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Data:
353.4 329.08 331.89 339.94 330.8 361.26 358.02 356.15 322.56 306.1 303.99 322.23 330.2 343.91 367.07 375.22 375.35 389.81 371.18 387.18 395.43 387.86 392.46 375.11 417.03 408.79 412.68 403.67 414.95 415.35 408.2 424.19 414.03 417.8 418.66 431.35 435.7 438.78 443.38 451.67 440.19 450.23 450.54 448.13 463.55 458.93 467.83 461.93 466.51 481.6 467.19 445.66 450.91 456.5 444.27 458.28 475.49 462.69 472.26 453.55 459.21 470.42 487.39 500.7 514.76 533.4 544.75 562.06 561.88 584.41 581.5 605.37 615.93 636.02 640.43 645.5 654.17 669.12 670.63 639.95 651.99 687.31 705.27 757.02 740.74 786.16 790.82 757.12 801.34 848.28 885.14 954.29 899.47 947.28 914.62 955.4 970.43 980.28 1049.34 1101.75 1111.75 1090.82 1133.84 1120.67 957.28 1017.01 1098.67 1163.63 1129.23 1279.64 1238.33 1286.37 1335.18 1301.84 1372.71 1328.72 1320.41 1282.71 1362.93 1388.91 1469.25 1394.46 1366.42 1498.58 1452.43 1420.6 1454.6 1430.83 1517.68 1436.52 1429.4 1314.95 1320.28 1366.01 1239.94 1160.33 1249.46 1255.82 1224.42 1211.23 1133.58 1040.94 1059.78 1139.45 1148.08 1130.2 1106.73 1147.39 1076.92 1067.14 989.82 911.62 916.07 815.28 885.76 936.31 879.82 855.7 841.15 848.18 916.92 963.59 974.5 990.31 1008.01 995.97 1050.71 1058.2 1111.92 1131.13 1144.94 1113.89 1107.3 1120.68 1140.84 1101.72 1104.24 1114.58 1130.2 1173.78 1211.92 1181.27 1203.6 1180.59 1156.85 1191.5 1191.33 1234.18 1220.33 1228.81 1207.01 1249.48 1248.29 1280.08 1280.66 1302.88 1310.61 1270.05 1270.06 1278.53 1303.8 1335.83 1377.76 1400.63 1418.03 1437.9 1406.8 1420.83 1482.37 1530.63 1504.66 1455.18 1473.96 1527.29 1545.79 1479.63 1467.97 1378.6 1330.45 1326.41 1385.97 1399.62 1276.69 1269.42 1287.83 1164.17 968.67 888.61 902.99 823.09 729.57 793.59 872.74 923.26 920.82 990.22 1019.52 1054.91 1036.18 1098.89
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Seasonal Period
12
12
4
6
12
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R Code
par1 <- as.numeric(par1) (n <- length(x)) (np <- floor(n / par1)) arr <- array(NA,dim=c(par1,np)) j <- 0 k <- 1 for (i in 1:(np*par1)) { j = j + 1 arr[j,k] <- x[i] if (j == par1) { j = 0 k=k+1 } } arr arr.mean <- array(NA,dim=np) arr.sd <- array(NA,dim=np) arr.range <- array(NA,dim=np) for (j in 1:np) { arr.mean[j] <- mean(arr[,j],na.rm=TRUE) arr.sd[j] <- sd(arr[,j],na.rm=TRUE) arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE) } arr.mean arr.sd arr.range (lm1 <- lm(arr.sd~arr.mean)) (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) (lm2 <- lm(arr.range~arr.mean)) bitmap(file='test1.png') plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation') dev.off() bitmap(file='test2.png') plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range') dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Section',header=TRUE) a<-table.element(a,'Mean',header=TRUE) a<-table.element(a,'Standard Deviation',header=TRUE) a<-table.element(a,'Range',header=TRUE) a<-table.row.end(a) for (j in 1:np) { a<-table.row.start(a) a<-table.element(a,j,header=TRUE) a<-table.element(a,arr.mean[j]) a<-table.element(a,arr.sd[j] ) a<-table.element(a,arr.range[j] ) 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,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'alpha',header=TRUE) a<-table.element(a,lm1$coefficients[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'beta',header=TRUE) a<-table.element(a,lm1$coefficients[[2]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,summary(lm1)$coefficients[2,2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,summary(lm1)$coefficients[2,3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,summary(lm1)$coefficients[2,4]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'alpha',header=TRUE) a<-table.element(a,lnlm1$coefficients[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'beta',header=TRUE) a<-table.element(a,lnlm1$coefficients[[2]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,summary(lnlm1)$coefficients[2,2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,summary(lnlm1)$coefficients[2,3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,summary(lnlm1)$coefficients[2,4]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Lambda',header=TRUE) a<-table.element(a,1-lnlm1$coefficients[[2]]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
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R Server
Big Analytics Cloud Computing Center
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