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Data X:
58.58527778 145 30 94 112285 14.36009776 1 33.60611111 101 28 103 84786 11.30900293 1 49.03 98 38 93 83123 12.45810893 1 49.81138889 132 30 103 101193 13.50394283 1 34.21805556 60 22 51 38361 7.219358341 1 14.65166667 38 26 70 68504 7.381367182 1 107.0927778 144 25 91 119182 15.26096233 1 9.213888889 5 18 22 22807 3.165415029 1 41.40583333 84 26 93 116174 11.78143911 1 45.95722222 79 25 60 57635 9.277808364 1 65.8925 127 38 123 66198 14.22780485 1 48.14611111 78 44 148 71701 13.3753608 1 36.98083333 60 30 90 57793 9.487750499 1 71.90916667 131 40 124 80444 15.21511671 1 50.02305556 84 34 70 53855 10.42712012 1 90.22194444 133 47 168 97668 17.84978648 1 64.15666667 150 30 115 133824 15.89534882 1 65.77361111 91 31 71 101481 12.55115438 1 37.63138889 132 23 66 99645 11.43396085 1 56.36805556 136 36 134 114789 15.53507626 1 59.76305556 124 36 117 99052 14.52239325 1 95.63805556 118 30 108 67654 13.89331579 1 42.75972222 70 25 84 65553 9.712278591 1 36.92861111 107 39 156 97500 14.12942504 1 48.53444444 119 34 120 69112 12.89537363 1 48.44861111 89 31 114 82753 12.1602659 1 62.65222222 112 31 94 85323 13.01181689 1 62.12 108 33 120 72654 13.29873685 1 34.67138889 52 25 81 30727 7.775415068 1 61.58277778 112 33 110 77873 13.28429728 1 58.54638889 116 35 133 117478 15.11451039 1 47.29611111 123 42 122 74007 13.74797244 1 72.37805556 125 43 158 90183 16.42942616 1 23.57027778 27 30 109 61542 8.584377305 1 81.78444444 162 33 124 101494 16.47410685 1 59.90027778 64 32 92 55813 10.83539947 1 90.3075 92 36 126 79215 14.48035784 1 46.53944444 83 28 70 55461 9.817153316 1 29.55777778 41 14 37 31081 5.345232896 1 73.82472222 120 32 120 83122 14.39310875 1 74.90305556 105 30 93 70106 12.87630593 1 41.42 79 35 95 60578 10.76877965 1 42.46416667 70 28 90 79892 10.49694082 1 31.01805556 55 28 80 49810 8.443671407 1 32.33555556 39 39 31 71570 8.407782836 1 100.6391667 67 34 110 100708 13.94065255 1 21.88888889 21 26 66 33032 6.16326461 1 50.87972222 127 39 138 82875 14.43688293 1 77.2125 152 39 133 139077 17.77583855 1 41.84138889 113 33 113 71595 12.2486023 1 46.89138889 99 28 100 72260 11.45685566 1 6.718888889 7 4 7 5950 1.153093302 1 91.46305556 141 39 140 115762 17.35686844 1 18.06361111 21 18 61 32551 5.224652076 1 28.0825 35 14 41 31701 5.238696901 1 60.81833333 109 29 96 80670 12.60523152 1 67.79222222 133 44 164 143558 17.98404039 1 64.81333333 230 28 102 120733 15.30279334 1 71.23944444 166 35 124 105195 16.2306506 1 57.26694444 68 28 99 73107 11.17728596 1 86.52027778 147 38 129 132068 17.64250157 1 65.5 179 23 62 149193 14.43288916 1 49.4275 61 36 73 46821 9.841441509 1 57.54888889 101 32 114 87011 13.10185804 1 54.59805556 108 29 99 95260 12.78229154 1 48.38444444 90 25 70 55183 9.841353028 1 39.79055556 114 27 104 106671 12.52267132 1 52.09972222 103 36 116 73511 12.85177133 1 52.13361111 142 28 91 92945 13.17889823 1 33.06 79 23 74 78664 9.459926349 1 50.60888889 88 40 138 70054 13.14460233 1 20.435 25 23 67 22618 5.679993974 1 54.16083333 83 40 151 74011 13.621099 1 46.52444444 113 28 72 83737 11.4493559 1 39.93222222 118 34 120 69094 12.45612266 1 76.53916667 110 33 115 93133 14.52255588 1 67.55527778 129 28 105 95536 14.00916003 1 50.83305556 51 34 104 225920 12.87917848 1 37.68027778 93 30 108 62133 10.91453539 1 42.30527778 76 33 98 61370 10.67710167 1 33.39472222 49 22 69 43836 7.500506329 1 96.24583333 118 38 111 106117 15.71511893 1 40.49722222 38 26 99 38692 8.451414364 1 53.70527778 141 35 71 84651 13.04281114 1 22.48694444 58 8 27 56622 5.475735648 1 34.10388889 27 24 69 15986 6.318561991 1 36.27361111 91 29 107 95364 11.67102852 1 79.57444444 63 29 107 89691 12.88262502 1 66.96277778 56 45 93 67267 12.34103121 1 41.235 144 37 129 126846 15.31887313 1 56.86472222 73 33 69 41140 10.00450285 1 50.5775 168 33 118 102860 14.86835659 1 38.98444444 64 25 73 51715 8.706868119 1 61.25444444 97 32 119 55801 12.38401963 1 67.51666667 117 29 104 111813 14.23699191 1 45.2125 100 28 107 120293 12.98177075 1 50.72583333 149 28 99 138599 14.75112225 1 64.48277778 187 31 90 161647 15.68212424 1 73.69944444 127 52 197 115929 18.1400865 1 86.34416667 245 24 85 162901 15.91867058 1 62.51666667 87 41 139 109825 14.96179352 1 64.5325 177 33 106 129838 16.04078615 1 40.26833333 49 32 50 37510 7.958769343 1 12.02416667 49 19 64 43750 6.117022453 1 43.265 73 20 31 40652 7.408636579 1 45.7525 177 31 63 87771 12.70501195 1 56.09444444 94 31 92 85872 12.21180573 1 65.40388889 117 32 106 89275 13.7541755 1 27.62944444 55 23 69 192565 10.16398398 1 27.98611111 58 30 93 140867 11.38027871 1 62.37472222 95 31 114 120662 14.09406222 1 67.64194444 129 42 110 101338 15.38882422 1 6.371666667 11 1 0 1168 0.694512385 1 42.35388889 101 32 83 65567 10.98992814 1 17.1825 28 11 30 25162 3.847727223 1 36.80194444 89 36 98 40735 10.36537965 1 88.165 193 31 82 91413 15.07282766 1 5.848333333 4 0 0 855 0.406821823 1 58.23361111 84 24 60 97068 11.07449882 1 8.726111111 39 8 9 14116 2.651799226 1 67.98583333 101 33 115 76643 13.40079885 1 51.25277778 82 40 140 110681 14.266541 1 35.67305556 36 38 120 92696 11.1935173 1 27.1775 75 24 66 94785 9.43355457 1 10.615 16 8 21 8773 2.298582242 1 41.9725 55 35 124 83209 11.55994194 1 75.68277778 131 43 152 93815 16.70032444 1 47.915 131 43 139 86687 14.84074833 1 91.14083333 144 41 144 105547 17.38403831 1 69.60527778 139 38 120 103487 15.73039586 1 97.51861111 211 45 160 213688 19.36265885 1 43.89305556 78 31 114 71220 11.32605381 1 23.73305556 39 28 78 56926 7.851840967 1 63.67833333 90 31 119 91721 13.30225262 1 97.67194444 166 40 141 115168 17.89762797 1 23.39083333 12 30 101 111194 9.46320715 1 90.16611111 133 37 133 135777 17.41915883 1 36.40805556 69 30 83 51513 9.332238773 1 56.74194444 119 35 116 74163 13.4187927 1 45.98416667 119 32 90 51633 11.37725913 1 39.36722222 65 27 36 75345 8.702323456 1 83.27083333 101 31 97 98952 14.19611665 1 54.39944444 196 31 98 102372 14.3980936 1 48.12777778 15 21 78 37238 7.301383906 1 70.69111111 136 39 117 103772 15.72027262 1 28.99694444 89 41 148 123969 14.03405468 1 55.41 123 32 105 135400 14.75498272 1 62.31388889 163 39 132 130115 17.03707132 1 4.08 5 0 0 6023 0.498724543 1 50.45361111 96 30 73 64466 10.82410397 1 75.51555556 151 37 86 54990 13.99003257 1 1.999722222 6 0 0 1644 0.295218143 1 12.96111111 13 5 13 6179 1.833602184 1 4.874166667 3 1 4 3926 0.59944272 1 26.45194444 23 32 48 34777 6.512322257 1 42.38916667 57 24 46 73224 8.594439161 1 28.23472222 28 11 38 17140 10.17463007 0 28.05861111 32 13 39 27570 11.70685947 0 1.993333333 0 0 0 1423 0.336629956 0 26.82222222 47 17 38 22996 12.81545815 0 48.84 65 20 77 39992 18.48134929 0 94.88055556 123 21 78 117105 19.15 0 28.77694444 26 16 49 23789 11.98976509 0 31.28083333 48 20 73 26706 15.96311694 0 23.77055556 37 21 36 24266 12.36867636 0 61.33361111 60 18 63 44418 17.85359417 0 25.73916667 39 17 41 35232 13.33972078 0 37.03555556 64 20 56 40909 17.43586247 0 17.04472222 26 12 25 13294 7.971763963 0 34.98055556 64 17 65 32387 16.49423975 0 22.86555556 25 10 38 21233 9.589676646 0 28.33611111 26 13 44 44332 13.0201353 0 28.20083333 76 22 87 61056 18.30998406 0 11.54611111 2 9 27 13497 5.311440585 0 27.75638889 36 25 80 32334 16.14544025 0 6.291111111 23 13 28 44339 9.61483208 0 12.97166667 14 13 33 10288 6.97001928 0 36.58277778 78 19 59 65622 17.8328823 0 25.48194444 14 18 49 16563 10.43631816 0 22.18416667 24 22 49 29011 12.49521893 0 30.01194444 39 14 38 34553 13.13014142 0 27.46277778 50 13 39 23517 12.5995365 0 33.45694444 57 16 56 51009 16.89272827 0 32.23555556 61 20 50 33416 15.95822221 0 69.4575 49 18 61 83305 17.44624506 0 37.80111111 40 13 41 27142 13.36096345 0 25.69416667 21 18 55 21399 11.70208318 0 37.71694444 29 14 44 24874 12.6700532 0 20.66888889 35 7 21 34988 9.942922584 0 22.56666667 13 17 50 45549 12.47787034 0 37.04666667 56 16 57 32755 16.1794073 0 27.26277778 14 17 48 27114 11.34269609 0 22.11638889 43 11 32 20760 10.61458743 0 16.44277778 20 24 68 37636 13.63362964 0 38.87277778 72 22 87 65461 19.3 0 32.94777778 87 12 43 30080 14.17862044 0 20.24444444 21 19 67 24094 12.13110302 0 18.1875 56 13 46 69008 14.29122225 0 27.67861111 59 17 46 54968 15.90424527 0 19.99027778 82 15 56 46090 15.30537883 0 21.46444444 43 16 48 27507 12.71711954 0 13.69138889 25 24 44 10672 10.1041787 0 37.53638889 38 15 60 34029 15.10633448 0 30.12388889 25 17 65 46300 14.97472539 0 24.92944444 38 18 55 24760 13.14940394 0 12.30444444 12 20 38 18779 8.803370342 0 21.56888889 29 16 52 21280 11.37671367 0 50.42444444 47 16 60 40662 16.48829016 0 37.2275 45 18 54 28987 15.28690517 0 34.46222222 40 22 86 22827 15.87449367 0 25.73055556 30 8 24 18513 8.991891655 0 33.84666667 41 17 52 30594 14.52069002 0 14.69861111 25 18 49 24006 10.73329293 0 22.74222222 23 16 61 27913 12.11255989 0 16.38361111 14 23 61 42744 13.12039076 0 14.86527778 16 22 81 12934 11.17618446 0 16.89222222 26 13 43 22574 9.852452086 0 15.65972222 21 13 40 41385 10.83826353 0 18.19166667 27 16 40 18653 10.01602482 0 22.48583333 9 16 56 18472 9.98429329 0 21.195 33 20 68 30976 13.90654664 0 28.89194444 42 22 79 63339 17.43829448 0 27.25111111 68 17 47 25568 14.13651406 0 18.88583333 32 18 57 33747 12.95465875 0 8.608055556 6 17 41 4154 6.405875596 0 37.62722222 67 12 29 19474 13.00827743 0 20.41777778 33 7 3 35130 8.843896805 0 17.53416667 77 17 60 39067 14.95275123 0 17.015 46 14 30 13310 9.985399433 0 20.80944444 30 23 79 65892 15.85289071 0 8.826111111 0 17 47 4143 6.304871788 0 22.62138889 36 14 40 28579 11.70744131 0 24.21833333 46 15 48 51776 14.6847188 0 13.91388889 18 17 36 21152 9.063740747 0 18.2625 48 21 42 38084 14.09669644 0 15.73694444 29 18 49 27717 11.45807414 0 43.99972222 28 18 57 32928 14.5766967 0 12.90416667 34 17 12 11342 8.013459929 0 20.45111111 33 17 40 19499 10.91719297 0 10.66527778 34 16 43 16380 9.680449615 0 25.5275 33 15 33 36874 12.30631181 0 38.75722222 80 21 77 48259 19.15 0 14.49 32 16 43 16734 9.974038576 0 14.32416667 30 14 45 28207 10.61396958 0 19.5975 41 15 47 30143 12.39969169 0 23.57111111 41 17 43 41369 13.89235106 0 28.48277778 51 15 45 45833 15.3368091 0 24.07722222 18 15 50 29156 11.27570477 0 23.80805556 34 10 35 35944 11.46890639 0 9.628333333 31 6 7 36278 7.705473469 0 41.82777778 39 22 71 45588 17.80919895 0 27.66972222 54 21 67 45097 17.4519668 0 5.374722222 14 1 0 3895 2.080910919 0 27.60361111 24 18 62 28394 13.09820752 0 23.95277778 24 17 54 18632 11.29135539 0 8.565833333 8 4 4 2325 2.507172277 0 8.807222222 26 10 25 25139 7.823664497 0 24.94611111 19 16 40 27975 10.96662538 0 17.24666667 11 16 38 14483 8.284173862 0 11.15305556 14 9 19 13127 5.69261529 0 7.676111111 1 16 17 5839 4.687276457 0 21.38611111 39 17 67 24069 13.25987215 0 10.40555556 5 7 14 3738 3.580093526 0 15.04361111 37 15 30 18625 9.733116837 0 13.85055556 32 14 54 36341 11.88651299 0 23.42694444 38 14 35 24548 11.3271032 0 17.82638889 47 18 59 21792 12.99614065 0 16.495 47 12 24 26263 10.51715507 0 33.14111111 37 16 58 23686 13.71616844 0 21.30611111 51 21 42 49303 15.32447894 0 28.72916667 45 19 46 25659 13.82274766 0 19.54 21 16 61 28904 11.71158746 0 12.05833333 1 1 3 2781 1.908370816 0 29.12166667 42 16 52 29236 13.82088431 0 17.28194444 26 10 25 19546 8.241327352 0 19.25111111 21 19 40 22818 10.5053078 0 14.75472222 4 12 32 32689 8.180917703 0 5.49 10 2 4 5752 2.322655495 0 24.07777778 43 14 49 22197 12.28598307 0 23.3625 34 17 63 20055 12.54906448 0 21.65138889 31 19 67 25272 13.11109062 0 24.75361111 19 14 32 82206 11.7934321 0 25.27916667 34 11 23 32073 10.81286184 0 11.18 6 4 7 5444 3.068772109 0 17.82972222 11 16 54 20154 9.674855276 0 14.12694444 24 20 37 36944 11.39973018 0 15.72583333 16 12 35 8019 7.166265475 0 17.44222222 72 15 51 30884 13.46103292 0 20.14861111 21 16 39 19540 9.81357389 0
Names of X columns:
Tijd totblogs Reviews LFM totsize Score Student
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
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
1 seconds
R Server
Big Analytics Cloud Computing Center
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