Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
1.2998 1.3146 1.3225 1.3321 1.3339 1.3496 1.3647 1.3674 1.3647 1.3481 1.3612 1.3626 1.3711 1.37 1.377 1.3945 1.3917 1.4084 1.4244 1.4014 1.4018 1.3926 1.3857 1.3857 1.3803 1.3912 1.4031 1.3934 1.4016 1.3861 1.3859 1.3896 1.4089 1.4101 1.3958 1.3833 1.3936 1.3874 1.397 1.3856 1.378 1.3705 1.3726 1.3648 1.3611 1.346 1.3477 1.3412 1.3323 1.3364 1.312 1.3074 1.306 1.3078 1.2989 1.285 1.2801 1.2725 1.2715 1.2697 1.2744 1.2874 1.2834 1.2818 1.28 1.268 1.27 1.2713 1.2693 1.2613 1.2611 1.2704 1.2711 1.2836 1.288 1.286 1.282 1.2799 1.279 1.3016 1.3133 1.3253 1.3176 1.3184 1.3206 1.3221 1.3073 1.3028 1.3069 1.2992 1.3033 1.2931 1.2897 1.285 1.2817 1.2844 1.2957 1.3 1.2828 1.2703 1.2569 1.2572 1.2637 1.266 1.2567 1.2579 1.2531 1.2548 1.2328 1.2271 1.2198 1.2339 1.2294 1.2262 1.2271 1.2258 1.2391 1.2372 1.2363 1.2277 1.2258 1.2249 1.2127 1.2045 1.201 1.1942 1.1959 1.206 1.2268 1.2218 1.2155 1.2307 1.2384 1.2255 1.2309 1.2223 1.236 1.2497 1.2334 1.227 1.2428 1.2349 1.2492 1.2587 1.2686 1.2698 1.2969 1.2746 1.2727 1.2924 1.3089 1.3238 1.3315 1.3256 1.3245 1.329 1.3321 1.3311 1.3339 1.3373 1.3486 1.3432 1.3535 1.3544 1.3615 1.3583 1.3585 1.3384 1.3296 1.334 1.3396 1.3468 1.3479 1.3482 1.3471 1.3353 1.3356 1.3338 1.3519 1.3471 1.3548 1.366 1.3756 1.3723 1.3705 1.3765 1.3657 1.361 1.3557 1.3662 1.3582 1.3668 1.3641 1.3548 1.3525 1.357 1.3489 1.3547 1.3577 1.3626 1.3519 1.3567 1.3726 1.3649 1.3607 1.3572 1.3718 1.374 1.376 1.3675 1.3691 1.3847 1.3984 1.3937 1.3913 1.3966 1.3999 1.4072 1.4085 1.4151 1.4135 1.4064 1.4132 1.4279 1.4369 1.4374 1.4486 1.4563 1.4481 1.4528 1.4273 1.4304 1.435 1.4442 1.4389 1.4406 1.4338 1.4433 1.4405 1.4398 1.4276 1.4279 1.4368 1.4337 1.4343 1.456 1.4541 1.4647 1.4757 1.473 1.4768 1.4774 1.4787 1.5068 1.512 1.509 1.5074 1.5023 1.4918 1.5071 1.5083 1.4969 1.4968 1.4815 1.4863 1.4957 1.4875 1.4965 1.4868 1.4922 1.5037 1.4966 1.4984 1.4862 1.4867 1.4761 1.4658 1.4772 1.48 1.4788 1.4785 1.4874 1.5019 1.502 1.5 1.4921 1.4971 1.4918 1.4869 1.4864 1.4881 1.4864 1.4765 1.475 1.4763 1.4694 1.4722 1.4616 1.4537 1.4539 1.4643 1.4549 1.465 1.467 1.4768 1.4783 1.478 1.4658 1.4705 1.4712 1.4671 1.4611 1.4561 1.4594 1.4545 1.4522 1.4473 1.433 1.4262 1.4335 1.422 1.4314 1.4272 1.4364 1.4268 1.427 1.4324 1.4323 1.433 1.4243 1.4112 1.4101 1.4072 1.4294 1.4293 1.417 1.4166 1.4202 1.4357 1.437 1.441 1.4384 1.4303 1.4138 1.4053 1.4104 1.4229 1.4269 1.4227 1.4229 1.4191 1.4223 1.4217 1.409 1.413 1.4089 1.3991 1.3975 1.3901 1.399 1.3901 1.4019 1.3897 1.4009 1.4049 1.4096 1.4134 1.4058 1.4096 1.394 1.4029 1.3978 1.3858 1.3932 1.392 1.384 1.389 1.385 1.4004 1.3969 1.4102 1.3959 1.3866 1.4177 1.4095 1.4207 1.4238 1.422 1.4098 1.3856 1.3901 1.3908 1.401 1.3972 1.3771 1.369 1.3612 1.3494 1.3518 1.3563 1.3623 1.3683 1.3574 1.3425 1.3363 1.3322 1.3403 1.3223 1.3275 1.3266 1.2992 1.3125 1.3232 1.305 1.2947 1.2932 1.2966 1.3058 1.3196 1.3173 1.3276 1.3273 1.3231 1.3255 1.3496 1.3425 1.3392 1.3246 1.3308 1.3193 1.3295 1.3607 1.3494 1.3507 1.3558 1.3549 1.3671 1.313 1.2942 1.3042 1.2905 1.2782 1.2786 1.2783 1.2565 1.2658 1.2555 1.2555 1.2615 1.2596 1.2644 1.2782 1.2795 1.2763 1.2798 1.2591 1.2705 1.2596 1.2634 1.2765 1.2823 1.2833 1.2938 1.2967 1.3008 1.2796 1.2829 1.2818 1.2849 1.276 1.2816 1.3111 1.326 1.3174 1.299 1.2795 1.2984 1.291 1.293 1.3182 1.327 1.3085 1.3173 1.3262 1.3394 1.3684 1.3617 1.3595 1.3332 1.3582 1.3866
Sample Range:
(leave blank to include all observations)
From:
To:
Seasonal Period
4
12
4
6
12
Chart options
R Code
par1 <- 5 (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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation