Case Seatbeltlaw_Q2
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 2 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Central Tendency - Ungrouped Data | |||
| Measure | Value | S.E. | Value/S.E. |
| Arithmetic Mean | -8.0729257479187e-10 | 10.6188693305822 | -7.60243439917735e-11 |
| Geometric Mean | NaN | ||
| Harmonic Mean | -66.3453559002448 | ||
| Quadratic Mean | 146.755693924481 | ||
| Winsorized Mean ( 1 / 64 ) | -0.00225296070312601 | 10.5976619071988 | -0.000212590354632432 |
| Winsorized Mean ( 2 / 64 ) | -0.389141709661460 | 10.5114574691667 | -0.0370207186589424 |
| Winsorized Mean ( 3 / 64 ) | -0.60872395966146 | 10.3675898727051 | -0.0587141242212961 |
| Winsorized Mean ( 4 / 64 ) | -0.114079874244793 | 10.2542623571223 | -0.0111251175629963 |
| Winsorized Mean ( 5 / 64 ) | -0.192055671119792 | 10.2312339214555 | -0.0187715062126612 |
| Winsorized Mean ( 6 / 64 ) | 0.152952069505207 | 10.1275534909170 | 0.0151025684181657 |
| Winsorized Mean ( 7 / 64 ) | -0.871577887265624 | 9.93937503757568 | -0.0876894054174065 |
| Winsorized Mean ( 8 / 64 ) | -0.654044528932291 | 9.86473213624514 | -0.0663012963655841 |
| Winsorized Mean ( 9 / 64 ) | -0.306871802369791 | 9.80392154052977 | -0.031300923931426 |
| Winsorized Mean ( 10 / 64 ) | 0.0231576038802075 | 9.74110968753737 | 0.00237730655161752 |
| Winsorized Mean ( 11 / 64 ) | -0.303784900286457 | 9.62851670103616 | -0.0315505398929999 |
| Winsorized Mean ( 12 / 64 ) | -0.79974414403646 | 9.46661829130887 | -0.0844804469163704 |
| Winsorized Mean ( 13 / 64 ) | -0.948001838307294 | 9.30090307559516 | -0.101925784045076 |
| Winsorized Mean ( 14 / 64 ) | -2.40296787268229 | 9.0117021156137 | -0.266649722977295 |
| Winsorized Mean ( 15 / 64 ) | -2.69665819299479 | 8.91418257299237 | -0.302513233368694 |
| Winsorized Mean ( 16 / 64 ) | -2.37954018466146 | 8.81648245626583 | -0.269896775325666 |
| Winsorized Mean ( 17 / 64 ) | -2.26686442372396 | 8.7885608223368 | -0.257933519440697 |
| Winsorized Mean ( 18 / 64 ) | -2.08081655497396 | 8.73527438797356 | -0.238208493809738 |
| Winsorized Mean ( 19 / 64 ) | -2.22847411486979 | 8.71726451071785 | -0.255639152870707 |
| Winsorized Mean ( 20 / 64 ) | -2.10152466695313 | 8.65444936006814 | -0.242825924506488 |
| Winsorized Mean ( 21 / 64 ) | -0.994736685703131 | 8.53712761933898 | -0.116518896057003 |
| Winsorized Mean ( 22 / 64 ) | -1.01157040966145 | 8.47548455483973 | -0.119352516439172 |
| Winsorized Mean ( 23 / 64 ) | -2.77960388882813 | 8.24320501987889 | -0.337199412379648 |
| Winsorized Mean ( 24 / 64 ) | -3.21506388882813 | 8.17498013935353 | -0.393280941852217 |
| Winsorized Mean ( 25 / 64 ) | -4.38361129768229 | 8.0425511241079 | -0.545052338497697 |
| Winsorized Mean ( 26 / 64 ) | -3.85657224455729 | 7.93390877424922 | -0.486087293702496 |
| Winsorized Mean ( 27 / 64 ) | -4.97438463361979 | 7.78044088983913 | -0.63934482686143 |
| Winsorized Mean ( 28 / 64 ) | -5.4714284711198 | 7.70549918371714 | -0.710068009958619 |
| Winsorized Mean ( 29 / 64 ) | -5.91446325341146 | 7.64892241045275 | -0.77324137127198 |
| Winsorized Mean ( 30 / 64 ) | -6.70059055028645 | 7.54541021544002 | -0.888035290192066 |
| Winsorized Mean ( 31 / 64 ) | -5.71889572528647 | 7.37680556624331 | -0.775253688596113 |
| Winsorized Mean ( 32 / 64 ) | -6.22706670861979 | 7.2178171405957 | -0.86273544858825 |
| Winsorized Mean ( 33 / 64 ) | -6.4199369445573 | 7.13245319004063 | -0.900102219180595 |
| Winsorized Mean ( 34 / 64 ) | -7.96024804664063 | 6.93823462900092 | -1.14730165125403 |
| Winsorized Mean ( 35 / 64 ) | -7.2561786586198 | 6.826758119697 | -1.06290255658594 |
| Winsorized Mean ( 36 / 64 ) | -7.3219533773698 | 6.75061613251324 | -1.08463482942021 |
| Winsorized Mean ( 37 / 64 ) | -7.3288117898698 | 6.73667398133189 | -1.08789764951945 |
| Winsorized Mean ( 38 / 64 ) | -8.03388989507812 | 6.54335984850556 | -1.22779276718412 |
| Winsorized Mean ( 39 / 64 ) | -8.47547549976562 | 6.45758888474338 | -1.3124829794894 |
| Winsorized Mean ( 40 / 64 ) | -8.34599770809896 | 6.33932914709153 | -1.31654273101249 |
| Winsorized Mean ( 41 / 64 ) | -8.38180296539062 | 6.28470994379208 | -1.33368175148163 |
| Winsorized Mean ( 42 / 64 ) | -9.48999190476563 | 6.13437862059099 | -1.54701763482792 |
| Winsorized Mean ( 43 / 64 ) | -8.84197473809896 | 5.89291733807751 | -1.50044099226811 |
| Winsorized Mean ( 44 / 64 ) | -8.92710865601562 | 5.84557313781894 | -1.52715712309887 |
| Winsorized Mean ( 45 / 64 ) | -9.15383368179687 | 5.8124302807431 | -1.57487199667995 |
| Winsorized Mean ( 46 / 64 ) | -9.13834411377604 | 5.80142125581835 | -1.57519058017224 |
| Winsorized Mean ( 47 / 64 ) | -7.81407211867188 | 5.65457334772996 | -1.38190304345576 |
| Winsorized Mean ( 48 / 64 ) | -8.05252711367187 | 5.55494447381642 | -1.44961433037323 |
| Winsorized Mean ( 49 / 64 ) | -8.71862968622396 | 5.48836172361314 | -1.58856688485253 |
| Winsorized Mean ( 50 / 64 ) | -8.76540532945313 | 5.48143726057306 | -1.59910711602977 |
| Winsorized Mean ( 51 / 64 ) | -7.91104225820312 | 5.20760440241907 | -1.51913272339355 |
| Winsorized Mean ( 52 / 64 ) | -6.17215160632813 | 5.0422597930784 | -1.22408441048610 |
| Winsorized Mean ( 53 / 64 ) | -6.99832114372396 | 4.93967985958686 | -1.41675601307273 |
| Winsorized Mean ( 54 / 64 ) | -7.29493259122396 | 4.73135234065629 | -1.54182822710939 |
| Winsorized Mean ( 55 / 64 ) | -6.30494060632813 | 4.62225515591229 | -1.36403993151774 |
| Winsorized Mean ( 56 / 64 ) | -8.3232230642448 | 4.42817324509417 | -1.87960646604462 |
| Winsorized Mean ( 57 / 64 ) | -9.01121064471354 | 4.34929759167664 | -2.07187722954588 |
| Winsorized Mean ( 58 / 64 ) | -9.96145125825521 | 4.26188282477518 | -2.33733578979396 |
| Winsorized Mean ( 59 / 64 ) | -10.0913346602865 | 4.1961407053366 | -2.40490855024294 |
| Winsorized Mean ( 60 / 64 ) | -10.4004823571615 | 4.15038204969003 | -2.50590963256942 |
| Winsorized Mean ( 61 / 64 ) | -10.2953297883073 | 4.11580888159069 | -2.50141104324755 |
| Winsorized Mean ( 62 / 64 ) | -9.51945532028646 | 4.01521937281708 | -2.37084314364812 |
| Winsorized Mean ( 63 / 64 ) | -9.61593325122396 | 3.87918120438278 | -2.47885642474233 |
| Winsorized Mean ( 64 / 64 ) | -9.41303989789063 | 3.80076565429347 | -2.47661675411567 |
| Trimmed Mean ( 1 / 64 ) | -0.42606354713158 | 10.3568008047625 | -0.0411385286985201 |
| Trimmed Mean ( 2 / 64 ) | -0.858891380079788 | 10.0981272767792 | -0.0850545211541172 |
| Trimmed Mean ( 3 / 64 ) | -1.10134282287634 | 9.8689617898274 | -0.111596624480963 |
| Trimmed Mean ( 4 / 64 ) | -1.27268851442935 | 9.67916990269295 | -0.131487361749406 |
| Trimmed Mean ( 5 / 64 ) | -1.57825562832418 | 9.509815142173 | -0.165960705305944 |
| Trimmed Mean ( 6 / 64 ) | -1.87397828586111 | 9.33405392164412 | -0.200767887307322 |
| Trimmed Mean ( 7 / 64 ) | -2.23837025985955 | 9.16768192563276 | -0.244158804593895 |
| Trimmed Mean ( 8 / 64 ) | -2.45137686338068 | 9.0249192212105 | -0.271623136262474 |
| Trimmed Mean ( 9 / 64 ) | -2.69928477158046 | 8.8844278037427 | -0.303822016589897 |
| Trimmed Mean ( 10 / 64 ) | -2.99601816311047 | 8.74302226810227 | -0.342675343975850 |
| Trimmed Mean ( 11 / 64 ) | -3.33700742620588 | 8.60010010613043 | -0.388019602681968 |
| Trimmed Mean ( 12 / 64 ) | -3.65214742889881 | 8.46175253022228 | -0.431606504191027 |
| Trimmed Mean ( 13 / 64 ) | -3.92707786599398 | 8.3336693208854 | -0.471230344615683 |
| Trimmed Mean ( 14 / 64 ) | -4.19536238631098 | 8.2157944548438 | -0.510645977010478 |
| Trimmed Mean ( 15 / 64 ) | -4.34709948799383 | 8.12189114744502 | -0.535232424207179 |
| Trimmed Mean ( 16 / 64 ) | -4.47913479159375 | 8.03122431706607 | -0.557715064946667 |
| Trimmed Mean ( 17 / 64 ) | -4.63859767313291 | 7.94349181354002 | -0.583949449689899 |
| Trimmed Mean ( 18 / 64 ) | -4.81030686766026 | 7.85155426677624 | -0.612656641502819 |
| Trimmed Mean ( 19 / 64 ) | -4.99936247373377 | 7.75738436168478 | -0.644464969201034 |
| Trimmed Mean ( 20 / 64 ) | -5.18357665825658 | 7.65727337640403 | -0.676948099336485 |
| Trimmed Mean ( 21 / 64 ) | -5.3808279857 | 7.55477905474281 | -0.712241608485158 |
| Trimmed Mean ( 22 / 64 ) | -5.65178343280405 | 7.45428708999416 | -0.758192348184497 |
| Trimmed Mean ( 23 / 64 ) | -5.92915606681507 | 7.35082475752667 | -0.806597390414467 |
| Trimmed Mean ( 24 / 64 ) | -6.11173880177083 | 7.25954284274178 | -0.841890313779395 |
| Trimmed Mean ( 25 / 64 ) | -6.27493175461268 | 7.16630380543269 | -0.87561620676139 |
| Trimmed Mean ( 26 / 64 ) | -6.37868419110714 | 7.07639752907352 | -0.901402749760763 |
| Trimmed Mean ( 27 / 64 ) | -6.5136467032971 | 6.98759367512287 | -0.932173077906189 |
| Trimmed Mean ( 28 / 64 ) | -6.59413099452206 | 6.90372821218926 | -0.955155068659775 |
| Trimmed Mean ( 29 / 64 ) | -6.65158272279851 | 6.81835340032509 | -0.9755409161516 |
| Trimmed Mean ( 30 / 64 ) | -6.68855423223485 | 6.72966381584848 | -0.993891287181863 |
| Trimmed Mean ( 31 / 64 ) | -6.68796167503846 | 6.64113613735975 | -1.00705083237419 |
| Trimmed Mean ( 32 / 64 ) | -6.73485196292969 | 6.55793987881809 | -1.02697677736922 |
| Trimmed Mean ( 33 / 64 ) | -6.75903221313492 | 6.47955309447262 | -1.04313246833346 |
| Trimmed Mean ( 34 / 64 ) | -6.77494284157258 | 6.40020758527323 | -1.05855048470015 |
| Trimmed Mean ( 35 / 64 ) | -6.72007818405738 | 6.32844883550935 | -1.0618839400819 |
| Trimmed Mean ( 36 / 64 ) | -6.69557073379167 | 6.2581388790653 | -1.06989807404078 |
| Trimmed Mean ( 37 / 64 ) | -6.66725965385593 | 6.18640467605322 | -1.07772769532262 |
| Trimmed Mean ( 38 / 64 ) | -6.63766552288793 | 6.1079457145493 | -1.08672634517311 |
| Trimmed Mean ( 39 / 64 ) | -6.57578300223684 | 6.03669984155524 | -1.08930097152929 |
| Trimmed Mean ( 40 / 64 ) | -6.4922800353125 | 5.96435237096559 | -1.08851382874641 |
| Trimmed Mean ( 41 / 64 ) | -6.41139053686364 | 5.89344672190226 | -1.08788470302726 |
| Trimmed Mean ( 42 / 64 ) | -6.32595259958333 | 5.81870513468505 | -1.08717531704341 |
| Trimmed Mean ( 43 / 64 ) | -6.18949807429245 | 5.74750441532219 | -1.07690183896022 |
| Trimmed Mean ( 44 / 64 ) | -6.07561714418269 | 5.68799044760653 | -1.06814826785430 |
| Trimmed Mean ( 45 / 64 ) | -5.9536282025 | 5.62470607192731 | -1.0584781011428 |
| Trimmed Mean ( 46 / 64 ) | -5.81708610205 | 5.55589541015832 | -1.04701144867021 |
| Trimmed Mean ( 47 / 64 ) | -5.67563057005102 | 5.47892242158904 | -1.03590270738036 |
| Trimmed Mean ( 48 / 64 ) | -5.58463305734375 | 5.40529132329462 | -1.03317892104654 |
| Trimmed Mean ( 49 / 64 ) | -5.47961628898936 | 5.33065271827507 | -1.02794471495087 |
| Trimmed Mean ( 50 / 64 ) | -5.34166363320652 | 5.25160726304147 | -1.01714834443139 |
| Trimmed Mean ( 51 / 64 ) | -5.1955839875 | 5.16190388584821 | -1.00652474404727 |
| Trimmed Mean ( 52 / 64 ) | -5.07941464971591 | 5.08655658556653 | -0.998595919315853 |
| Trimmed Mean ( 53 / 64 ) | -5.03249928843023 | 5.01650879994209 | -1.00318757309632 |
| Trimmed Mean ( 54 / 64 ) | -4.94771990922619 | 4.94565767157857 | -1.00041697945644 |
| Trimmed Mean ( 55 / 64 ) | -4.84594374957317 | 4.88449892656826 | -0.992106625966201 |
| Trimmed Mean ( 56 / 64 ) | -4.7822784321875 | 4.82436660032699 | -0.991275918348196 |
| Trimmed Mean ( 57 / 64 ) | -4.62663251429487 | 4.77317384662577 | -0.969298974426735 |
| Trimmed Mean ( 58 / 64 ) | -4.43230218164474 | 4.72021827301802 | -0.939003648831435 |
| Trimmed Mean ( 59 / 64 ) | -4.18495907290541 | 4.66576487368917 | -0.896950272077557 |
| Trimmed Mean ( 60 / 64 ) | -3.91800424409722 | 4.60761724721257 | -0.850331968539154 |
| Trimmed Mean ( 61 / 64 ) | -3.6216623875 | 4.54260523161166 | -0.797265490361591 |
| Trimmed Mean ( 62 / 64 ) | -3.31275589257353 | 4.46849340063723 | -0.74135857336191 |
| Trimmed Mean ( 63 / 64 ) | -3.02153245901515 | 4.39267562391628 | -0.687856950457295 |
| Trimmed Mean ( 64 / 64 ) | -2.70751337367188 | 4.31847906990830 | -0.626959938867869 |
| Median | 4.709752322 | ||
| Midrange | 40.4760369 | ||
| 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 observations | 192 | ||
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/20/t1195562381wuq1xgtap29h4t8/1scet1195562806.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/20/t1195562381wuq1xgtap29h4t8/1scet1195562806.ps (open in new window) |
 |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/20/t1195562381wuq1xgtap29h4t8/2wb151195562806.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/20/t1195562381wuq1xgtap29h4t8/2wb151195562806.ps (open in new window) |
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
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Software written by Ed van Stee & Patrick Wessa
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