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Data X:
112285 210907 56 79 146283 144 145 3 84786 120982 56 58 98364 103 101 4 83123 176508 54 60 86146 98 98 12 101193 179321 89 108 96933 135 132 2 38361 123185 40 49 79234 61 60 1 68504 52746 25 0 42551 39 38 3 119182 385534 92 121 195663 150 144 0 22807 33170 18 1 6853 5 5 0 17140 101645 63 20 21529 28 28 0 116174 149061 44 43 95757 84 84 5 57635 165446 33 69 85584 80 79 0 66198 237213 84 78 143983 130 127 0 71701 173326 88 86 75851 82 78 7 57793 133131 55 44 59238 60 60 7 80444 258873 60 104 93163 131 131 3 53855 180083 66 63 96037 84 84 9 97668 324799 154 158 151511 140 133 0 133824 230964 53 102 136368 151 150 4 101481 236785 119 77 112642 91 91 3 99645 135473 41 82 94728 138 132 0 114789 202925 61 115 105499 150 136 7 99052 215147 58 101 121527 124 124 0 67654 344297 75 80 127766 119 118 1 65553 153935 33 50 98958 73 70 5 97500 132943 40 83 77900 110 107 7 69112 174724 92 123 85646 123 119 0 82753 174415 100 73 98579 90 89 0 85323 225548 112 81 130767 116 112 5 72654 223632 73 105 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61370 152299 53 62 67808 78 76 0 43836 120221 37 53 61724 51 49 1 106117 346485 90 118 131722 121 118 0 38692 145790 63 30 68580 38 38 5 84651 193339 78 100 106175 145 141 2 56622 80953 25 49 55792 59 58 0 15986 122774 45 24 25157 27 27 0 95364 130585 46 67 76669 91 91 5 26706 112611 41 46 57283 48 48 0 89691 286468 144 57 105805 68 63 1 67267 241066 82 75 129484 58 56 0 126846 148446 91 135 72413 150 144 1 41140 204713 71 68 87831 74 73 1 102860 182079 63 124 96971 181 168 2 51715 140344 53 33 71299 65 64 6 55801 220516 62 98 77494 97 97 1 111813 243060 63 58 120336 121 117 4 120293 162765 32 68 93913 99 100 2 138599 182613 39 81 136048 152 149 3 161647 232138 62 131 181248 188 187 0 115929 265318 117 110 146123 138 127 10 24266 85574 34 37 32036 40 37 0 162901 310839 92 130 186646 254 245 9 109825 225060 93 93 102255 87 87 7 129838 232317 54 118 168237 178 177 0 37510 144966 144 39 64219 51 49 0 43750 43287 14 13 19630 49 49 4 40652 155754 61 74 76825 73 73 4 87771 164709 109 81 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68847 83 82 7 29011 79863 37 29 17659 24 24 1 92696 128423 64 32 20112 37 36 8 94785 97839 38 25 61023 77 75 2 8773 38214 34 16 13983 16 16 0 83209 151101 32 48 65176 56 55 2 93815 272458 65 100 132432 132 131 0 86687 172494 52 46 112494 144 131 0 34553 108043 62 45 45109 40 39 1 105547 328107 65 129 170875 153 144 3 103487 250579 83 130 180759 143 139 0 213688 351067 95 136 214921 220 211 3 71220 158015 29 59 100226 79 78 0 23517 98866 18 25 32043 50 50 0 56926 85439 33 32 54454 39 39 0 91721 229242 247 63 78876 95 90 4 115168 351619 139 95 170745 169 166 4 111194 84207 29 14 6940 12 12 11 51009 120445 118 36 49025 63 57 0 135777 324598 110 113 122037 134 133 0 51513 131069 67 47 53782 69 69 4 74163 204271 42 92 127748 119 119 0 51633 165543 65 70 86839 119 119 1 75345 141722 94 19 44830 75 65 0 33416 116048 64 50 77395 63 61 0 83305 250047 81 41 89324 55 49 0 98952 299775 95 91 103300 103 101 9 102372 195838 67 111 112283 197 196 1 37238 173260 63 41 10901 16 15 3 103772 254488 83 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89746 36 28 28910 38 38 3 18779 44296 25 10 13339 12 12 0 21280 77648 47 31 25319 29 29 0 40662 181528 54 32 66956 49 47 0 28987 134019 53 32 47487 46 45 0 22827 124064 40 43 52785 41 40 1 18513 92630 40 27 44683 31 30 4 30594 121848 39 37 35619 41 41 0 24006 52915 14 20 21920 26 25 0 27913 81872 45 32 45608 23 23 0 42744 58981 36 0 7721 14 14 7 12934 53515 28 5 20634 16 16 2 22574 60812 44 26 29788 25 26 0 41385 56375 30 10 31931 21 21 7 18653 65490 22 27 37754 32 27 3 18472 80949 17 11 32505 9 9 0 30976 76302 31 29 40557 35 33 0 63339 104011 55 25 94238 42 42 6 25568 98104 54 55 44197 68 68 2 33747 67989 21 23 43228 32 32 0 4154 30989 14 5 4103 6 6 0 19474 135458 81 43 44144 68 67 3 35130 73504 35 23 32868 33 33 0 39067 63123 43 34 27640 84 77 1 13310 61254 46 36 14063 46 46 1 65892 74914 30 35 28990 30 30 0 4143 31774 23 0 4694 0 0 1 28579 81437 38 37 42648 36 36 0 51776 87186 54 28 64329 47 46 0 21152 50090 20 16 21928 20 18 0 38084 65745 53 26 25836 50 48 0 27717 56653 45 38 22779 30 29 0 32928 158399 39 23 40820 30 28 0 11342 46455 20 22 27530 34 34 0 19499 73624 24 30 32378 33 33 0 16380 38395 31 16 10824 34 34 0 36874 91899 35 18 39613 37 33 0 48259 139526 151 28 60865 83 80 0 16734 52164 52 32 19787 32 32 0 28207 51567 30 21 20107 30 30 2 30143 70551 31 23 36605 43 41 0 41369 84856 29 29 40961 41 41 1 45833 102538 57 50 48231 51 51 1 29156 86678 40 12 39725 19 18 0 35944 85709 44 21 21455 37 34 0 36278 34662 25 18 23430 33 31 0 45588 150580 77 27 62991 41 39 0 45097 99611 35 41 49363 54 54 0 3895 19349 11 13 9604 14 14 0 28394 99373 63 12 24552 25 24 1 18632 86230 44 21 31493 25 24 0 2325 30837 19 8 3439 8 8 0 25139 31706 13 26 19555 26 26 0 27975 89806 42 27 21228 20 19 0 14483 62088 38 13 23177 11 11 1 13127 40151 29 16 22094 14 14 0 5839 27634 20 2 2342 3 1 0 24069 76990 27 42 38798 40 39 0 3738 37460 20 5 3255 5 5 0 18625 54157 19 37 24261 38 37 0 36341 49862 37 17 18511 32 32 0 24548 84337 26 38 40798 41 38 0 21792 64175 42 37 28893 46 47 0 26263 59382 49 29 21425 47 47 0 23686 119308 30 32 50276 37 37 0 49303 76702 49 35 37643 51 51 0 25659 103425 67 17 30377 49 45 1 28904 70344 28 20 27126 21 21 0 2781 43410 19 7 13 1 1 0 29236 104838 49 46 42097 44 42 1 19546 62215 27 24 24451 26 26 0 22818 69304 30 40 14335 21 21 6 32689 53117 22 3 5084 4 4 3 5752 19764 12 10 9927 10 10 1 22197 86680 31 37 43527 43 43 2 20055 84105 20 17 27184 34 34 0 25272 77945 20 28 21610 32 31 0 82206 89113 39 19 20484 20 19 0 32073 91005 29 29 20156 34 34 3 5444 40248 16 8 6012 6 6 1 20154 64187 27 10 18475 12 11 0 36944 50857 21 15 12645 24 24 0 8019 56613 19 15 11017 16 16 1 30884 62792 35 28 37623 72 72 0 19540 72535 14 17 35873 27 21 0
Names of X columns:
totsize time_in_rfc logins blogged_computations totseconds tothyperlinks totblogs shared_compendiums
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
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11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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