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Data X:
1418 210907 56 3 79 30 115 112285 24188 869 120982 56 4 58 28 109 84786 18273 1530 176508 54 12 60 38 146 83123 14130 2172 179321 89 2 108 30 116 101193 32287 901 123185 40 1 49 22 68 38361 8654 463 52746 25 3 0 26 101 68504 9245 3201 385534 92 0 121 25 96 119182 33251 371 33170 18 0 1 18 67 22807 1271 1192 101645 63 0 20 11 44 17140 5279 1583 149061 44 5 43 26 100 116174 27101 1439 165446 33 0 69 25 93 57635 16373 1764 237213 84 0 78 38 140 66198 19716 1495 173326 88 7 86 44 166 71701 17753 1373 133131 55 7 44 30 99 57793 9028 2187 258873 60 3 104 40 139 80444 18653 1491 180083 66 9 63 34 130 53855 8828 4041 324799 154 0 158 47 181 97668 29498 1706 230964 53 4 102 30 116 133824 27563 2152 236785 119 3 77 31 116 101481 18293 1036 135473 41 0 82 23 88 99645 22530 1882 202925 61 7 115 36 139 114789 15977 1929 215147 58 0 101 36 135 99052 35082 2242 344297 75 1 80 30 108 67654 16116 1220 153935 33 5 50 25 89 65553 15849 1289 132943 40 7 83 39 156 97500 16026 2515 174724 92 0 123 34 129 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1381 162765 32 2 68 28 108 120293 25131 1369 182613 39 3 81 28 99 138599 30910 1659 232138 62 0 131 31 117 161647 35947 2888 265318 117 10 110 52 199 115929 29848 1290 85574 34 0 37 21 78 24266 6943 2845 310839 92 9 130 24 91 162901 42705 1982 225060 93 7 93 41 158 109825 31808 1904 232317 54 0 118 33 126 129838 26675 1391 144966 144 0 39 32 122 37510 8435 602 43287 14 4 13 19 71 43750 7409 1743 155754 61 4 74 20 75 40652 14993 1559 164709 109 0 81 31 115 87771 36867 2014 201940 38 0 109 31 119 85872 33835 2143 235454 73 0 151 32 124 89275 24164 2146 220801 75 1 51 18 72 44418 12607 874 99466 50 0 28 23 91 192565 22609 1590 92661 61 1 40 17 45 35232 5892 1590 133328 55 0 56 20 78 40909 17014 1210 61361 77 0 27 12 39 13294 5394 2072 125930 75 4 37 17 68 32387 9178 1281 100750 72 0 83 30 119 140867 6440 1401 224549 50 4 54 31 117 120662 21916 834 82316 32 4 27 10 39 21233 4011 1105 102010 53 3 28 13 50 44332 5818 1272 101523 42 0 59 22 88 61056 18647 1944 243511 71 0 133 42 155 101338 20556 391 22938 10 0 12 1 0 1168 238 761 41566 35 5 0 9 36 13497 70 1605 152474 65 0 106 32 123 65567 22392 530 61857 25 4 23 11 32 25162 3913 1988 99923 66 0 44 25 99 32334 12237 1386 132487 41 0 71 36 136 40735 8388 2395 317394 86 1 116 31 117 91413 22120 387 21054 16 0 4 0 0 855 338 1742 209641 42 5 62 24 88 97068 11727 620 22648 19 0 12 13 39 44339 3704 449 31414 19 0 18 8 25 14116 3988 800 46698 45 0 14 13 52 10288 3030 1684 131698 65 0 60 19 75 65622 13520 1050 91735 35 0 7 18 71 16563 1421 2699 244749 95 2 98 33 124 76643 20923 1606 184510 49 7 64 40 151 110681 20237 1502 79863 37 1 29 22 71 29011 3219 1204 128423 64 8 32 38 145 92696 3769 1138 97839 38 2 25 24 87 94785 12252 568 38214 34 0 16 8 27 8773 1888 1459 151101 32 2 48 35 131 83209 14497 2158 272458 65 0 100 43 162 93815 28864 1111 172494 52 0 46 43 165 86687 21721 1421 108043 62 1 45 14 54 34553 4821 2833 328107 65 3 129 41 159 105547 33644 1955 250579 83 0 130 38 147 103487 15923 2922 351067 95 3 136 45 170 213688 42935 1002 158015 29 0 59 31 119 71220 18864 1060 98866 18 0 25 13 49 23517 4977 956 85439 33 0 32 28 104 56926 7785 2186 229242 247 4 63 31 120 91721 17939 3604 351619 139 4 95 40 150 115168 23436 1035 84207 29 11 14 30 112 111194 325 1417 120445 118 0 36 16 59 51009 13539 3261 324598 110 0 113 37 136 135777 34538 1587 131069 67 4 47 30 107 51513 12198 1424 204271 42 0 92 35 130 74163 26924 1701 165543 65 1 70 32 115 51633 12716 1249 141722 94 0 19 27 107 75345 8172 946 116048 64 0 50 20 75 33416 10855 1926 250047 81 0 41 18 71 83305 11932 3352 299775 95 9 91 31 120 98952 14300 1641 195838 67 1 111 31 116 102372 25515 2035 173260 63 3 41 21 79 37238 2805 2312 254488 83 10 120 39 150 103772 29402 1369 104389 45 5 135 41 156 123969 16440 1577 136084 30 0 27 13 51 27142 11221 2201 199476 70 2 87 32 118 135400 28732 961 92499 32 0 25 18 71 21399 5250 1900 224330 83 1 131 39 144 130115 28608 1254 135781 31 2 45 14 47 24874 8092 1335 74408 67 4 29 7 28 34988 4473 1597 81240 66 0 58 17 68 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18 72 21792 6928 652 59382 49 0 29 12 41 26263 1514 707 119308 30 0 32 16 61 23686 9238 954 76702 49 0 35 21 67 49303 8204 1461 103425 67 1 17 19 76 25659 5926 672 70344 28 0 20 16 64 28904 5785 778 43410 19 0 7 1 3 2781 4 1141 104838 49 1 46 16 63 29236 5930 680 62215 27 0 24 10 40 19546 3710 1090 69304 30 6 40 19 69 22818 705 616 53117 22 3 3 12 48 32689 443 285 19764 12 1 10 2 8 5752 2416 1145 86680 31 2 37 14 52 22197 7747 733 84105 20 0 17 17 66 20055 5432 888 77945 20 0 28 19 76 25272 4913 849 89113 39 0 19 14 43 82206 2650 1182 91005 29 3 29 11 39 32073 2370 528 40248 16 1 8 4 14 5444 775 642 64187 27 0 10 16 61 20154 5576 947 50857 21 0 15 20 71 36944 1352 819 56613 19 1 15 12 44 8019 3080 757 62792 35 0 28 15 60 30884 10205 894 72535 14 0 17 16 64 19540 6095
Names of X columns:
pageviews timeInRfc logins SharedComp BloggedComp CompRev FeedbackMes Totsize TotRev.
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
none
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
5
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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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