CaseStatistiek - EDA Fixed Location Inflatie

*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, 29 Dec 2009 15:05:28 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/29/t1262124722frm1yk76m23p2ex.htm/, Retrieved Tue, 29 Dec 2009 23:12:04 +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/Dec/29/t1262124722frm1yk76m23p2ex.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:

CaseStatistiek - EDA Fixed Location Inflatie

Dataseries X:

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2.1 2.1 2.6 2.6 2.7 2.5 2.4 1.9 2.2 1.9 2 2.2 2.5 2.5 2.7 2.6 2.3 2 2.3 2.9 2.5 2.5 2.3 2.5 2.3 2.4 2.2 2.4 2.6 2.8 2.8 2.5 2.5 2.2 2.1 1.9 1.9 1.7 1.7 1.6 1.4 1.1 0.8 0.9 1 1 1.1 1.3 1.4 1.4 1.6 2 2.1 1.9 1.5 1.2 1.5 2.2 2.1 2.1 2.1 1.9 1.3 1.1 1.4 1.6 1.9 1.7 1.6 1.2 1.3 0.9 0.5 0.8 1 1.3 1.3 1.2 1.2 1 0.8 0.7 0.6 0.7 1 1 1.3 1.1 0.8 0.7 0.7 0.9 1.3 1.4 1.6 2.1 0.3 2.1 2.5 2.3 2.4 3 1.7 3.5 4 3.7 3.7 3 2.7 2.5 2.2 2.9 3.1 3 2.8 2.5 1.9 1.9 1.8 2 2.6 2.5 2.5 1.6 1.4 0.8 1.1 1.3 1.2 1.3 1.1 1.3 1.2 1.6 1.7 1.5 0.9 1.5 1.4 1.6 1.7 1.4 1.8 1.7 1.4 1.2 1 1.7 2.4 2 2.1 2 1.8 2.7 2.3 1.9 2 2.3 2.8 2.4 2.3 2.7 2.7 2.9 3 2.2 2.3 2.8 2.8 2.8 2.2 2.6 2.8 2.5 2.4 2.3 1.9 1.7 2 2.1 1.7 1.8 1.8 1.8 1.3 1.3 1.3 1.2 1.4 2.2 2.9 3.1 3.5 3.6 4.4 4.1 5.1 5.8 5.9 5.4 5.5 4.8 3.2 2.7 2.1 1.9 0.6 0.7 -0.2 -1 -1.7 -0.7 -1 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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 1.96168224299065 0.0722112602968043 27.1658773843263 Geometric Mean NaN Harmonic Mean 1.82656313752954 Quadratic Mean 2.22685394776418 Winsorized Mean ( 1 / 71 ) 1.96448598130841 0.0713838486122725 27.5200345666243 Winsorized Mean ( 2 / 71 ) 1.96168224299065 0.070700717296759 27.7462848751123 Winsorized Mean ( 3 / 71 ) 1.96168224299065 0.070102408274157 27.9830934669019 Winsorized Mean ( 4 / 71 ) 1.95981308411215 0.0681487648228943 28.7578665468895 Winsorized Mean ( 5 / 71 ) 1.96448598130841 0.064676215782442 30.3741639417581 Winsorized Mean ( 6 / 71 ) 1.96728971962617 0.0604436793686255 32.5474845372721 Winsorized Mean ( 7 / 71 ) 1.96401869158879 0.0578513646695564 33.9493925995894 Winsorized Mean ( 8 / 71 ) 1.96401869158879 0.0567796167738815 34.5902068943204 Winsorized Mean ( 9 / 71 ) 1.95140186915888 0.0547699685065198 35.6290485894032 Winsorized Mean ( 10 / 71 ) 1.95607476635514 0.0542452335646206 36.0598459590912 Winsorized Mean ( 11 / 71 ) 1.95093457943925 0.0534851809789958 36.4761704780508 Winsorized Mean ( 12 / 71 ) 1.94532710280374 0.0526908188234433 36.9196597479753 Winsorized Mean ( 13 / 71 ) 1.94532710280374 0.0526908188234433 36.9196597479753 Winsorized Mean ( 14 / 71 ) 1.92570093457944 0.0501563185121079 38.3939848797828 Winsorized Mean ( 15 / 71 ) 1.92570093457944 0.0485561448054072 39.6592633599073 Winsorized Mean ( 16 / 71 ) 1.92570093457944 0.0485561448054072 39.6592633599073 Winsorized Mean ( 17 / 71 ) 1.91775700934579 0.0476816579665456 40.2200152245405 Winsorized Mean ( 18 / 71 ) 1.91775700934579 0.0476816579665456 40.2200152245405 Winsorized Mean ( 19 / 71 ) 1.91775700934579 0.0476816579665456 40.2200152245405 Winsorized Mean ( 20 / 71 ) 1.92710280373832 0.0466843716778504 41.2793989611012 Winsorized Mean ( 21 / 71 ) 1.91728971962617 0.0456588112218892 41.9916696978524 Winsorized Mean ( 22 / 71 ) 1.91728971962617 0.0456588112218892 41.9916696978524 Winsorized Mean ( 23 / 71 ) 1.91728971962617 0.0456588112218892 41.9916696978524 Winsorized Mean ( 24 / 71 ) 1.92850467289720 0.0445227623801111 43.3150274107584 Winsorized Mean ( 25 / 71 ) 1.91682242990654 0.0433653616205986 44.2016936622534 Winsorized Mean ( 26 / 71 ) 1.91682242990654 0.0433653616205986 44.2016936622534 Winsorized Mean ( 27 / 71 ) 1.91682242990654 0.0433653616205986 44.2016936622534 Winsorized Mean ( 28 / 71 ) 1.91682242990654 0.0433653616205986 44.2016936622534 Winsorized Mean ( 29 / 71 ) 1.91682242990654 0.0433653616205986 44.2016936622534 Winsorized Mean ( 30 / 71 ) 1.91682242990654 0.0433653616205986 44.2016936622534 Winsorized Mean ( 31 / 71 ) 1.93130841121495 0.0419721983022131 46.0139923410474 Winsorized Mean ( 32 / 71 ) 1.93130841121495 0.0419721983022131 46.0139923410474 Winsorized Mean ( 33 / 71 ) 1.91588785046729 0.0405216904883711 47.280550919195 Winsorized Mean ( 34 / 71 ) 1.91588785046729 0.0405216904883711 47.280550919195 Winsorized Mean ( 35 / 71 ) 1.91588785046729 0.0405216904883711 47.280550919195 Winsorized Mean ( 36 / 71 ) 1.91588785046729 0.0405216904883711 47.280550919195 Winsorized Mean ( 37 / 71 ) 1.93317757009346 0.0389392610810386 49.6459746904344 Winsorized Mean ( 38 / 71 ) 1.93317757009346 0.0389392610810386 49.6459746904344 Winsorized Mean ( 39 / 71 ) 1.93317757009346 0.0389392610810386 49.6459746904344 Winsorized Mean ( 40 / 71 ) 1.91448598130841 0.0372668460019480 51.3723640902784 Winsorized Mean ( 41 / 71 ) 1.91448598130841 0.0372668460019480 51.3723640902784 Winsorized Mean ( 42 / 71 ) 1.91448598130841 0.0372668460019480 51.3723640902784 Winsorized Mean ( 43 / 71 ) 1.91448598130841 0.0372668460019480 51.3723640902784 Winsorized Mean ( 44 / 71 ) 1.91448598130841 0.0372668460019480 51.3723640902784 Winsorized Mean ( 45 / 71 ) 1.93551401869159 0.0354336212890984 54.6236582171485 Winsorized Mean ( 46 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 47 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 48 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 49 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 50 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 51 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 52 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 53 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 54 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 55 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 56 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 57 / 71 ) 1.91401869158879 0.0336057744198025 56.9550538451789 Winsorized Mean ( 58 / 71 ) 1.94112149532710 0.0313426917945215 61.9321884684582 Winsorized Mean ( 59 / 71 ) 1.94112149532710 0.0313426917945215 61.9321884684582 Winsorized Mean ( 60 / 71 ) 1.91308411214953 0.0290638171025235 65.8235669940074 Winsorized Mean ( 61 / 71 ) 1.91308411214953 0.0290638171025235 65.8235669940074 Winsorized Mean ( 62 / 71 ) 1.91308411214953 0.0290638171025235 65.8235669940074 Winsorized Mean ( 63 / 71 ) 1.91308411214953 0.0290638171025235 65.8235669940074 Winsorized Mean ( 64 / 71 ) 1.91308411214953 0.0290638171025235 65.8235669940074 Winsorized Mean ( 65 / 71 ) 1.91308411214953 0.0290638171025235 65.8235669940074 Winsorized Mean ( 66 / 71 ) 1.91308411214953 0.0290638171025235 65.8235669940074 Winsorized Mean ( 67 / 71 ) 1.88177570093458 0.0266769724311829 70.5393277212732 Winsorized Mean ( 68 / 71 ) 1.91355140186916 0.0240435946509772 79.5867435650429 Winsorized Mean ( 69 / 71 ) 1.91355140186916 0.0240435946509772 79.5867435650429 Winsorized Mean ( 70 / 71 ) 1.91355140186916 0.0240435946509772 79.5867435650429 Winsorized Mean ( 71 / 71 ) 1.91355140186916 0.0240435946509772 79.5867435650429 Trimmed Mean ( 1 / 71 ) 1.96168224299065 0.0683159235286163 28.7148609235875 Trimmed Mean ( 2 / 71 ) 1.96037735849057 0.0649687434498229 30.1741615182171 Trimmed Mean ( 3 / 71 ) 1.95336538461538 0.0617090690472977 31.6544296449879 Trimmed Mean ( 4 / 71 ) 1.95336538461538 0.058381142820223 33.4588411643553 Trimmed Mean ( 5 / 71 ) 1.94803921568627 0.0553758388087066 35.1785048785570 Trimmed Mean ( 6 / 71 ) 1.94455445544554 0.0530436623387231 36.6595059562087 Trimmed Mean ( 7 / 71 ) 1.94455445544554 0.0514859472460117 37.7686448333954 Trimmed Mean ( 8 / 71 ) 1.9405 0.0503183880710774 38.5644309046415 Trimmed Mean ( 9 / 71 ) 1.93316326530612 0.0492525074668447 39.2500476571161 Trimmed Mean ( 10 / 71 ) 1.93092783505155 0.0484287691465012 39.8715034282685 Trimmed Mean ( 11 / 71 ) 1.928125 0.0476223867078335 40.4877859614466 Trimmed Mean ( 12 / 71 ) 1.92578947368421 0.0468630643444057 41.0939724199684 Trimmed Mean ( 13 / 71 ) 1.92393617021277 0.0461519252661228 41.6870186697283 Trimmed Mean ( 14 / 71 ) 1.92393617021277 0.0453920324204902 42.3848871183017 Trimmed Mean ( 15 / 71 ) 1.92204301075269 0.0448635915546278 42.84193360695 Trimmed Mean ( 16 / 71 ) 1.92173913043478 0.0444635004340256 43.2205991808098 Trimmed Mean ( 17 / 71 ) 1.92111111111111 0.0440359094543356 43.6260119279077 Trimmed Mean ( 18 / 71 ) 1.92134831460674 0.0436608279619246 44.006227190247 Trimmed Mean ( 19 / 71 ) 1.92159090909091 0.0432592436430262 44.4203538311444 Trimmed Mean ( 20 / 71 ) 1.92183908045977 0.0428290827207128 44.8722914051657 Trimmed Mean ( 21 / 71 ) 1.92151162790698 0.0424539682302368 45.2610605794543 Trimmed Mean ( 22 / 71 ) 1.92176470588235 0.0421364161987505 45.6081669788363 Trimmed Mean ( 23 / 71 ) 1.92202380952381 0.0417948606391617 45.9870850178856 Trimmed Mean ( 24 / 71 ) 1.92228915662651 0.041427402166379 46.4013927039472 Trimmed Mean ( 25 / 71 ) 1.92195121951220 0.0411204802418736 46.7395129679212 Trimmed Mean ( 26 / 71 ) 1.92222222222222 0.0408764496336491 47.0251756072247 Trimmed Mean ( 27 / 71 ) 1.92222222222222 0.040611965736791 47.3314252917546 Trimmed Mean ( 28 / 71 ) 1.9225 0.040325399152397 47.6746675893901 Trimmed Mean ( 29 / 71 ) 1.92278481012658 0.040014949169372 48.051661942340 Trimmed Mean ( 30 / 71 ) 1.92307692307692 0.039678619463591 48.4663264265415 Trimmed Mean ( 31 / 71 ) 1.92337662337662 0.039314189221253 48.9232173287922 Trimmed Mean ( 32 / 71 ) 1.92368421052632 0.0390194888111603 49.3006000113488 Trimmed Mean ( 33 / 71 ) 1.92297297297297 0.0386989003155874 49.6906360979571 Trimmed Mean ( 34 / 71 ) 1.92328767123288 0.038451867917308 50.0180556993738 Trimmed Mean ( 35 / 71 ) 1.92361111111111 0.0381815952816391 50.380585120186 Trimmed Mean ( 36 / 71 ) 1.92394366197183 0.0378860223790241 50.7824137019208 Trimmed Mean ( 37 / 71 ) 1.92428571428571 0.0375628480348418 51.2284295509441 Trimmed Mean ( 38 / 71 ) 1.92391304347826 0.0373193926169476 51.552635468257 Trimmed Mean ( 39 / 71 ) 1.92352941176471 0.0370513877106221 51.9151786375131 Trimmed Mean ( 40 / 71 ) 1.92313432835821 0.0367565331974461 52.3208845085486 Trimmed Mean ( 41 / 71 ) 1.92348484848485 0.0365474453657564 52.6298029653006 Trimmed Mean ( 42 / 71 ) 1.92384615384615 0.0363151225935547 52.9764466274334 Trimmed Mean ( 43 / 71 ) 1.92421875 0.0360573228169793 53.3655468479177 Trimmed Mean ( 44 / 71 ) 1.92460317460317 0.0357715237981276 53.8026611744148 Trimmed Mean ( 45 / 71 ) 1.925 0.0354548763355117 54.2943650905337 Trimmed Mean ( 46 / 71 ) 1.92459016393443 0.0352341630761431 54.6228431700017 Trimmed Mean ( 47 / 71 ) 1.925 0.0351118660463226 54.8247705621904 Trimmed Mean ( 48 / 71 ) 1.92542372881356 0.0349703717785409 55.0587148746033 Trimmed Mean ( 49 / 71 ) 1.92586206896552 0.0348076894788972 55.3286385220458 Trimmed Mean ( 50 / 71 ) 1.92631578947368 0.0346215736749603 55.6391747977323 Trimmed Mean ( 51 / 71 ) 1.92678571428571 0.0344094820701196 55.9957778602802 Trimmed Mean ( 52 / 71 ) 1.92727272727273 0.0341685243788833 56.4049154099207 Trimmed Mean ( 53 / 71 ) 1.92777777777778 0.0338953996775831 56.8743191145411 Trimmed Mean ( 54 / 71 ) 1.92777777777778 0.0335863189583206 57.3977094712308 Trimmed Mean ( 55 / 71 ) 1.92830188679245 0.0332369083685546 58.0168848862253 Trimmed Mean ( 56 / 71 ) 1.92884615384615 0.0328420868696795 58.7309253976459 Trimmed Mean ( 57 / 71 ) 1.92941176470588 0.0323959094562099 59.5572650094575 Trimmed Mean ( 58 / 71 ) 1.93 0.0318913631453416 60.5179525003125 Trimmed Mean ( 59 / 71 ) 1.93061224489796 0.0315227497794049 61.2450455118388 Trimmed Mean ( 60 / 71 ) 1.93020833333333 0.0311022912612182 62.060004426109 Trimmed Mean ( 61 / 71 ) 1.92978723404255 0.0308247489326758 62.6051241571307 Trimmed Mean ( 62 / 71 ) 1.93043478260870 0.0305032465656904 63.286207205892 Trimmed Mean ( 63 / 71 ) 1.93111111111111 0.0301314368438246 64.0895793028499 Trimmed Mean ( 64 / 71 ) 1.93181818181818 0.0297017486468655 65.0405538335889 Trimmed Mean ( 65 / 71 ) 1.93333333333333 0.0292050603788236 66.1985734066546 Trimmed Mean ( 66 / 71 ) 1.93414634146341 0.0286302531379994 67.5560335474758 Trimmed Mean ( 67 / 71 ) 1.935 0.0279635839140203 69.1971388914078 Trimmed Mean ( 68 / 71 ) 1.93717948717949 0.0274398184680406 70.5973871305212 Trimmed Mean ( 69 / 71 ) 1.93815789473684 0.0271427096129677 71.4062052894996 Trimmed Mean ( 70 / 71 ) 1.93918918918919 0.0267876291141329 72.3912213703934 Trimmed Mean ( 71 / 71 ) 1.94027777777778 0.0263641676479212 73.5952601913746 Median 1.9 Midrange 2.1 Midmean - Weighted Average at Xnp 1.91951219512195 Midmean - Weighted Average at X(n+1)p 1.91951219512195 Midmean - Empirical Distribution Function 1.91951219512195 Midmean - Empirical Distribution Function - Averaging 1.91951219512195 Midmean - Empirical Distribution Function - Interpolation 1.91951219512195 Midmean - Closest Observation 1.91951219512195 Midmean - True Basic - Statistics Graphics Toolkit 1.91951219512195 Midmean - MS Excel (old versions) 1.91951219512195 Number of observations 214

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/29/t1262124722frm1yk76m23p2ex/1jatn1262124325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t1262124722frm1yk76m23p2ex/1jatn1262124325.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t1262124722frm1yk76m23p2ex/2ylws1262124325.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t1262124722frm1yk76m23p2ex/2ylws1262124325.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')

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