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Data X:
112285 1418 210907 56 396 81 3 79 30 115 94 24188 146283 144 145 84786 869 120982 56 297 55 4 58 28 109 103 18273 98364 103 101 83123 1530 176508 54 559 50 12 60 38 146 93 14130 86146 98 98 101193 2172 179321 89 967 125 2 108 30 116 103 32287 96933 135 132 38361 901 123185 40 270 40 1 49 22 68 51 8654 79234 61 60 68504 463 52746 25 143 37 3 0 26 101 70 9245 42551 39 38 119182 3201 385534 92 1562 63 0 121 25 96 91 33251 195663 150 144 22807 371 33170 18 109 44 0 1 18 67 22 1271 6853 5 5 17140 1192 101645 63 371 88 0 20 11 44 38 5279 21529 28 28 116174 1583 149061 44 656 66 5 43 26 100 93 27101 95757 84 84 57635 1439 165446 33 511 57 0 69 25 93 60 16373 85584 80 79 66198 1764 237213 84 655 74 0 78 38 140 123 19716 143983 130 127 71701 1495 173326 88 465 49 7 86 44 166 148 17753 75851 82 78 57793 1373 133131 55 525 52 7 44 30 99 90 9028 59238 60 60 80444 2187 258873 60 885 88 3 104 40 139 124 18653 93163 131 131 53855 1491 180083 66 497 36 9 63 34 130 70 8828 96037 84 84 97668 4041 324799 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1255 121848 39 339 45 0 37 17 64 52 5401 35619 41 41 24006 613 52915 14 181 54 0 20 18 68 49 6521 21920 26 25 27913 721 81872 45 245 25 0 32 16 64 61 10856 45608 23 23 42744 1109 58981 36 384 38 7 0 23 91 61 2154 7721 14 14 12934 740 53515 28 212 52 2 5 22 88 81 6117 20634 16 16 22574 1126 60812 44 399 67 0 26 13 52 43 5238 29788 25 26 41385 728 56375 30 229 74 7 10 13 49 40 4820 31931 21 21 18653 689 65490 22 224 38 3 27 16 62 40 5615 37754 32 27 18472 592 80949 17 203 30 0 11 16 61 56 4272 32505 9 9 30976 995 76302 31 333 26 0 29 20 76 68 8702 40557 35 33 63339 1613 104011 55 384 67 6 25 22 88 79 15340 94238 42 42 25568 2048 98104 54 636 132 2 55 17 66 47 8030 44197 68 68 33747 705 67989 21 185 42 0 23 18 71 57 9526 43228 32 32 4154 301 30989 14 93 35 0 5 17 68 41 1278 4103 6 6 19474 1803 135458 81 581 118 3 43 12 48 29 4236 44144 68 67 35130 799 73504 35 248 68 0 23 7 25 3 3023 32868 33 33 39067 861 63123 43 304 43 1 34 17 68 60 7196 27640 84 77 13310 1186 61254 46 344 76 1 36 14 41 30 3394 14063 46 46 65892 1451 74914 30 407 64 0 35 23 90 79 6371 28990 30 30 4143 628 31774 23 170 48 1 0 17 66 47 1574 4694 0 0 28579 1161 81437 38 312 64 0 37 14 54 40 9620 42648 36 36 51776 1463 87186 54 507 56 0 28 15 59 48 6978 64329 47 46 21152 742 50090 20 224 71 0 16 17 60 36 4911 21928 20 18 38084 979 65745 53 340 75 0 26 21 77 42 8645 25836 50 48 27717 675 56653 45 168 39 0 38 18 68 49 8987 22779 30 29 32928 1241 158399 39 443 42 0 23 18 72 57 5544 40820 30 28 11342 676 46455 20 204 39 0 22 17 67 12 3083 27530 34 34 19499 1049 73624 24 367 93 0 30 17 64 40 6909 32378 33 33 16380 620 38395 31 210 38 0 16 16 63 43 3189 10824 34 34 36874 1081 91899 35 335 60 0 18 15 59 33 6745 39613 37 33 48259 1688 139526 151 364 71 0 28 21 84 77 16724 60865 83 80 16734 736 52164 52 178 52 0 32 16 64 43 4850 19787 32 32 28207 617 51567 30 206 27 2 21 14 56 45 7025 20107 30 30 30143 812 70551 31 279 59 0 23 15 54 47 6047 36605 43 41 41369 1051 84856 29 387 40 1 29 17 67 43 7377 40961 41 41 45833 1656 102538 57 490 79 1 50 15 58 45 9078 48231 51 51 29156 705 86678 40 238 44 0 12 15 59 50 4605 39725 19 18 35944 945 85709 44 343 65 0 21 10 40 35 3238 21455 37 34 36278 554 34662 25 232 10 0 18 6 22 7 8100 23430 33 31 45588 1597 150580 77 530 124 0 27 22 83 71 9653 62991 41 39 45097 982 99611 35 291 81 0 41 21 81 67 8914 49363 54 54 3895 222 19349 11 67 15 0 13 1 2 0 786 9604 14 14 28394 1212 99373 63 397 92 1 12 18 72 62 6700 24552 25 24 18632 1143 86230 44 467 42 0 21 17 61 54 5788 31493 25 24 2325 435 30837 19 178 10 0 8 4 15 4 593 3439 8 8 25139 532 31706 13 175 24 0 26 10 32 25 4506 19555 26 26 27975 882 89806 42 299 64 0 27 16 62 40 6382 21228 20 19 14483 608 62088 38 154 45 1 13 16 58 38 5621 23177 11 11 13127 459 40151 29 106 22 0 16 9 36 19 3997 22094 14 14 5839 578 27634 20 189 56 0 2 16 59 17 520 2342 3 1 24069 826 76990 27 194 94 0 42 17 68 67 8891 38798 40 39 3738 509 37460 20 135 19 0 5 7 21 14 999 3255 5 5 18625 717 54157 19 201 35 0 37 15 55 30 7067 24261 38 37 36341 637 49862 37 207 32 0 17 14 54 54 4639 18511 32 32 24548 857 84337 26 280 35 0 38 14 55 35 5654 40798 41 38 21792 830 64175 42 260 48 0 37 18 72 59 6928 28893 46 47 26263 652 59382 49 227 49 0 29 12 41 24 1514 21425 47 47 23686 707 119308 30 239 48 0 32 16 61 58 9238 50276 37 37 49303 954 76702 49 333 62 0 35 21 67 42 8204 37643 51 51 25659 1461 103425 67 428 96 1 17 19 76 46 5926 30377 49 45 28904 672 70344 28 230 45 0 20 16 64 61 5785 27126 21 21 2781 778 43410 19 292 63 0 7 1 3 3 4 13 1 1 29236 1141 104838 49 350 71 1 46 16 63 52 5930 42097 44 42 19546 680 62215 27 186 26 0 24 10 40 25 3710 24451 26 26 22818 1090 69304 30 326 48 6 40 19 69 40 705 14335 21 21 32689 616 53117 22 155 29 3 3 12 48 32 443 5084 4 4 5752 285 19764 12 75 19 1 10 2 8 4 2416 9927 10 10 22197 1145 86680 31 361 45 2 37 14 52 49 7747 43527 43 43 20055 733 84105 20 261 45 0 17 17 66 63 5432 27184 34 34 25272 888 77945 20 299 67 0 28 19 76 67 4913 21610 32 31 82206 849 89113 39 300 30 0 19 14 43 32 2650 20484 20 19 32073 1182 91005 29 450 36 3 29 11 39 23 2370 20156 34 34 5444 528 40248 16 183 34 1 8 4 14 7 775 6012 6 6 20154 642 64187 27 238 36 0 10 16 61 54 5576 18475 12 11 36944 947 50857 21 165 34 0 15 20 71 37 1352 12645 24 24 8019 819 56613 19 234 37 1 15 12 44 35 3080 11017 16 16 30884 757 62792 35 176 46 0 28 15 60 51 10205 37623 72 72 19540 894 72535 14 329 44 0 17 16 64 39 6095 35873 27 21
Names of X columns:
totsize pageviews time_in_rfc logins compendium_views_info compendium_views_pr shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p1 feedback_messages_p120 totrevisions totseconds tothyperlinks totblogs
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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