095994-3.094856-3.094098-3.094098-3.080765-3.094098-3.102821-3.094098
0.14-3.063886-3.061412-3.056816-3.056816-3.035912-3.056816-3.069895-3.056816
0.16-3.004775-3.002136-2.994879-2.994879-2.991166-3.011373-3.004116-2.994879
0.18-2.978317-2.977336-2.973954-2.973954-2.973531-2.979408-2.976027-2.979408
0.2-2.952778-2.951892-2.952778-2.950562-2.949232-2.952778-2.949232-2.952778
0.22-2.940652-2.938624-2.938729-2.938729-2.935689-2.938729-2.933593-2.938729
0.24-2.922592-2.918234-2.915328-2.915328-2.914568-2.915328-2.930582-2.915328
0.26-2.912907-2.912706-2.912443-2.912443-2.91195-2.913216-2.912953-2.912443
0.28-2.907061-2.90445-2.8996-2.8996-2.900347-2.908927-2.904077-2.908927
0.3-2.896831-2.895777-2.896831-2.895075-2.894372-2.896831-2.894372-2.896831
0.32-2.892139-2.88975-2.891843-2.891843-2.883472-2.891843-2.876495-2.891843
0.34-2.874023-2.873808-2.87377-2.87377-2.872161-2.87377-2.874364-2.87377
0.36-2.863994-2.860764-2.858611-2.858611-2.857883-2.867583-2.865429-2.858611
0.38-2.839881-2.838887-2.837788-2.837788-2.838259-2.840405-2.839306-2.837788
0.4-2.83648-2.834328-2.83648-2.83379-2.833252-2.83648-2.833252-2.83648
0.42-2.821109-2.818191-2.818611-2.818611-2.817885-2.818611-2.817122-2.818611
0.44-2.813203-2.810634-2.81087-2.81087-2.809926-2.81087-2.805208-2.81087
0.46-2.799853-2.793966-2.792174-2.792174-2.792942-2.804973-2.803181-2.792174
0.48-2.781075-2.777166-2.77456-2.77456-2.77684-2.782704-2.780098-2.77456
0.5-2.771371-2.762107-2.771371-2.762107-2.762107-2.771371-2.762107-2.762107
0.52-2.750435-2.749825-2.749833-2.749833-2.749826-2.749833-2.749817-2.749833
0.54-2.74728-2.744657-2.745593-2.745593-2.745192-2.745593-2.739842-2.745593
0.56-2.738677-2.738357-2.738334-2.738334-2.738425-2.738906-2.738883-2.738334
0.58-2.730306-2.727851-2.72692-2.72692-2.728528-2.731152-2.730221-2.72692
0.6-2.708602-2.707564-2.708602-2.707737-2.70791-2.708602-2.70791-2.706873
0.62-2.702651-2.701127-2.701596-2.701596-2.701395-2.701596-2.700948-2.701596
0.64-2.694257-2.689958-2.690109-2.690109-2.690523-2.690109-2.689631-2.690109
0.66-2.689475-2.689436-2.689469-2.689469-2.689472-2.68948-2.688959-2.689469
0.68-2.686643-2.678878-2.677507-2.677507-2.682989-2.688927-2.687556-2.677507
0.7-2.674128-2.667835-2.674128-2.674128-2.671431-2.674128-2.671431-2.665139
0.72-2.660614-2.653443-2.659483-2.659483-2.658554-2.659483-2.653908-2.647867
0.74-2.646229-2.643098-2.645137-2.645137-2.64552-2.645137-2.641178-2.645137
0.76-2.637152-2.632015-2.634171-2.634171-2.635959-2.639139-2.622851-2.634171
0.78-2.619521-2.614946-2.614829-2.614829-2.618231-2.620694-2.620577-2.614829
0.8-2.605302-2.603142-2.605302-2.603952-2.604762-2.605302-2.604762-2.602602
0.82-2.5934-2.588852-2.591099-2.591099-2.591329-2.591099-2.589722-2.587475
0.84-2.584081-2.580655-2.581818-2.581818-2.583176-2.581818-2.580338-2.581818
0.86-2.572243-2.55962-2.561844-2.561844-2.569816-2.579175-2.555516-2.561844
0.88-2.551203-2.542209-2.542846-2.542846-2.549949-2.553292-2.535517-2.542846
0.9-2.534879-2.520387-2.534879-2.526828-2.533269-2.534879-2.533269-2.518777
0.92-2.515699-2.505626-2.51493-2.51493-2.515392-2.51493-2.511312-2.502008
0.94-2.498821-2.496199-2.496696-2.496696-2.498502-2.496696-2.496273-2.495776
0.96-2.492976-2.484208-2.488775-2.488775-2.492696-2.495776-2.480656-2.488775
0.98-2.475911-2.473612-2.475195-2.475195-2.475893-2.47609-2.467983-2.475195


Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[-3.3,-3.2[-3.2550.0555560.0555560.555555
[-3.2,-3.1[-3.1550.0555560.1111110.555556
[-3.1,-3[-3.0540.0444440.1555560.444444
[-3,-2.9[-2.95110.1222220.2777781.222222
[-2.9,-2.8[-2.85160.1777780.4555561.777778
[-2.8,-2.7[-2.75160.1777780.6333331.777778
[-2.7,-2.6[-2.65160.1777780.8111111.777778
[-2.6,-2.5[-2.55110.1222220.9333331.222222
[-2.5,-2.4]-2.4560.06666710.666667


Properties of Density Trace
Bandwidth0.07438259770065
#Observations90
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/1pnfj1256756588.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/1pnfj1256756588.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/2gx9e1256756588.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/2gx9e1256756588.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/590zc1256756589.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/590zc1256756589.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/7cdde1256756589.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/7cdde1256756589.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/9kkq31256756589.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Oct/28/t1256756659ds5omql85vnlas4/9kkq31256756589.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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