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Data X:
10 14688 10 4 0 0 6023 10 46660 20 12 5 13 6179 10 7199 5 7 0 0 1644 10 21054 16 4 0 0 855 10 80953 25 49 8 27 56622 10 19349 11 13 1 0 3895 11 173260 63 41 21 78 37238 11 38214 34 16 8 21 8773 11 17547 5 0 1 4 3926 11 43287 14 13 19 64 43750 11 78800 42 20 26 66 33032 11 65029 17 21 18 61 32551 11 31414 19 18 8 9 14116 11 7176 17 0 0 0 1423 11 24188 24 8 4 7 5950 11 61857 25 23 11 30 25162 11 22938 10 12 1 0 1168 11 43410 19 7 1 3 2781 11 19764 12 10 2 4 5752 11 53117 22 3 12 32 32689 11 33170 18 1 18 22 22807 11 232138 62 131 31 90 161647 11 111665 34 39 28 80 49810 11 152871 58 59 28 90 79892 11 100750 72 83 30 93 140867 11 221698 45 105 33 110 77873 11 209641 42 62 24 60 97068 11 52746 25 0 26 70 68504 11 215641 46 71 32 92 55813 11 85439 33 32 28 78 56926 11 145790 63 30 26 99 38692 11 132943 40 83 39 156 97500 11 132487 41 71 36 98 40735 11 135473 41 82 23 66 99645 11 84853 31 38 30 109 61542 11 210767 60 94 35 133 117478 11 97839 38 25 24 66 94785 11 120221 37 53 22 69 43836 11 224549 50 54 31 114 120662 11 116408 61 34 39 31 71570 11 158015 29 59 31 114 71220 12 150629 44 85 33 113 71595 12 123185 40 49 22 51 38361 12 124817 40 47 25 81 30727 12 104389 45 135 41 148 123969 12 162765 32 68 28 107 120293 12 149061 44 43 26 93 116174 12 84207 29 14 30 101 111194 12 201940 38 109 31 92 85872 12 168809 66 76 28 100 72260 12 176508 54 60 38 93 83123 12 99466 50 28 23 69 192565 12 106408 30 33 14 37 31081 12 344297 75 80 30 108 67654 12 218946 41 76 29 96 80670 12 122774 45 24 24 69 15986 12 153935 33 50 25 84 65553 12 206161 71 75 28 99 73107 12 183167 66 91 39 138 82875 12 144966 144 39 32 50 37510 12 34662 25 18 6 7 36278 12 116048 64 50 20 50 33416 12 77945 20 28 19 67 25272 12 65490 22 27 16 40 18653 12 81872 45 32 16 61 27913 12 101011 34 30 13 39 27570 12 71965 35 32 15 56 46090 12 72880 33 14 19 67 24094 12 62792 35 28 15 51 30884 12 31774 23 0 17 47 4143 12 182192 52 77 40 138 70054 12 67989 21 23 18 57 33747 12 52915 14 20 18 49 24006 12 56375 30 10 13 40 41385 12 30989 14 5 17 41 4154 12 135131 66 38 15 60 34029 12 76990 27 42 17 67 24069 12 64175 42 37 18 59 21792 12 59194 31 7 24 68 37636 12 53515 28 5 22 81 12934 12 89746 36 28 18 55 24760 12 49862 37 17 14 54 36341 12 85574 34 37 21 36 24266 12 220801 75 51 18 63 44418 12 49289 19 15 24 44 10672 12 135781 31 45 14 44 24874 12 82316 32 27 10 38 21233 12 133368 36 37 16 57 32755 12 92499 32 25 18 55 21399 12 37460 20 5 7 14 3738 12 91735 35 7 18 49 16563 12 187681 62 114 28 91 92945 12 79619 43 42 11 32 20760 12 98104 54 55 17 47 25568 12 80949 17 11 16 56 18472 12 31706 13 26 10 25 25139 12 70344 28 20 16 61 28904 12 99923 66 44 25 80 32334 12 91899 35 18 15 33 36874 12 235454 73 151 32 106 89275 12 46455 20 22 17 12 11342 12 60812 44 26 13 43 22574 12 77648 47 31 16 52 21280 12 101523 42 59 22 87 61056 12 254488 83 120 39 117 103772 12 103597 43 27 16 49 23789 12 136084 30 27 13 41 27142 12 62088 38 13 16 38 14483 12 86680 31 37 14 49 22197 12 38395 31 16 16 43 16380 12 179321 89 108 30 103 101193 12 40248 16 8 4 7 5444 12 70551 31 23 15 47 30143 12 84105 20 17 17 63 20055 12 112611 41 46 20 73 26706 12 73624 24 30 17 40 19499 12 89806 42 27 16 40 27975 12 98866 18 25 13 39 23517 12 61254 46 36 14 30 13310 12 99643 55 33 17 46 54968 12 310839 92 130 24 85 162901 12 181633 70 47 30 73 64466 12 50090 20 16 17 36 21152 12 92661 61 40 17 41 35232 12 46698 45 14 13 33 10288 12 61361 77 27 12 25 13294 12 84337 26 38 14 35 24548 12 72535 14 17 16 39 19540 12 84856 29 29 17 43 41369 12 124064 40 43 22 86 22827 12 54157 19 37 15 30 18625 12 76302 31 29 20 68 30976 12 59382 49 29 12 24 26263 12 65745 53 26 21 42 38084 12 74914 30 35 23 79 65892 12 101645 63 20 11 38 17140 12 73504 35 23 7 3 35130 12 99373 63 12 18 62 28394 12 135458 81 43 12 29 19474 12 184510 49 64 40 140 110681 12 385534 92 121 25 91 119182 12 108043 62 45 14 38 34553 12 328107 65 129 41 144 105547 12 362301 119 76 34 110 100708 12 130585 46 67 29 107 95364 12 85709 44 21 10 35 35944 12 174184 53 72 25 70 55183 12 81437 38 37 14 40 28579 12 120982 56 58 28 103 84786 12 187559 121 75 36 116 73511 12 235800 94 105 23 62 149193 12 220516 62 98 32 119 55801 12 210907 56 79 30 94 112285 12 155754 61 74 20 31 40652 12 152299 53 62 33 98 61370 12 79863 37 29 22 49 29011 12 346485 90 118 38 111 106117 12 135649 46 99 30 108 62133 12 134019 53 32 18 54 28987 12 167488 45 69 28 72 83737 12 108446 60 22 17 65 46300 12 27634 20 2 16 17 5839 12 232317 54 118 33 106 129838 12 22648 19 12 13 28 44339 12 119308 30 32 16 58 23686 12 194979 62 66 40 151 74011 12 81240 66 58 17 50 45549 12 121848 39 37 17 52 30594 12 86678 40 12 15 50 29156 12 73566 32 22 23 67 22618 12 102010 53 28 13 44 44332 12 151101 32 48 35 124 83209 12 207176 70 56 32 114 87011 12 165543 65 70 32 90 51633 12 182999 88 73 34 104 225920 12 256462 105 123 35 124 105195 12 244052 68 101 44 164 143558 12 63123 43 34 17 60 39067 12 87186 54 28 15 48 51776 12 182079 63 124 33 118 102860 12 95227 34 37 32 48 34777 12 165446 33 69 25 60 57635 12 229242 247 63 31 119 91721 12 167542 66 59 28 70 55461 12 225060 93 93 41 139 109825 12 164709 109 81 31 63 87771 12 131698 65 60 19 59 65622 12 56613 19 15 12 35 8019 12 250579 83 130 38 120 103487 12 44296 25 10 20 38 18779 12 152474 65 106 32 83 65567 12 86230 44 21 17 54 18632 12 258873 60 104 40 124 80444 12 41566 35 0 9 27 13497 12 250047 81 41 18 61 83305 12 243511 71 133 42 110 101338 12 62215 27 24 10 25 19546 12 104838 49 46 16 52 29236 12 77272 59 21 16 48 27507 12 148446 91 135 37 129 126846 12 69304 30 40 19 40 22818 12 102538 57 50 15 45 45833 12 101097 64 30 14 41 31701 12 223632 73 105 33 120 72654 12 141722 94 19 27 36 75345 12 351067 95 136 45 160 213688 12 311473 112 128 38 129 132068 12 152601 48 46 24 46 73224 12 215147 58 101 36 117 99052 12 52164 52 32 16 43 16734 12 91005 29 29 11 23 32073 12 265318 117 110 52 197 115929 12 299775 95 91 31 97 98952 12 241066 82 75 45 93 67267 12 118612 46 54 12 43 30080 12 173326 88 86 44 148 71701 12 225548 112 81 31 94 85323 12 277965 89 115 39 133 139077 12 317394 86 116 31 82 91413 12 177939 82 55 36 73 46821 12 92630 40 27 8 24 18513 12 140344 53 33 25 73 51715 12 230964 53 102 30 115 133824 12 199476 70 87 32 105 135400 12 174724 92 123 34 120 69112 12 128423 64 32 38 120 92696 12 341570 168 94 21 78 117105 12 64187 27 10 16 54 20154 12 150580 77 27 22 71 45588 12 204713 71 68 33 69 41140 12 244749 95 98 33 115 76643 12 98146 40 15 17 48 27114 12 351619 139 95 40 141 115168 12 58981 36 0 23 61 42744 12 233328 132 92 28 102 120733 12 182613 39 81 28 99 138599 12 89113 39 19 14 32 82206 12 324799 154 158 47 168 97668 12 143246 103 67 27 104 106671 12 40151 29 16 9 19 13127 12 158399 39 23 18 57 32928 12 195838 67 111 31 98 102372 12 286468 144 57 29 107 89691 12 175824 107 57 20 77 39992 12 143756 46 105 34 120 69094 12 139942 42 54 22 87 65461 12 104011 55 25 22 79 63339 12 196553 57 41 29 99 95260 12 180083 66 63 34 70 53855 12 260561 75 114 43 158 90183 12 294424 77 107 33 124 101494 12 99611 35 41 21 67 45097 12 131069 67 47 30 83 51513 12 65475 18 16 13 46 69008 12 237213 84 78 38 123 66198 12 324598 110 113 37 133 135777 12 170266 62 44 42 122 74007 12 269651 67 106 30 93 70106 12 243060 63 58 29 104 111813 12 149112 56 56 35 95 60578 12 174415 100 73 31 114 82753 12 133131 55 44 30 90 57793 12 133328 55 56 20 56 40909 12 56653 45 38 18 49 27717 12 50857 21 15 20 37 36944 12 74408 67 29 7 21 34988 12 193339 78 100 35 71 84651 12 275541 63 116 33 115 93133 12 96560 76 42 17 38 22996 12 243199 75 88 28 105 95536 12 76702 49 35 21 42 49303 12 272458 65 100 43 152 93815 12 103425 67 17 19 46 25659 12 271856 103 109 37 86 54990 12 139526 151 28 21 77 48259 12 172494 52 46 43 139 86687 12 120445 118 36 16 56 51009 12 181528 54 32 16 60 40662 12 236785 119 77 31 71 101481 12 224330 83 131 39 132 130115 12 202925 61 115 36 134 114789 12 30837 19 8 4 4 2325 12 329267 259 79 39 140 115762 12 125930 75 37 17 65 32387 12 204271 42 92 35 116 74163 12 51567 30 21 14 45 28207 12 325107 99 84 36 126 79215 12 119016 52 118 23 74 78664 12 265769 146 96 32 120 83122
Names of X columns:
Maand Time_in_rfc Logins Blogged_computations Compendiums_reviewed Feedback_messages_p120 Total_number_of_characters
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
10
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') }
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Summary of computational transaction
Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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