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StatOpdr3 Q2 - Mean

R Software Module: rwasp_centraltendency.wasp (opens new window with default values)
Title produced by software: Central Tendency
Date of computation: Tue, 20 Nov 2007 07:03:42 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/20/t1195567659xqm4uvoz7wzhsw2.htm/, Retrieved Tue, 20 Nov 2007 15:07:43 +0100
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
-183.9235445 -177.0726091 -228.6351091 -237.4476091 -127.7601091 -193.0101091 -220.6351091 -164.5101091 -268.3226091 -333.6976091 -34.26010911 -154.8851091 -97.74528053 101.1056549 2.543154874 -43.26934513 -163.5818451 -162.8318451 46.54315487 26.66815487 -107.1443451 42.48065487 76.91815487 196.2931549 201.4329835 12.28391886 -0.278581137 42.90891886 87.59641886 84.34641886 57.72141886 173.8464189 -185.9660811 47.65891886 89.09641886 -68.52858114 272.6112475 146.4621829 162.8996829 10.08718285 279.7746829 212.5246829 248.8996829 -41.97531715 -5.787817149 52.83718285 274.2746829 414.6496829 310.7895114 362.6404468 26.07794684 403.2654468 327.9529468 193.7029468 317.0779468 202.2029468 321.3904468 178.0154468 16.45294684 -68.17205316 -157.0322246 -76.18128917 -81.74378917 -134.5562892 77.13121083 199.8812108 105.2562108 198.3812108 262.5687108 196.1937108 11.63121083 -145.9937892 -166.8539606 -202.0030252 43.43447482 -113.3780252 -113.6905252 -155.9405252 -210.5655252 -124.4405252 -64.25302518 -298.6280252 -154.1905252 23.18447482 -249.6756966 118.1752388 -180.3872612 -79.19976119 -81.51226119 -246.7622612 -105.3872612 -319.2622612 -72.07476119 -90.44976119 -80.01226119 119.3627388 -53.49743261 -114.6464972 -155.2089972 -50.02149721 -196.3339972 -14.58399721 -82.20899721 17.91600279 -162.8964972 -132.2714972 -16.83399721 81.54100279 275.6808314 -32.46823322 17.96926678 27.15676678 -123.1557332 108.5942668 67.96926678 34.09426678 -13.71823322 -113.0932332 54.34426678 149.7192668 153.8590954 -28.28996923 238.1475308 50.33503077 8.022530771 -61.22746923 -140.8524692 -28.72746923 9.460030771 -121.9149692 41.52253077 115.8975308 27.03735936 -91.11170524 3.325794759 -29.48670524 -73.79920524 50.95079476 -86.67420524 -9.54920524 -66.36170524 73.26329476 -216.2992052 -128.9242052 -142.7843767 27.06655875 60.50405875 35.69155875 16.37905875 -64.87094125 115.5040587 -30.37094125 87.81655875 205.4415587 -64.12094125 -322.7459413 -139.6061127 35.24482274 -4.317677263 17.86982274 2.557322737 129.3073227 -16.31767726 164.8073227 21.99482274 138.6198227 87.05732274 51.43232274 -80.42784867 -105.1918797 5.245620328 68.43312033 -0.879379672 -105.1293797 -82.75437967 -132.6293797 102.5581203 23.18312033 -180.3793797 -267.0043797 30.13544892 23.98638432 90.42388432 31.61138432 81.29888432 25.04888432 -13.57611568 33.54888432 140.7363843 132.3613843 94.79888432 4.173884316
 
Text written by user:
 
Output produced by software:


Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean-8.0729257479187e-1010.6188693305822-7.60243439917735e-11
Geometric MeanNaN
Harmonic Mean-66.3453559002448
Quadratic Mean146.755693924481
Winsorized Mean ( 1 / 64 )-0.0022529607031260110.5976619071988-0.000212590354632432
Winsorized Mean ( 2 / 64 )-0.38914170966146010.5114574691667-0.0370207186589424
Winsorized Mean ( 3 / 64 )-0.6087239596614610.3675898727051-0.0587141242212961
Winsorized Mean ( 4 / 64 )-0.11407987424479310.2542623571223-0.0111251175629963
Winsorized Mean ( 5 / 64 )-0.19205567111979210.2312339214555-0.0187715062126612
Winsorized Mean ( 6 / 64 )0.15295206950520710.12755349091700.0151025684181657
Winsorized Mean ( 7 / 64 )-0.8715778872656249.93937503757568-0.0876894054174065
Winsorized Mean ( 8 / 64 )-0.6540445289322919.86473213624514-0.0663012963655841
Winsorized Mean ( 9 / 64 )-0.3068718023697919.80392154052977-0.031300923931426
Winsorized Mean ( 10 / 64 )0.02315760388020759.741109687537370.00237730655161752
Winsorized Mean ( 11 / 64 )-0.3037849002864579.62851670103616-0.0315505398929999
Winsorized Mean ( 12 / 64 )-0.799744144036469.46661829130887-0.0844804469163704
Winsorized Mean ( 13 / 64 )-0.9480018383072949.30090307559516-0.101925784045076
Winsorized Mean ( 14 / 64 )-2.402967872682299.0117021156137-0.266649722977295
Winsorized Mean ( 15 / 64 )-2.696658192994798.91418257299237-0.302513233368694
Winsorized Mean ( 16 / 64 )-2.379540184661468.81648245626583-0.269896775325666
Winsorized Mean ( 17 / 64 )-2.266864423723968.7885608223368-0.257933519440697
Winsorized Mean ( 18 / 64 )-2.080816554973968.73527438797356-0.238208493809738
Winsorized Mean ( 19 / 64 )-2.228474114869798.71726451071785-0.255639152870707
Winsorized Mean ( 20 / 64 )-2.101524666953138.65444936006814-0.242825924506488
Winsorized Mean ( 21 / 64 )-0.9947366857031318.53712761933898-0.116518896057003
Winsorized Mean ( 22 / 64 )-1.011570409661458.47548455483973-0.119352516439172
Winsorized Mean ( 23 / 64 )-2.779603888828138.24320501987889-0.337199412379648
Winsorized Mean ( 24 / 64 )-3.215063888828138.17498013935353-0.393280941852217
Winsorized Mean ( 25 / 64 )-4.383611297682298.0425511241079-0.545052338497697
Winsorized Mean ( 26 / 64 )-3.856572244557297.93390877424922-0.486087293702496
Winsorized Mean ( 27 / 64 )-4.974384633619797.78044088983913-0.63934482686143
Winsorized Mean ( 28 / 64 )-5.47142847111987.70549918371714-0.710068009958619
Winsorized Mean ( 29 / 64 )-5.914463253411467.64892241045275-0.77324137127198
Winsorized Mean ( 30 / 64 )-6.700590550286457.54541021544002-0.888035290192066
Winsorized Mean ( 31 / 64 )-5.718895725286477.37680556624331-0.775253688596113
Winsorized Mean ( 32 / 64 )-6.227066708619797.2178171405957-0.86273544858825
Winsorized Mean ( 33 / 64 )-6.41993694455737.13245319004063-0.900102219180595
Winsorized Mean ( 34 / 64 )-7.960248046640636.93823462900092-1.14730165125403
Winsorized Mean ( 35 / 64 )-7.25617865861986.826758119697-1.06290255658594
Winsorized Mean ( 36 / 64 )-7.32195337736986.75061613251324-1.08463482942021
Winsorized Mean ( 37 / 64 )-7.32881178986986.73667398133189-1.08789764951945
Winsorized Mean ( 38 / 64 )-8.033889895078126.54335984850556-1.22779276718412
Winsorized Mean ( 39 / 64 )-8.475475499765626.45758888474338-1.3124829794894
Winsorized Mean ( 40 / 64 )-8.345997708098966.33932914709153-1.31654273101249
Winsorized Mean ( 41 / 64 )-8.381802965390626.28470994379208-1.33368175148163
Winsorized Mean ( 42 / 64 )-9.489991904765636.13437862059099-1.54701763482792
Winsorized Mean ( 43 / 64 )-8.841974738098965.89291733807751-1.50044099226811
Winsorized Mean ( 44 / 64 )-8.927108656015625.84557313781894-1.52715712309887
Winsorized Mean ( 45 / 64 )-9.153833681796875.8124302807431-1.57487199667995
Winsorized Mean ( 46 / 64 )-9.138344113776045.80142125581835-1.57519058017224
Winsorized Mean ( 47 / 64 )-7.814072118671885.65457334772996-1.38190304345576
Winsorized Mean ( 48 / 64 )-8.052527113671875.55494447381642-1.44961433037323
Winsorized Mean ( 49 / 64 )-8.718629686223965.48836172361314-1.58856688485253
Winsorized Mean ( 50 / 64 )-8.765405329453135.48143726057306-1.59910711602977
Winsorized Mean ( 51 / 64 )-7.911042258203125.20760440241907-1.51913272339355
Winsorized Mean ( 52 / 64 )-6.172151606328135.0422597930784-1.22408441048610
Winsorized Mean ( 53 / 64 )-6.998321143723964.93967985958686-1.41675601307273
Winsorized Mean ( 54 / 64 )-7.294932591223964.73135234065629-1.54182822710939
Winsorized Mean ( 55 / 64 )-6.304940606328134.62225515591229-1.36403993151774
Winsorized Mean ( 56 / 64 )-8.32322306424484.42817324509417-1.87960646604462
Winsorized Mean ( 57 / 64 )-9.011210644713544.34929759167664-2.07187722954588
Winsorized Mean ( 58 / 64 )-9.961451258255214.26188282477518-2.33733578979396
Winsorized Mean ( 59 / 64 )-10.09133466028654.1961407053366-2.40490855024294
Winsorized Mean ( 60 / 64 )-10.40048235716154.15038204969003-2.50590963256942
Winsorized Mean ( 61 / 64 )-10.29532978830734.11580888159069-2.50141104324755
Winsorized Mean ( 62 / 64 )-9.519455320286464.01521937281708-2.37084314364812
Winsorized Mean ( 63 / 64 )-9.615933251223963.87918120438278-2.47885642474233
Winsorized Mean ( 64 / 64 )-9.413039897890633.80076565429347-2.47661675411567
Trimmed Mean ( 1 / 64 )-0.4260635471315810.3568008047625-0.0411385286985201
Trimmed Mean ( 2 / 64 )-0.85889138007978810.0981272767792-0.0850545211541172
Trimmed Mean ( 3 / 64 )-1.101342822876349.8689617898274-0.111596624480963
Trimmed Mean ( 4 / 64 )-1.272688514429359.67916990269295-0.131487361749406
Trimmed Mean ( 5 / 64 )-1.578255628324189.509815142173-0.165960705305944
Trimmed Mean ( 6 / 64 )-1.873978285861119.33405392164412-0.200767887307322
Trimmed Mean ( 7 / 64 )-2.238370259859559.16768192563276-0.244158804593895
Trimmed Mean ( 8 / 64 )-2.451376863380689.0249192212105-0.271623136262474
Trimmed Mean ( 9 / 64 )-2.699284771580468.8844278037427-0.303822016589897
Trimmed Mean ( 10 / 64 )-2.996018163110478.74302226810227-0.342675343975850
Trimmed Mean ( 11 / 64 )-3.337007426205888.60010010613043-0.388019602681968
Trimmed Mean ( 12 / 64 )-3.652147428898818.46175253022228-0.431606504191027
Trimmed Mean ( 13 / 64 )-3.927077865993988.3336693208854-0.471230344615683
Trimmed Mean ( 14 / 64 )-4.195362386310988.2157944548438-0.510645977010478
Trimmed Mean ( 15 / 64 )-4.347099487993838.12189114744502-0.535232424207179
Trimmed Mean ( 16 / 64 )-4.479134791593758.03122431706607-0.557715064946667
Trimmed Mean ( 17 / 64 )-4.638597673132917.94349181354002-0.583949449689899
Trimmed Mean ( 18 / 64 )-4.810306867660267.85155426677624-0.612656641502819
Trimmed Mean ( 19 / 64 )-4.999362473733777.75738436168478-0.644464969201034
Trimmed Mean ( 20 / 64 )-5.183576658256587.65727337640403-0.676948099336485
Trimmed Mean ( 21 / 64 )-5.38082798577.55477905474281-0.712241608485158
Trimmed Mean ( 22 / 64 )-5.651783432804057.45428708999416-0.758192348184497
Trimmed Mean ( 23 / 64 )-5.929156066815077.35082475752667-0.806597390414467
Trimmed Mean ( 24 / 64 )-6.111738801770837.25954284274178-0.841890313779395
Trimmed Mean ( 25 / 64 )-6.274931754612687.16630380543269-0.87561620676139
Trimmed Mean ( 26 / 64 )-6.378684191107147.07639752907352-0.901402749760763
Trimmed Mean ( 27 / 64 )-6.51364670329716.98759367512287-0.932173077906189
Trimmed Mean ( 28 / 64 )-6.594130994522066.90372821218926-0.955155068659775
Trimmed Mean ( 29 / 64 )-6.651582722798516.81835340032509-0.9755409161516
Trimmed Mean ( 30 / 64 )-6.688554232234856.72966381584848-0.993891287181863
Trimmed Mean ( 31 / 64 )-6.687961675038466.64113613735975-1.00705083237419
Trimmed Mean ( 32 / 64 )-6.734851962929696.55793987881809-1.02697677736922
Trimmed Mean ( 33 / 64 )-6.759032213134926.47955309447262-1.04313246833346
Trimmed Mean ( 34 / 64 )-6.774942841572586.40020758527323-1.05855048470015
Trimmed Mean ( 35 / 64 )-6.720078184057386.32844883550935-1.0618839400819
Trimmed Mean ( 36 / 64 )-6.695570733791676.2581388790653-1.06989807404078
Trimmed Mean ( 37 / 64 )-6.667259653855936.18640467605322-1.07772769532262
Trimmed Mean ( 38 / 64 )-6.637665522887936.1079457145493-1.08672634517311
Trimmed Mean ( 39 / 64 )-6.575783002236846.03669984155524-1.08930097152929
Trimmed Mean ( 40 / 64 )-6.49228003531255.96435237096559-1.08851382874641
Trimmed Mean ( 41 / 64 )-6.411390536863645.89344672190226-1.08788470302726
Trimmed Mean ( 42 / 64 )-6.325952599583335.81870513468505-1.08717531704341
Trimmed Mean ( 43 / 64 )-6.189498074292455.74750441532219-1.07690183896022
Trimmed Mean ( 44 / 64 )-6.075617144182695.68799044760653-1.06814826785430
Trimmed Mean ( 45 / 64 )-5.95362820255.62470607192731-1.0584781011428
Trimmed Mean ( 46 / 64 )-5.817086102055.55589541015832-1.04701144867021
Trimmed Mean ( 47 / 64 )-5.675630570051025.47892242158904-1.03590270738036
Trimmed Mean ( 48 / 64 )-5.584633057343755.40529132329462-1.03317892104654
Trimmed Mean ( 49 / 64 )-5.479616288989365.33065271827507-1.02794471495087
Trimmed Mean ( 50 / 64 )-5.341663633206525.25160726304147-1.01714834443139
Trimmed Mean ( 51 / 64 )-5.19558398755.16190388584821-1.00652474404727
Trimmed Mean ( 52 / 64 )-5.079414649715915.08655658556653-0.998595919315853
Trimmed Mean ( 53 / 64 )-5.032499288430235.01650879994209-1.00318757309632
Trimmed Mean ( 54 / 64 )-4.947719909226194.94565767157857-1.00041697945644
Trimmed Mean ( 55 / 64 )-4.845943749573174.88449892656826-0.992106625966201
Trimmed Mean ( 56 / 64 )-4.78227843218754.82436660032699-0.991275918348196
Trimmed Mean ( 57 / 64 )-4.626632514294874.77317384662577-0.969298974426735
Trimmed Mean ( 58 / 64 )-4.432302181644744.72021827301802-0.939003648831435
Trimmed Mean ( 59 / 64 )-4.184959072905414.66576487368917-0.896950272077557
Trimmed Mean ( 60 / 64 )-3.918004244097224.60761724721257-0.850331968539154
Trimmed Mean ( 61 / 64 )-3.62166238754.54260523161166-0.797265490361591
Trimmed Mean ( 62 / 64 )-3.312755892573534.46849340063723-0.74135857336191
Trimmed Mean ( 63 / 64 )-3.021532459015154.39267562391628-0.687856950457295
Trimmed Mean ( 64 / 64 )-2.707513373671884.31847906990830-0.626959938867869
Median4.709752322
Midrange40.4760369
Midmean - Weighted Average at Xnp-6.63164039798968
Midmean - Weighted Average at X(n+1)p-5.58463305734374
Midmean - Empirical Distribution Function-6.63164039798968
Midmean - Empirical Distribution Function - Averaging-5.58463305734374
Midmean - Empirical Distribution Function - Interpolation-5.58463305734374
Midmean - Closest Observation-6.63164039798968
Midmean - True Basic - Statistics Graphics Toolkit-5.58463305734374
Midmean - MS Excel (old versions)-5.67563057005101
Number of observations192
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/20/t1195567659xqm4uvoz7wzhsw2/1griq1195567418.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/20/t1195567659xqm4uvoz7wzhsw2/1griq1195567418.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/20/t1195567659xqm4uvoz7wzhsw2/2ckl91195567418.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/20/t1195567659xqm4uvoz7wzhsw2/2ckl91195567418.ps (open in new window)


 
Parameters:
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
R code (references can be found in the software module):
geomean <- function(x) {
return(exp(mean(log(x))))
}
harmean <- function(x) {
return(1/mean(1/x))
}
quamean <- function(x) {
return(sqrt(mean(x*x)))
}
winmean <- function(x) {
x <-sort(x[!is.na(x)])
n<-length(x)
denom <- 3
nodenom <- n/denom
if (nodenom>40) denom <- n/40
sqrtn = sqrt(n)
roundnodenom = floor(nodenom)
win <- array(NA,dim=c(roundnodenom,2))
for (j in 1:roundnodenom) {
win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n
win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn
}
return(win)
}
trimean <- function(x) {
x <-sort(x[!is.na(x)])
n<-length(x)
denom <- 3
nodenom <- n/denom
if (nodenom>40) denom <- n/40
sqrtn = sqrt(n)
roundnodenom = floor(nodenom)
tri <- array(NA,dim=c(roundnodenom,2))
for (j in 1:roundnodenom) {
tri[j,1] <- mean(x,trim=j/n)
tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2)
}
return(tri)
}
midrange <- function(x) {
return((max(x)+min(x))/2)
}
q1 <- function(data,n,p,i,f) {
np <- n*p;
i <<- floor(np)
f <<- np - i
qvalue <- (1-f)*data[i] + f*data[i+1]
}
q2 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
qvalue <- (1-f)*data[i] + f*data[i+1]
}
q3 <- function(data,n,p,i,f) {
np <- n*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
qvalue <- data[i+1]
}
}
q4 <- function(data,n,p,i,f) {
np <- n*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- (data[i]+data[i+1])/2
} else {
qvalue <- data[i+1]
}
}
q5 <- function(data,n,p,i,f) {
np <- (n-1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i+1]
} else {
qvalue <- data[i+1] + f*(data[i+2]-data[i+1])
}
}
q6 <- function(data,n,p,i,f) {
np <- n*p+0.5
i <<- floor(np)
f <<- np - i
qvalue <- data[i]
}
q7 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
qvalue <- f*data[i] + (1-f)*data[i+1]
}
}
q8 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
if (f == 0.5) {
qvalue <- (data[i]+data[i+1])/2
} else {
if (f < 0.5) {
qvalue <- data[i]
} else {
qvalue <- data[i+1]
}
}
}
}
midmean <- function(x,def) {
x <-sort(x[!is.na(x)])
n<-length(x)
if (def==1) {
qvalue1 <- q1(x,n,0.25,i,f)
qvalue3 <- q1(x,n,0.75,i,f)
}
if (def==2) {
qvalue1 <- q2(x,n,0.25,i,f)
qvalue3 <- q2(x,n,0.75,i,f)
}
if (def==3) {
qvalue1 <- q3(x,n,0.25,i,f)
qvalue3 <- q3(x,n,0.75,i,f)
}
if (def==4) {
qvalue1 <- q4(x,n,0.25,i,f)
qvalue3 <- q4(x,n,0.75,i,f)
}
if (def==5) {
qvalue1 <- q5(x,n,0.25,i,f)
qvalue3 <- q5(x,n,0.75,i,f)
}
if (def==6) {
qvalue1 <- q6(x,n,0.25,i,f)
qvalue3 <- q6(x,n,0.75,i,f)
}
if (def==7) {
qvalue1 <- q7(x,n,0.25,i,f)
qvalue3 <- q7(x,n,0.75,i,f)
}
if (def==8) {
qvalue1 <- q8(x,n,0.25,i,f)
qvalue3 <- q8(x,n,0.75,i,f)
}
midm <- 0
myn <- 0
roundno4 <- round(n/4)
round3no4 <- round(3*n/4)
for (i in 1:n) {
if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){
midm = midm + x[i]
myn = myn + 1
}
}
midm = midm / myn
return(midm)
}
(arm <- mean(x))
sqrtn <- sqrt(length(x))
(armse <- sd(x) / sqrtn)
(armose <- arm / armse)
(geo <- geomean(x))
(har <- harmean(x))
(qua <- quamean(x))
(win <- winmean(x))
(tri <- trimean(x))
(midr <- midrange(x))
midm <- array(NA,dim=8)
for (j in 1:8) midm[j] <- midmean(x,j)
midm
bitmap(file='test1.png')
lb <- win[,1] - 2*win[,2]
ub <- win[,1] + 2*win[,2]
if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax))
lines(ub,lty=3)
lines(lb,lty=3)
grid()
dev.off()
bitmap(file='test2.png')
lb <- tri[,1] - 2*tri[,2]
ub <- tri[,1] + 2*tri[,2]
if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax))
lines(ub,lty=3)
lines(lb,lty=3)
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Measure',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.element(a,'S.E.',header=TRUE)
a<-table.element(a,'Value/S.E.',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE)
a<-table.element(a,arm)
a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean'))
a<-table.element(a,armose)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE)
a<-table.element(a,geo)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE)
a<-table.element(a,har)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE)
a<-table.element(a,qua)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
for (j in 1:length(win[,1])) {
a<-table.row.start(a)
mylabel <- paste('Winsorized Mean (',j)
mylabel <- paste(mylabel,'/')
mylabel <- paste(mylabel,length(win[,1]))
mylabel <- paste(mylabel,')')
a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE)
a<-table.element(a,win[j,1])
a<-table.element(a,win[j,2])
a<-table.element(a,win[j,1]/win[j,2])
a<-table.row.end(a)
}
for (j in 1:length(tri[,1])) {
a<-table.row.start(a)
mylabel <- paste('Trimmed Mean (',j)
mylabel <- paste(mylabel,'/')
mylabel <- paste(mylabel,length(tri[,1]))
mylabel <- paste(mylabel,')')
a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE)
a<-table.element(a,tri[j,1])
a<-table.element(a,tri[j,2])
a<-table.element(a,tri[j,1]/tri[j,2])
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE)
a<-table.element(a,median(x))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE)
a<-table.element(a,midr)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[1])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[2])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[3])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[4])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[5])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[6])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[7])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[8])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of observations',header=TRUE)
a<-table.element(a,length(x))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
 





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