FLA central tendency

*The author of this computation has been verified*

R Software Module: /rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Tue, 23 Nov 2010 15:12:11 +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/Nov/23/t12905251438wb5r1eo5uktp8r.htm/, Retrieved Tue, 23 Nov 2010 16:12:25 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t12905251438wb5r1eo5uktp8r.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 «

0.0060913893934256 -0.0060913893933763 2.5757174171304E-14 2.5757174171304E-14 2.5757174171304E-14 0.016161967957026 -0.016161967956973 2.5757174171304E-14 2.5757174171304E-14 0.016161967957026 -0.016161967956973 2.5757174171304E-14 2.5757174171304E-14 2.5757174171304E-14 0.016161967957026 2.7089441800854E-14 0.0040000053333271 -1.8651746813703E-14 -0.018127384592519 3.8191672047105E-14 -0.0020345886977617 -0.016427474141974 -2.6645352591004E-14 -2.6645352591004E-14 0.0010346612407734 -2.6645352591004E-15 -0.0010346612408028 0.0010346612407734 -2.6645352591004E-15 -2.6645352591004E-15 -2.6645352591004E-15 -0.0010346612408028 -2.6645352591004E-14 0.0010346612407734 -2.6645352591004E-15 -0.0010346612408028 -2.6645352591004E-14 -2.6645352591004E-14 -2.6645352591004E-14 -2.6645352591004E-14 -2.6645352591004E-14 0.0010346612407734 -2.6645352591004E-15 -0.0010346612408028 -2.6645352591004E-14 -2.6645352591004E-14 -2.6645352591004E-14 -2.6645352591004E-14 -2.6645352591 etc...

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.00312145047808877 0.00417631842850798 -0.747416781436356 Geometric Mean NaN Harmonic Mean -1.37448771518915e-14 Quadratic Mean 0.0589968553461109 Winsorized Mean ( 1 / 66 ) -0.00319961080658492 0.00409826633749335 -0.780723004093951 Winsorized Mean ( 2 / 66 ) -0.00227140766410652 0.00321707460232691 -0.706047557138902 Winsorized Mean ( 3 / 66 ) -0.00255550340876426 0.00176858793363279 -1.44493997734967 Winsorized Mean ( 4 / 66 ) -0.00214589258337164 0.00095186625684113 -2.25440556165213 Winsorized Mean ( 5 / 66 ) -0.00187524171034159 0.000856587839071025 -2.18919954826268 Winsorized Mean ( 6 / 66 ) -0.00167024539075006 0.000787994700030055 -2.11961500589580 Winsorized Mean ( 7 / 66 ) -0.000518494962088747 0.000428310486316771 -1.21055864531245 Winsorized Mean ( 8 / 66 ) -0.000518494962088747 0.000428310486316771 -1.21055864531245 Winsorized Mean ( 9 / 66 ) -0.000518494962088747 0.000428310486316771 -1.21055864531245 Winsorized Mean ( 10 / 66 ) -0.00101927716573577 0.000350387393801468 -2.90900067687166 Winsorized Mean ( 11 / 66 ) -0.00104080821426121 0.000316986873429717 -3.28344263281293 Winsorized Mean ( 12 / 66 ) -0.00120279848871437 0.000302562584648886 -3.97537088106971 Winsorized Mean ( 13 / 66 ) -0.00120279848871437 0.000302562584648886 -3.97537088106971 Winsorized Mean ( 14 / 66 ) -0.00101107215497466 0.000255290900893677 -3.96047078621009 Winsorized Mean ( 15 / 66 ) -0.000461199834568884 0.000123773241151268 -3.72616754864835 Winsorized Mean ( 16 / 66 ) -0.00038661530789886 0.000106868772598715 -3.61766396766411 Winsorized Mean ( 17 / 66 ) -0.000121033308358519 4.99788220624837e-05 -2.42169189596352 Winsorized Mean ( 18 / 66 ) -3.10398372322183e-05 3.43315041166301e-05 -0.904121098999058 Winsorized Mean ( 19 / 66 ) -0.000129332655101494 2.42566418119699e-05 -5.3318450304886 Winsorized Mean ( 20 / 66 ) -0.000129332655102093 2.42566418119538e-05 -5.33184503051685 Winsorized Mean ( 21 / 66 ) -0.000129332655103259 2.42566418119226e-05 -5.33184503057177 Winsorized Mean ( 22 / 66 ) -0.000129332655103332 2.42566418119206e-05 -5.33184503057522 Winsorized Mean ( 23 / 66 ) -0.000129332655103332 2.42566418119206e-05 -5.33184503057522 Winsorized Mean ( 24 / 66 ) -0.000129332655103332 2.42566418119206e-05 -5.33184503057522 Winsorized Mean ( 25 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 26 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 27 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 28 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 29 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 30 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 31 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 32 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 33 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 34 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 35 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 36 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 37 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 38 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 39 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 40 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 41 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 42 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 43 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 44 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 45 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 46 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 47 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 48 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 49 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 50 / 66 ) -9.03277452835027e-15 2.16324394633961e-15 -4.17556907700329 Winsorized Mean ( 51 / 66 ) -9.2026386511179e-15 2.14930643904289e-15 -4.28167825859952 Winsorized Mean ( 52 / 66 ) -9.2026386511179e-15 2.14930643904289e-15 -4.28167825859952 Winsorized Mean ( 53 / 66 ) -9.2026386511179e-15 2.14930643904289e-15 -4.28167825859952 Winsorized Mean ( 54 / 66 ) -9.2026386511179e-15 2.14930643904289e-15 -4.28167825859952 Winsorized Mean ( 55 / 66 ) -9.2026386511179e-15 2.14930643904289e-15 -4.28167825859952 Winsorized Mean ( 56 / 66 ) -9.2026386511179e-15 2.14930643904289e-15 -4.28167825859952 Winsorized Mean ( 57 / 66 ) -9.2026386511179e-15 2.14930643904289e-15 -4.28167825859952 Winsorized Mean ( 58 / 66 ) -9.2026386511179e-15 2.14930643904289e-15 -4.28167825859952 Winsorized Mean ( 59 / 66 ) -4.09339229179305e-15 1.49332101274028e-15 -2.74113352512303 Winsorized Mean ( 60 / 66 ) -4.09339229179305e-15 1.49332101274028e-15 -2.74113352512303 Winsorized Mean ( 61 / 66 ) -4.09339229179305e-15 1.49332101274028e-15 -2.74113352512303 Winsorized Mean ( 62 / 66 ) -4.09339229179305e-15 1.49332101274028e-15 -2.74113352512303 Winsorized Mean ( 63 / 66 ) -4.09339229179305e-15 1.49332101274028e-15 -2.74113352512303 Winsorized Mean ( 64 / 66 ) -7.21978032913737e-15 1.23934840622154e-15 -5.82546464972563 Winsorized Mean ( 65 / 66 ) -7.21978032913737e-15 1.23934840622154e-15 -5.82546464972563 Winsorized Mean ( 66 / 66 ) -7.21978032913737e-15 1.23934840622154e-15 -5.82546464972563 Trimmed Mean ( 1 / 66 ) -0.00248976904194452 0.0032706232719814 -0.761252163547461 Trimmed Mean ( 2 / 66 ) -0.00176544071067880 0.00208290121670185 -0.84758734428811 Trimmed Mean ( 3 / 66 ) -0.00150463300272636 0.00106828068110955 -1.40846224155584 Trimmed Mean ( 4 / 66 ) -0.00113974744507431 0.000647540546386019 -1.76011749601681 Trimmed Mean ( 5 / 66 ) -0.000874972408680281 0.000531439457321887 -1.64641973159008 Trimmed Mean ( 6 / 66 ) -0.000662149153007661 0.000422691439080145 -1.56650712976024 Trimmed Mean ( 7 / 66 ) -0.000481486744810099 0.000303486976369821 -1.58651534431373 Trimmed Mean ( 8 / 66 ) -0.000475740127220247 0.000276751231857598 -1.71901719832286 Trimmed Mean ( 9 / 66 ) -0.000469867210342705 0.000245698704917385 -1.91237153855042 Trimmed Mean ( 10 / 66 ) -0.000463863784201219 0.000208321783921455 -2.22666960444287 Trimmed Mean ( 11 / 66 ) -0.000401457786275989 0.000181913930745629 -2.20685565217844 Trimmed Mean ( 12 / 66 ) -0.000335409188343631 0.000157666018054973 -2.12733975577847 Trimmed Mean ( 13 / 66 ) -0.000252325922024595 0.000130428871540038 -1.93458640748216 Trimmed Mean ( 14 / 66 ) -0.000167310486721395 9.33075588361618e-05 -1.79310753392631 Trimmed Mean ( 15 / 66 ) -9.6406144851372e-05 5.212386832364e-05 -1.84955852188063 Trimmed Mean ( 16 / 66 ) -6.74542647150616e-05 3.80860312801207e-05 -1.77110248686557 Trimmed Mean ( 17 / 66 ) -4.34210536319443e-05 2.21690637349513e-05 -1.95863272130376 Trimmed Mean ( 18 / 66 ) -3.78534600360638e-05 1.76516727501454e-05 -2.14446871817017 Trimmed Mean ( 19 / 66 ) -3.83207867029942e-05 1.53995543249015e-05 -2.4884347880788 Trimmed Mean ( 20 / 66 ) -3.23331637820403e-05 1.42767872181842e-05 -2.26473668675666 Trimmed Mean ( 21 / 66 ) -2.61939554706445e-05 1.29712438344143e-05 -2.01938656038127 Trimmed Mean ( 22 / 66 ) -1.98973315614495e-05 1.14133747097095e-05 -1.74333464619563 Trimmed Mean ( 23 / 66 ) -1.34371589792841e-05 9.47040465170133e-06 -1.41885795522688 Trimmed Mean ( 24 / 66 ) -6.80698185548271e-06 6.80698184733323e-06 -1.00000000119722 Trimmed Mean ( 25 / 66 ) -8.37996338987069e-15 2.27820185420352e-15 -3.67832348762634 Trimmed Mean ( 26 / 66 ) -8.34467630130423e-15 2.28091858298706e-15 -3.65847179445404 Trimmed Mean ( 27 / 66 ) -8.308422443188e-15 2.28334884452774e-15 -3.63870043909405 Trimmed Mean ( 28 / 66 ) -8.27116153345743e-15 2.28546639712584e-15 -3.61902565877104 Trimmed Mean ( 29 / 66 ) -8.27116153345743e-15 2.28724255312904e-15 -3.61621530787897 Trimmed Mean ( 30 / 66 ) -8.19344592173367e-15 2.28864591038549e-15 -3.58004088118358 Trimmed Mean ( 31 / 66 ) -8.1528986460517e-15 2.28964204834040e-15 -3.56077433674017 Trimmed Mean ( 32 / 66 ) -8.11115880343792e-15 2.29019318320770e-15 -3.54169196856889 Trimmed Mean ( 33 / 66 ) -8.06817299537298e-15 2.29025777560573e-15 -3.52282309935138 Trimmed Mean ( 34 / 66 ) -8.02388458706365e-15 2.28979008277465e-15 -3.50420095161768 Trimmed Mean ( 35 / 66 ) -7.97823345849864e-15 2.2887396459319e-15 -3.48586326657096 Trimmed Mean ( 36 / 66 ) -7.93115573216598e-15 2.28705070139593e-15 -3.46785304205329 Trimmed Mean ( 37 / 66 ) -7.88258347483863e-15 2.28466150171506e-15 -3.45021941715274 Trimmed Mean ( 38 / 66 ) -7.83244437050072e-15 2.28150353004658e-15 -3.43301873845482 Trimmed Mean ( 39 / 66 ) -7.78066136110255e-15 2.27750058726324e-15 -3.41631585283242 Trimmed Mean ( 40 / 66 ) -7.72715225139111e-15 2.27256772648382e-15 -3.40018568482744 Trimmed Mean ( 41 / 66 ) -7.67182927355386e-15 2.26661000360844e-15 -3.38471517435303 Trimmed Mean ( 42 / 66 ) -7.61459860682567e-15 2.25952100454634e-15 -3.37000567443475 Trimmed Mean ( 43 / 66 ) -7.55535984652807e-15 2.25118109953959e-15 -3.35617594163049 Trimmed Mean ( 44 / 66 ) -7.49400541621984e-15 2.24145536144816e-15 -3.34336589749354 Trimmed Mean ( 45 / 66 ) -7.43041991571858e-15 2.23019106683986e-15 -3.33174140377459 Trimmed Mean ( 46 / 66 ) -7.36447939668024e-15 2.21721467444456e-15 -3.32150038585016 Trimmed Mean ( 47 / 66 ) -7.29605055616875e-15 2.20232814237191e-15 -3.31288077185032 Trimmed Mean ( 48 / 66 ) -7.22498983717605e-15 2.18530439954781e-15 -3.30617091086764 Trimmed Mean ( 49 / 66 ) -7.1511424233209e-15 2.16588172214117e-15 -3.30172342756156 Trimmed Mean ( 50 / 66 ) -7.07434111291154e-15 2.14375667305964e-15 -3.29997392046123 Trimmed Mean ( 51 / 66 ) -6.99440505513853e-15 2.1185751271295e-15 -3.30146661573214 Trimmed Mean ( 52 / 66 ) -6.90419943438774e-15 2.09107363368434e-15 -3.30174859611374 Trimmed Mean ( 53 / 66 ) -6.81015527658372e-15 2.05973585045524e-15 -3.30632458287238 Trimmed Mean ( 54 / 66 ) -6.71202224235344e-15 2.02399396309521e-15 -3.31622641407934 Trimmed Mean ( 55 / 66 ) -6.60952773993515e-15 1.98316166285428e-15 -3.33282347260704 Trimmed Mean ( 56 / 66 ) -6.50237439649785e-15 1.93639841924810e-15 -3.35797340664155 Trimmed Mean ( 57 / 66 ) -6.50237439649785e-15 1.88265843504297e-15 -3.45382586424894 Trimmed Mean ( 58 / 66 ) -6.3902371766216e-15 1.82061519444520e-15 -3.50993290406373 Trimmed Mean ( 59 / 66 ) -6.14955241200917e-15 1.74854522040188e-15 -3.51695360249006 Trimmed Mean ( 60 / 66 ) -6.23667784083188e-15 1.73990136121688e-15 -3.58450081128161 Trimmed Mean ( 61 / 66 ) -6.32827124036346e-15 1.72906480862007e-15 -3.65993871878863 Trimmed Mean ( 62 / 66 ) -6.42468534513353e-15 1.71567679198568e-15 -3.74469444078554 Trimmed Mean ( 63 / 66 ) -6.52631102313443e-15 1.69930491693176e-15 -3.84057679001967 Trimmed Mean ( 64 / 66 ) -6.63358257213537e-15 1.67942311948934e-15 -3.94991738243573 Trimmed Mean ( 65 / 66 ) -6.60741302941207e-15 1.68338452461673e-15 -3.92507649487655 Trimmed Mean ( 66 / 66 ) -6.57970410182269e-15 1.68633698038461e-15 -3.9017730016939 Median -2.6645352591004e-15 Midrange -0.06565791265637 Midmean - Weighted Average at Xnp -7.69754630406778e-15 Midmean - Weighted Average at X(n+1)p -7.69754630406778e-15 Midmean - Empirical Distribution Function -7.69754630406778e-15 Midmean - Empirical Distribution Function - Averaging -7.69754630406778e-15 Midmean - Empirical Distribution Function - Interpolation -7.69754630406778e-15 Midmean - Closest Observation -7.69754630406778e-15 Midmean - True Basic - Statistics Graphics Toolkit -7.69754630406778e-15 Midmean - MS Excel (old versions) -7.69754630406778e-15 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t12905251438wb5r1eo5uktp8r/1yp2m1290525124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905251438wb5r1eo5uktp8r/1yp2m1290525124.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905251438wb5r1eo5uktp8r/2yp2m1290525124.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905251438wb5r1eo5uktp8r/2yp2m1290525124.ps (open in new window)

Parameters (Session):

Parameters (R input):

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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by