thr3ads.net - R devel - p.s. regarding stripchart missing-data report (PR#2019) [Sep 2002]

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baron@cattell.psych.upenn.edu
2002-Sep-14 17:49 UTC
p.s. regarding stripchart missing-data report (PR#2019)

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In case you want this - and feel free to delete this - here are the
data for the bug I just reported concerning missing data in
stripchart.

You need to run hrp2.R.  Please excuse the extraneous junk.  This was
written mostly by a grad student who was learning R in the process
(Andrea Gurmankin).  She doesn't like to delete things.

Jon


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t2 <- read.table("hrp2.data")
# t1 <- t2[-c(1,2,11,19,20,54,83)] # less than 6 on log scale
t1 <- t2[-c(52,124),] # less than 5

ns <- nrow(t1) # number of Ss remaining

# ----------- define sub-matrices and summary variables

t1[which(t1[,1]==0),1] <- NA
t1[which(t1[,4]==0),4] <- NA
t1[which(t1[,12]==0),12] <- NA
t1[which(t1[,13]==0),13] <- NA
t1[which(t1[,15]==0),15] <- NA
t1[which(t1[,17]==0),17] <- NA
t1[which(t1[,18]==0),18] <- NA
t1[which(t1[,19]==0),19] <- NA
t1[which(t1[,20]==0),20] <- NA
t1[which(t1[,31]==7),31] <- NA
t1[which(t1[,12]==21),12] <- NA
t1[which(t1[,34]==0),34] <- NA
t1[which(t1[,37]==0),37] <- NA
t1[which(t1[,38]==0),38] <- NA
t1[which(t1[,39]=="y"),39] <- 1
t1[which(t1[,41]==5),41] <- NA
t1[which(t1[,43]==5),43] <- NA
t1[which(t1[,44]==5),44] <- NA
t1[which(t1[,46]==3),46] <- NA
t1[which(t1[,50]==5),50] <- NA
t1[which(t1[,51]==5),51] <- NA
t1[which(t1[,52]==5),52] <- NA
t1[which(t1[,53]==5),53] <- NA
t1[which(t1[,56]==5),56] <- NA
t1[which(t1[,58]==4),58] <- NA
t1[which(t1[,59]==4),59] <- NA
t1[which(t1[,60]==3),60] <- NA
t1[which(t1[,60]==11),60] <- 1
t1[which(t1[,61]==3),61] <- NA
t1[which(t1[,64]==5),64] <- NA
t1[which(t1[,65]==5),65] <- NA
t1[which(t1[,66]==5),66] <- NA
t1[which(t1[,67]==5),67] <- NA
t1[which(t1[,71]==3),71] <- NA
t1[which(t1[,72]==11),72] <- 1
t1[which(t1[,72]==4),72] <- NA
t1[which(t1[,74]==0),74] <- NA
t1[which(t1[,74]=="n"),74] <- 2
t1[which(t1[,75]=="n"),75] <- 2
t1[which(t1[,76]=="y"),76] <- 1
t1[which(t1[,77]==11),77] <- 1
t1[which(t1[,77]=="y"),77] <- 1
t1[which(t1[,78]==11),78] <- 1
t1[which(t1[,78]=="y"),78] <- 1
t1[which(t1[,79]=="n"),79] <- 2
t1[which(t1[,80]=="n"),80] <- 2
t1[which(t1[,81]=="y"),81] <- 1
t1[which(t1[,82]=="y"),82] <- 1
t1[which(t1[,87]==0),87] <- NA
t1[which(t1[,88]==0),88] <- NA
t1[which(t1[,90]==0),90] <- NA
t1[which(t1[,96]==13),96] <- NA
t1[which(t1[,100]==13),100] <- NA
t1[which(t1[,101]==11),101] <- 1
t1[which(t1[,101]==110),101] <- NA
t1[which(t1[,105]==0),105] <- NA
t1[which(t1[,113]==110),113] <- NA
t1[which(t1[,115]==0),115] <- NA
t1[which(t1[,213]==5),213] <- NA
t1[which(t1[,214]==6),214] <- NA
t1[which(t1[,215]==11),215] <- 1
t1[which(t1[,217]==0),217] <- NA
t1[which(t1[,218]==24),218] <- NA
t1[which(t1[,222]==23),222] <- NA
t1[which(t1[,227]==5),227] <- NA
t1[which(t1[,231]==5),231] <- NA
t1[which(t1[,236]==5),236] <- NA
t1[which(t1[,242]==12),242] <- NA
t1[which(t1[,243]==42),243] <- NA
t1[which(t1[,245]==41),245] <- NA
t1[which(t1[,246]==50),246] <- NA
t1[which(t1[,247]==100),247] <- NA
t1[which(t1[,248]==50),248] <- NA
t1[which(t1[,248]==55),248] <- NA
t1[which(t1[,250]==50),250] <- NA
t1[which(t1[,250]==83),250] <- NA
t1[which(t1[,250]==92),250] <- NA
t1[which(t1[,250]==93),250] <- NA

qs <- as.matrix(t1[,116:211])
colnames(qs) <-
c(paste("q01",1:24,sep="."),paste("q02",1:24,sep="."),
                 
paste("q03",1:24,sep="."),paste("q04",1:24,sep="."))
times <- qs[,c(0:3*24+3,0:3*24+24)]
tmean <- apply(times,1,mean,trim=.15)

#tmean is time mean for each Ss and trims off top and bottom 15%
plot(sort(tmean))

#each Ss has 6 times
#throw out the longest and shortest 15% times for each Ss and then compute time
means for each Ss
#look at Ss mean for times (plot(sort(tmean)) and eliminate fast outliers. 

act <- as.matrix(t1[,c(1:20,31:40,73:82)]) # 1=y 2=n
colnames(act) <-
c("B.actl.100","B.actl.20","B.actl.40","B.actl.60","B.actl.80","Bfr.actl.100",
"Bfr.actl.20","Bfr.actl.40","Bfr.actl.60","Bfr.actl.80",
"C.actl.100","C.actl.20","C.actl.40","C.actl.60","C.actl.80","Cfr.actl.100",
"Cfr.actl.20","Cfr.actl.40","Cfr.actl.60","Cfr.actl.80",
"L.actl.100","L.actl.20","L.actl.40","L.actl.60","L.actl.80","Lfr.actl.100",
"Lfr.actl.20","Lfr.actl.40","Lfr.actl.60","Lfr.actl.80",
"P.actl.100","P.actl.20","P.actl.40","P.actl.60","P.actl.80","Pfr.actl.100",
"Pfr.actl.20","Pfr.actl.40","Pfr.actl.60","Pfr.actl.80")
act[act=="y"] <- 1 # clean up responses
act[act=="n"] <- 2
act[act=="0"] <- NA # don't know what else to do with these (4
of them)
act[act=="11"] <- 1
act <- apply(act,2,as.numeric) # convert to numeric

lot <- as.matrix(t1[,21:30]) # 5=strongly agree, 1=strongly disagree
colnames(lot) <-
 
c("LOTa","LOTb","LOTc","LOTd","LOTe","LOTf","LOTg","LOTh","LOTi","LOTj")

lot[,c(3,7,9)] <- 6-lot[,c(3,7,9)]
lot.score <- apply(lot[,c(1,3,4,7,9,10)],1,mean,na.rm=T)
#columns correspond to LOTa, LOTc, LOTd, LOTg, LOTi, LOTj

mss <- as.matrix(t1[,41:72]) # 1=do 2=not do
colnames(mss) <-
c("MBSSa1","MBSSa2","MBSSa3","MBSSa4",
                  
"MBSSa5","MBSSa6","MBSSa7","MBSSa8",
                  
"MBSSb1","MBSSb2","MBSSb3","MBSSb4",
                  
"MBSSb5","MBSSb6","MBSSb7","MBSSb8",
                  
"MBSSc1","MBSSc2","MBSSc3","MBSSc4",
                  
"MBSSc5","MBSSc6","MBSSc7","MBSSc8",
                  
"MBSSd1","MBSSd2","MBSSd3","MBSSd4",
                  
"MBSSd5","MBSSd6","MBSSd7","MBSSd8")
mss[mss==11] <- 3 # mss is numeric
mss[mss==2] <- 0

mss.m.score <- (mss[,1] + mss[,4] + mss[,6] + mss[,7] + mss[,10] + mss[,12] +
mss[,13] + mss[,16] + mss[,17] + mss[,18]
 + mss[,20] + mss[,23] + mss[,25] + mss[,28] + mss[,30] + mss[,31])
 
mss.b.score <- (mss[,2] + mss[,3] + mss[,5] + mss[,8] + mss[,9] + mss[,11] +
mss[,14] + mss[,15] + mss[,19] + mss[,21]
 + mss[,22] + mss[,24] + mss[,26] + mss[,27] + mss[,29] + mss[,32])

demos <- as.matrix(t1[,83:115])
denial <- apply(demos[,11:14],c(1,2),as.numeric) # convert to numeric
denial.score <- rowMeans(denial) # subject means (no missing data)

hb <- apply(demos[,15:22],c(1,2),as.numeric) # convert to numeric
num <- demos[,26:30]
num <- demos[,c(26:30,7,10,25,33)] # include bctrans,cc..,lc..,pc..
num[num=="a"] <- 1 # recode a-c to numbers to make scoring easier
num[num=="b"] <- 2
num[num=="c"] <- 3
num[num=="1/4"] <- .25 # happened 2x in pctrans - really a wrong
answer
num <- apply(num,c(1,2),as.numeric)
num.correct <- t(num)==c(500,3,10,100,2,60,.03,10,25)
num.score <- colMeans(num.correct)
demos <- demos[,c(1:6,8,9,23,24,31,32)]
demos[demos=="y"] <- 1
demos[demos=="n"] <- 2
demos[demos=="m"] <- 1
demos[demos=="f"] <- 2
demos[demos=="0"] <- 2 # these were responses to "die"
questions, counted as "no"
demos[demos=="mi"] <- NA # demos is still character, not numeric
demos <- apply(demos,2,as.numeric)
educ <- as.vector(demos[,1])
age <- as.vector(demos[,2])
# health <- as.vector(demos[,3]) # missing
sex <- as.vector(demos[,4])
bc.die <- as.vector(demos[,5])
bc.had <- as.vector(demos[,6])
cc.die <- as.vector(demos[,7])
cc.had <- as.vector(demos[,8])
lc.die <- as.vector(demos[,9])
lc.had <- as.vector(demos[,10])
pc.die <- as.vector(demos[,11])
pc.had <- as.vector(demos[,12])
rm(demos)



sf12 <- as.matrix(t1[,212:222])
colnames(sf12) <-
c("sf12.1","sf12.2a","sf12.2b","sf12.3","sf12.4a","sf12.4b",
"sf12.5","sf12.6a","sf12.6b","sf12.6c","sf12.7")
sf12[sf12=="y"] <- 1
sf12[sf12=="11"] <- 1
sf12[sf12=="n"] <- 2
sf12 <- apply(sf12,2,as.numeric)

stai <- as.matrix(t1[,223:242])
colnames(stai) <-
c("stai.a","stai.b","stai.c","stai.d","stai.e","stai.f",
"stai.g","stai.h","stai.i","stai.j","stai.k","stai.l","stai.m","stai.n",
"stai.o","stai.p","stai.q","stai.r","stai.s","stai.t")
stai[stai==12] <- NA # assume this was an effort to change 1 to 2 (one
response)
stai[stai==5] <- 4
stai.score <- 50+((stai-2.5) %*%
                   c(-1,1,-1,1,1,-1,-1,1,1,-1,1,1,-1,-1,1,-1,1,1,-1,1))


truste <- as.matrix(t1[,243:250])
colnames(truste) <- 
c("trust1a","trust1b","trust1c","trust1d","trust1e",
"trust1f","trust1g","trust2")

truste[,1] <- 6 - truste[,1]
truste[,3] <- 6 - truste[,3]
truste[,5] <- 6 - truste[,5]
truste[,6] <- 6 - truste[,6]
truste[,8] <- ceiling(truste[,8]/2 + (0+truste[,8]==0))

truste.score <- 100*(apply(truste,1,mean,na.rm=T)-1)/4


# write csv file of all data
#write.table(data.frame(act,lot,mss,denial,hb,num,age,truste,
#sex, bc.die, bc.had, cc.die, cc.had, lc.die, lc.had, pc.die,
#pc.had,denial,qs,sf12,stai), file="hrp2.csv",sep=",",
row.names=F)

qs <- array(qs,c(ns,24,4)) # subjects, items, cancers
cancer <- as.vector(qs[,1,])
 cancer[cancer>4] <- 4 # correct some js error
 cancer <- as.factor(cancer) # like "cat" in Systat
probtype <- as.factor(as.vector(floor(qs[,2,]/4)))
subj <- as.factor(rep(1:ns,4))

# ---- this makes one array where the third argument is the condition
# conditions are 1-4=verbal, 5-8=percent, 9-12 fraction
# 1-4 are the four cancers, also 5-8 and 9-12

qsall <- array(NA,c(ns,24,12))
for (i in 1:ns) {for (j in 1:4) {condition <- qs[i,2,j]+1
                                 qsall[i,,condition] <- qs[i,,j]}}
qsall[,1,] <- qsall[,2,] %% 4 + 1
qsall[,2,] <- floor(qsall[,2,]/4)

vnames <-
c("Cancer","Probtype","time1","nprob","conf","vprob","worry",
 
"QOL","trust","comfort","getit.100","getit.80","getit.60","getit.40",
 
"getit.20","docsaid","notnotice","minrisk","exagg","docwrong","notapply",
  "trick","misunderst","time2")
cnames <-
c("prostate.v","colon.v","lung.v","breast.v",
           
"prostate.p","colon.p","lung.p","breast.p",
           
"prostate.f","colon.f","lung.f","breast.f")
dimnames(qsall) <- list(NULL,vnames,cnames)
prostate <- c(1,5,9)
colon <- c(2,6,10)
lung <- c(3,7,11)
breast <- c(4,8,12)
words <- 1:4
percent <- 5:8
fraction <- 9:12
numbers <- 5:12

#Now if you want just the nprob data for numbers (fractions or
#percent), you can refer to nprob.m[,numbers].  You need the comma
#because "numbers" refers to the columns in the matrix.

# use .m for these to distinguish them from later ones
cancer.m <- qsall[,1,]
probtype.m <- qsall[,2,] # skipped time
verbal.m <- (probtype.m==0) + 0 # now coded 1,0
nprob.m <- qsall[,4,]
conf.m <- qsall[,5,]
vprob.m <- qsall[,6,]
worry.m <- qsall[,7,] 
QOL.m <- qsall[,8,]
trust.m <- qsall[,9,]
comfort.m <- qsall[,10,]
getit.100.m <- qsall[,11,]
getit.80.m <- qsall[,12,]
getit.60.m <- qsall[,13,]
getit.40.m <- qsall[,14,] 
getit.20.m <- qsall[,15,]
docsaid.m <- qsall[,16,]
notnotice.m <- qsall[,17,]
minrisk.m <- qsall[,18,]
exagg.m <- qsall[,19,]
docwrong.m <- qsall[,20,]
notapply.m <- qsall[,21,] 
trick.m <- qsall[,22,]
misunderst.m <- qsall[,23,]

# ----------- this part creates a whole bunch of variables from qs for anova

nprob <- as.vector(qs[,4,])
conf <- as.vector(qs[,5,])
vprob <- as.vector(qs[,6,])
worry <- as.vector(qs[,7,])
QOL <- as.vector(qs[,8,])
trust <- as.vector(qs[,9,])
comfort <- as.vector(qs[,10,])
getit.100 <- as.vector(qs[,11,])
getit.80 <- as.vector(qs[,12,])
getit.60 <- as.vector(qs[,13,])
getit.40 <- as.vector(qs[,14,])
getit.20 <- as.vector(qs[,15,])
docsaid <- as.vector(qs[,16,])
docsaid[docsaid==9] <- NA
notnotice <- as.vector(qs[,17,])
notnotice[notnotice==9] <- NA
minrisk <- as.vector(qs[,18,])
exagg <- as.vector(qs[,19,])
docwrong <- as.vector(qs[,20,])
notapply <- as.vector(qs[,21,])
trick <- as.vector(qs[,22,])
misunderst <- as.vector(qs[,23,])
rm(qs)

group <- as.factor((1-is.na(nprob.m[,1:3])) %*% c(1,2,3))

#--------------------
# NEW SECTION 8/6/02
# We need both matrix and vector forms of each deviation score
# New names: deviat
#log odds transformation of nprob to stretch out differences near zero.
rightans.m <- matrix(c(25,.03,10,60),ns,12,byrow=T)
rightans <- rep(c(25,.03,10,60),rep(ns,4))
logo <- function(x) log(x/(100-x))
trim <- function(x) x+(.0001)*(x<.0001)-(.0001)*(x==100)
deviat.logo.m <- logo(trim(nprob.m))-logo(rightans.m)
deviat.logo <- logo(trim(nprob))-logo(rightans)
# apply this to nprob.dev.logo
ctrim <- function(x) {x*(x>0-10 &
x<10)-10*(x<0-10)+10*(x>10)}

deviatt.logo.m <- ctrim(deviat.logo.m)
deviatt.logo <- ctrim(deviat.logo)

plot(deviat.logo,ctrim(deviat.logo))

#within-Ss mean of deviation score
deviat.logo.sm <- apply(deviat.logo.m[,5:12],1,mean,na.rm=T)

mean(deviat.logo.sm,na.rm=T)
              
# Looking at this you can see right away what is going on.
# Estimates are too high, especially for cancer #2 (the low
# one).  But the outliers are also apparent.  I thought that
# cutting them off at + or - 10 seemed reasonable.  So:
                       
#linear transformation of nprob
deviat.lino.m <-
(nprob.m>rightans.m)*(nprob.m-rightans.m)/(100-rightans.m) +
                 (nprob.m<rightans.m)*(nprob.m-rightans.m)/rightans.m
deviat.lino <- (nprob>rightans)*(nprob-rightans)/(100-rightans) +
               (nprob<rightans)*(nprob-rightans)/rightans
deviat.raw.m <- nprob.m-rightans.m

par(las=1)
stripchart(c(t(deviat.lino.m)) ~ rep(cnames,ns))
stripchart(c(t(deviat.logo.m)) ~ rep(cnames,ns))
stripchart(c(t(ctrim(deviat.logo.m))) ~ rep(cnames,ns))
stripchart(c(t(deviat.raw.m)) ~ rep(cnames,ns))

# end of new section (but nprob.dev changed to deviat throughout

# ----- everything after this is commented out and is deletable


nprob.p0 <- nprob[probtype==0]
nprob.p12 <- nprob[probtype!=0]

round(nprob[cancer==0],2)
round(nprob[cancer==1],2)
round(nprob[cancer==2],2)

table(sex)/sum(table(sex))

#na.rm=T  removes NAs so you can take the mean of it.

mean(stai.score,na.rm=T)
mean(denial,na.rm=T)
mean(age,na.rm=T)
sd(age,na.rm=T)
mean(educ,na.rm=T)
sd(educ,na.rm=T)
mean(sf12[,1],na.rm=T)


verbal <- probtype==0

deviat.p.fr <- (25 - (nprob[cancer==1 & probtype==2]))
deviat.p.pt <- (25 - (nprob[cancer==1 & probtype==1]))
deviat.c.fr <- (.03 - (nprob[cancer==2 & probtype==2]))
deviat.c.pt <- (.03 - (nprob[cancer==2 & probtype==1]))
deviat.l.fr <- (10 - (nprob[cancer==3 & probtype==2]))
deviat.l.pt <- (10 - (nprob[cancer==3 & probtype==1]))
deviat.b.fr <- (60 - (nprob[cancer==4 & probtype==2]))
deviat.b.pt <- (60 - (nprob[cancer==4 & probtype==1]))


mean(deviat.p.fr,na.rm=T)
mean(deviat.p.pt,na.rm=T)
mean(deviat.c.fr,na.rm=T)
mean(deviat.c.pt,na.rm=T)
mean(deviat.l.fr,na.rm=T)
mean(deviat.l.pt,na.rm=T)
mean(deviat.b.fr,na.rm=T)
mean(deviat.b.pt,na.rm=T)

nprob.p.fr.acc <- sign(deviat.p.fr)
nprob.p.pt.acc <- sign(deviat.p.fr)
nprob.c.fr.acc <- sign(deviat.c.fr)
nprob.c.pt.acc <- sign(deviat.c.fr)
nprob.l.fr.acc <- sign(deviat.l.fr)
nprob.l.pt.acc <- sign(deviat.l.fr)
nprob.b.fr.acc <- sign(deviat.b.fr)
nprob.b.pt.acc <- sign(deviat.b.fr)

table(nprob.p.fr.acc)/sum(table(nprob.p.fr.acc))
table(nprob.p.pt.acc)/sum(table(nprob.p.pt.acc))
table(nprob.c.fr.acc)/sum(table(nprob.c.fr.acc))
table(nprob.c.pt.acc)/sum(table(nprob.c.pt.acc))
table(nprob.l.fr.acc)/sum(table(nprob.l.fr.acc))
table(nprob.l.pt.acc)/sum(table(nprob.l.pt.acc))
table(nprob.b.fr.acc)/sum(table(nprob.b.fr.acc))
table(nprob.b.pt.acc)/sum(table(nprob.b.pt.acc))

 #------------ notes:
 #P.actl.100 not included in comput, so missing
# recoding work needed on hb (high numbers), trust (same), stai (y/n)

# --------------- do basic analyses (examples)

#readline("examples of data summary follow - press any key to
continue")
#summary(num)
#readline("ugly summary of nprob by cancer and probtype, and plot (look at
it!)")
#by(nprob,list(cancer,probtype),summary)
#stripchart(nprob~cancer,method="stack",offset=1/6,main="by
cancer")
#readline("prostate (look at the plot!)")

stripchart(nprob[cancer==1] ~ probtype[cancer==1],method="stack",
main="Prostate, 0=words 1=pct 2=frac")

#readline("colon")
stripchart(nprob[cancer==2] ~
probtype[cancer==2],method="stack",main="Colon, 0=words 1=pct
2=frac")
#window()
#readline("lung")

stripchart(nprob[cancer==3] ~ probtype[cancer==3],method="stack",
main="Lung, 0=words 1=pct 2=frac")
#window()

#readline("breast")

#commented this below out b/c file is crashing on it.
#stripchart(nprob[cancer==4] ~ probtype[cancer==4],method="stack",
main="Breast, 0=words 1=pct 2=frac")

#readline("analysis of variance - very crude")
#summary(aov(nprob ~ subj+cancer*probtype))

table(sex)/sum(table(sex))

#na.rm=T  removes NAs so you can take the mean of it.

mean(stai.score,na.rm=T)
mean(denial,na.rm=T)
mean(age,na.rm=T)
mean(educ,na.rm=T)
mean(sf12[,1],na.rm=T)

by(nprob, list(cancer,probtype), range)

# calculates the % of times that each response was endorsed across cancer and
probtypes.
# this has double-counting problem
table(docsaid)/sum(table(docsaid))
table(notnotice)/sum(table(notnotice))
table(minrisk)/sum(table(minrisk))
table(exagg)/sum(table(exagg))
table(docwrong)/sum(table(docwrong))
table(notapply)/sum(table(notapply))
table(trick)/sum(table(trick))
table(misunderst)/sum(table(misunderst))

#hist(nprob[cancer==1 & probtype==0], main="Prostate, words only",
xlab="Probability", ylim=range(12,0), br=c(0, 10, 20,
#30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==1 & probtype==1], main="Prostate, words &
percent", xlab="Probability", ylim=range(12,0), br=c(0,
#10, 20, 30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==1 & probtype==2], main="Prostate, words &
fraction", xlab="Probability", ylim=range(12,0), br=c(0,
#10, 20, 30, 40, 50, 60, 70, 80, 90, 100))

#hist(nprob[cancer==2 & probtype==0], main="Colon, words only",
xlab="Probability", ylim=range(12,0), br=c(0, 10, 20,
#30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==2 & probtype==1], main="Colon, words &
percent", xlab="Probability", ylim=range(12,0), br=c(0, 10,
#20, 30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==2 & probtype==2], main="Colon, words &
fraction", xlab="Probability", ylim=range(12,0), br=c(0, 10,
#20, 30, 40, 50, 60, 70, 80, 90, 100))

#hist(nprob[cancer==3 & probtype==0], main="Lung, words only",
xlab="Probability", ylim=range(12,0), br=c(0, 10, 20, 30,
#40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==3 & probtype==1], main="Lung, words &
percent", xlab="Probability", ylim=range(12,0), br=c(0, 10,
#20, 30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==3 & probtype==2], main="Lung, words &
fraction", xlab="Probability", ylim=range(12,0), br=c(0, 10,
#20, 30, 40, 50, 60, 70, 80, 90, 100))

#hist(nprob[cancer==4 & probtype==0], main="Breast, words only",
xlab="Probability", ylim=range(12,0), br=c(0, 10, 20,
#30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==4 & probtype==1], main="Breast, words &
percent", xlab="Probability", ylim=range(12,0), br=c(0, 10,
#20, 30, 40, 50, 60, 70, 80, 90, 100))
#hist(nprob[cancer==4 & probtype==2], main="Breast, words &
fraction", xlab="Probability", ylim=range(12,0), br=c(0, 10,
#20, 30, 40, 50, 60, 70, 80, 90, 100))

#could do for prostate in words only b/c in cnames it is column 1
#hist(nprob.m[,1], main="Prostate, words only",
xlab="Probability", ylim=range(12,0), br=c(0, 10, 20, 30, 40, 50, 60,
#70, 80, 90, 100))

#could also do means this way, converting to a dataframe:

#nprob.d <- as.data.frame(nprob.m)
#mean(nprob.d$prostate.v, na.rm=T)

table(pc.die)/sum(table(pc.die))
table(cc.die)/sum(table(cc.die))
table(lc.die)/sum(table(lc.die))
table(bc.die)/sum(table(bc.die))

table(pc.had)/sum(table(pc.had))
table(cc.had)/sum(table(cc.had))
table(lc.had)/sum(table(lc.had))
table(bc.had)/sum(table(bc.had))

table(num.score)/sum(table(num.score))

cor.test(QOL, nprob)
# want to do the same test, but with deviations.

cor.test(worry, nprob)

#So the within subject mean trust for numbers would be
apply(trust.m[,numbers],1,mean,na.rm=T)
apply(comfort.m[,numbers],1,mean,na.rm=T)

#The 1 is for rows, and you need na.rm=T because most data are
#missing.  Of course, most of the time, this would just be the
#value for the one trial that the subject got in that condition.
#The whole t test would be

t.test(apply(trust.m[,5:12],1,mean,na.rm=T)-apply(trust.m[,1:4],1,mean,na.rm=T))
t.test(apply(comfort.m[,5:12],1,mean,na.rm=T)-apply(comfort.m[,1:4],1,mean,na.rm=T))
#These are within-Ss ttests of trust in numbers versions compared to trust in
words versions.

#Notice that this simply tests the difference.  Of course, this
#totally ignores cancer type.  This works, at p<.05.  But that is
#still important because it rejects the view that the effect goes
#the other way.

verbal <- probtype==0
lm0 <- lm(trust ~ subj)
lm1 <- lm(trust ~ subj+cancer)
lm2 <- lm(trust ~ subj+cancer+verbal)

#Then I compared successive models:
anova(lm0,lm1)
anova(lm1,lm2)
#The first comparison shows that cancer matters.  The second shows
#that verbal is still significant.  The result is slightly
#different from the t test, and slightly not as good, but I
#suspect that this is just random.  


lm13 <- lm(comfort ~ subj)
lm14 <- lm(comfort ~ subj+cancer)
lm15 <- lm(comfort ~ subj+cancer+verbal)

anova(lm1)
anova(lm2)
anova(lm14)
anova(lm15)


#to get one probtype at a time, "sort" removes missing data
#stripchart(sort(nprob.m[,c(1)]), method="stack", main="Prostate,
words only")
#window()
#stripchart(sort(nprob.m[,c(5)]), method="stack", main="Prostate,
percent")
#window()
#stripchart(sort(nprob.m[,c(9)]), method="stack", main="Prostate,
fraction")
#window()

#t.test(nprob.m[pc.had==1,c(1,5,9)],nprob.m[pc.had==2,c(1,5,9)])
#t.test(nprob.m[cc.had==1,c(2,6,10)],nprob.m[cc.had==2,c(2,6,10)])
#t.test(nprob.m[lc.had==1,c(3,7,11)],nprob.m[lc.had==2,c(3,7,11)])
#t.test(nprob.m[bc.had==1,c(4,8,12)],nprob.m[bc.had==2,c(4,8,12)])

c.had <- c(pc.had,cc.had,lc.had,bc.had)
# to use for an anova to see if having known someone w/ a certain cancer
#  makes your nprob deviate more in the pos
#direction
#do this w/ the transformed nprob!

summary(aov(deviat.logo~subj+cancer+verbal+c.had))

#to test within-Ss to see if Ss were more likely to say yes to minrisk (etc) in
words than numbers versions
minrisk.words.sm <- apply(minrisk.m[,1:4],1,mean,na.rm=T)
minrisk.numbers.sm <- apply(minrisk.m[,5:12],1,mean,na.rm=T)

exagg.words.sm <- apply(exagg.m[,1:4],1,mean,na.rm=T)
exagg.numbers.sm <- apply(exagg.m[,5:12],1,mean,na.rm=T)

#or just go directly to:
wilcox.test(apply(minrisk.m[,1:4],1,mean,na.rm=T)-
       apply(minrisk.m[,5:12],1,mean,na.rm=T))
wilcox.test(apply(exagg.m[,1:4],1,mean,na.rm=T)-
       apply(exagg.m[,5:12],1,mean,na.rm=T))

minrisk.sm <- (apply(minrisk.m[,1:4],1,mean,na.rm=T)+
               apply(minrisk.m[,5:12],1,mean,na.rm=T))/2
   
exagg.sm <- (apply(exagg.m[,1:4],1,mean,na.rm=T)+
apply(exagg.m[,5:12],1,mean,na.rm=T))/2
       
sf12.1 <- rep(sf12[,1],4)
   
QOL.numbers.sm <-apply(QOL.m[,5:12],1,mean,na.rm=T)
worry.numbers.sm <-apply(worry.m[,5:12],1,mean,na.rm=T)
trust.numbers.sm <-apply(trust.m[,5:12],1,mean,na.rm=T)
comfort.numbers.sm <-apply(comfort.m[,5:12],1,mean,na.rm=T)


lm4 <- lm(deviat.logo.sm ~ 
stai.score+lot.score+educ+denial.score+sex+truste.score+minrisk.numbers.sm+exagg.numbers.sm+QOL.numbers.sm
+worry.numbers.sm+trust.numbers.sm+comfort.numbers.sm+mss.m.score+mss.b.score)
anova(lm4)

#lm5 <- lm(deviat.logo.sm ~
stai.score+lot.score+educ+sex+minrisk.numbers.sm+QOL.numbers.sm
#+worry.numbers.sm)
#anova(lm5)

#lm6 <- lm(deviat.logo.sm ~ educ+sex+age)
#anova(lm6)

#lm7 <- lm(deviat.logo.sm ~ stai.score)
#anova(lm7)

#lm8 <- lm(deviat.logo.sm ~ worry.numbers.sm)
#anova(lm8)
 
#lm9 <- lm(minrisk.numbers.sm ~
truste.score+mss.m.score+worry.numbers.sm+stai.score)
#anova(lm9)

lm10 <- lm(minrisk.numbers.sm ~
truste.score+mss.m.score+worry.numbers.sm+sex+educ+age)
anova(lm10)

lm11 <- lm(minrisk.numbers.sm ~ truste.score+worry.numbers.sm)
anova(lm11)


#lm12 <- lm(nprob.m[,1,5,9] ~ worry.numbers.sm+pc.had+stai.score)
#anova(lm12)

#lm 13-15 used above

# tells you which Ss said both
#apply((minrisk.m==1 & exagg.m==1)+0,1,sum,na.rm=T)

toohigh.sm <- apply(nprob.m>rightans.m,1,mean,na.rm=T)

toohigh.numbers.sm <-
apply(nprob.m[,5:12]>rightans.m[,5:12],1,mean,na.rm=T)

#If in a given scenario, your nprob is greater than the right answer, you 
#get a 1 and if not, you get a 0. So if we take the avg of these across the 
#2-3 numbers versions that a given Ss got, we get a mean for "how often was
#nprob > right ans".  

summary(lm(toohigh.sm ~ minrisk.numbers.sm+worry.numbers.sm+sex+educ))
rawdev.sm <- apply(nprob.m-rightans.m,1,mean,na.rm=T)

lm16 <- lm(toohigh.sm ~ 
stai.score+lot.score+educ+denial.score+sex+truste.score+minrisk.numbers.sm+exagg.numbers.sm+QOL.numbers.sm
+worry.numbers.sm+trust.numbers.sm+comfort.numbers.sm+mss.m.score+mss.b.score)
anova(lm16)

lm17 <- lm(toohigh.sm ~
stai.score+lot.score+educ+denial.score+sex+truste.score+minrisk.numbers.sm+QOL.numbers.sm
+worry.numbers.sm+trust.numbers.sm+comfort.numbers.sm+mss.m.score)
anova(lm17)

#lm18 <- lm(toohigh.sm ~
stai.score+lot.score+educ+sex+truste.score+minrisk.numbers.sm+QOL.numbers.sm
#+worry.numbers.sm+trust.numbers.sm+mss.m.score)
#anova(lm18)

#lm19 <- lm(toohigh.sm ~
stai.score+educ+sex+truste.score+minrisk.numbers.sm+QOL.numbers.sm
#+worry.numbers.sm+comfort.numbers.sm)
#anova(lm19) 

lm20 <- lm(toohigh.sm ~ stai.score)
anova(lm20)

lm21 <- lm(toohigh.sm ~ educ)
anova(lm21)

t.test(toohigh.sm[sex==1], toohigh.sm[sex==2])

lm22 <- lm(toohigh.sm ~ educ)
anova(lm22)

lm23 <- lm(toohigh.sm ~ lot.score)
anova(lm23)

lm24 <- lm(toohigh.sm ~ denial.score)
anova(lm24)

lm25 <- lm(toohigh.sm ~ truste.score)
anova(lm25)
lm26 <- lm(toohigh.sm ~ QOL.numbers.sm)
anova(lm26)
lm27 <- lm(toohigh.sm ~ worry.numbers.sm)
anova(lm27)
lm28 <- lm(toohigh.sm ~ mss.m.score)
anova(lm28)
lm29 <- lm(toohigh.sm ~ trust.numbers.sm)
anova(lm29)

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2 1 3 2 3 2 1 3 3 2 2 3 2 2 2 2 1 3 2 2 3 2 4 2 4 2 4 4 2 8
 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 2 2 1 1 4 4 1 4 4 4 1 1 1 5 1 2 2 1 1 1 2 2 1 1
1 2 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 2 1 2 1 NA 2 2 1 1 1 2 1
1 1 17 28 NA 2 1 1 60 2 2 .0003 1 1 1 1 1 1 2 2 1 1 1 1 1 1 10 500 c 10 100 200
2 2 25 1 8 1522 25 1 2 2 75 8 1 1 1 1 2 2 1 2 2 2 2 2 2 2 401 2 1 948 1 1 1 1 50
9 1 1 1 1 2 2 9 9 2 2 2 2 2 2 225 3 6 737 10 1 2 2 80 10 1 1 1 1 2 2 1 2 2 2 2 2
2 2 242 4 11 595 60 1 4 4 60 9 5 1 1 1 1 2 1 2 2 2 2 2 2 2 101 5 1 1 2 2 1 1 4 4
4 2 3 2 3 1 1 4 4 1 2 4 1 2 3 3 1 4 2 2 4 1 4 3 4 3 5 4 3 9
 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 2 3 2 3 2 3 3 3 4 1 1 1 1 1 1 1 1 1 1
1 2 2 1 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 NA 1 1 1 1 1 1 1
1 1 14 45 NA 1 2 1 60 2 2 .03 1 1 1 1 1 1 2 2 1 0 2 1 2 2 10 500 c 10 100 2 2 2
25 1 4 299 25 1 3 3 50 9 3 1 1 1 1 1 1 2 2 2 2 2 2 2 177 2 9 885 .03 1 1 1 20 9
5 2 2 2 2 2 1 2 2 2 2 2 2 2 369 3 2 711 10 2 2 3 10 7 3 1 1 1 1 1 9 9 2 2 2 2 2
2 230 4 7 447 60 2 3 4 1 8 2 1 1 1 1 1 1 2 2 2 2 2 2 2 121 3 1 1 2 2 2 1 4 3 2 1
3 2 2 2 1 2 2 1 2 3 1 2 2 3 1 2 2 2 2 3 4 2 4 3 5 3 2 7
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 3 5 4 5 5 3 4 4 4 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1
1 1 1 11 16 NA 2 2 2 60 2 2 .03 3 4 4 4 1 1 2 2 1 0 1 3 1 1 10 500 C 10 100 2 2
2 25 1 0 962 65 1 3 4 40 8 2 1 1 1 1 1 9 9 1 2 2 2 2 2 209 2 5 563 5 1 2 2 50 9
4 1 1 1 1 1 1 2 2 2 2 2 2 2 196 3 10 482 15 2 2 3 35 8 3 1 1 1 1 1 1 2 2 2 2 2 2
2 123 4 3 415 85 3 4 4 40 8 1 1 1 1 1 1 9 9 1 2 2 2 2 2 91 4 1 1 2 1 2 1 3 3 2 1
3 2 3 2 2 3 3 2 2 3 2 2 3 2 2 3 2 2 3 3 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 5 1 5 1 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 2 2 2 2 1 1 1 1 2 1 1 1 1 2 2 2 2 2 1 1 1 2 1 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 15 53 NA 2 2 2 60 2 2 3 3 1 1 1 1 1 1 2 1 0 1 1 2 2 10 500 c 10 100 20 2 2
25 1 8 899 100 1 5 5 50 7 4 1 1 1 1 1 1 1 1 2 2 2 2 2 658 2 1 754 75 1 5 5 20 1
1 1 1 1 1 1 9 9 2 2 2 2 2 2 208 3 6 731 10 1 1 3 30 1 4 1 1 1 1 1 2 2 2 2 2 2 2
2 760 4 11 682 100 1 5 5 90 10 1 1 1 1 1 1 1 2 2 1 2 2 2 2 267 2 1 1 2 2 2 4 2 3
2 3 2 2 1 4 2 1 1 2 4 2 3 3 1 1 4 1 4 4 2 4 1 5 1 5 1 1 5 0
 1 2 2 1 1 1 2 2 1 1 1 2 2 2 2 1 2 2 2 1 4 4 2 3 5 3 2 2 2 4 1 2 2 2 1 1 2 2 1 1
1 1 1 2 2 2 2 2 2 1 2 1 1 2 2 1 1 1 2 1 2 2 2 1 1 2 2 2 2 1 1 2 NA 2 2 2 1 1 2 2
2 1 14 45 NA 2 2 2 60 2 2 .03 2 3 1 1 2 1 2 2 1 4 1 1 1 1 10 500 c 10 100 .2 2 1
25 1 4 1321 40 2 3 2 50 7 3 1 1 1 2 2 1 2 2 2 2 2 2 2 395 2 9 1256 10 4 2 1 50 8
5 1 1 2 2 2 1 2 2 2 2 2 2 2 168 3 2 913 10 4 2 2 50 8 4 1 1 1 2 2 9 9 1 2 2 2 2
2 358 4 7 788 50 3 4 4 40 8 4 1 1 1 2 2 1 2 2 2 2 2 2 2 269 3 1 1 2 2 2 2 4 3 2
2 3 2 3 1 1 2 3 2 2 3 2 2 3 3 1 3 2 1 3 1 5 2 4 1 5 4 1 9
 1 2 2 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 5 3 1 3 3 1 1 3 3 5 1 2 2 2 1 1 2 2 2 1
1 1 1 1 2 2 2 2 2 2 2 1 2 2 1 2 2 1 2 2 2 2 1 1 1 2 2 1 2 2 1 2 NA NA 2 2 1 1 2
2 2 1 16 65 NA 2 2 2 60 2 1 0.03 1 1 1 1 1 2 2 2 1 0 2 1 2 2 10 500 C 10 100 2 2
2 25 1 8 1297 60 1 3 3 50 9 4 1 1 1 2 2 2 1 2 2 2 1 2 2 530 2 1 1086 1 1 1 2 50
10 5 2 2 2 2 2 9 9 2 2 2 2 2 2 192 3 6 819 10 1 2 2 20 10 3 1 1 2 2 2 1 2 2 2 2
2 2 2 166 4 11 689 60 1 4 4 20 10 1 1 1 1 2 2 1 2 2 2 2 2 2 2 192 4 1 1 2 2 2 1
2 3 2 1 3 2 2 1 1 3 2 1 1 2 1 1 3 4 2 3 2 2 3 2 NA NA NA NA NA NA NA NA
 1 2 1 1 1 1 2 1 1 1 1 2 2 2 2 1 2 2 2 2 5 4 2 4 5 3 2 4 2 4 1 2 2 1 1 1 2 2 1 1
1 2 1 1 1 2 2 1 2 1 2 1 1 2 1 1 1 1 1 1 2 2 1 1 1 2 1 2 2 1 1 2 NA 2 1 1 1 1 2 2
1 1 16 23 NA 2 1 1 60 1 1 .03 1 1 1 1 1 1 1 2 1 15 2 1 2 2 10 500 c 10 100 2 1 1
25 1 0 946 25 3 2 3 65 9 4 1 1 1 2 2 9 9 2 2 2 2 2 2 497 2 5 789 .03 2 1 2 50 9
4 1 2 2 2 2 1 2 2 2 2 2 2 2 246 3 10 603 10 2 3 3 60 8 4 1 1 1 2 2 1 2 2 2 2 2 2
2 96 4 3 496 65 3 4 3 85 8 4 1 1 1 1 2 9 9 2 2 2 2 2 2 115 4 1 1 2 2 2 2 4 3 2 2
4 1 4 1 1 3 4 1 2 4 1 2 3 3 1 4 2 2 4 1 5 1 5 2 5 4 1 10
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 2 3 3 4 4 4 2 4 3 4 1 2 2 1 1 1 2 2 1 1
1 2 1 1 1 1 2 2 2 1 1 1 1 2 1 1 2 1 2 1 2 2 1 1 1 1 2 2 2 1 1 1 NA 2 2 2 1 1 2 2
2 1 14 58 NA 1 2 2 60 2 1 .03 1 1 1 2 1 1 2 1 1 0 2 2 2 1 10 50 c 10 100 2 2 2
25 1 4 1435 25 1 2 3 60 9 5 1 1 2 2 2 1 2 2 2 2 2 2 2 480 2 9 1166 .03 1 1 1 50
9 5 1 2 2 2 2 1 2 2 2 2 2 2 2 98 3 2 988 5 2 2 3 60 9 4 1 1 1 2 2 9 9 2 2 2 2 2
2 469 4 7 870 60 2 4 4 60 10 5 1 1 1 1 2 1 2 2 2 2 2 2 2 153 2 1 1 2 2 2 2 4 3 1
1 2 3 3 3 2 3 3 2 2 4 2 3 3 4 1 3 2 1 3 2 5 1 4 1 4 4 2 9
 1 1 1 1 1 1 2 1 1 1 1 2 2 2 1 1 2 1 1 1 3 2 3 3 2 3 2 3 3 3 1 2 2 2 1 1 2 1 1 1
1 1 1 1 2 2 2 2 2 1 1 1 1 2 1 1 2 1 1 2 1 2 1 1 1 1 1 1 2 1 1 11 NA 2 1 1 1 1 2
1 1 1 12 38 NA 2 2 2 60 2 2 3 1 1 1 1 1 1 1 1 1 0 1 2 2 2 10 500 C 10 100 200 2
2 25 1 0 941 40 4 2 2 60 5 4 1 1 1 2 2 9 9 2 2 2 2 2 2 434 2 5 814 10 1 1 1 45 7
4 1 2 2 2 2 1 2 2 2 2 2 2 2 198 3 10 698 10 1 1 2 45 6 4 1 1 2 2 2 1 2 2 2 2 2 2
2 134 4 3 767 65 3 4 4 50 6 4 1 1 1 1 2 9 9 2 2 2 2 2 2 149 1 3 2 1 2 2 5 3 2 2
2 3 2 2 2 1 1 2 2 1 2 1 2 2 2 2 2 2 1 3 1 2 4 1 1 3 2 3 6
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2 2 1 1 3 3 2 3 3 5 1 4 1 5 1 1 1 1 1 1 1 1 1
NA 1 2 1 2 1 2 2 2 2 1 2 1 1 2 1 1 1 1 1 2 1 2 2 1 1 1 1 1 2 1 1 1 NA 2 1 1 1 1
2 1 1 1 14 53 NA f 2 2 60 2 2 .0003 1 1 1 1 1 2 2 1 3 0 2 2 2 2 10 100 b 1 100
.02 2 2 25 1 8 1105 30 1 2 2 70 5 2 1 1 1 2 2 1 2 2 2 2 2 2 2 505 2 1 1024 20 3
2 2 10 5 2 1 1 2 2 2 9 9 2 2 2 2 2 2 251 3 6 1007 40 3 2 4 10 7 1 1 1 1 1 2 1 2
2 2 2 2 2 2 236 4 11 630 90 2 4 5 5 7 1 1 1 1 1 1 1 2 2 2 2 2 2 2 191 2 1 1 2 1
1 2 3 3 3 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 2 1 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 4 1 4 5 4 1 4 1 5 1 2 2 2 1 1 2 2 2 1
1 2 1 1 2 1 1 1 2 1 1 1 1 2 2 1 2 1 1 1 2 2 1 1 1 1 1 2 2 1 2 1 NA 2 2 2 1 1 2 2
1 1 16 26 NA 2 2 1 60 2 1 .03 1 1 1 2 1 1 2 1 2 10 1 2 2 2 10 500 C 10 100 2 2 1
25 1 0 1454 20 1 2 2 70 8 4 1 1 1 2 2 9 9 2 2 2 2 2 2 657 2 5 1079 .03 1 2 2 70
9 5 1 2 2 2 2 1 2 2 2 2 2 2 2 308 3 10 1136 10 1 2 2 70 8 4 1 1 2 2 2 1 2 2 2 2
2 2 2 215 4 3 887 85 2 4 3 65 8 4 1 1 1 2 2 9 9 1 2 2 2 2 2 197 4 1 1 2 2 1 2 4
4 2 2 4 2 4 1 1 3 3 2 2 4 2 1 4 3 1 3 2 1 3 2 4 2 4 2 5 3 2 3
 1 2 2 2 1 1 2 2 2 1 1 2 2 2 2 1 2 2 2 2 3 4 4 2 3 4 3 3 3 3 1 2 2 2 2 1 2 2 2 1
1 1 2 2 1 1 2 2 2 2 1 1 1 2 1 1 2 1 2 1 2 1 1 2 1 1 2 1 1 1 1 2 NA 2 2 2 2 1 2 2
2 1 16 43 NA 2 2 2 60 2 2 .003 4 2 1 2 1 1 2 1 3 4 2 2 1 1 10 500 c 10 100 .02 2
2 25 1 8 1721 90 1 4 4 5 9 1 1 1 2 2 2 1 2 2 2 2 2 2 2 917 2 1 1127 10 1 2 2 5 9
3 1 2 2 2 2 9 9 2 2 2 2 2 2 802 3 6 1188 1 1 1 1 1 10 5 1 2 2 2 2 1 2 2 2 2 2 2
2 357 4 11 909 90 1 4 4 20 9 4 1 1 2 2 2 1 2 2 2 2 2 2 2 209 4 1 2 2 1 1 1 3 3 2
2 3 3 2 3 2 2 2 3 2 2 2 2 2 2 2 2 2 1 3 2 2 3 3 4 4 4 4 8
 1 2 1 1 1 1 2 1 1 1 1 2 2 1 1 1 2 2 2 1 3 2 5 2 2 3 4 2 3 2 1 2 2 1 1 1 2 2 1 1
1 2 1 2 1 2 2 1 2 1 1 1 2 2 1 1 2 1 2 1 2 2 2 1 1 1 1 2 1 2 1 2 NA 2 1 1 1 1 2 1
1 1 13 31 NA 2 2 1 60 1 1 .03 1 1 2 1 2 2 2 1 1 3 3 3 1 1 10 500 c 10 100 2 2 2
25 1 8 1082 50 1 2 3 75 8 4 1 1 1 2 2 1 2 2 2 2 2 2 2 396 2 1 1466 20 2 1 2 75 7
4 1 1 2 2 2 9 9 2 2 2 1 2 2 278 3 6 853 10 2 1 2 60 9 4 1 1 1 2 2 1 2 2 2 2 2 2
2 228 4 11 705 60 2 3 4 50 9 4 1 1 1 1 2 1 2 2 2 2 2 2 2 133 4 1 2 2 1 2 2 3 2 2
2 3 1 2 3 1 2 2 2 1 3 1 2 2 2 1 3 1 1 3 1 NA NA NA NA NA NA NA NA
 1 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 4 4 3 2 5 4 1 3 2 2 2 2 2 2 1 2 2 2 1
1 2 2 1 2 1 2 2 2 1 2 1 1 2 1 1 2 1 2 1 2 2 1 1 1 2 2 1 2 1 2 2 NA 2 2 2 2 1 2 2
2 1 14 37 NA 2 2 2 6 2 2 .003 1 1 1 1 1 2 2 1 3 0 2 1 2 2 1 50 C 10 100 2 2 2 25
1 0 1258 20 1 2 2 60 8 5 1 1 2 2 2 9 9 2 2 2 2 2 2 364 2 5 1151 .03 1 1 1 50 10
5 1 1 2 2 2 1 2 2 2 2 2 2 2 641 3 10 1447 .01 1 1 1 40 10 5 2 2 2 2 2 1 2 2 2 2
2 2 2 206 4 3 732 60 2 4 5 60 10 5 1 1 2 2 2 9 9 2 2 2 2 2 2 162 4 1 2 2 2 2 3 4
4 1 1 3 1 4 2 1 3 4 1 2 4 2 1 4 2 1 2 2 2 4 1 5 2 3 2 5 3 2 8
 1 2 1 1 1 1 2 1 1 1 1 2 2 2 2 1 2 2 2 1 3 4 2 4 4 2 1 2 1 5 1 2 2 1 1 1 2 2 1 1
1 2 1 2 2 2 2 1 2 1 2 1 1 2 1 1 1 1 1 2 2 1 2 1 1 1 2 2 2 1 1 2 NA 2 2 1 1 1 2 1
1 1 16 54 NA 1 2 1 60 2 2 .03 1 1 2 3 1 1 2 2 1 2 2 1 2 2 10 500 c 10 100 2 2 2
25 1 8 2054 25 3 3 4 50 8 2 1 1 1 1 2 1 2 2 2 2 2 2 2 449 2 1 1088 20 3 2 4 40 8
4 1 1 2 2 2 9 9 1 2 2 2 2 2 175 3 6 1183 10 2 2 3 30 7 3 1 1 1 2 2 1 2 2 2 2 2 2
2 232 4 11 776 80 3 4 5 50 9 1 1 1 1 1 2 1 2 2 1 2 2 2 2 246 3 1 1 2 2 2 2 4 3 2
1 4 2 3 1 1 3 3 2 3 3 1 2 3 1 2 3 2 3 3 3 2 3 2 3 2 2 4 4
 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 5 5 1 5 5 1 2 5 3 5 1 2 2 1 1 1 2 2 1 1
1 2 1 1 2 1 1 2 2 1 2 1 1 2 1 1 1 1 2 1 1 2 1 1 1 1 2 1 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 13 30 NA 1 2 2 60 2 2 .03 1 1 1 1 2 2 2 2 1 0 1 1 2 2 10 500 C 10 100 2 2 2
25 1 8 1150 40 3 3 3 40 4 3 2 2 2 2 2 1 1 2 2 2 2 2 2 1060 2 1 660 60 3 3 3 60 9
3 1 1 1 2 2 9 9 2 2 2 2 2 2 200 3 6 440 50 3 3 3 50 5 3 1 1 1 2 2 2 2 2 2 2 2 2
2 5090 4 11 450 50 3 3 3 50 8 3 1 1 1 2 2 2 2 2 2 2 2 2 2 120 5 1 1 2 2 2 1 5 5
1 1 4 1 4 1 1 4 4 1 1 4 1 4 4 4 1 4 1 1 4 1 5 1 4 1 5 5 1 9
 1 2 2 1 1 1 2 2 1 1 1 2 2 2 1 1 2 2 2 1 3 2 3 3 4 3 2 2 3 3 1 2 2 2 1 1 2 2 2 1
1 2 1 2 1 2 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1 NA 2 2 1 1 1 2 2
1 1 12 19 NA 2 2 2 60 2 2 0.0003 2 2 2 1 1 2 2 1 1 0 1 1 2 1 10 500 C 10 100 2 2
2 25 1 8 1741 65 2 3 3 75 7 2 1 1 1 2 2 1 2 1 2 2 2 2 2 536 2 1 1316 20 2 2 2 70
8 5 1 1 2 2 2 9 9 2 2 2 2 2 2 240 3 6 916 15 2 2 2 85 8 4 1 1 2 2 2 1 2 2 2 2 2
2 2 393 4 11 985 70 2 4 4 60 8 1 1 1 1 2 2 1 2 2 1 2 2 2 2 245 4 1 1 2 1 2 2 4 3
3 2 3 3 2 2 2 2 3 2 2 4 2 3 4 2 2 3 2 3 2 2 4 2 4 1 5 4 1 7
 1 2 1 1 1 1 1 1 1 1 2 2 2 2 2 1 2 2 1 1 1 1 3 1 3 4 2 4 3 3 1 2 2 1 1 1 1 1 1 1
1 1 2 1 2 1 2 2 2 1 1 1 1 2 2 1 1 1 2 1 2 2 1 1 1 1 2 1 1 1 1 2 NA 2 1 1 1 1 2 2
1 1 14 51 NA 2 2 2 60 2 2 .0003 1 1 1 1 2 2 2 2 1 1 2 2 2 2 10 50 c 10 100 200 2
2 25 1 0 1669 90 1 4 4 75 10 5 1 1 1 2 2 9 9 1 2 2 2 2 2 541 2 5 1226 10 1 1 1
95 9 3 1 2 2 2 2 2 2 2 2 2 2 2 2 307 3 10 1256 50 3 2 2 70 8 2 1 1 1 1 2 2 2 1 2
2 2 2 2 246 4 3 1001 80 2 4 4 80 9 4 1 1 1 1 2 9 9 2 2 2 2 2 2 251 3 2 1 1 1 2 2
4 2 2 3 2 3 1 3 2 1 2 1 2 2 1 2 3 2 1 2 1 2 3 3 4 3 2 1 4 3 3 5
 1 2 1 1 1 1 2 1 1 1 1 2 2 2 2 1 2 2 1 1 4 3 1 5 5 3 1 4 1 1 1 2 2 1 1 1 2 1 1 1
2 1 1 1 2 1 2 2 2 1 2 1 1 2 1 1 2 2 1 1 2 2 1 1 1 1 2 2 1 1 1 1 NA 2 2 1 1 1 2 2
1 1 20 25 NA 2 2 2 60 2 2 .03 1 1 1 1 2 2 2 1 3 10 1 2 2 2 10 500 c 10 100 2 1 1
25 1 0 1092 30 2 2 3 40 7 2 1 1 1 2 2 9 9 2 1 2 2 2 2 237 2 5 840 5 1 2 1 40 7 4
1 1 2 2 2 1 2 1 2 2 2 2 2 196 3 10 675 20 3 2 2 30 8 2 1 1 1 2 2 1 2 2 1 2 2 2 2
201 4 3 645 60 2 3 4 40 8 1 1 1 1 1 2 9 9 2 2 2 2 2 2 210 4 1 1 2 1 1 1 4 3 2 2
3 1 3 1 1 3 3 2 1 4 1 2 4 2 1 4 1 1 4 1 4 3 4 3 4 3 3 8
 1 2 1 1 1 1 2 1 1 1 1 2 2 2 2 1 2 2 2 2 1 3 3 1 2 4 3 3 4 2 1 2 2 1 1 1 2 2 1 1
1 2 2 2 2 1 2 2 2 1 2 1 1 2 2 1 1 1 2 1 2 2 1 1 1 2 1 1 2 1 1 2 NA n n y y y n n
y y 14 23 NA f 2 2 60 2 2 .03 1 1 1 1 1 2 2 2 1 0 3 2 1 1 10 50 c 10 100 2 1 1
25 1 4 1905 25 1 2 3 20 8 5 1 1 1 2 2 1 2 2 2 2 2 2 2 566 2 9 1259 .03 1 1 1 20
8 5 1 2 2 2 2 1 2 2 2 2 2 2 2 245 3 2 1174 35 4 3 4 20 6 2 1 1 1 2 2 9 9 1 2 1 2
2 2 429 4 7 883 60 1 4 5 60 8 4 1 1 1 1 2 1 2 2 2 2 2 2 2 447 1 3 3 1 2 2 2 2 2
2 2 2 2 2 1 2 3 3 1 3 3 2 3 2 1 1 3 3 3 3 2 4 1 5 1 3 4 2 9
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 3 3 2 3 2 3 1 1 1 5 1 1 1 1 1 1 1 1 1 1
1 2 1 1 2 2 2 1 2 1 2 1 1 2 1 1 2 1 1 1 2 2 1 1 1 2 1 2 2 1 2 1 NA 1 1 1 1 1 1 1
1 1 16 38 NA 2 2 1 60 2 2 .0003 2 1 2 2 1 1 2 2 1 3 1 2 1 1 .1 50 C 10 100 2 2 2
25 1 8 2295 25 1 3 3 50 7 3 1 1 1 1 1 1 2 2 2 2 2 2 2 862 2 1 731 5 2 2 2 25 7 2
1 2 2 2 2 9 9 1 2 2 2 2 2 169 3 6 793 10 2 2 3 10 7 3 1 1 1 1 1 1 2 1 2 2 2 2 2
176 4 11 1077 60 1 3 5 25 7 2 1 1 1 1 1 1 2 2 2 2 2 2 2 155 3 1 2 2 2 2 2 4 3 2
2 3 2 3 2 2 3 2 2 3 3 2 3 3 3 3 2 2 3 3 3 4 2 4 2 5 3 3 8
 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 3 3 3 4 5 4 3 4 3 4 2 2 2 1 1 1 2 2 1 1
1 2 1 1 2 1 1 1 2 2 2 2 1 2 1 1 NA NA NA NA NA NA NA NA 1 1 2 1 2 1 1 2 NA 2 2 1
1 1 2 2 1 1 12 43 NA f 2 2 10 2 2 60 2 2 1 1 1 1 2 1 3 0 1 1 2 2 10 500 a 10 10
10 NA NA 50 1 8 907 50 2 3 3 50 5 3 1 1 1 2 2 1 2 1 2 2 1 2 2 612 2 1 554 50 2 3
3 50 5 3 1 1 1 2 2 9 9 1 2 1 1 1 1 282 3 6 554 50 1 2 2 50 5 2 1 1 1 2 2 1 2 1 2
2 2 2 2 257 4 11 363 50 2 2 2 50 5 2 1 1 1 2 2 1 2 1 2 2 2 2 2 186 3 1 1 2 2 2 1
4 4 2 1 3 2 3 1 2 3 3 2 1 4 1 1 3 3 1 3 2 3 2 1 4 1 4 2 4 4 2 8
 1 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 4 2 3 2 3 3 3 1 3 3 1 2 2 2 2 1 2 2 2 2
1 2 2 1 2 1 2 2 1 2 2 1 1 2 1 1 1 1 2 1 2 2 2 2 1 1 2 1 2 1 1 2 NA 2 2 2 2 1 2 2
2 1 16 44 NA 1 2 2 60 2 2 .03 2 1 2 1 1 1 2 1 1 30 1 1 2 2 10 500 C 10 1002 2 2
2 25 1 0 1530 50 2 3 4 40 6 3 1 1 2 2 2 9 9 1 2 2 2 2 2 467 2 5 1087 5 1 1 1 20
7 3 2 2 2 2 2 1 2 2 2 2 2 2 2 365 3 10 1312 10 1 2 4 5 7 2 1 1 2 2 2 1 2 1 2 2 2
2 2 331 4 3 700 80 1 4 5 5 8 5 1 1 2 2 2 9 9 1 2 2 2 2 2 124 3 1 1 1 1 2 2 3 3 3
2 3 2 2 1 2 2 2 2 4 2 3 3 2 2 2 2 2 3 2 2 2 5 2 2 3 2 3 5
 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 2 4 2 1 3 2 3 1 2 2 2 2 1 2 2 2 2
1 2 2 1 2 1 1 2 2 1 2 1 1 2 1 1 1 1 1 1 2 2 1 2 1 1 1 2 1 2 1 2 NA 2 2 2 2 1 2 2
2 2 14 48 NA f 2 2 60 2 2 .03 1 1 1 1 1 2 2 1 1 25 2 2 2 2 10 500 C 10 100 2 2 2
25 1 4 1606 25 4 2 2 50 2 3 1 1 2 2 2 1 2 2 2 2 2 2 2 641 2 9 1888 5 1 1 1 25 8
4 2 2 2 2 2 1 2 2 2 2 2 2 2 273 3 2 1074 5 2 2 2 10 7 4 1 2 2 2 2 9 9 2 2 2 2 2
2 282 4 7 841 60 2 3 4 25 8 4 1 2 2 2 2 1 2 2 2 2 2 2 2 275 4 1 2 2 1 1 2 2 1 2
2 2 3 2 3 2 2 2 2 2 2 2 2 2 3 2 2 3 3 2 2 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 3 3 5 2 5 5 5 1 5 3 1 2 2 2 2 1 2 2 1 1
2 2 1 1 2 1 2 1 2 1 1 1 1 2 1 1 2 1 2 1 2 1 1 1 1 1 2 1 2 1 1 2 NA 2 2 2 1 NA 2
2 2 2 12 36 NA 2 2 1 60 2 2 .0003 1 1 1 1 1 1 1 1 3 2 2 2 1 1 10 500 c 10 100
200 2 2 25 1 4 1457 10 1 1 1 70 10 5 1 1 2 2 2 1 2 2 2 2 1 2 2 844 2 9 857 1 1 1
1 10 10 1 2 2 2 2 2 1 2 2 2 2 2 2 2 225 3 2 1089 15 1 2 2 5 10 1 1 1 2 2 2 9 9 1
2 2 2 2 2 196 4 7 815 60 1 3 3 15 10 5 1 1 2 2 2 1 2 2 2 2 2 2 2 307 3 3 3 1 1 2
3 2 2 2 3 2 3 2 3 2 1 1 2 3 2 1 3 3 4 3 3 1 3 3 3 5 5 3 5 5 2 5 8
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 2 3 1 5 5 2 1 2 2 4 1 2 1 1 1 1 2 1 1 1
1 2 1 1 1 2 2 2 2 1 2 1 1 2 1 1 2 1 2 1 2 2 1 1 1 2 2 2 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 18 25 NA 2 2 2 60 2 2 .03 1 1 1 1 1 1 1 2 1 0 2 2 1 1 10 500 C 10 100 2 2 2
25 1 4 1533 12 2 2 3 75 9 4 1 1 1 1 2 1 2 2 2 2 1 2 2 743 2 9 1057 .03 2 1 1 75
9 5 1 2 2 2 2 1 2 2 2 2 2 2 2 181 3 2 1407 1 2 2 2 70 10 5 1 1 1 1 2 9 9 2 2 2 2
2 2 248 4 7 878 60 2 4 4 70 9 4 1 1 1 1 1 1 2 2 2 2 2 2 2 140 4 1 1 2 2 2 2 3 2
3 1 2 3 3 2 2 2 2 2 4 3 1 3 2 2 2 3 3 1 3 2 4 2 4 1 5 3 2 10
 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 3 1 1 2 4 3 1 3 1 4 2 2 2 2 2 1 2 2 1 1
1 2 1 1 2 1 1 2 2 1 2 2 1 2 2 1 1 2 2 2 2 2 1 1 1 2 2 1 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 16 38 NA 2 2 2 4 2 2 70 2 1 1 1 1 1 2 1 3 10 2 1 2 2 3 200 b 10 40 10 2 2 1
1 0 2568 40 1 3 5 80 10 1 1 1 2 1 1 9 9 1 2 2 2 2 2 722 2 5 1155 100 1 5 5 20 10
1 1 1 1 2 2 1 2 2 2 2 2 2 2 313 3 10 800 50 3 1 1 50 8 1 1 1 1 2 2 1 2 1 2 2 2 2
2 446 4 3 767 50 1 3 5 40 9 1 1 1 1 2 2 9 9 2 1 2 2 2 2 329 2 1 1 2 2 2 1 3 4 1
1 3 1 4 3 1 2 2 1 1 4 1 1 NA 4 1 2 1 1 2 1 1 1 5 3 5 5 1 10
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 5 4 2 3 3 4 5 3 3 1 1 1 1 1 1 1 1 1 1
1 2 1 1 2 2 2 1 2 1 2 1 1 2 2 1 1 1 2 1 2 2 1 1 1 2 2 1 2 1 2 2 NA 1 1 1 1 1 1 1
1 1 16 32 NA 1 2 2 60 2 2 0.03 1 1 1 1 1 2 2 2 1 0 1 1 2 2 10 500 c 10 100 2 2 2
25 1 4 1574 20 2 2 2 50 8 2 1 1 1 1 1 1 2 2 2 2 2 2 2 339 2 9 1227 1 1 1 1 60 7
4 1 1 1 1 1 1 2 2 2 2 2 2 2 172 3 2 1091 50 1 3 4 80 9 3 1 1 1 1 1 9 9 1 2 2 2 2
2 165 4 7 837 80 2 4 4 50 9 3 1 1 1 1 1 1 2 2 2 2 2 2 2 176 5 1 1 2 2 2 1 4 4 1
1 4 2 4 1 2 3 4 1 1 4 1 3 4 3 1 4 2 1 4 1 4 1 4 2 5 4 3 8
 1 2 2 1 1 1 2 1 1 1 1 2 2 2 1 1 2 2 2 1 2 1 3 3 3 3 2 1 3 3 1 2 2 2 2 1 2 2 1 1
1 1 1 1 2 2 2 1 2 1 1 1 1 2 2 1 2 1 2 1 2 2 2 1 1 2 1 2 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 16 25 NA 2 2 2 60 2 1 .0003 3 1 1 1 1 1 1 1 2 4 1 2 1 1 10 500 C 10 100 0.02
2 2 25 1 0 951 75 2 4 4 25 8 4 1 1 1 2 2 9 9 2 2 2 2 2 2 520 2 5 809 30 2 3 3 25
4 1 1 1 1 2 2 2 2 1 2 1 1 2 2 202 3 10 930 40 1 3 3 50 9 4 1 1 1 2 2 1 2 2 2 2 2
2 2 236 4 3 652 75 1 4 4 20 6 2 1 1 1 2 2 9 9 2 1 2 2 2 2 161 3 1 1 2 1 2 2 3 2
2 2 3 2 2 3 1 2 2 3 4 3 1 2 3 3 2 3 2 2 2 3 5 2 5 2 5 5 2 9
 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 2 2 1 1 3 5 2 3 5 2 2 4 1 4 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 1 2 2 2 1 1 1 1 2 1 1 2 1 1 1 1 2 1 1 1 1 2 1 2 1 1 1 NA 1 1 1 1 1 1 1
1 1 16 56 NA 2 1 1 60 1 1 .003 1 1 1 2 2 1 2 2 1 0 2 2 2 2 10 50 c 10 100 200 2
2 25 1 8 1323 50 1 3 4 25 8 3 1 1 1 1 1 2 2 1 2 2 2 2 2 697 2 1 1107 10 1 2 2 10
7 2 1 1 1 1 1 9 9 1 2 2 2 2 2 234 3 6 954 5 1 2 3 10 10 4 1 1 1 1 1 1 2 1 2 2 2
2 2 315 4 11 737 50 1 4 5 10 10 5 1 1 1 1 1 1 2 2 2 2 2 2 2 187 2 3 3 1 2 2 2 3
1 2 2 4 1 3 2 1 1 2 2 1 3 1 1 4 3 1 3 1 1 4 1 1 1 5 1 5 3 1 10
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1 2 1 2 2 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 2 2 2 1 1 2 2 1 1 1 1 1 NA 2 1 1 1 1 2 2
1 1 10 26 NA 1 2 2 60 2 2 3 3 3 2 1 2 2 2 2 2 2 2 2 1 1 10 50 c 10 100 200 2 2
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1 1 11 25 NA 2 1 1 60 2 2 3 5 5 4 3 2 2 2 2 1 2 2 2 1 1 10 50 C 10 100 200 2 2
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1 1 14 44 NA 2 0 1 60 0 1 .03 1 1 1 1 1 2 1 1 3 0 1 1 0 2 10 50 C 10 100 2 0 2
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1 1 16 55 NA 2 1 1 60 2 2 3 2 1 1 1 1 2 2 2 1 1 1 1 1 1 10 50 A 10 10 2 1 1 25 1
4 2508 25 2 2 3 50 5 4 1 1 1 2 2 1 2 1 2 2 1 2 2 1543 2 9 1208 10 1 1 2 20 8 3 1
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1 1 12 33 NA 2 2 2 60 2 2 .0003 1 1 1 1 2 2 2 2 1 0 2 1 2 2 10 500 C 10 100 2 2
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1 2 2 1 2 1 2 1 2 1 1 2 1 2 1 1 1 2 2 1 2 2 1 1 1 1 2 2 1 1 1 2 NA 2 1 1 1 1 2 2
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1 1 14 52 NA 2 2 2 60 2 2 003 2 2 1 1 1 1 2 1 1 0 2 2 2 2 10 50 C 10 100 2 2 2
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1 1 14 42 NA 2 2 2 60 2 2 .03 2 1 2 2 1 1 2 1 3 15 1 2 2 2 10 50 C 10 100 2 2 2
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1 1 1 15 69 NA f 2 2 60 2 2 .3 3 1 1 1 1 1 2 1 1 0 1 1 1 1 1 500 C 10 100 20 2 2
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1 1 12 25 NA 2 2 2 60 2 2 .03 2 2 2 2 1 1 2 1 3 3 2 2 2 2 10 500 c 10 100 .2 2 2
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1 1 1 16 24 NA 2 1 1 30 2 2 40 2 2 2 1 1 2 2 2 1 1 2 1 1 1 20 30 C 5 100 2 2 2
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1 1 10 31 NA f 2 1 60 2 1 .0003 1 2 2 1 1 2 2 1 3 1 1 1 2 2 1 825 C 10 100 .02 2
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1 1 14 30 NA 2 2 2 65 2 2 3 1 1 1 1 2 1 2 1 1 20 2 2 1 1 20 500 C 10 100 200 2 2
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1 1 16 28 NA 2 2 1 60 1 1 .01 1 1 1 3 1 1 2 2 1 5 1 1 1 1 10 500 C 10 100 .2 2 2
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1 1 15 35 NA 2 1 1 60 2 2 0.03 2 2 2 2 1 1 2 2 1 0 1 1 2 2 10 500 C 10 100 2 2 1
25 1 0 2562 10 3 2 2 25 6 3 1 1 1 2 2 9 9 2 1 2 2 2 2 606 2 5 1241 0.03 2 1 1 15
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4 2 1 2 2 3 3 2 2 2 2 2 3 1 3 2 2 3 2 2 2 2 3 NA NA NA NA NA NA NA NA
 1 2 2 1 1 1 2 2 1 1 1 2 2 2 2 1 2 2 2 2 5 4 3 4 4 4 2 3 4 5 1 2 2 2 1 1 2 2 2 1
1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 NA 2 2 2 1 1 2 2
1 1 24 37 NA 1 2 2 60 2 2 0.03 2 2 2 2 1 1 1 2 1 0 2 2 2 2 10 500 C 10 100 2 2 2
25 1 0 2351 50 1 3 3 20 6 3 1 1 1 2 2 9 9 2 2 2 1 2 2 998 2 5 1619 0.3 1 1 2 30
8 4 1 1 1 2 2 1 2 2 2 2 2 2 2 258 3 10 1520 10 1 2 2 10 8 2 1 1 1 2 2 1 2 2 1 2
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4 2 2 3 2 3 3 1 3 3 2 2 3 2 2 3 3 2 3 2 2 3 2 4 4 4 2 4 4 3 8
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2 2 2 1 1 1 5 1 4 4 5 1 1 1 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 2 2 2 2 1 NA 1 1 1 1 1 1 1
1 1 17 26 NA 2 2 2 60 2 2 .03 1 1 1 2 1 1 1 2 1 1 1 2 1 1 10 500 c 10 100 2 2 2
25 1 8 10124 25 1 3 4 50 8 4 1 1 1 1 1 1 2 2 2 2 2 2 2 228 2 1 413 .01 1 1 2 50
8 4 1 1 1 2 2 9 9 2 2 2 2 2 2 64 3 6 690 10 2 2 3 50 7 3 1 1 1 1 1 1 2 2 1 2 2 2
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4 1 4 1 4 4 1 1 4 4 1 4 4 1 1 4 1 3 4 1 4 2 2 3 3 3 3 3 5
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 1 1 4 2 3 3 3 2 2 3 2 4 1 1 1 1 1 1 1 1 1 1
1 2 1 1 2 2 2 1 2 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 1 2 1 2 1 1 1 NA 2 2 1 1 1 1 1
1 1 12 25 NA 2 2 2 60 2 2 .03 1 1 1 2 1 2 2 1 1 0 1 1 2 2 10 500 C 10 10 2 2 2
25 1 0 1170 50 2 3 4 70 8 2 1 1 1 2 2 9 9 1 2 2 1 2 2 387 2 5 964 1 2 1 2 20 9 4
1 1 1 2 2 1 2 1 2 2 2 2 2 186 3 10 1021 70 1 3 4 30 8 2 1 1 1 1 1 2 2 1 2 2 1 2
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3 2 2 2 1 2 2 2 2 3 1 2 2 2 2 3 2 1 3 1 3 4 2 3 3 2 4 4
 1 2 2 1 1 1 2 1 1 1 1 2 2 1 1 1 2 1 1 1 4 4 1 4 5 3 1 4 1 5 1 2 1 1 1 1 2 1 1 1
1 2 1 1 2 1 2 2 2 1 1 1 1 2 1 1 2 1 2 2 2 2 2 1 1 1 2 1 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 16 30 NA F 1 1 60 1 1 .00003 1 1 1 1 1 1 2 2 1 0 1 2 2 2 10 500 C 10 100 200
2 2 25 1 0 1433 25 4 2 2 65 9 5 1 1 1 1 2 9 9 2 2 2 2 2 2 436 2 5 932 1 1 1 2 30
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1 1 4 1 4 1 2 3 3 1 2 4 1 1 4 4 1 4 1 1 4 1 4 1 4 2 4 3 1 7
 1 2 2 2 1 1 2 2 2 1 1 2 2 2 1 1 2 2 2 1 3 5 1 5 5 3 1 5 1 5 1 1 2 2 1 1 2 2 2 1
1 2 1 2 2 2 2 2 1 1 2 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 2 2 2 1 1 NA 2 2 2 1 1 2 2
2 1 16 38 NA 1 2 2 60 2 2 .003 1 1 1 1 1 1 2 2 1 2 2 2 2 2 10 500 2 10 100 2 1 1
25 1 4 791 25 2 2 2 50 8 2 1 1 2 2 2 1 2 2 2 2 2 2 2 476 2 9 583 1 1 1 1 75 9 5
1 1 2 2 2 1 2 2 2 2 2 2 2 172 3 2 521 20 1 2 2 80 8 2 1 1 2 2 2 9 9 1 2 2 2 2 2
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4 1 4 1 1 4 4 1 1 4 1 1 4 4 1 4 1 1 4 1 4 4 3 2 4 3 3 7
 1 2 2 2 1 1 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 1 3 2 2 1 4 1 3 2 1 2 2 2 2 1 2 2 2 2
1 1 2 1 2 2 2 2 1 2 2 2 2 1 2 1 2 1 2 1 1 1 1 1 1 2 1 1 2 1 2 2 NA 2 2 2 1 1 2 2
1 1 14 25 NA 2 2 2 60 2 2 .03 1 1 1 2 1 1 2 2 1 1 2 2 2 2 10 50 c 10 100 2 2 2
25 1 8 7412 50 1 2 2 33 4 3 1 1 1 1 2 2 1 1 2 2 1 1 2 291 2 1 659 5 4 1 1 40 9 3
1 2 2 2 2 9 9 2 2 2 2 2 2 193 3 6 546 15 4 1 1 40 7 3 1 1 2 2 2 2 2 2 1 2 1 2 2
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2 2 2 4 3 1 1 2 3 1 3 1 2 2 2 1 4 4 4 1 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 2 2 2 2 3 3 4 4 4 3 4 4 4 2 1 2 2 1 1 1 2 1 1 1
1 2 1 1 2 1 2 1 2 1 1 1 1 2 2 1 1 1 2 1 2 2 1 1 1 1 2 2 2 1 1 2 NA 2 1 1 1 1 2 1
1 1 12 39 NA 2 2 2 60 2 2 3 1 2 2 3 1 2 2 1 3 0 1 1 2 1 10 500 c 10 10 5 2 2 25
1 0 1056 80 3 4 4 50 9 4 1 1 1 1 2 9 9 1 2 2 1 2 2 857 2 5 1497 2 1 1 1 20 10 5
2 2 2 2 2 1 2 2 2 2 2 2 2 261 3 10 886 10 2 2 2 10 9 4 1 1 1 1 1 1 2 1 2 2 2 2 2
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3 2 3 3 2 2 2 3 4 2 2 2 2 2 2 2 3 2 2 2 4 2 4 4 4 2 3 7
 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 4 4 2 3 3 4 2 4 2 1 2 1 1 1 1 2 2 1 1
1 2 1 2 1 2 2 1 1 1 1 1 1 2 1 1 2 1 2 2 1 2 2 1 1 2 1 2 2 2 2 1 NA 1 1 1 1 1 1 1
1 1 12 24 NA 2 1 1 60 2 2 .03 1 1 1 1 1 2 2 1 2 1 1 2 2 2 10 500 c 10 100 2 2 2
25 1 4 1915 75 2 4 4 40 9 3 1 1 1 1 1 2 2 1 2 2 1 2 2 482 2 9 1503 5 1 2 1 40 9
3 1 1 2 2 2 1 2 2 2 2 2 2 2 260 3 2 1170 10 2 2 2 30 9 4 1 1 1 1 1 9 9 2 2 2 2 2
2 153 4 7 811 70 2 4 4 60 9 4 1 1 1 1 1 1 2 2 2 2 2 2 2 160 4 1 1 2 1 2 1 2 2 3
1 4 2 1 3 2 1 2 4 3 3 1 4 2 3 4 2 3 3 3 2 1 1 4 1 5 5 1 9
 1 2 1 1 1 1 2 1 1 1 1 2 2 2 1 1 2 2 2 1 3 3 2 4 4 3 2 3 2 4 1 2 2 2 1 1 2 2 1 1
1 2 1 1 2 2 2 1 1 1 1 1 1 2 2 1 2 1 1 1 2 2 1 1 1 2 1 2 2 1 2 1 NA 2 2 1 1 1 2 2
1 1 16 24 NA 2 2 2 60 2 2 .03 1 1 1 1 1 1 1 2 1 3 1 2 2 2 10 500 C 10 100 2 2 2
25 1 8 1252 50 1 3 3 50 9 4 1 1 1 2 2 2 2 1 2 2 2 2 2 419 2 1 858 10 1 1 2 50 8
4 1 1 2 2 2 9 9 2 2 2 2 2 2 271 3 6 850 20 1 2 2 50 8 4 1 1 1 2 2 2 2 1 2 2 1 2
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1 3 2 3 2 1 3 2 1 1 3 1 2 3 3 1 3 2 2 2 2 5 2 4 2 4 4 2 9
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 1 1 5 1 5 5 5 1 5 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 2 1 1 2 2 1 1 1 1 2 1 1 1 1 2 1 1 2 1 1 1 2 2 1 2 1 1 2 NA 1 1 1 1 1 2 1
1 1 15 53 NA 2 2 2 60 2 2 3 1 1 1 1 1 1 1 2 1 0 1 1 1 1 10 500 c 10 100 20 2 2
25 1 8 849 60 2 4 5 40 1 1 1 1 1 2 2 2 2 1 2 2 2 2 2 714 2 1 713 10 1 1 2 20 8 5
1 1 1 2 2 9 9 2 2 2 2 2 2 281 3 6 680 50 1 3 3 40 5 3 1 1 1 1 2 2 2 1 2 2 2 2 2
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3 3 1 4 3 1 2 2 4 2 3 4 2 2 4 1 2 2 2 2 1 5 1 5 1 1 5 0
 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 2 2 1 1 4 4 3 3 4 2 2 4 4 4 1 1 1 1 1 1 1 1 1 1
1 2 1 1 2 1 2 1 2 1 1 1 1 2 1 1 2 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 NA 1 1 1 1 1 1 1
1 1 12 39 NA 2 2 1 60 2 1 .03 3 2 2 1 2 1 2 2 1 0 2 2 2 2 10 500 c 10 100 20 2 2
25 1 4 8334 90 2 4 5 80 9 2 1 1 1 1 1 2 2 1 2 2 1 2 2 422 2 9 1219 10 2 1 4 90 8
4 1 1 1 2 2 2 2 1 2 2 1 2 2 359 3 2 1025 50 2 3 4 90 9 4 1 1 1 1 1 9 9 1 2 2 1 2
2 149 4 7 588 60 1 4 5 95 9 5 1 1 1 1 1 1 2 2 2 2 2 2 2 152 4 2 3 1 1 2 5 4 2 2
2 3 1 3 2 2 1 3 1 1 4 1 3 3 3 2 3 1 1 3 1 5 3 4 2 5 4 2 5
 1 2 1 1 1 1 1 1 1 1 1 2 2 1 1 1 2 2 2 1 3 4 2 5 5 5 1 4 1 4 1 2 1 1 1 1 1 1 1 1
1 2 2 1 1 1 2 2 2 1 1 1 1 2 1 1 2 1 2 1 2 2 1 1 1 1 1 2 2 1 2 1 NA 2 2 1 1 1 2 2
1 1 12 53 NA 2 1 1 60 1 1 .oo3 1 1 1 2 1 2 1 1 2 2 1 1 1 1 10 50 c 10 100 20 2 1
25 1 8 1733 75 2 3 3 95 9 3 1 1 1 2 2 2 2 1 2 2 2 2 2 867 2 1 1330 10 1 1 1 10
10 1 1 2 2 2 2 9 9 2 2 1 2 2 2 384 3 6 1319 10 1 2 2 10 9 3 1 1 1 1 2 1 2 2 2 2
2 2 2 185 4 11 887 75 1 4 4 60 9 1 1 1 1 1 2 1 2 2 2 2 2 2 2 159 4 1 1 2 2 2 1 4
3 1 1 4 2 3 1 1 3 3 2 2 4 2 2 3 3 2 3 2 1 NA 2 3 3 4 3 5 4 3 8
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 1 3 4 4 2 3 3 2 3 3 3 1 2 2 2 1 1 2 1 1 1
2 2 2 1 1 1 2 2 2 1 1 1 1 1 1 1 2 1 1 1 2 2 1 1 1 1 1 2 2 1 1 1 NA 1 1 1 1 1 1 1
1 1 13 43 NA 2 2 1 60 2 2 .01 1 1 1 1 1 1 2 1 1 1 2 1 2 2 10 50 C 10 100 20 2 2
25 1 8 1262 100 1 5 5 50 10 4 1 1 1 1 1 1 2 1 2 2 2 2 2 615 2 1 1283 1 1 1 2 30
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4 4 2 1 3 1 3 2 1 3 3 1 1 3 1 2 3 3 1 4 2 2 4 1 4 1 5 1 3 5 2 10
 1 2 1 1 1 1 2 1 1 1 1 2 2 1 1 1 2 1 1 1 3 3 4 1 3 3 3 1 5 2 1 2 2 1 1 1 2 1 1 1
1 2 2 2 2 1 1 2 2 2 2 2 1 2 1 1 1 1 2 1 2 2 1 1 1 1 1 2 1 1 1 1 NA 2 2 1 1 1 2 2
1 1 14 30 NA f 0 0 60 0 1 .03 1 2 1 1 1 2 2 2 2 0 2 2 1 1 10 500 c 10 100 10 0 0
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4 2 2 1 4 2 2 1 4 3 2 2 2 1 2 2 2 2 2 1 2 5 1 5 1 5 5 2 9
 1 2 1 1 1 1 1 1 1 1 1 2 2 1 1 1 2 2 2 1 4 4 1 4 4 4 1 2 1 5 1 2 2 1 1 1 2 1 1 1
1 1 1 2 2 2 2 2 2 1 2 1 1 2 1 1 2 2 2 2 1 2 2 1 2 2 2 2 1 1 2 2 NA 2 1 1 1 1 2 1
1 1 12 30 NA m 2 1 60 2 2 30 1 1 1 1 2 1 2 1 1 3 2 2 1 1 10 500 c 10 100 2 2 2
25 1 8 8131 50 2 2 2 50 7 3 1 1 1 1 2 1 1 1 2 2 2 2 2 816 2 1 1049 50 1 1 2 50 7
3 1 1 1 2 2 9 9 2 2 2 2 2 2 308 3 6 574 50 2 3 3 50 7 3 1 1 1 2 2 2 2 2 2 2 2 2
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 1 2 1 1 1 1 2 1 1 1 1 2 2 1 1 1 2 2 2 1 1 1 3 1 4 3 2 1 4 1 1 2 2 1 1 1 2 1 1 1
1 1 2 1 1 1 1 2 2 1 1 1 1 2 2 1 2 1 2 1 2 1 1 2 1 1 1 2 1 1 2 2 NA 2 1 1 1 1 2 1
1 1 16 24 NA 2 2 1 60 2 2 .0003 1 1 1 2 1 1 1 2 2 20 3 2 2 2 10 50 c 10 10 .02 2
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4 2 1 1 1 1 2 9 9 1 2 1 2 2 2 150 3 6 644 20 1 1 1 10 6 2 1 1 1 1 2 1 2 1 2 2 2
2 2 149 4 11 677 80 1 5 5 40 8 1 1 1 1 1 1 1 2 2 2 2 2 2 2 226 3 1 1 2 1 1 1 3 2
4 3 3 4 2 2 2 1 2 2 4 2 3 2 2 2 2 2 3 3 3 3 3 2 2 4 4 2 3 7
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1 2 1 1 1 2 2 2 2 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 2 2 2 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 16 23 NA 2 2 2 60 2 2 .03 2 1 2 2 2 2 1 1 1 2 1 1 2 2 10 500 c 10 100 2 2 2
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4 3 2 1 4 1 3 1 1 3 4 1 2 4 1 2 4 4 1 4 2 1 4 1 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2 2 2 1 4 3 3 4 4 4 2 3 2 4 1 1 1 1 1 1 1 1 1 1
1 2 2 1 2 1 1 2 2 1 2 1 1 2 2 1 2 1 2 2 1 2 1 1 1 2 2 1 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 12 45 NA m 2 2 60 2 2 .03 2 1 1 3 2 1 2 1 3 30 2 2 2 2 10 500 c 10 100 20 2
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3 2 1 3 2 3 3 1 2 3 1 1 3 2 1 4 3 1 3 1 1 4 2 NA NA NA NA NA NA NA NA
 1 2 1 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 4 5 2 5 5 3 1 2 1 5 1 2 2 2 1 1 2 2 1 1
1 2 1 1 1 2 2 2 1 2 2 2 2 1 1 1 1 1 2 1 2 2 1 1 1 2 1 2 2 1 2 2 NA 2 2 2 1 1 2 2
2 1 14 27 NA 2 2 2 60 2 2 .03 2 1 1 1 2 2 2 1 1 2 1 1 2 2 10 500 c 10 100 2 2 2
25 1 8 1468 75 2 4 4 90 8 4 1 1 2 2 2 2 2 2 2 2 2 2 2 773 2 1 2162 1 1 1 1 25 9
1 2 2 2 2 2 9 9 2 2 2 2 2 2 419 3 6 1238 10 1 1 2 25 10 4 1 1 1 2 2 1 2 2 2 2 2
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 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 1 1 1 3 2 3 2 2 3 3 2 4 1 2 2 1 1 1 1 1 1 1
1 2 1 1 2 1 2 1 2 1 1 1 1 2 1 1 1 1 2 1 2 2 1 2 1 1 1 2 2 1 1 1 NA 1 1 1 1 1 1 1
1 1 14 37 NA f 1 2 60 2 2 .003 2 2 1 1 1 1 2 1 1 0 1 1 2 2 10 50 c 10 100 2 2 2
25 1 4 1677 50 1 3 3 80 5 3 1 1 1 1 1 1 2 2 2 2 2 2 2 451 2 9 1217 1 1 1 1 100 6
3 1 1 1 1 2 1 2 2 2 2 2 2 2 168 3 2 713 20 1 3 2 85 9 2 1 1 1 1 1 9 9 2 2 2 2 2
2 304 4 7 720 40 1 3 4 80 10 2 1 1 1 1 1 1 2 2 2 2 2 2 2 148 3 1 1 2 1 1 1 4 4 1
1 3 1 3 2 2 3 3 2 2 3 2 2 3 3 2 3 1 1 2 1 3 2 3 2 3 2 3 5
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1 1 1 1 1 2 2 2 2 1 2 1 1 2 1 1 2 2 1 2 2 2 2 2 1 2 2 1 2 1 1 2 NA 2 1 1 1 1 1 1
1 1 16 38 NA 2 2 2 8050 1 1 30 2 1 1 1 1 1 2 13 3 1 2 1 1 1 45 500 C 1 20 30 1 2
60 1 4 2442 5 1 2 3 25 8 1 1 1 1 2 2 2 2 1 2 2 2 2 2 916 2 9 982 10 1 2 2 80 10
1 1 1 1 2 2 2 2 1 2 2 2 2 2 366 3 2 2672 100 1 5 5 10 10 1 1 1 1 1 2 9 9 2 2 2 2
2 2 221 4 7 1189 100 3 5 4 9 10 1 1 1 1 1 2 1 2 2 2 2 2 2 2 260 2 1 1 2 2 2 1 2
2 1 1 3 1 2 2 1 2 2 1 1 2 1 1 2 3 1 2 1 1 2 1 5 3 4 1 5 5 3 10
 1 2 2 1 1 1 2 1 1 1 1 2 2 2 2 1 2 2 2 1 NA 3 2 4 3 4 2 3 2 4 1 2 2 2 1 1 2 2 2
1 1 2 2 2 1 2 2 1 2 1 1 1 1 2 2 1 1 1 2 1 2 2 1 1 1 2 2 2 2 1 1 2 NA 2 2 2 1 1 2
2 1 1 20 50 NA 1 2 2 60 2 2 .03 1 1 1 13 1 1 1 1 1 0 2 2 2 2 10 500 C 10 100 2 2
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9 1 1 1 2 2 2 1 2 2 2 2 2 2 2 231 3 2 959 20 1 2 2 15 9 1 1 1 1 2 2 9 9 2 2 2 2
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 1 1 1 1 1 1 2 1 1 1 2 2 2 2 2 1 2 2 2 2 4 4 2 4 4 2 3 4 2 4 2 2 2 2 2 1 2 1 1 1
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1 1 1 16 42 NA 2 1 1 60 2 2 .0003 1 1 1 1 1 1 1 2 1 0 1 1 2 2 10 500 C 10 100 2
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4 4 2 1 4 1 4 2 1 4 4 1 1 4 1 1 4 3 1 4 1 1 4 1 4 2 2 4 4 4 2 8
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1 2 1 1 1 1 2 2 2 1 2 1 1 2 1 1 2 1 1 1 2 2 1 1 1 1 2 2 2 1 1 2 NA 2 1 1 1 1 2 1
1 1 14 39 NA 1 2 1 .6 2 2 .03 1 2 1 1 2 2 2 2 1 0 1 2 2 1 .1 500 C 10 100 2 2 1
25 1 4 2817 75 2 4 4 80 9 5 1 1 1 2 2 2 1 1 2 1 1 2 2 1653 2 9 2459 5 1 2 2 70 9
1 1 1 2 2 2 1 2 2 2 2 2 2 2 567 3 2 1878 30 1 2 2 75 9 1 1 1 1 2 2 9 9 2 2 2 2 2
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3 2 3 2 2 3 1 2 2 2 2 2 2 2 2 3 2 2 3 3 2 3 2 4 2 5 4 2 9
 1 2 2 1 1 1 2 2 1 1 1 2 2 2 2 1 2 2 2 2 2 3 4 2 4 3 3 2 3 4 1 2 2 2 1 1 2 2 2 1
1 2 1 1 2 1 1 2 1 1 1 1 1 2 1 1 1 2 1 1 2 2 2 1 1 2 2 2 1 1 1 2 NA 2 2 2 1 1 2 2
1 1 17 23 NA f 2 1 60 1 1 .0003 1 1 2 2 1 2 2 1 3 4 1 2 1 1 10 500 c  10 100 2 2
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1 2 1 2 1 1 1 1 2 1 1 1 1 2 2 1 2 1 1 1 2 2 1 1 1 1 1 1 1 1 2 1 NA 2 2 1 1 1 2 2
1 1 16 24 NA 2 2 2 60 2 2 .03 1 1 1 1 1 2 1 1 2 10 1 1 2 2 10 500 c 10 100 2 1 1
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4 4 3 2 3 1 3 2 1 2 3 2 2 3 2 1 3 3 2 3 2 1 3 1 NA NA NA NA NA NA NA NA
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1 1 2 2 1 1 14 39 NA 2 2 2 60 2 2 .0003 1 2 1 1 2 1 2 2 1 0 2 2 2 2 10 45 c 10
100 200 2 2 25 1 4 889 75 1 3 3 50 6 4 1 1 1 1 2 1 2 2 2 2 2 2 2 472 2 9 829 25
2 2 2 40 6 4 1 1 1 2 2 2 2 2 2 2 2 2 2 298 3 2 642 25 2 2 2 25 6 4 1 1 1 2 2 9 9
2 2 2 2 2 2 172 4 7 498 75 2 4 4 25 7 5 1 1 1 2 2 1 2 2 2 2 2 2 2 292 1 3 3 1 1
1 1 1 1 4 3 1 3 1 3 3 1 1 2 2 1 3 3 1 1 4 2 3 3 3 3 3 4 2 2 4 3 4 4
 1 2 1 1 1 1 2 2 1 1 1 2 2 2 1 1 2 2 2 1 1 1 4 2 3 3 3 1 2 3 1 2 2 1 1 1 2 2 2 1
1 2 2 1 1 1 1 2 2 2 2 1 1 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 18 24 NA 2 2 2 60 2 2 .03 2 2 2 2 2 1 2 2 1 0 1 1 2 2 10 500 c 10 100 2 2 2
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3 3 3 2 3 2 2 2 2 4 2 4 4 2 3 3 2 4 4 2 4 NA NA NA NA NA NA NA NA
 1 2 2 1 1 1 2 2 2 1 1 2 2 2 2 1 2 2 2 2 3 2 2 3 4 3 3 3 2 4 1 2 2 1 1 1 2 2 2 1
1 2 1 1 2 1 2 2 2 1 2 2 1 2 1 1 1 1 2 1 2 1 1 1 1 2 2 1 2 1 1 2 NA 2 2 2 1 1 2 2
1 1 16 40 NA 2 2 2 60 2 2 .03 2 2 2 2 1 1 2 2 1 0 3 2 2 1 10 500 c 10 100 2 2 2
25 1 8 1234 75 1 4 5 30 9 2 1 1 1 1 2 1 2 1 2 2 2 2 2 526 2 1 861 1 1 1 1 25 10
5 1 2 2 2 2 9 9 2 2 2 2 2 2 230 3 6 923 8 2 2 3 40 9 2 1 1 1 2 2 1 2 1 2 1 1 2 2
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2 3 2 2 2 1 3 3 1 3 3 1 3 2 3 2 3 1 2 4 2 3 2 3 1 4 3 3 8
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1 2 1 2 2 1 2 1 1 1 2 1 1 2 1 1 2 1 1 2 2 1 2 1 1 2 1 2 2 1 1 1 NA 2 1 1 1 1 2 1
1 1 14 20 NA 2 2 2 60 2 2 .03 1 1 1 2 1 1 2 2 1 0 3 1 2 2 10 500 C 10 100 2 2 2
25 1 4 1200 25 2 2 2 60 8 3 1 1 1 1 2 1 2 2 2 2 2 2 2 627 2 9 1342 .03 2 2 2 50
8 4 1 1 1 2 2 1 2 1 2 1 2 2 2 254 3 2 755 30 2 2 2 40 7 2 1 1 1 1 1 9 9 1 2 2 2
2 2 278 4 7 695 60 1 4 3 60 8 3 1 1 1 1 2 1 2 2 2 2 2 2 2 149 3 1 1 2 2 2 2 3 2
2 2 3 2 3 2 1 2 2 2 2 4 3 2 2 2 2 3 3 1 2 1 3 3 2 1 4 4 1 6
 1 2 2 1 1 1 2 1 1 1 1 2 2 2 2 1 2 2 2 1 3 3 2 4 4 3 2 2 1 5 1 2 2 2 1 1 2 2 1 1
1 1 1 2 1 2 2 1 2 1 1 1 1 2 1 1 2 2 1 1 2 2 1 1 2 1 1 2 2 1 1 1 NA 2 2 1 1 1 2 2
1 1 18 26 NA 2 1 1 60 2 2 .0003 2 1 2 1 1 1 2 1 2 4 1 1 2 2 10 500 a 10 100 2 2
2 25 1 8 1147 40 1 3 3 72 9 2 1 1 2 2 2 1 2 1 2 2 1 2 2 316 2 1 905 5 2 1 2 60 8
2 1 1 1 2 2 9 9 2 2 2 2 2 2 140 3 6 873 15 2 2 1 68 8 2 1 1 1 2 2 1 2 1 2 2 1 2
2 256 4 11 669 60 1 3 3 75 10 4 1 1 1 2 2 1 2 2 2 2 2 2 2 135 4 1 1 2 1 2 1 4 3
2 2 3 2 3 2 1 1 2 4 2 3 1 1 3 3 1 3 3 1 3 2 4 1 4 2 5 4 2 9
 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 1 3 2 2 1 1 3 2 1 3 1 2 2 2 1 1 2 2 1 1
1 1 1 1 1 2 2 1 1 1 1 1 1 2 1 1 2 1 2 1 1 2 2 1 1 1 2 2 2 2 2 1 NA 2 2 1 1 NA NA
NA NA NA 14 53 NA f 1 1 70 2 2 03 1 2 2 2 1 1 1 2 1 5 3 3 2 2 20 450 b 10 100 20
2 2 25 1 8 1061 75 1 4 4 50 8 5 1 1 1 1 2 1 2 2 2 2 2 2 2 1163 2 1 1439 10 1 1 1
80 8 1 1 1 2 2 2 9 9 2 2 2 2 2 2 241 3 6 1011 10 1 1 1 80 8 5 1 1 1 2 2 1 2 2 2
2 2 2 2 731 4 11 545 50 2 3 4 80 8 5 1 1 1 2 2 1 2 1 2 2 2 2 2 602 2 2 2 2 2 2 3
4 4 2 2 4 3 2 3 2 3 3 1 3 3 2 2 3 2 2 4 2 1 4 3 5 1 5 1 5 5 2 10
 1 1 1 1 1 1 1 1 1 1 1 21 2 2 1 1 2 2 2 1 3 4 3 5 5 3 1 1 5 5 1 1 1 1 1 1 1 1 1
1 1 2 1 1 1 2 2 1 1 2 2 1 1 1 1 1 2 1 1 2 1 1 1 1 1 2 1 2 1 2 2 1 NA 2 2 1 1 1 1
1 1 1 18 54 NA 1 2 1 60 2 2 .3 2 2 1 1 1 1 1 2 1 5 1 2 1 2 10 500 c 10 100 2 2 1
25 1 0 1720 75 1 3 4 30 9 4 1 1 1 2 2 9 9 1 2 2 2 2 2 560 2 5 967 5 1 1 2 25 10
1 1 1 1 1 2 1 2 2 2 2 2 2 2 458 3 10 1076 10 1 1 2 10 10 5 1 1 1 1 1 1 2 2 2 2 2
2 2 329 4 3 788 80 2 4 5 40 9 4 1 1 1 1 1 9 9 1 2 2 2 2 2 319 4 1 1 2 1 1 2 4 3
3 2 4 2 4 1 1 4 4 1 1 3 1 1 4 4 1 4 2 2 4 12 5 2 5 5 5 5 1 10
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 4 3 3 4 4 5 2 3 1 5 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 1 1 2 2 1 1 1 1 2 1 1 2 1 1 1 2 2 1 1 1 2 2 1 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 20 39 NA 2 2 2 60 2 2 .03 1 1 1 1 1 2 1 2 1 17 1 1 1 1 10 500 c 10 100 2 2 2
25 1 4 1693 50 2 3 3 70 9 4 1 1 1 1 2 1 2 2 2 2 2 2 2 594 2 9 1424 5 2 2 2 80 8
4 1 1 1 1 2 1 2 2 2 2 2 2 2 203 3 2 1348 30 2 2 2 60 6 2 1 1 1 1 1 9 9 1 2 1 2 2
2 407 4 7 1296 95 1 5 5 75 6 1 1 1 1 1 1 1 2 1 2 1 2 2 2 238 4 1 1 2 2 2 24 4 4
1 1 4 2 3 1 2 3 3 2 2 4 1 2 3 2 1 4 1 2 3 1 5 4 2 1 1 2 3 7
 1 2 1 1 1 1 2 1 1 1 1 2 2 2 1 1 2 2 1 1 4 4 2 4 4 4 2 3 2 4 1 2 2 1 1 1 2 2 1 1
1 1 1 1 1 2 2 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2 1 1 NA 2 2 1 1 1 2 2
1 1 19 37 NA 2 0 NA 60 1 NA .03 NA NA NA NA 1 1 2 2 1 3 1 1 1 NA 10 500 c 10 100
2 0 NA 25 1 0 687 50 2 3 4 60 8 2 1 1 1 2 2 9 9 1 2 1 2 1 2 180 2 5 611 30 2 2 4
5 8 2 1 1 2 2 2 1 2 1 2 2 2 2 2 182 3 10 359 30 2 2 3 5 9 2 1 1 1 2 2 1 2 1 2 2
2 2 2 179 4 3 316 65 2 3 4 50 9 2 1 1 1 1 2 9 9 1 2 2 2 2 2 91 4 1 1 2 2 2 1 4 3
3 2 3 2 3 2 2 3 3 2 4 3 1 1 4 3 1 2 2 1 3 2 3 2 4 1 4 4 1 7
 1 2 1 1 1 1 2 1 1 1 1 2 2 2 1 1 2 2 1 1 3 3 3 2 3 3 3 2 3 2 1 2 2 1 1 1 2 1 1 1
1 2 1 1 2 2 2 1 2 1 1 1 1 2 1 1 2 1 1 1 2 2 1 1 1 2 2 1 1 1 1 2 NA 2 2 1 1 1 2 1
1 1 13 36 NA 2 2 2 60 2 2 .03 1 2 3 3 2 1 1 1 3 0 2 2 1 1 10 50 c 10 100 200 1 1
25 1 4 1675 35 2 3 3 50 7 4 1 1 1 2 2 1 2 2 2 2 1 2 2 537 2 9 978 10 1 1 1 60 9
4 1 1 1 2 2 1 2 2 2 2 2 2 2 234 3 2 872 50 2 3 4 40 8 4 1 1 1 1 2 9 9 1 2 1 1 2
2 182 4 7 859 90 1 4 5 40 9 4 1 1 1 1 2 1 2 2 2 2 2 2 2 163 4 1 1 2 1 2 1 2 3 3
2 2 2 2 2 1 1 2 2 2 3 1 1 3 3 1 3 1 2 3 1 4 1 4 2 4 4 1 9
 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2 1 1 4 3 1 4 4 4 1 1 1 5 1 2 2 1 1 1 2 2 1 1
5 1 5 5 1 3 1 1 1 5 5 5 5 1 5 5 1 4 4 3 3 1 1 5 5 5 5 2 1 1 3 4 NA 2 1 1 1 1 2 2
1 1 12 34 NA 1 1 1 6 2 2 1 1 1 1 1 1 1 2 1 1 0 2 2 2 2 1 500 c 10 100 20 2 1 1/4
1 0 978 90 1 4 4 80 9 1 1 1 1 1 2 9 9 2 2 2 2 2 2 486 2 5 584 50 1 2 2 60 9 2 1
1 1 1 1 1 1 2 2 2 2 2 2 295 3 10 914 10 4 2 2 20 8 4 1 1 1 2 2 1 2 2 2 2 2 2 1
193 4 3 609 50 1 2 3 75 9 4 1 1 1 1 1 9 9 1 2 2 2 2 2 212 4 1 1 2 1 2 1 4 3 2 2
3 2 2 3 1 3 3 2 1 4 1 3 3 2 1 4 1 1 3 1 5 2 4 1 5 5 1 9
 1 2 1 1 1 1 2 1 1 1 1 2 1 1 1 1 2 1 1 1 4 2 3 3 3 3 3 2 3 3 1 2 1 1 1 1 1 1 1 1
1 2 1 1 2 1 2 1 2 1 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 2 1 NA 2 1 1 1 11 2
1 1 1 14 47 NA f 2 2 6 2 2 10 2 2 2 2 1 1 2 2 1 20 1 2 2 2 1 600 c 10 100 2 2 2
1 1 0 2546 50 1 3 3 70 8 3 1 1 1 1 1 9 9 2 1 2 2 2 2 433 2 5 676 50 1 3 3 70 8 3
1 1 1 1 1 1 2 2 1 2 2 2 2 419 3 10 604 50 2 3 3 70 7 3 1 1 1 1 1 1 2 2 1 2 2 2 2
366 4 3 371 50 1 3 3 80 9 3 1 1 1 1 1 9 9 2 1 2 2 2 2 173 2 2 2 1 1 2 2 3 2 2 3
2 3 2 3 1 2 2 2 2 2 2 2 2 2 2 3 3 2 2 3 1 4 3 3 3 3 3 5
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 2 1 4 5 3 3 2 1 3 1 1 1 1 1 1 1 1 1 1
1 2 1 2 1 2 2 1 1 1 2 1 1 2 1 1 2 1 1 1 2 2 1 1 1 1 2 1 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 12 30 NA 2 2 2 1.3 2 2 .003 1 1 1 1 1 1 2 2 1 0 2 2 2 2 1 500 c 10 100 20 2
2 25 1 8 2605 25 1 3 3 50 8 2 1 1 1 1 1 1 2 2 2 2 2 2 2 1518 2 1 2465 1 1 1 1 20
9 4 1 1 1 1 1 9 9 2 2 2 2 2 2 255 3 6 1096 30 1 3 3 50 8 3 1 1 1 1 1 1 2 2 2 2 2
2 2 333 4 11 908 80 1 3 3 60 8 2 1 1 1 1 1 1 2 2 1 2 2 2 2 426 3 1 1 2 2 2 1 4 4
2 1 3 1 4 1 1 3 3 1 2 3 1 2 3 4 1 3 1 2 3 1 5 1 5 1 5 4 1 9
 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 3 3 3 3 1 2 2 4 3 3 1 2 1 1 1 1 2 2 1 1
1 2 1 1 1 2 2 1 1 1 1 1 1 2 1 1 2 1 2 1 1 2 2 1 1 1 1 2 2 2 2 1 NA 2 2 1 1 1 2 2
1 1 14 54 NA m 2 2 75 2 2 03 1 2 2 1 1 1 1 2 1 3 3 3 2 2 20 375 b 10 100 200 2 2
25 1 8 435 80 1 4 4 75 8 5 1 1 1 2 2 1 2 2 2 2 2 2 2 448 2 1 1368 10 1 1 1 80 8
5 1 1 2 2 2 9 9 2 2 2 2 2 2 720 3 6 1168 10 1 1 1 80 8 5 1 1 1 2 2 1 2 2 2 2 2 2
2 276 4 11 968 50 2 3 4 80 8 5 1 1 1 2 2 1 2 2 2 2 2 2 2 156 2 3 3 1 1 2 4 4 3 2
3 3 2 2 2 3 3 2 2 2 3 2 2 3 2 2 3 3 1 3 2 5 1 5 1 5 5 1 10
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 3 3 4 4 3 3 3 4 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 2 2 1 2 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1
1 1 14 27 NA 2 1 1 .60 1 1 .00003 2 1 1 1 1 1 1 2 1 1 1 1 1 1 .10 .001 C 10 100
.02 2 2 .25 1 4 1783 .50 2 2 2 85 8 2 1 1 1 1 1 2 1 2 2 2 2 2 2 855 2 9 1133 .10
2 1 2 90 9 3 1 1 1 1 1 1 1 1 1 1 1 1 1 134 3 2 1092 .20 2 2 2 85 8 2 1 1 1 1 1 9
9 1 2 2 2 2 2 266 4 7 1402 .25 1 3 3 90 8 2 1 1 1 1 1 2 2 1 2 2 2 2 2 204 2 3 2
2 1 2 1 4 4 2 2 3 2 2 3 2 2 2 2 3 3 1 3 2 2 2 3 2 2 3 2 5 1 5 1 5 5 1 9
 1 2 2 2 1 1 2 2 2 1 1 2 2 2 1 1 2 2 2 1 4 4 1 4 5 1 1 4 1 5 7 2 2 2 1 1 2 2 2 1
1 2 1 1 2 2 2 1 2 1 1 1 1 2 1 1 2 1 1 2 1 2 2 1 1 1 1 2 2 1 1 1 NA 2 2 2 1 1 2 2
2 1 15 30 NA 2 1 1 60 2 2 .03 1 1 1 1 1 1 2 2 1 0 1 1 2 2 10 600 c 10 100 2 2 1
25 1 0 1561 80 2 4 5 90 9 5 1 1 2 2 2 9 9 1 2 2 2 2 2 342 2 5 1179 3 2 2 1 70 9
5 1 1 2 2 2 1 2 2 2 2 2 2 2 301 3 10 1850 25 2 3 3 70 10 5 1 1 2 2 2 1 2 1 2 2 2
2 2 187 4 3 2547 90 2 5 5 70 10 5 1 1 2 2 2 9 9 2 2 2 2 2 2 138 5 1 1 2 2 2 2 4
4 2 1 4 1 4 1 1 3 4 1 1 4 1 1 4 3 1 4 2 1 4 1 5 1 5 1 5 5 1 10
 1 2 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 2 1 1 3 2 2 3 4 3 2 3 2 3 1 2 2 1 1 1 2 2 1 1
1 2 1 1 1 2 2 1 2 1 1 1 1 2 1 1 2 1 1 2 1 2 2 1 1 1 1 2 2 1 1 1 NA 2 2 1 1 1 1 1
1 1 12 46 NA 2 2 2 60 2 2 .03 1 1 1 1 2 1 1 1 3 0 1 2 1 1 10 50 a 10 100 20 2 2
25 1 8 1507 50 1 3 3 80 9 5 1 1 1 1 1 1 2 2 2 2 2 2 2 766 2 1 3050 10 1 1 1 50 9
5 1 1 1 1 1 9 9 2 2 2 2 2 2 323 3 6 904 10 1 2 2 50 9 4 1 1 1 1 1 1 2 2 2 2 2 2
2 270 4 11 1289 60 1 4 3 25 9 5 1 1 1 1 1 1 2 2 2 2 2 2 2 155 2 3 3 1 1 1 5 2 2
3 3 2 2 2 3 2 2 2 3 1 2 1 2 2 2 2 2 1 2 2 2 2 2 3 4 4 3 4 50
 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 4 3 2 4 4 5 2 5 4 4 1 2 2 1 1 1 2 2 1 1
1 2 1 1 2 2 2 2 1 1 1 1 1 2 1 1 1 1 2 1 2 2 1 2 1 2 2 2 1 1 1 2 NA 2 2 1 1 1 2 2
2 1 12 35 NA 2 2 2 60 2 2 .03 2 2 1 1 1 1 2 1 1 2 1 1 1 2 10 500 A 10 100 200 2
2 25 1 4 1080 50 1 2 4 50 9 4 1 1 1 2 2 2 1 1 2 2 2 2 2 377 2 9 813 5 1 2 1 10
10 5 1 1 1 2 2 1 1 2 2 2 2 2 2 156 3 2 672 20 1 2 2 45 9 1 1 1 1 2 2 9 9 2 2 2 2
2 2 73 4 7 562 60 1 5 4 50 9 1 1 1 1 2 2 1 1 2 2 2 2 2 2 63 4 1 1 1 1 1 2 4 4 2
2 3 2 2 2 2 3 2 3 3 2 1 2 3 3 3 3 2 2 4 3 3 3 3 3 3 3 3 5
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1 2 1 2 1 2 2 1 2 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 1 2 2 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 14 41 NA f 2 1 60 2 2 .03 2 2 1 1 1 2 2 1 3 2 2 1 1 1 10 500 a 10 100 .02 2
2 25 1 8 1735 50 2 3 3 60 8 4 1 1 1 2 2 2 2 1 2 2 2 2 2 1089 2 1 2356 10 1 2 2
40 8 4 1 1 1 2 2 9 9 1 2 2 2 2 2 873 3 6 1040 20 2 2 2 10 8 2 1 1 1 2 2 2 2 1 2
2 2 2 2 301 4 11 1487 50 2 3 4 80 8 2 1 1 1 1 2 1 2 2 2 2 2 2 2 316 3 1 2 1 1 1
3 2 3 3 2 3 2 2 3 3 2 2 3 2 2 1 4 2 3 3 2 2 2 3 2 4 2 4 1 5 4 1 93
 1 1 1 1 1 1 2 1 1 1 1 2 2 1 1 1 2 2 1 1 5 4 1 5 5 5 1 5 1 5 1 1 1 1 1 1 2 2 1 1
1 2 1 1 2 2 2 1 2 1 1 1 1 2 1 1 2 1 1 2 1 2 2 1 1 2 1 2 2 2 2 1 NA 2 2 1 1 1 2 1
1 1 14 39 NA 2 2 2 60 1 2 .0003 1 1 1 1 2 1 2 2 1 2 1 1 1 2 10 600 c 10 100 2 1
2 25 1 4 2372 50 2 3 3 60 9 2 1 1 1 1 1 2 1 2 1 2 2 2 2 892 2 9 1336 25 1 2 2 30
9 2 1 1 1 2 2 1 2 2 2 2 2 2 2 384 3 2 1653 30 2 2 2 40 9 2 1 1 1 1 2 9 9 2 1 2 2
2 2 615 4 7 1341 80 1 5 5 65 9 1 1 1 1 1 1 1 2 2 2 2 2 2 2 370 3 1 1 2 2 2 1 4 4
2 1 4 1 4 1 1 2 4 1 1 4 1 1 4 4 1 4 1 1 4 1 5 1 5 4 4 5 1 10
 1 2 1 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 3 4 2 3 4 4 1 4 2 4 1 2 2 2 1 1 2 2 2 1
1 1 1 1 1 2 2 1 2 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 14 32 NA f 2 2 60 2 2 .03 1 1 1 1 1 1 1 2 1 1 3 3 2 2 10 50 C 10 100 2 2 2
25 1 8 2003 50 1 4 5 50 8 4 1 1 2 2 2 1 2 1 2 2 2 2 2 1054 2 1 1415 10 1 1 1 50
9 4 2 2 2 2 2 9 9 1 2 2 2 2 2 390 3 6 1066 10 1 2 2 50 9 4 1 1 2 2 2 1 2 2 2 2 2
2 2 261 4 11 999 60 1 4 4 75 9 4 1 1 1 1 2 1 2 2 2 2 2 2 2 215 4 1 1 2 2 2 2 3 3
2 1 3 2 3 1 1 3 3 1 1 3 1 1 3 4 1 3 1 1 4 1 4 2 4 1 4 3 3 8
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 2 2 3 4 3 1 1 4 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 1 2 2 1 1 1 1 1 2 1 1 2 1 2 2 1 2 2 1 1 2 2 2 2 1 1 1 NA 1 1 1 1 1 1 1
1 1 14 55 NA 2 2 1 60 2 2 NA 4 3 3 3 1 1 2 1 3 0 2 2 1 1 10 500 c 10 100 500 1 1
25 1 8 647 50 1 3 2 50 6 3 1 1 1 1 1 1 2 1 2 2 2 2 2 548 2 1 1116 5 1 1 1 50 9 3
1 1 1 1 1 9 9 2 2 2 1 2 2 212 3 6 628 20 1 1 2 20 9 4 1 1 1 1 1 1 2 1 2 2 1 2 2
202 4 11 910 60 1 4 5 5 10 3 1 1 1 1 1 1 2 1 2 2 2 2 2 238 2 1 1 2 1 1 1 1 1 1 4
2 2 2 3 3 2 2 2 3 2 2 2 2 2 3 2 3 3 2 2 4 3 4 4 4 3 2 8
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 3 2 3 3 3 3 3 3 3 3 1 2 2 2 1 1 2 2 1 1
2 2 1 1 2 1 2 1 1 1 1 1 1 2 1 1 1 1 2 1 2 1 1 1 2 2 1 2 2 1 2 1 NA 2 1 1 1 1 2 1
1 1 17 29 NA 2 2 1 6 2 2 .03 1 1 1 1 2 2 2 2 1 8 2 2 1 1 .1 500 c 10 100 2 2 2
25 1 0 1604 40 4 3 4 50 8 2 1 1 1 2 2 9 9 2 2 2 2 2 2 337 2 5 1015 10 2 1 2 35 8
2 2 2 2 2 2 1 1 2 2 2 2 2 2 375 3 10 863 35 2 2 2 40 9 2 1 1 1 1 2 1 2 2 2 2 2 2
2 309 4 3 854 75 3 4 5 55 8 2 1 1 1 1 1 9 9 2 2 2 2 2 2 132 3 2 1 1 1 1 2 2 2 3
3 NA 2 2 2 2 1 2 2 2 2 3 2 2 2 2 2 3 3 2 3 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 2 3 2 3 2 2 3 4 2 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 1 2 2 2 1 1 1 1 2 2 1 1 1 2 1 2 2 1 1 2 1 1 2 2 1 1 1 NA 1 1 1 1 1 1 1
1 1 12 31 NA f 2 2 .6 2 2 .0003 2 1 1 1 1 1 2 2 1 0 1 1 1 1 .1 500 c 10 100 2 2
2 25 1 4 6833 80 1 4 4 50 10 4 1 1 1 1 1 2 2 1 2 2 1 2 2 600 2 9 1162 5 1 1 2 50
10 5 2 2 2 2 2 1 2 2 2 2 2 2 2 209 3 2 1071 25 1 2 2 1 9 4 1 1 1 1 1 9 9 2 2 2 2
2 2 1747 4 7 900 95 1 5 5 1 10 5 1 1 1 1 1 1 2 1 2 2 2 2 2 205 3 1 1 2 1 2 1 4 3
2 2 4 1 3 2 2 2 2 1 2 4 1 2 4 3 1 4 1 2 4 2 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 2 1 1 1 2 2 5 2 3 5 3 2 5 2 1 1 1 1 1 1 2 1 1 1
1 1 1 2 1 2 2 2 1 1 2 2 1 1 2 1 1 1 2 1 2 2 2 1 2 2 2 2 1 1 2 1 NA 2 1 1 1 1 1 1
1 1 16 48 NA 1 2 2 60 2 2 .03 1 1 1 1 1 1 2 1 3 2 2 NA 2 1 10 50 c 10 100 2 2 1
25 1 4 869 90 2 4 4 40 8 1 1 1 1 1 1 2 2 1 2 1 1 2 2 522 2 9 594 10 4 2 3 50 4 2
1 1 1 1 2 1 2 2 1 2 1 2 2 178 3 2 640 40 3 3 4 40 5 1 1 1 1 1 2 9 9 2 2 2 1 2 2
274 4 7 424 30 1 4 4 30 5 1 1 1 1 1 2 1 2 2 2 2 1 2 2 147 3 1 1 2 2 2 2 3 2 4 NA
2 4 2 2 2 1 2 3 3 2 3 3 2 4 2 2 3 2 2 3 4 4 2 2 5 2 4 3
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1 2 1 1 2 1 2 2 2 1 1 1 1 2 1 1 2 2 2 2 1 2 2 1 1 1 1 2 2 1 1 1 NA 2 1 1 1 1 2 1
1 1 18 50 NA 2 2 1 60 1 1 .03 1 1 1 1 1 1 2 1 3 1 2 2 2 1 10 500 C 10 100 20 2 1
25 1 8 1683 50 2 3 3 70 7 2 1 1 1 2 2 2 2 2 2 2 2 2 2 527 2 1 1477 10 1 2 2 100
9 5 2 2 2 2 2 9 9 2 2 2 2 2 2 244 3 6 1475 50 2 2 3 40 10 4 1 1 1 1 1 2 2 2 2 2
2 2 2 330 4 11 1111 80 1 4 4 70 8 3 1 1 1 1 1 1 2 2 2 2 2 2 2 276 4 1 2 1 2 2 2
5 4 1 1 4 2 4 2 1 3 4 1 1 4 1 2 4 4 1 4 1 2 4 1 1 3 1 2 4 4 2 4
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 2 3 4 4 3 2 4 3 1 1 1 1 1 1 1 1 1 1
1 2 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 2 2 2 1 1 2 2 2 1 1 1 2 NA 1 1 1 1 1 1 1
1 1 12 33 NA 2 2 1 1 1 1 30 3 2 2 3 1 1 2 1 3 3 1 2 1 1 10 50 c 1 100 50 2 2 40
1 8 1290 75 1 2 5 25 8 2 1 1 1 1 1 1 2 1 2 2 2 2 2 432 2 1 796 50 1 3 3 50 8 2 1
1 1 1 1 9 9 1 2 2 1 2 1 356 3 6 5253 75 2 2 4 75 7 1 1 1 1 1 1 1 2 2 2 2 2 2 2
338 4 11 726 80 4 4 5 30 10 2 1 1 1 1 1 1 2 1 2 2 2 2 2 197 2 5 6 1 1 1 3 3 2 2
3 2 3 2 3 2 1 2 3 4 3 1 2 3 3 2 3 2 3 3 3 2 2 2 3 4 2 4 5
 1 2 1 1 1 1 2 1 1 1 1 2 2 2 2 1 2 2 2 1 2 2 3 4 3 2 4 2 1 2 1 2 2 2 1 1 2 2 2 1
1 2 2 1 1 1 1 2 2 1 1 1 1 2 2 1 1 1 1 1 2 2 1 1 1 2 1 1 2 1 1 2 NA 2 2 2 1 1 2 2
1 1 12 19 NA 2 2 1 60 2 2 .03 3 2 1 2 1 2 2 2 1 0 1 2 1 1 10 500 c 10 100 2 2 2
25 1 4 1393 60 2 3 4 40 7 2 1 1 1 2 2 2 2 1 2 2 1 2 2 964 2 9 803 10 1 1 1 40 9
4 1 1 2 2 2 1 2 2 2 2 2 2 2 183 3 2 1337 30 4 2 2 15 6 2 1 1 1 1 2 9 9 1 2 2 2 2
2 180 4 7 1938 70 1 4 5 20 8 1 1 1 2 2 2 1 2 1 2 2 2 2 2 126 2 1 2 2 1 1 2 3 2 3
4 3 2 1 4 3 3 2 4 3 2 2 4 3 1 2 1 3 2 3 2 3 4 2 4 3 3 4 4
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1 1 1 1 2 1 1 2 2 1 2 1 1 2 2 1 2 1 2 1 2 2 1 1 1 2 2 1 1 1 2 2 NA 1 1 1 1 1 1 1
1 1 14 39 NA F 2 2 60 2 2 3333 1 1 1 1 1 2 2 1 3 30 2 1 2 2 10 500 C 10 100 20 2
2 25 1 8 833 90 1 4 5 5 10 5 1 1 1 1 1 2 2 1 2 1 1 2 2 366 2 1 545 5 1 1 1 5 10
1 2 2 2 2 2 9 9 2 2 2 2 2 2 174 3 6 472 10 1 1 2 2 10 5 1 1 1 1 1 1 2 2 2 2 2 2
2 133 4 11 376 95 1 5 5 10 10 5 1 1 1 1 1 1 2 2 2 2 2 2 2 81 4 1 1 2 2 2 1 4 3 3
1 3 2 4 2 1 3 2 1 1 4 1 1 4 4 1 4 2 2 3 1 5 2 5 1 5 3 1 10
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1 2 2 1 2 2 2 2 2 1 2 1 1 2 1 1 2 1 2 1 1 2 2 1 1 1 2 1 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 12 63 NA 1 2 2 60 1 1 .03 1 1 2 1 1 1 1 1 1 20 1 1 2 2 10 500 C 10 100 .02 2
2 25 1 0 2429 50 2 3 3 60 7 2 1 1 1 1 2 9 9 2 1 2 2 2 2 1161 2 5 3535 10 1 1 2
60 9 4 1 1 1 2 2 1 2 2 2 2 2 2 2 456 3 10 1452 50 1 2 2 70 8 2 1 1 1 1 2 1 2 2 1
2 2 2 2 406 4 3 988 60 1 3 3 70 8 2 1 1 1 1 1 9 9 2 2 2 2 2 2 209 3 1 1 2 2 2 1
3 4 1 1 3 2 3 1 1 2 3 1 2 3 1 1 3 3 1 3 1 1 3 1 4 2 4 3 4 4 2 8
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1 2 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 NA 1 1 1 1 1 1 1
1 1 9 41 NA 2 1 1 60 1 1 .003 2 2 1 1 1 2 2 1 3 0 1 1 1 1 10 500 c 1 100 2 2 2
25 1 0 805 85 1 4 5 25 9 5 1 1 1 1 1 9 9 1 2 2 2 2 2 271 2 5 703 1 1 1 2 5 10 5
1 1 1 1 1 1 2 2 2 2 2 2 2 159 3 10 532 25 1 3 4 5 9 5 1 1 1 1 1 1 2 2 2 2 2 2 2
106 4 3 357 75 1 4 5 40 9 5 1 1 1 1 1 9 9 1 2 2 2 2 2 160 2 1 3 1 1 2 4 3 2 3 3
2 2 2 3 1 2 2 2 2 3 2 2 2 2 2 2 2 1 2 2 3 4 3 2 4 3 3 8
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1 2 1 1 2 2 2 2 2 1 2 1 1 2 1 1 1 1 2 1 2 2 1 1 1 1 2 1 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 23 46 NA 2 2 2 60 2 2 .03 1 1 1 1 1 1 2 2 2 0 2 3 2 2 10 500 c 10 100 2 2 2
25 1 0 2052 50 2 2 3 10 8 2 1 1 1 1 2 9 9 1 2 2 1 2 2 764 2 5 1586 5 1 1 1 30 9
4 1 2 2 2 2 1 2 1 2 2 2 2 2 410 3 10 1498 20 1 2 2 15 8 3 1 1 2 2 2 1 2 1 2 2 1
2 2 588 4 3 982 80 1 4 4 10 9 1 1 1 1 1 1 9 9 2 2 2 2 2 2 419 2 1 1 2 2 2 1 4 4
2 1 3 2 4 4 1 3 3 2 3 4 2 3 2 2 2 3 2 2 3 2 3 4 3 2 4 3 3 7
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1 2 1 2 2 1 2 2 1 2 1 1 2 1 1 1 1 1 2 1 2 2 1 1 1 1 2 2 2 1 1 2 NA 2 2 2 1 1 2 2
2 1 18 46 NA 2 0 0 60 0 0 3 2 2 2 2 1 1 1 2 1 0 1 1 1 1 10 500 C 10 100 20 0 0
25 1 0 1995 60 1 4 4 70 8 4 1 1 1 2 2 9 9 1 2 2 2 2 2 836 2 5 1231 25 2 2 1 25 9
5 2 2 2 2 2 1 2 2 2 2 2 2 2 645 3 10 1259 10 1 2 2 20 9 1 1 1 1 2 2 1 2 2 2 2 2
2 2 555 4 3 5742 90 1 5 5 50 10 5 1 1 1 1 2 9 9 2 2 2 2 2 2 318 4 1 1 1 2 2 2 3
2 2 2 3 2 2 2 1 2 4 1 1 3 1 2 4 2 1 3 2 2 2 2 42 2 41 1 4 4 2 9
 1 2 2 1 1 1 2 2 2 1 1 2 1 1 1 1 2 2 1 1 5 4 1 5 5 5 1 1 5 5 1 2 2 2 1 1 2 2 2 1
1 2 1 1 1 2 2 1 1 1 2 2 1 2 1 1 2 1 2 1 2 2 2 2 1 2 2 1 2 1 2 2 NA 2 1 1 1 1 2 2
1 1 18 42 NA 2 1 1 60 2 2 .03 1 1 1 1 1 1 2 2 1 0 1 1 1 1 10 500 c 10 100 2 1 1
25 1 0 1286 60 1 4 2 80 9 2 1 1 1 2 2 9 9 1 2 2 2 2 2 604 2 5 929 5 1 1 1 90 10
5 1 1 1 2 2 1 2 2 2 2 2 2 2 271 3 10 662 70 1 2 2 70 10 1 1 1 2 2 2 1 2 2 2 2 2
2 2 204 4 3 674 65 1 3 2 89 10 1 1 1 2 2 2 9 9 1 2 2 2 2 2 143 4 1 1 2 1 1 1 4 4
3 3 4 1 4 1 1 4 4 2 1 4 1 1 4 4 1 4 1 1 4 1 5 1 5 1 5 5 1 10
 1 2 1 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 3 2 3 3 5 3 2 2 1 4 1 2 2 2 2 1 2 2 1 1
1 1 1 1 2 2 2 2 2 1 1 1 1 2 1 1 1 1 2 1 2 2 1 1 2 1 2 2 1 1 1 2 NA 2 2 1 1 1 2 1
1 1 14 33 NA 2 2 2 60 2 2 .003 1 1 1 1 1 2 2 1 2 3 3 3 2 2 10 50 C 10 100 2 2 2
25 1 8 813 80 1 3 4 10 8 4 1 1 1 2 2 1 2 2 2 2 2 2 2 330 2 1 632 1 1 1 1 20 9 5
2 2 2 2 2 9 9 2 2 2 2 2 2 120 3 6 585 20 1 2 2 10 7 4 1 1 1 2 2 1 2 2 2 2 2 2 2
159 4 11 548 95 1 5 5 10 9 5 1 1 1 1 2 1 2 2 2 2 2 2 2 109 4 1 1 2 2 2 1 3 3 3 3
3 2 2 3 1 2 2 3 2 2 2 2 2 2 2 2 2 1 2 1 4 2 4 2 5 4 2 9
 1 2 2 1 1 1 2 2 1 1 2 2 2 2 2 1 2 2 2 1 3 3 2 1 4 4 3 2 3 2 1 2 2 2 1 1 2 2 2 1
1 2 1 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 1 2 2 1 2 1 2 2 2 1 2 2 2 2 NA 2 2 2 2 1 2 2
2 1 16 38 NA 2 2 2 60 2 2 NA 1 1 1 2 2 2 2 2 1 4 2 2 2 2 1 50 c 10 100 5 2 2 25
1 4 2175 50 2 3 3 40 6 3 1 1 1 1 1 2 2 2 2 2 1 2 2 1951 2 9 1599 10 4 2 1 80 6 4
1 1 2 2 2 1 2 2 2 2 2 2 2 385 3 2 1295 50 3 3 1 60 6 4 1 1 2 2 1 9 9 2 2 2 2 2 2
374 4 7 720 80 1 4 3 50 6 3 1 1 2 2 2 1 2 2 2 2 2 2 2 240 3 1 1 2 1 2 2 3 3 2 1
3 1 2 1 1 1 2 1 2 3 2 2 3 3 1 2 1 1 3 1 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 2 2 4 5 5 1 4 1 5 1 1 1 1 1 1 1 1 1 1
1 1 2 1 2 1 1 2 2 1 2 1 1 2 2 1 2 2 2 2 1 1 2 1 1 2 1 1 1 1 2 2 NA 1 1 1 1 1 1 1
1 1 12 31 NA 1 2 2 6 2 2 .0003 1 1 1 1 2 2 2 1 3 0 2 2 1 1 1 500 c 10 100 .02 2
2 25 1 0 22012 50 1 3 2 75 7 4 1 1 1 2 2 9 9 2 2 2 2 2 2 180 2 5 462 5 1 2 2 75
8 4 2 2 2 2 2 1 2 1 2 2 2 2 2 177 3 10 712 50 1 3 4 50 8 4 1 1 1 1 1 1 2 2 2 2 2
2 2 257 4 3 491 100 1 5 5 25 7 4 1 1 1 1 1 9 9 2 2 2 2 2 2 133 2 1 1 2 1 2 3 3 4
3 3 2 3 2 3 2 1 2 2 2 2 2 1 2 2 1 2 2 1 2 2 NA NA NA NA NA NA NA NA
 1 2 2 2 1 1 2 2 2 1 1 2 2 2 1 1 2 2 2 1 3 4 2 4 3 3 2 5 2 3 1 2 2 2 1 1 2 2 2 1
1 2 1 1 2 1 2 2 2 1 2 2 1 2 1 1 2 2 2 2 1 2 2 1 2 2 1 2 2 1 2 2 NA 2 2 2 NA 1 2
2 2 1 13 51 NA 1 2 2 60 2 2 .003 1 1 1 2 2 1 2 1 1 0 2 1 2 2 10 50 c 10 100 2 2
2 25 1 8 2847 50 1 3 2 60 6 4 1 1 2 2 2 1 2 2 2 2 2 2 2 757 2 1 2180 10 1 1 1 20
8 1 1 1 2 2 2 9 9 2 2 2 2 2 2 532 3 6 2198 50 1 2 2 20 6 4 1 1 2 2 2 1 2 2 2 2 2
2 2 413 4 11 1921 60 2 3 4 40 6 2 1 1 2 2 2 1 2 2 1 2 2 2 2 384 1 3 3 1 2 2 4 2
1 3 5 2 2 1 2 2 1 2 2 1 2 1 1 2 3 3 2 1 1 3 1 4 4 3 2 3 3 3 5
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 3 4 5 5 3 3 3 3 4 1 1 1 1 1 1 1 1 1 1
1 2 1 1 2 2 2 2 2 1 1 1 1 2 1 1 2 1 1 1 2 2 2 2 2 2 1 2 2 1 2 2 NA 1 1 1 1 1 1 1
1 1 14 30 NA 2 1 1 60 2 2 75 1 1 1 2 1 1 2 1 1 0 1 1 1 1 10 100 a 10 100 20 2 2
25 1 0 2113 80 1 3 5 80 8 4 1 1 2 2 2 9 9 1 2 2 2 2 2 818 2 5 1571 1 1 2 1 100
10 5 1 1 2 2 2 1 2 2 2 2 2 2 2 351 3 10 1236 30 1 3 3 30 10 4 1 1 1 2 1 1 2 2 2
2 2 2 2 129 4 3 810 99 1 5 4 50 10 4 1 1 1 2 2 9 9 1 2 2 2 2 2 121 3 2 2 2 1 2 4
3 3 3 3 2 2 2 1 2 2 3 1 1 3 1 2 3 3 2 1 1 1 3 2 5 1 4 1 5 4 1 9
 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 1 2 2 1 1 3 3 3 4 3 3 1 1 1 5 1 2 2 1 1 1 2 2 1 1
1 2 1 1 2 1 2 2 2 1 1 1 1 2 1 1 2 1 1 2 2 2 2 1 1 2 2 2 2 1 1 2 NA 2 2 1 1 1 2 2
1 1 12 31 NA 2 1 1 60 NA NA .03 1 1 1 1 1 1 2 2 1 1 2 2 NA NA 10 500 C 10 100 2
NA 1 25 1 0 3138 80 2 4 3 67 7 3 2 1 1 1 1 9 9 2 2 2 2 2 2 1437 2 5 2697 5 1 1 1
90 9 4 1 1 1 1 1 1 2 2 2 2 2 2 2 861 3 10 1760 10 1 1 1 85 8 4 1 1 1 1 1 1 2 2 2
2 2 2 2 283 4 3 1065 95 1 5 5 1 10 1 1 1 1 1 1 9 9 2 2 2 2 2 2 208 4 2 1 1 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 3 3 1 2 1 1 3 1 5 2 4 2 4 4 2 10
 1 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 4 3 4 4 4 2 4 4 4 1 2 2 1 1 1 2 2 2 1
1 1 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 NA 2 2 2 1 1 2 2
2 2 12 49 NA 2 1 1 60 1 1 .03 1 3 2 3 2 1 2 1 3 30 1 1 1 1 10 50 C 10 100 2 2 2
25 1 0 2340 50 2 3 3 50 6 2 1 1 1 1 1 9 9 2 1 2 2 2 2 1532 2 5 1618 .03 1 1 2 90
10 2 2 2 2 2 2 1 2 2 2 2 2 2 2 569 3 10 1505 10 1 2 4 75 9 4 1 2 2 2 2 1 2 2 2 2
2 2 2 673 4 3 1100 99 1 4 5 80 10 1 1 1 1 2 2 9 9 2 1 2 2 2 2 784 3 2 2 1 1 1 2
3 3 3 4 2 3 2 3 2 2 2 2 4 2 3 3 3 1 2 2 3 2 3 2 2 4 2 3 4 2 4 5
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2 1 1 1 4 4 2 3 4 3 2 4 1 5 1 2 2 1 1 1 2 1 1 1
1 2 1 1 2 2 2 1 2 1 1 1 1 2 1 1 2 1 2 2 2 2 2 1 1 2 2 1 2 1 2 2 NA 2 1 1 1 1 2 1
1 1 16 50 NA 2 1 1 60 1 1 .03 1 1 1 1 1 1 1 1 3 0 1 1 1 1 10 500 C 10 100 200 2
1 25 1 0 908 50 2 3 3 50 8 4 1 1 1 2 2 9 9 2 2 2 2 2 2 154 2 5 693 25 1 2 2 1 8
5 1 1 1 2 2 1 2 2 2 2 2 2 2 198 3 10 520 50 1 3 3 25 7 4 1 1 1 2 2 2 2 2 2 2 2 2
2 148 4 3 514 85 1 4 4 50 9 5 1 1 1 1 1 9 9 2 2 2 2 2 2 114 1 2 2 1 1 1 1 4 2 2
2 3 2 3 2 1 2 3 2 2 3 2 2 3 3 1 3 2 1 3 2 4 1 4 3 4 3 2 8
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2 2 1 1 5 1 3 4 4 4 4 4 4 5 1 2 2 2 1 1 2 1 1 1
2 2 1 2 2 2 2 2 2 1 2 2 1 2 1 1 2 1 2 1 2 2 1 2 1 2 1 2 2 1 2 1 NA 2 1 1 1 1 2 1
1 1 14 41 NA 1 2 1 60 2 2 .03 1 2 1 2 2 2 2 1 3 4 1 1 1 2 10 500 c 10 100 2 2 2
25 1 4 2000 65 2 2 3 80 7 4 1 1 1 1 2 1 2 2 2 2 2 2 2 820 2 9 1374 2 1 1 1 91 9
5 2 2 2 2 2 1 2 2 2 2 2 2 2 542 3 2 1604 22 2 2 2 86 8 4 1 1 2 2 2 9 9 1 2 2 2 2
2 599 4 7 1286 64 2 2 3 66 9 2 1 1 1 1 2 1 2 2 2 2 2 2 2 218 5 1 2 1 2 1 1 4 4 2
2 3 2 3 1 2 4 4 1 2 4 2 2 3 3 2 4 2 2 4 2 NA NA NA NA NA NA NA NA
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 2 4 3 4 1 4 1 5 1 1 1 1 1 1 1 1 1 1
1 2 1 1 2 1 1 2 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 NA 1 1 1 1 1 1 1
1 1 16 58 NA m 2 1 60 2 2 .0003 1 1 1 1 1 1 2 1 3 0 2 1 1 1 10 500 C 10 100 2 2
2 25 1 4 2448 50 1 4 4 50 8 4 1 1 1 1 1 1 2 1 2 2 1 2 2 1122 2 9 1554 1 1 2 2 50
8 4 1 1 1 1 1 1 2 2 2 2 2 2 2 379 3 2 1346 1 1 2 2 50 8 4 1 1 1 1 1 9 9 1 2 2 2
2 2 491 4 7 1623 50 1 3 3 80 9 4 1 1 1 1 1 1 2 2 2 2 2 2 2 296 3 1 1 2 2 2 2 4 4
2 1 3 2 4 1 1 3 4 1 2 4 1 1 3 4 1 4 1 1 4 1 4 2 4 3 4 3 4 8
 1 2 2 1 1 1 2 2 1 1 1 2 2 2 1 1 2 2 2 1 1 1 5 1 2 4 5 2 5 1 1 2 2 2 1 1 2 2 2 1
1 2 1 1 2 1 1 2 2 1 2 1 1 2 1 1 1 1 2 1 2 1 1 2 1 2 2 1 1 1 2 2 NA 2 2 2 1 1 2 2
2 1 14 34 NA 2 2 2 60 2 2 .03 2 2 2 2 1 2 1 1 3 0 1 1 2 2 10 525 c 10 100 2 2 2
25.03 1 0 1158 5 1 1 2 25 8 4 1 1 2 2 2 9 9 2 2 2 2 2 2 265 2 5 798 1 2 1 2 20 8
4 1 1 2 2 2 1 2 2 2 2 2 2 2 268 3 10 772 10 2 2 2 10 8 4 1 1 2 2 2 1 2 2 2 2 2 2
2 240 4 3 785 50 3 3 3 40 7 4 1 1 1 2 2 9 9 2 1 1 2 2 2 201 2 1 2 1 1 1 2 1 2 2
4 2 3 1 3 3 1 2 4 4 1 3 3 1 3 3 1 3 3 1 3 1 5 1 4 4 1 4 1
 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 5 1 5 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 2 2 1 2 1 1 2 2 1 1 1 1 2 1 1 1 1 2 1 2 2 1 1 1 2 2 2 1 1 1 2 NA 1 1 1 1 1 1 1
1 1 12 45 NA 2 2 2 60 2 2 .0003 1 1 1 1 2 1 2 1 3 6 2 2 2 2 10 500 c 10 100 2 2
2 25 1 4 2398 50 3 3 3 75 5 2 1 1 1 1 1 2 2 1 2 2 2 2 2 832 2 9 1739 1 1 1 1 50
10 5 1 1 1 1 2 1 2 2 2 2 2 2 2 368 3 2 1249 75 2 3 3 20 9 2 1 1 1 1 1 9 9 1 2 2
2 2 2 300 4 7 840 100 1 5 5 25 8 1 1 1 1 1 1 1 2 1 2 1 2 2 2 276 3 1 1 2 2 2 1 3
4 4 1 3 2 3 2 1 1 2 3 3 2 1 2 2 3 2 2 1 2 2 4 5 1 5 1 5 4 1 9
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2 3 2 2 2 2 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1 1 2 1 1 2 2 1 2 1 1 1 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 13 47 NA 2 2 2 6 2 2 30 2 2 2 3 2 2 2 1 3 1 2 1 2 2 10 50 c 10 100 200 2 2
25 1 8 1283 100 1 4 5 100 10 5 1 1 1 1 1 1 2 2 2 2 2 2 2 444 2 1 369 100 1 4 5
100 9 4 1 1 1 1 1 9 9 1 2 2 2 2 2 211 3 6 486 100 1 4 5 100 10 5 1 1 1 1 1 1 2 2
2 2 2 2 2 139 4 11 288 100 1 4 5 100 10 4 1 1 1 1 1 1 2 2 2 2 2 2 2 105 2 1 1 2
1 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 3 2 2 3 2 2 3 2 4 4 4 2 4 NA 2 7
 1 2 2 1 1 1 2 2 1 1 1 2 2 2 1 1 2 2 2 1 4 3 4 3 3 4 2 4 4 3 1 2 2 1 1 1 2 2 2 1
1 2 2 1 1 2 2 1 2 1 2 1 1 2 2 1 1 1 1 2 2 2 2 1 1 1 1 2 2 2 1 2 NA 2 2 1 1 1 2 2
1 1 16 40 NA 1 2 2 60 1 1 .03 1 1 2 1 1 1 2 1 1 6 1 2 2 2 10 500 C 10 100 2 2 2
25 1 4 1484 35 1 2 2 65 7 2 1 1 1 2 2 1 1 2 2 2 2 2 2 705 2 9 1279 1 1 1 1 1 4 1
1 1 2 2 2 1 2 2 2 2 2 2 2 243 3 2 1005 5 1 1 1 5 9 1 1 1 2 2 2 9 9 2 2 2 2 2 2
156 4 7 916 60 2 3 3 4 6 2 1 1 1 2 2 1 2 2 2 2 2 2 2 234 4 1 2 2 2 2 1 4 3 2 3 2
2 2 3 2 1 2 3 4 2 1 2 3 2 2 3 4 3 2 2 5 1 5 1 4 5 2 10
 1 2 1 1 1 1 2 1 1 1 1 2 2 1 1 1 2 2 1 1 4 3 3 4 4 4 2 4 3 1 1 2 2 1 1 1 2 2 1 1
1 2 2 1 2 2 1 2 1 1 2 1 1 2 1 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
NA 2 2 1 1 1 2 2 1 1 NA NA NA NA NA NA 60 NA NA .3 1 1 1 1 NA NA NA NA NA NA NA
NA NA NA 10 500 a 1 100 2 NA NA 25 1 0 1995 60 1 2 5 50 6 2 1 1 1 2 2 9 9 2 1 2
2 2 2 727 2 5 1386 3 1 1 1 75 7 3 1 1 1 2 2 1 2 2 2 2 2 2 2 365 3 10 855 25 1 4
2 75 7 2 1 1 1 2 2 1 2 2 2 2 2 2 2 236 4 3 844 65 1 2 4 80 7 2 1 1 1 1 2 9 9 2 2
2 2 2 2 321 4 1 1 2 2 2 2 4 2 2 NA 3 2 3 2 1 3 3 1 2 3 2 2 3 3 2 3 2 3 3 2 4 2 4
3 4 3 2 7
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 3 4 5 5 3 5 3 NA 1 1 1 1 1 1 1 1 1
1 1 1 1 1 2 1 1 1 2 1 2 1 1 2 1 1 2 2 1 2 2 2 1 1 1 1 2 1 1 1 1 1 NA 1 1 1 1 1 1
1 1 1 15 51 NA 1 2 1 60 2 2 .03 2 2 1 1 1 1 2 1 3 10 1 1 2 2 10 500 C 10 100 2 2
2 25 1 8 1561 50 4 3 3 70 8 4 1 1 1 1 1 1 2 1 2 2 2 2 2 993 2 1 1458 50 2 3 3 70
5 3 1 1 1 1 1 9 9 1 2 2 2 2 2 338 3 6 1213 50 2 2 3 50 6 3 1 1 1 1 1 2 2 2 1 2 2
2 2 353 4 11 1046 75 1 4 4 40 9 5 1 1 1 1 1 1 2 2 1 2 2 2 2 297 3 1 1 2 2 2 1 4
4 1 2 3 2 4 1 1 3 4 2 1 4 1 1 4 4 NA 4 1 1 4 1 5 3 4 2 4 4 2 9
 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 5 1 1 1 5 1 1 5 1 1 1 2 2 1 1 1 1 1 1 1
1 2 1 1 1 1 2 2 1 1 1 1 1 2 1 1 2 1 1 2 1 2 2 1 1 1 1 1 2 1 1 1 NA 1 1 1 1 1 1 1
1 1 NA 31 NA 2 2 2 100 2 1 25 1 1 1 1 2 1 2 1 3 0 1 1 1 1 75 50 c 1 3 6 2 2 100
1 4 2479 100 1 3 4 50 5 2 1 1 1 1 1 1 2 2 2 2 2 2 2 962 2 9 2785 20 3 3 3 20 5 2
1 1 1 1 1 2 2 1 2 2 2 2 2 477 3 2 1239 50 1 3 3 20 10 4 1 1 1 1 1 9 9 2 2 2 2 2
2 208 4 7 855 50 1 3 3 50 10 4 1 1 1 1 1 1 2 2 2 2 2 2 2 259 1 3 3 1 1 2 5 2 1 1
5 1 4 2 3 2 1 2 2 2 3 2 2 3 4 3 2 1 1 3 2 5 1 5 1 5 5 2 10
 1 2 1 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 4 2 4 4 3 2 4 3 1 2 2 2 1 1 2 2 2 1
1 2 1 1 1 2 2 2 1 1 2 1 1 2 2 1 2 1 1 1 2 2 1 2 2 2 2 2 2 2 2 1 NA 2 2 1 1 1 2 2
1 1 22 32 NA 2 2 2 60 2 2 .03 2 2 2 2 1 1 2 1 2 6 1 1 2 2 10 NA c 10 100 2 2 2
25 1 8 1039 50 2 3 4 25 8 2 1 1 2 2 2 1 2 1 2 1 1 2 2 430 2 1 680 10 2 2 3 25 8
3 2 2 2 2 2 9 9 2 2 2 1 2 2 130 3 6 621 15 2 2 3 25 8 3 1 1 2 2 2 1 2 1 2 2 2 2
2 159 4 11 566 75 2 4 4 19 8 2 1 1 1 1 2 1 2 2 2 2 1 2 2 142 3 1 1 2 1 1 1 2 2 3
3 2 3 2 3 3 2 2 3 4 2 2 3 2 2 3 2 3 3 2 2 4 3 4 4 4 2 3 8
 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 2 3 5 4 3 4 1 2 1 3 1 1 1 1 1 1 1 1 1 1
1 2 1 1 1 1 2 2 2 1 1 1 1 2 1 1 1 1 2 1 2 1 1 1 1 1 2 1 2 1 1 2 NA 1 1 1 1 1 1 1
1 1 16 32 NA 2 2 2 60 1 1 0.003 2 2 1 1 1 1 2 2 1 0 2 2 1 1 10 500 c 10 100 2 2
2 25 1 0 2085 70 1 4 4 1 9 1 1 1 1 1 1 9 9 1 2 2 2 2 2 914 2 5 1468 90 1 1 1 1 9
5 2 2 2 2 2 1 2 2 2 2 2 2 2 398 3 10 1217 60 1 3 4 1 9 3 1 1 1 1 1 1 2 1 2 2 2 2
2 324 4 3 1008 90 1 5 4 1 9 3 1 1 1 1 1 9 9 2 2 2 2 2 2 288 3 2 1 2 2 2 1 4 4 2
1 4 2 4 1 1 3 3 1 2 3 1 1 4 3 1 4 2 2 3 2 4 4 3 3 5 3 4 7
 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 2 1 5 2 3 1 5 2 5 4 1 2 2 1 1 1 1 1 1 1
1 2 1 1 1 2 2 1 2 1 1 1 1 2 2 1 1 1 2 2 2 2 1 1 1 1 2 1 1 1 1 1 NA 1 1 1 1 1 1 1
1 1 15 42 NA 2 2 1 60 2 2 .03 1 1 1 1 1 1 1 1 2 2 1 2 2 1 10 500 C 10 100 2 2 2
25 1 4 1209 95 1 5 5 85 9 5 1 1 1 1 1 1 2 2 2 2 2 2 2 1450 2 9 1535 1 1 1 1 85 9
5 1 1 1 2 2 1 2 2 2 2 2 2 2 1245 3 2 1061 15 1 2 2 85 9 5 1 1 1 1 1 9 9 2 2 2 2
2 2 275 4 7 918 85 1 4 4 10 9 4 1 1 1 1 1 1 2 2 2 2 1 2 2 321 3 2 2 1 1 1 4 3 1
3 3 3 3 2 4 3 1 2 4 4 2 2 1 2 3 2 1 2 1 2 4 4 1 4 1 5 3 4 8
 1 2 2 1 1 1 2 1 1 1 1 2 2 1 1 1 2 2 2 1 3 3 2 3 3 3 3 2 2 4 1 2 2 2 1 1 2 2 2 1
2 1 1 1 1 1 2 2 2 1 2 1 1 2 2 1 2 1 1 1 2 2 1 1 1 1 1 2 1 1 1 1 NA 2 2 1 1 1 2 2
1 1 12 42 NA 2 2 2 60 2 2 .03 2 2 1 1 1 2 2 1 1 5 2 2 2 2 10 500 C 10 100 2 2 2
25 1 0 1681 60 1 3 4 60 7 2 1 1 1 2 2 9 9 1 2 2 1 2 2 688 2 5 1158 5 2 1 1 50 9
4 1 2 2 2 2 1 2 2 2 2 2 2 2 318 3 10 1161 10 1 2 2 25 8 4 1 1 1 2 2 1 2 2 2 2 2
2 2 160 4 3 837 90 2 5 5 60 9 4 1 1 1 2 2 9 9 1 2 2 2 2 2 235 3 1 1 2 1 1 1 3 3
2 2 3 2 3 2 1 3 3 2 2 3 2 3 3 3 2 3 1 2 2 3 3 2 4 1 4 3 2 8
 1 2 1 1 1 1 2 1 1 1 1 2 1 1 1 1 2 1 1 1 3 3 3 3 3 3 2 NA 3 3 1 2 1 1 1 1 2 1 1
1 1 2 1 2 1 1 1 1 2 1 2 1 1 2 2 1 1 1 2 1 1 2 1 1 1 1 2 1 2 1 1 2 NA 2 1 1 1 1 2
1 1 1 14 48 NA 2 2 2 60 2 2 .003 2 2 1 1 2 1 1 1 1 3 1 1 2 2 10 500 c 10 100 2 2
1 25 1 8 2315 25 1 2 3 35 8 2 1 1 1 1 1 1 2 1 2 2 2 2 2 1083 2 1 1833 3 1 2 2 35
8 2 1 1 1 2 2 9 9 1 2 2 2 2 2 793 3 6 1831 10 1 2 2 35 8 2 1 1 1 1 2 1 2 2 2 2 2
2 2 409 4 11 1506 60 1 4 5 35 8 2 1 1 1 1 1 1 2 2 2 2 2 2 2 734 2 2 2 1 1 2 3 3
2 2 2 3 2 2 2 1 2 3 2 2 3 1 2 4 3 1 5 3 2 2 2 5 3 2 3 3 2 5 4
 1 1 1 1 1 1 NA 1 1 1 2 2 2 2 2 2 2 2 2 2 5 4 1 4 4 2 1 5 1 5 1 1 1 1 1 1 1 1 1
1 1 2 1 1 1 1 1 1 2 1 2 1 1 2 1 1 1 2 2 1 1 2 1 1 1 1 2 2 2 1 1 1 NA 1 1 1 1 1 1
1 1 1 12 48 NA 2 2 2 60 2 2 .0033 1 1 1 2 2 1 2 1 1 0 2 2 2 2 10 500 C 10 100 2
2 2 25 1 8 3151 2 2 2 2 60 10 4 1 1 1 1 1 1 2 2 2 2 1 2 1 1950 2 1 2879 1 1 1 1
45 10 5 2 2 2 2 2 9 9 1 2 2 2 2 2 1350 3 6 2038 12 1 3 3 75 8 2 1 1 1 1 1 1 2 1
2 1 1 2 2 397 4 11 3651 75 2 4 4 75 9 4 1 1 1 1 1 1 2 1 2 1 1 2 2 372 1 2 3 1 2
2 3 4 2 2 2 4 1 3 1 1 3 4 1 1 3 1 2 3 3 2 4 1 1 4 1 2 3 2 2 4 3 3 8

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