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R Software Module: rwasp_summary1.wasp (opens new window with default values)
Title produced by software: Univariate Summary Statistics
Date of computation: Fri, 16 May 2008 13:15:51 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs.htm/, Retrieved Fri, 16 May 2008 21:18:22 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
883 891 893 895 896 898 901 903 910 917 889 898 887 899 908 904 905 883 885 888 910 899 900 900 898 900 902 904 905 911 891 895 903 898 903 890 883 915 896 900 892 907 893 894 898 893 888 889 898 907 904 902 901 876 892 899 906 893 902 881 911 886 896 889 883 903 922 881 896 911 896 911 900 882 890 914 901 905 923 897 920 906 885 885 893 905 901 894 914 895 877 899 891 901 888 902 893 898 890 902
 
Text written by user:
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean897.870.972724487865361923.046567862572
Geometric Mean897.81789596773
Harmonic Mean897.76585070294
Quadratic Mean897.922162550853
Winsorized Mean ( 1 / 33 )897.870.9679359482941927.613032228439
Winsorized Mean ( 2 / 33 )897.910.942176182885112953.017085669093
Winsorized Mean ( 3 / 33 )897.820.922050854225393973.720696516517
Winsorized Mean ( 4 / 33 )897.780.898661181531682999.019450767663
Winsorized Mean ( 5 / 33 )897.780.8804934337453561019.63281677310
Winsorized Mean ( 6 / 33 )897.780.8804934337453561019.63281677310
Winsorized Mean ( 7 / 33 )897.570.8440229543529891063.44264142444
Winsorized Mean ( 8 / 33 )897.570.8440229543529891063.44264142444
Winsorized Mean ( 9 / 33 )897.750.8140651203693841102.79875348626
Winsorized Mean ( 10 / 33 )897.750.8140651203693841102.79875348626
Winsorized Mean ( 11 / 33 )897.640.796395922042841127.12782066671
Winsorized Mean ( 12 / 33 )897.760.7776057529820031154.51820740423
Winsorized Mean ( 13 / 33 )897.630.7182547591139921249.73762945515
Winsorized Mean ( 14 / 33 )897.630.6777346401241731324.45642712838
Winsorized Mean ( 15 / 33 )897.630.6777346401241731324.45642712838
Winsorized Mean ( 16 / 33 )897.470.6560449294788811368.00081773803
Winsorized Mean ( 17 / 33 )897.640.6318994933507781420.54236384979
Winsorized Mean ( 18 / 33 )897.460.6085950039130121474.64240460357
Winsorized Mean ( 19 / 33 )897.460.6085950039130121474.64240460357
Winsorized Mean ( 20 / 33 )897.660.5812247605033891544.42835371045
Winsorized Mean ( 21 / 33 )897.660.5812247605033891544.42835371045
Winsorized Mean ( 22 / 33 )897.440.554015643581781619.88205639454
Winsorized Mean ( 23 / 33 )897.670.5235957651208851714.43327046916
Winsorized Mean ( 24 / 33 )897.670.5235957651208851714.43327046916
Winsorized Mean ( 25 / 33 )897.420.494041261107581816.48795484834
Winsorized Mean ( 26 / 33 )897.680.460759145214711948.26301186423
Winsorized Mean ( 27 / 33 )897.680.460759145214711948.26301186423
Winsorized Mean ( 28 / 33 )897.960.4268560185894782103.66015914982
Winsorized Mean ( 29 / 33 )897.670.3933936779596062281.86178450019
Winsorized Mean ( 30 / 33 )897.670.3933936779596062281.86178450019
Winsorized Mean ( 31 / 33 )897.670.3933936779596062281.86178450019
Winsorized Mean ( 32 / 33 )897.670.3933936779596062281.86178450019
Winsorized Mean ( 33 / 33 )897.670.3933936779596062281.86178450019
Trimmed Mean ( 1 / 33 )897.8367346938780.9320139966513963.329668781564
Trimmed Mean ( 2 / 33 )897.8020833333330.8909412270752431007.70068333308
Trimmed Mean ( 3 / 33 )897.7446808510640.8598862100653361044.02730308101
Trimmed Mean ( 4 / 33 )897.7173913043480.8332446963946911077.37546388068
Trimmed Mean ( 5 / 33 )897.70.8108887557995531107.05690956936
Trimmed Mean ( 6 / 33 )897.6818181818180.7905769249277231135.47687755228
Trimmed Mean ( 7 / 33 )897.6627906976740.767252183997221169.97098140672
Trimmed Mean ( 8 / 33 )897.6785714285710.7489063960864351198.65256341724
Trimmed Mean ( 9 / 33 )897.695121951220.7277129254686091233.58413810384
Trimmed Mean ( 10 / 33 )897.68750.7092503061934081265.68503694830
Trimmed Mean ( 11 / 33 )897.6794871794870.6877680120574481305.20680148251
Trimmed Mean ( 12 / 33 )897.6842105263160.6660200003097191347.83371386575
Trimmed Mean ( 13 / 33 )897.6756756756760.6439730287791031393.96470901516
Trimmed Mean ( 14 / 33 )897.6805555555560.6285544899423111428.16664254191
Trimmed Mean ( 15 / 33 )897.6857142857140.617060035850511454.77856631634
Trimmed Mean ( 16 / 33 )897.6911764705880.6033479002501371487.85000511052
Trimmed Mean ( 17 / 33 )897.7121212121210.5904307429706091520.43593918484
Trimmed Mean ( 18 / 33 )897.718750.5788384433089951550.89690461485
Trimmed Mean ( 19 / 33 )897.7419354838710.5683971748914891579.42715963579
Trimmed Mean ( 20 / 33 )897.7666666666670.5555913359475991615.87592998632
Trimmed Mean ( 21 / 33 )897.7758620689650.5444784733060561648.873015342
Trimmed Mean ( 22 / 33 )897.7857142857140.5306907320287631691.73053174227
Trimmed Mean ( 23 / 33 )897.8148148148150.5180690874458041733.00209676907
Trimmed Mean ( 24 / 33 )897.8269230769230.5076497422624391768.59525048823
Trimmed Mean ( 25 / 33 )897.840.494343514374451816.22692296498
Trimmed Mean ( 26 / 33 )897.8750.4825179598318431860.81156505119
Trimmed Mean ( 27 / 33 )897.8913043478260.4736825216059191895.55506777771
Trimmed Mean ( 28 / 33 )897.909090909090.4618857635623631944.00685568620
Trimmed Mean ( 29 / 33 )897.909090909090.4531596696418641981.44087186469
Trimmed Mean ( 30 / 33 )897.9250.4477686845591092005.33228643297
Trimmed Mean ( 31 / 33 )897.9473684210530.4397519157415552041.94077678284
Trimmed Mean ( 32 / 33 )897.9722222222220.4281486745021572097.33738698681
Trimmed Mean ( 33 / 33 )8980.4115099797794832182.20710098261
Median898
Midrange899.5
Midmean - Weighted Average at Xnp897.576923076923
Midmean - Weighted Average at X(n+1)p897.576923076923
Midmean - Empirical Distribution Function897.576923076923
Midmean - Empirical Distribution Function - Averaging897.576923076923
Midmean - Empirical Distribution Function - Interpolation897.576923076923
Midmean - Closest Observation897.576923076923
Midmean - True Basic - Statistics Graphics Toolkit897.576923076923
Midmean - MS Excel (old versions)897.927272727273
Number of observations100


Variability - Ungrouped Data
Absolute range47
Relative range (unbiased)4.83178953406851
Relative range (biased)4.85613119711254
Variance (unbiased)94.619292929293
Variance (biased)93.6731
Standard Deviation (unbiased)9.72724487865361
Standard Deviation (biased)9.67848645192005
Coefficient of Variation (unbiased)0.0108336895972174
Coefficient of Variation (biased)0.0107793850467440
Mean Squared Error (MSE versus 0)806264.21
Mean Squared Error (MSE versus Mean)93.6731
Mean Absolute Deviation from Mean (MAD Mean)7.623
Mean Absolute Deviation from Median (MAD Median)7.61
Median Absolute Deviation from Mean6.13
Median Absolute Deviation from Median6
Mean Squared Deviation from Mean93.6731
Mean Squared Deviation from Median93.69
Interquartile Difference (Weighted Average at Xnp)12
Interquartile Difference (Weighted Average at X(n+1)p)12.75
Interquartile Difference (Empirical Distribution Function)12
Interquartile Difference (Empirical Distribution Function - Averaging)12.5
Interquartile Difference (Empirical Distribution Function - Interpolation)12.25
Interquartile Difference (Closest Observation)12
Interquartile Difference (True Basic - Statistics Graphics Toolkit)12.25
Interquartile Difference (MS Excel (old versions))13
Semi Interquartile Difference (Weighted Average at Xnp)6
Semi Interquartile Difference (Weighted Average at X(n+1)p)6.375
Semi Interquartile Difference (Empirical Distribution Function)6
Semi Interquartile Difference (Empirical Distribution Function - Averaging)6.25
Semi Interquartile Difference (Empirical Distribution Function - Interpolation)6.125
Semi Interquartile Difference (Closest Observation)6
Semi Interquartile Difference (True Basic - Statistics Graphics Toolkit)6.125
Semi Interquartile Difference (MS Excel (old versions))6.5
Coefficient of Quartile Variation (Weighted Average at Xnp)0.00668896321070234
Coefficient of Quartile Variation (Weighted Average at X(n+1)p)0.00710405348934392
Coefficient of Quartile Variation (Empirical Distribution Function)0.00668896321070234
Coefficient of Quartile Variation (Empirical Distribution Function - Averaging)0.00696572861521315
Coefficient of Quartile Variation (Empirical Distribution Function - Interpolation)0.0068273651943709
Coefficient of Quartile Variation (Closest Observation)0.00668896321070234
Coefficient of Quartile Variation (True Basic - Statistics Graphics Toolkit)0.0068273651943709
Coefficient of Quartile Variation (MS Excel (old versions))0.00724233983286908
Number of all Pairs of Observations4950
Squared Differences between all Pairs of Observations189.238585858586
Mean Absolute Differences between all Pairs of Observations10.9933333333333
Gini Mean Difference10.9933333333333
Leik Measure of Dispersion0.505004155176229
Index of Diversity0.98999883804858
Index of Qualitative Variation0.999998826311697
Coefficient of Dispersion0.00848886414253897
Observations100


Percentiles - Ungrouped Data
pWeighted Average at XnpWeighted Average at X(n+1)pEmpirical Distribution FunctionEmpirical Distribution Function - AveragingEmpirical Distribution Function - InterpolationClosest ObservationTrue Basic - Statistics Graphics ToolkitMS Excel (old versions)
0.01876876.01876876.5876.99876876.99876
0.02877877.08877879880.92877880.92877
0.03881881881881881881881881
0.04881881.04881881.5881.96881881.96881
0.05882882.05882882.5882.95882882.95882
0.06883883883883883883883883
0.07883883883883883883883883
0.08883883883883883883883883
0.09883883.18883884884.82883884.82883
0.1885885885885885885885885
0.11885885885885885885885885
0.12885885.12885885.5885.88885885.88885
0.13886886.13886886.5886.87886886.87886
0.14887887.14888888887.86887887.86887
0.15888888888888888888888888
0.16888888888888888888888888
0.17888888.17888888.5888.83888888.83888
0.18889889889889889889889889
0.19889889889889889889889889
0.2889889.2889889.5889.8889889.8889
0.21890890890890890890890890
0.22890890890890890890890890
0.23890890.23890890.5890.77890890.77890
0.24891891891891891891891891
0.25891891891891891891891891
0.26891891.26891891.5891.74891891.74891
0.27892892892892892892892892
0.28892892.28893893892.72892892.72892
0.29893893893893893893893893
0.3893893893893893893893893
0.31893893893893893893893893
0.32893893893893893893893893
0.33893893893893893893893893
0.34893893.34893893.5893.66893893.66893
0.35894894894894894894894894
0.36894894.36894894.5894.64894894.64894
0.37895895895895895895895895
0.38895895895895895895895895
0.39895895.39895895.5895.61895895.61895
0.4896896896896896896896896
0.41896896896896896896896896
0.42896896896896896896896896
0.43896896896896896896896896
0.44896896.44896896.5896.56896896.56896
0.45897897.45897897.5897.55897897.55897
0.46898898898898898898898898
0.47898898898898898898898898
0.48898898898898898898898898
0.49898898898898898898898898
0.5898898898898898898898898
0.51898898898898898898898898
0.52898898.52898898.5898.48898898.48899
0.53899899899899899899899899
0.54899899899899899899899899
0.55899899899899899899899899
0.56899899.56900900899.44899899.44900
0.57900900900900900900900900
0.58900900900900900900900900
0.59900900900900900900900900
0.6900900900900900900900900
0.61900900.61900900.5900.39900900.39901
0.62901901901901901901901901
0.63901901901901901901901901
0.64901901901901901901901901
0.65901901901901901901901901
0.66901901.66901901.5901.34901901.34902
0.67902902902902902902902902
0.68902902902902902902902902
0.69902902902902902902902902
0.7902902902902902902902902
0.71902902.71902902.5902.29902902.29903
0.72903903903903903903903903
0.73903903903903903903903903
0.74903903903903903903903903
0.75903903.75903903.5903.25903903.25904
0.76904904904904904904904904
0.77904904904904904904904904
0.78904904.78904904.5904.22904904.22905
0.79905905905905905905905905
0.8905905905905905905905905
0.81905905905905905905905905
0.82905905.82905905.5905.18905905.18906
0.83906906906906906906906906
0.84906906.84906906.5906.16906906.16907
0.85907907907907907907907907
0.86907907.86907907.5907.14907907.14908
0.87908909.74908909908.26908908.26910
0.88910910910910910910910910
0.89910910.89910910.5910.11910910.11911
0.9911911911911911911911911
0.91911911911911911911911911
0.92911911911911911911911911
0.93911913.79911912.5911.21911911.21914
0.94914914914914914914914914
0.95914914.95914914.5914.05914914.05915
0.96915916.92915916915.08915915.08917
0.97917919.91917918.5917.09917917.09920
0.98920921.96920921920.04920920.04922
0.99922922.99922922.5922.01922922.01923


Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[875,880[877.520.020.020.004
[880,885[882.5100.10.120.02
[885,890[887.5110.110.230.022
[890,895[892.5160.160.390.032
[895,900[897.5220.220.610.044
[900,905[902.5210.210.820.042
[905,910[907.570.070.890.014
[910,915[912.570.070.960.014
[915,920[917.520.020.980.004
[920,925]922.520.0210.004


Properties of Density Trace
Bandwidth3.27547130996441
#Observations100
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/1064y1210965341.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/1064y1210965341.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/2xujp1210965341.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/2xujp1210965341.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/535i51210965341.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/535i51210965341.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/7y1jf1210965341.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/7y1jf1210965341.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/9adxc1210965342.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/16/t1210965502cuzw2x76c6pmehs/9adxc1210965342.ps (open in new window)


 
Parameters (Session):
 
Parameters (R input):
 
R code (references can be found in the software module):
load(file='createtable')
x <-sort(x[!is.na(x)])
num <- 50
res <- array(NA,dim=c(num,3))
geomean <- function(x) {
return(exp(mean(log(x))))
}
harmean <- function(x) {
return(1/mean(1/x))
}
quamean <- function(x) {
return(sqrt(mean(x*x)))
}
winmean <- function(x) {
x <-sort(x[!is.na(x)])
n<-length(x)
denom <- 3
nodenom <- n/denom
if (nodenom>40) denom <- n/40
sqrtn = sqrt(n)
roundnodenom = floor(nodenom)
win <- array(NA,dim=c(roundnodenom,2))
for (j in 1:roundnodenom) {
win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n
win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn
}
return(win)
}
trimean <- function(x) {
x <-sort(x[!is.na(x)])
n<-length(x)
denom <- 3
nodenom <- n/denom
if (nodenom>40) denom <- n/40
sqrtn = sqrt(n)
roundnodenom = floor(nodenom)
tri <- array(NA,dim=c(roundnodenom,2))
for (j in 1:roundnodenom) {
tri[j,1] <- mean(x,trim=j/n)
tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2)
}
return(tri)
}
midrange <- function(x) {
return((max(x)+min(x))/2)
}
q1 <- function(data,n,p,i,f) {
np <- n*p;
i <<- floor(np)
f <<- np - i
qvalue <- (1-f)*data[i] + f*data[i+1]
}
q2 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
qvalue <- (1-f)*data[i] + f*data[i+1]
}
q3 <- function(data,n,p,i,f) {
np <- n*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
qvalue <- data[i+1]
}
}
q4 <- function(data,n,p,i,f) {
np <- n*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- (data[i]+data[i+1])/2
} else {
qvalue <- data[i+1]
}
}
q5 <- function(data,n,p,i,f) {
np <- (n-1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i+1]
} else {
qvalue <- data[i+1] + f*(data[i+2]-data[i+1])
}
}
q6 <- function(data,n,p,i,f) {
np <- n*p+0.5
i <<- floor(np)
f <<- np - i
qvalue <- data[i]
}
q7 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
qvalue <- f*data[i] + (1-f)*data[i+1]
}
}
q8 <- function(data,n,p,i,f) {
np <- (n+1)*p
i <<- floor(np)
f <<- np - i
if (f==0) {
qvalue <- data[i]
} else {
if (f == 0.5) {
qvalue <- (data[i]+data[i+1])/2
} else {
if (f < 0.5) {
qvalue <- data[i]
} else {
qvalue <- data[i+1]
}
}
}
}
iqd <- function(x,def) {
x <-sort(x[!is.na(x)])
n<-length(x)
if (def==1) {
qvalue1 <- q1(x,n,0.25,i,f)
qvalue3 <- q1(x,n,0.75,i,f)
}
if (def==2) {
qvalue1 <- q2(x,n,0.25,i,f)
qvalue3 <- q2(x,n,0.75,i,f)
}
if (def==3) {
qvalue1 <- q3(x,n,0.25,i,f)
qvalue3 <- q3(x,n,0.75,i,f)
}
if (def==4) {
qvalue1 <- q4(x,n,0.25,i,f)
qvalue3 <- q4(x,n,0.75,i,f)
}
if (def==5) {
qvalue1 <- q5(x,n,0.25,i,f)
qvalue3 <- q5(x,n,0.75,i,f)
}
if (def==6) {
qvalue1 <- q6(x,n,0.25,i,f)
qvalue3 <- q6(x,n,0.75,i,f)
}
if (def==7) {
qvalue1 <- q7(x,n,0.25,i,f)
qvalue3 <- q7(x,n,0.75,i,f)
}
if (def==8) {
qvalue1 <- q8(x,n,0.25,i,f)
qvalue3 <- q8(x,n,0.75,i,f)
}
iqdiff <- qvalue3 - qvalue1
return(c(iqdiff,iqdiff/2,iqdiff/(qvalue3 + qvalue1)))
}
midmean <- function(x,def) {
x <-sort(x[!is.na(x)])
n<-length(x)
if (def==1) {
qvalue1 <- q1(x,n,0.25,i,f)
qvalue3 <- q1(x,n,0.75,i,f)
}
if (def==2) {
qvalue1 <- q2(x,n,0.25,i,f)
qvalue3 <- q2(x,n,0.75,i,f)
}
if (def==3) {
qvalue1 <- q3(x,n,0.25,i,f)
qvalue3 <- q3(x,n,0.75,i,f)
}
if (def==4) {
qvalue1 <- q4(x,n,0.25,i,f)
qvalue3 <- q4(x,n,0.75,i,f)
}
if (def==5) {
qvalue1 <- q5(x,n,0.25,i,f)
qvalue3 <- q5(x,n,0.75,i,f)
}
if (def==6) {
qvalue1 <- q6(x,n,0.25,i,f)
qvalue3 <- q6(x,n,0.75,i,f)
}
if (def==7) {
qvalue1 <- q7(x,n,0.25,i,f)
qvalue3 <- q7(x,n,0.75,i,f)
}
if (def==8) {
qvalue1 <- q8(x,n,0.25,i,f)
qvalue3 <- q8(x,n,0.75,i,f)
}
midm <- 0
myn <- 0
roundno4 <- round(n/4)
round3no4 <- round(3*n/4)
for (i in 1:n) {
if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){
midm = midm + x[i]
myn = myn + 1
}
}
midm = midm / myn
return(midm)
}
range <- max(x) - min(x)
lx <- length(x)
biasf <- (lx-1)/lx
varx <- var(x)
bvarx <- varx*biasf
sdx <- sqrt(varx)
mx <- mean(x)
bsdx <- sqrt(bvarx)
x2 <- x*x
mse0 <- sum(x2)/lx
xmm <- x-mx
xmm2 <- xmm*xmm
msem <- sum(xmm2)/lx
axmm <- abs(x - mx)
medx <- median(x)
axmmed <- abs(x - medx)
xmmed <- x - medx
xmmed2 <- xmmed*xmmed
msemed <- sum(xmmed2)/lx
qarr <- array(NA,dim=c(8,3))
for (j in 1:8) {
qarr[j,] <- iqd(x,j)
}
sdpo <- 0
adpo <- 0
for (i in 1:(lx-1)) {
for (j in (i+1):lx) {
ldi <- x[i]-x[j]
aldi <- abs(ldi)
sdpo = sdpo + ldi * ldi
adpo = adpo + aldi
}
}
denom <- (lx*(lx-1)/2)
sdpo = sdpo / denom
adpo = adpo / denom
gmd <- 0
for (i in 1:lx) {
for (j in 1:lx) {
ldi <- abs(x[i]-x[j])
gmd = gmd + ldi
}
}
gmd <- gmd / (lx*(lx-1))
sumx <- sum(x)
pk <- x / sumx
ck <- cumsum(pk)
dk <- array(NA,dim=lx)
for (i in 1:lx) {
if (ck[i] <= 0.5) dk[i] <- ck[i] else dk[i] <- 1 - ck[i]
}
bigd <- sum(dk) * 2 / (lx-1)
iod <- 1 - sum(pk*pk)
res[1,] <- c('Absolute range','http://www.xycoon.com/absolute.htm', range)
res[2,] <- c('Relative range (unbiased)','http://www.xycoon.com/relative.htm', range/sd(x))
res[3,] <- c('Relative range (biased)','http://www.xycoon.com/relative.htm', range/sqrt(varx*biasf))
res[4,] <- c('Variance (unbiased)','http://www.xycoon.com/unbiased.htm', varx)
res[5,] <- c('Variance (biased)','http://www.xycoon.com/biased.htm', bvarx)
res[6,] <- c('Standard Deviation (unbiased)','http://www.xycoon.com/unbiased1.htm', sdx)
res[7,] <- c('Standard Deviation (biased)','http://www.xycoon.com/biased1.htm', bsdx)
res[8,] <- c('Coefficient of Variation (unbiased)','http://www.xycoon.com/variation.htm', sdx/mx)
res[9,] <- c('Coefficient of Variation (biased)','http://www.xycoon.com/variation.htm', bsdx/mx)
res[10,] <- c('Mean Squared Error (MSE versus 0)','http://www.xycoon.com/mse.htm', mse0)
res[11,] <- c('Mean Squared Error (MSE versus Mean)','http://www.xycoon.com/mse.htm', msem)
res[12,] <- c('Mean Absolute Deviation from Mean (MAD Mean)', 'http://www.xycoon.com/mean2.htm', sum(axmm)/lx)
res[13,] <- c('Mean Absolute Deviation from Median (MAD Median)', 'http://www.xycoon.com/median1.htm', sum(axmmed)/lx)
res[14,] <- c('Median Absolute Deviation from Mean', 'http://www.xycoon.com/mean3.htm', median(axmm))
res[15,] <- c('Median Absolute Deviation from Median', 'http://www.xycoon.com/median2.htm', median(axmmed))
res[16,] <- c('Mean Squared Deviation from Mean', 'http://www.xycoon.com/mean1.htm', msem)
res[17,] <- c('Mean Squared Deviation from Median', 'http://www.xycoon.com/median.htm', msemed)
mylink1 <- hyperlink('http://www.xycoon.com/difference.htm','Interquartile Difference','')
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[18,] <- c('', mylink2, qarr[1,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[19,] <- c('', mylink2, qarr[2,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[20,] <- c('', mylink2, qarr[3,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[21,] <- c('', mylink2, qarr[4,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[22,] <- c('', mylink2, qarr[5,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ')
res[23,] <- c('', mylink2, qarr[6,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[24,] <- c('', mylink2, qarr[7,1])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[25,] <- c('', mylink2, qarr[8,1])
mylink1 <- hyperlink('http://www.xycoon.com/deviation.htm','Semi Interquartile Difference','')
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[26,] <- c('', mylink2, qarr[1,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[27,] <- c('', mylink2, qarr[2,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[28,] <- c('', mylink2, qarr[3,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[29,] <- c('', mylink2, qarr[4,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[30,] <- c('', mylink2, qarr[5,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ')
res[31,] <- c('', mylink2, qarr[6,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[32,] <- c('', mylink2, qarr[7,2])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[33,] <- c('', mylink2, qarr[8,2])
mylink1 <- hyperlink('http://www.xycoon.com/variation1.htm','Coefficient of Quartile Variation','')
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ')
res[34,] <- c('', mylink2, qarr[1,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ')
res[35,] <- c('', mylink2, qarr[2,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ')
res[36,] <- c('', mylink2, qarr[3,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ')
res[37,] <- c('', mylink2, qarr[4,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ')
res[38,] <- c('', mylink2, qarr[5,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ')
res[39,] <- c('', mylink2, qarr[6,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ')
res[40,] <- c('', mylink2, qarr[7,3])
mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ')
res[41,] <- c('', mylink2, qarr[8,3])
res[42,] <- c('Number of all Pairs of Observations', 'http://www.xycoon.com/pair_numbers.htm', lx*(lx-1)/2)
res[43,] <- c('Squared Differences between all Pairs of Observations', 'http://www.xycoon.com/squared_differences.htm', sdpo)
res[44,] <- c('Mean Absolute Differences between all Pairs of Observations', 'http://www.xycoon.com/mean_abs_differences.htm', adpo)
res[45,] <- c('Gini Mean Difference', 'http://www.xycoon.com/gini_mean_difference.htm', gmd)
res[46,] <- c('Leik Measure of Dispersion', 'http://www.xycoon.com/leiks_d.htm', bigd)
res[47,] <- c('Index of Diversity', 'http://www.xycoon.com/diversity.htm', iod)
res[48,] <- c('Index of Qualitative Variation', 'http://www.xycoon.com/qualitative_variation.htm', iod*lx/(lx-1))
res[49,] <- c('Coefficient of Dispersion', 'http://www.xycoon.com/dispersion.htm', sum(axmm)/lx/medx)
res[50,] <- c('Observations', '', lx)
res
(arm <- mean(x))
sqrtn <- sqrt(length(x))
(armse <- sd(x) / sqrtn)
(armose <- arm / armse)
(geo <- geomean(x))
(har <- harmean(x))
(qua <- quamean(x))
(win <- winmean(x))
(tri <- trimean(x))
(midr <- midrange(x))
midm <- array(NA,dim=8)
for (j in 1:8) midm[j] <- midmean(x,j)
midm
bitmap(file='test1.png')
lb <- win[,1] - 2*win[,2]
ub <- win[,1] + 2*win[,2]
if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main='Robustness of Central Tendency', xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main='Robustness of Central Tendency', xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax))
lines(ub,lty=3)
lines(lb,lty=3)
grid()
dev.off()
bitmap(file='test2.png')
lb <- tri[,1] - 2*tri[,2]
ub <- tri[,1] + 2*tri[,2]
if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main='Robustness of Central Tendency', xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main='Robustness of Central Tendency', xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax))
lines(ub,lty=3)
lines(lb,lty=3)
grid()
dev.off()
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Measure',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.element(a,'S.E.',header=TRUE)
a<-table.element(a,'Value/S.E.',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE)
a<-table.element(a,arm)
a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean'))
a<-table.element(a,armose)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE)
a<-table.element(a,geo)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE)
a<-table.element(a,har)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE)
a<-table.element(a,qua)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
for (j in 1:length(win[,1])) {
a<-table.row.start(a)
mylabel <- paste('Winsorized Mean (',j)
mylabel <- paste(mylabel,'/')
mylabel <- paste(mylabel,length(win[,1]))
mylabel <- paste(mylabel,')')
a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE)
a<-table.element(a,win[j,1])
a<-table.element(a,win[j,2])
a<-table.element(a,win[j,1]/win[j,2])
a<-table.row.end(a)
}
for (j in 1:length(tri[,1])) {
a<-table.row.start(a)
mylabel <- paste('Trimmed Mean (',j)
mylabel <- paste(mylabel,'/')
mylabel <- paste(mylabel,length(tri[,1]))
mylabel <- paste(mylabel,')')
a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE)
a<-table.element(a,tri[j,1])
a<-table.element(a,tri[j,2])
a<-table.element(a,tri[j,1]/tri[j,2])
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE)
a<-table.element(a,median(x))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE)
a<-table.element(a,midr)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[1])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[2])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[3])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[4])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[5])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[6])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[7])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[8])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of observations',header=TRUE)
a<-table.element(a,length(x))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variability - Ungrouped Data',2,TRUE)
a<-table.row.end(a)
for (i in 1:num) {
a<-table.row.start(a)
if (res[i,1] != '') {
a<-table.element(a,hyperlink(res[i,2],res[i,1],''),header=TRUE)
} else {
a<-table.element(a,res[i,2],header=TRUE)
}
a<-table.element(a,res[i,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
lx <- length(x)
qval <- array(NA,dim=c(99,8))
mystep <- 25
mystart <- 25
if (lx>10){
mystep=10
mystart=10
}
if (lx>20){
mystep=5
mystart=5
}
if (lx>50){
mystep=2
mystart=2
}
if (lx>=100){
mystep=1
mystart=1
}
for (perc in seq(mystart,99,mystep)) {
qval[perc,1] <- q1(x,lx,perc/100,i,f)
qval[perc,2] <- q2(x,lx,perc/100,i,f)
qval[perc,3] <- q3(x,lx,perc/100,i,f)
qval[perc,4] <- q4(x,lx,perc/100,i,f)
qval[perc,5] <- q5(x,lx,perc/100,i,f)
qval[perc,6] <- q6(x,lx,perc/100,i,f)
qval[perc,7] <- q7(x,lx,perc/100,i,f)
qval[perc,8] <- q8(x,lx,perc/100,i,f)
}
bitmap(file='test3.png')
myqqnorm <- qqnorm(x,col=2)
qqline(x)
grid()
dev.off()
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Percentiles - Ungrouped Data',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p',1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_1.htm', 'Weighted Average at Xnp',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),1,TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),1,TRUE)
a<-table.row.end(a)
for (perc in seq(mystart,99,mystep)) {
a<-table.row.start(a)
a<-table.element(a,round(perc/100,2),1,TRUE)
for (j in 1:8) {
a<-table.element(a,round(qval[perc,j],6))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
bitmap(file='histogram1.png')
myhist<-hist(x)
dev.off()
myhist
n <- length(x)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/histogram.htm','Frequency Table (Histogram)',''),6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bins',header=TRUE)
a<-table.element(a,'Midpoint',header=TRUE)
a<-table.element(a,'Abs. Frequency',header=TRUE)
a<-table.element(a,'Rel. Frequency',header=TRUE)
a<-table.element(a,'Cumul. Rel. Freq.',header=TRUE)
a<-table.element(a,'Density',header=TRUE)
a<-table.row.end(a)
crf <- 0
mybracket <- '['
mynumrows <- (length(myhist$breaks)-1)
for (i in 1:mynumrows) {
a<-table.row.start(a)
if (i == 1)
dum <- paste('[',myhist$breaks[i],sep='')
else
dum <- paste(mybracket,myhist$breaks[i],sep='')
dum <- paste(dum,myhist$breaks[i+1],sep=',')
if (i==mynumrows)
dum <- paste(dum,']',sep='')
else
dum <- paste(dum,mybracket,sep='')
a<-table.element(a,dum,header=TRUE)
a<-table.element(a,myhist$mids[i])
a<-table.element(a,myhist$counts[i])
rf <- myhist$counts[i]/n
crf <- crf + rf
a<-table.element(a,round(rf,6))
a<-table.element(a,round(crf,6))
a<-table.element(a,round(myhist$density[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
bitmap(file='density1.png')
mydensity1<-density(x,kernel='gaussian',na.rm=TRUE)
plot(mydensity1,main='Gaussian Kernel')
grid()
dev.off()
mydensity1
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Properties of Density Trace',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bandwidth',header=TRUE)
a<-table.element(a,mydensity1$bw)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Observations',header=TRUE)
a<-table.element(a,mydensity1$n)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable4.tab')
 





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