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Data X:
1418 210907 56 3 79 30 115 112285 24188 144 145 869 120982 56 4 58 28 109 84786 18273 103 101 1530 176508 54 12 60 38 146 83123 14130 98 98 2172 179321 89 2 108 30 116 101193 32287 135 132 901 123185 40 1 49 22 68 38361 8654 61 60 463 52746 25 3 0 26 101 68504 9245 39 38 3201 385534 92 0 121 25 96 119182 33251 150 144 371 33170 18 0 1 18 67 22807 1271 5 5 1192 101645 63 0 20 11 44 17140 5279 28 28 1583 149061 44 5 43 26 100 116174 27101 84 84 1439 165446 33 0 69 25 93 57635 16373 80 79 1764 237213 84 0 78 38 140 66198 19716 130 127 1495 173326 88 7 86 44 166 71701 17753 82 78 1373 133131 55 7 44 30 99 57793 9028 60 60 2187 258873 60 3 104 40 139 80444 18653 131 131 1491 180083 66 9 63 34 130 53855 8828 84 84 4041 324799 154 0 158 47 181 97668 29498 140 133 1706 230964 53 4 102 30 116 133824 27563 151 150 2152 236785 119 3 77 31 116 101481 18293 91 91 1036 135473 41 0 82 23 88 99645 22530 138 132 1882 202925 61 7 115 36 139 114789 15977 150 136 1929 215147 58 0 101 36 135 99052 35082 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530 61857 25 4 23 11 32 25162 3913 31 28 1988 99923 66 0 44 25 99 32334 12237 36 36 1386 132487 41 0 71 36 136 40735 8388 120 89 2395 317394 86 1 116 31 117 91413 22120 195 193 387 21054 16 0 4 0 0 855 338 4 4 1742 209641 42 5 62 24 88 97068 11727 89 84 620 22648 19 0 12 13 39 44339 3704 24 23 449 31414 19 0 18 8 25 14116 3988 39 39 800 46698 45 0 14 13 52 10288 3030 14 14 1684 131698 65 0 60 19 75 65622 13520 78 78 1050 91735 35 0 7 18 71 16563 1421 15 14 2699 244749 95 2 98 33 124 76643 20923 106 101 1606 184510 49 7 64 40 151 110681 20237 83 82 1502 79863 37 1 29 22 71 29011 3219 24 24 1204 128423 64 8 32 38 145 92696 3769 37 36 1138 97839 38 2 25 24 87 94785 12252 77 75 568 38214 34 0 16 8 27 8773 1888 16 16 1459 151101 32 2 48 35 131 83209 14497 56 55 2158 272458 65 0 100 43 162 93815 28864 132 131 1111 172494 52 0 46 43 165 86687 21721 144 131 1421 108043 62 1 45 14 54 34553 4821 40 39 2833 328107 65 3 129 41 159 105547 33644 153 144 1955 250579 83 0 130 38 147 103487 15923 143 139 2922 351067 95 3 136 45 170 213688 42935 220 211 1002 158015 29 0 59 31 119 71220 18864 79 78 1060 98866 18 0 25 13 49 23517 4977 50 50 956 85439 33 0 32 28 104 56926 7785 39 39 2186 229242 247 4 63 31 120 91721 17939 95 90 3604 351619 139 4 95 40 150 115168 23436 169 166 1035 84207 29 11 14 30 112 111194 325 12 12 1417 120445 118 0 36 16 59 51009 13539 63 57 3261 324598 110 0 113 37 136 135777 34538 134 133 1587 131069 67 4 47 30 107 51513 12198 69 69 1424 204271 42 0 92 35 130 74163 26924 119 119 1701 165543 65 1 70 32 115 51633 12716 119 119 1249 141722 94 0 19 27 107 75345 8172 75 65 946 116048 64 0 50 20 75 33416 10855 63 61 1926 250047 81 0 41 18 71 83305 11932 55 49 3352 299775 95 9 91 31 120 98952 14300 103 101 1641 195838 67 1 111 31 116 102372 25515 197 196 2035 173260 63 3 41 21 79 37238 2805 16 15 2312 254488 83 10 120 39 150 103772 29402 140 136 1369 104389 45 5 135 41 156 123969 16440 89 89 1577 136084 30 0 27 13 51 27142 11221 40 40 2201 199476 70 2 87 32 118 135400 28732 125 123 961 92499 32 0 25 18 71 21399 5250 21 21 1900 224330 83 1 131 39 144 130115 28608 167 163 1254 135781 31 2 45 14 47 24874 8092 32 29 1335 74408 67 4 29 7 28 34988 4473 36 35 1597 81240 66 0 58 17 68 45549 1572 13 13 207 14688 10 0 4 0 0 6023 2065 5 5 1645 181633 70 2 47 30 110 64466 14817 96 96 2429 271856 103 1 109 37 147 54990 16714 151 151 151 7199 5 0 7 0 0 1644 556 6 6 474 46660 20 0 12 5 15 6179 2089 13 13 141 17547 5 0 0 1 4 3926 2658 3 3 1639 133368 36 1 37 16 64 32755 10695 57 56 872 95227 34 0 37 32 111 34777 1669 23 23 1318 152601 48 2 46 24 85 73224 16267 61 57 1018 98146 40 0 15 17 68 27114 7768 21 14 1383 79619 43 3 42 11 40 20760 7252 43 43 1314 59194 31 6 7 24 80 37636 6387 20 20 1335 139942 42 0 54 22 88 65461 18715 82 72 1403 118612 46 2 54 12 48 30080 7936 90 87 910 72880 33 0 14 19 76 24094 8643 25 21 616 65475 18 2 16 13 51 69008 7294 60 56 1407 99643 55 1 33 17 67 54968 4570 61 59 771 71965 35 1 32 15 59 46090 7185 85 82 766 77272 59 2 21 16 61 27507 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4154 1278 6 6 1803 135458 81 3 43 12 48 19474 4236 68 67 799 73504 35 0 23 7 25 35130 3023 33 33 861 63123 43 1 34 17 68 39067 7196 84 77 1186 61254 46 1 36 14 41 13310 3394 46 46 1451 74914 30 0 35 23 90 65892 6371 30 30 628 31774 23 1 0 17 66 4143 1574 0 0 1161 81437 38 0 37 14 54 28579 9620 36 36 1463 87186 54 0 28 15 59 51776 6978 47 46 742 50090 20 0 16 17 60 21152 4911 20 18 979 65745 53 0 26 21 77 38084 8645 50 48 675 56653 45 0 38 18 68 27717 8987 30 29 1241 158399 39 0 23 18 72 32928 5544 30 28 676 46455 20 0 22 17 67 11342 3083 34 34 1049 73624 24 0 30 17 64 19499 6909 33 33 620 38395 31 0 16 16 63 16380 3189 34 34 1081 91899 35 0 18 15 59 36874 6745 37 33 1688 139526 151 0 28 21 84 48259 16724 83 80 736 52164 52 0 32 16 64 16734 4850 32 32 617 51567 30 2 21 14 56 28207 7025 30 30 812 70551 31 0 23 15 54 30143 6047 43 41 1051 84856 29 1 29 17 67 41369 7377 41 41 1656 102538 57 1 50 15 58 45833 9078 51 51 705 86678 40 0 12 15 59 29156 4605 19 18 945 85709 44 0 21 10 40 35944 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29236 5930 44 42 680 62215 27 0 24 10 40 19546 3710 26 26 1090 69304 30 6 40 19 69 22818 705 21 21 616 53117 22 3 3 12 48 32689 443 4 4 285 19764 12 1 10 2 8 5752 2416 10 10 1145 86680 31 2 37 14 52 22197 7747 43 43 733 84105 20 0 17 17 66 20055 5432 34 34 888 77945 20 0 28 19 76 25272 4913 32 31 849 89113 39 0 19 14 43 82206 2650 20 19 1182 91005 29 3 29 11 39 32073 2370 34 34 528 40248 16 1 8 4 14 5444 775 6 6 642 64187 27 0 10 16 61 20154 5576 12 11 947 50857 21 0 15 20 71 36944 1352 24 24 819 56613 19 1 15 12 44 8019 3080 16 16 757 62792 35 0 28 15 60 30884 10205 72 72 894 72535 14 0 17 16 64 19540 6095 27 21
Names of X columns:
pageviews time_in_rfc logins shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p1 totsize totrevisions 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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