Regressie Master 1 juli 2008

*The author of this computation has been verified*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 12 Dec 2008 07:18:23 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/12/t12290915986zva0mlmq9nl93q.htm/, Retrieved Fri, 12 Dec 2008 15:19:59 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/12/t12290915986zva0mlmq9nl93q.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

8310 0 7649 0 7279 0 6857 0 6496 0 6280 0 8962 0 11205 0 10363 0 9175 0 8234 0 8121 0 7438 0 6876 0 6489 0 6319 0 5952 0 6055 0 9107 0 11493 0 10213 0 9238 0 8218 0 7995 0 7581 0 7051 0 6668 0 6433 0 6135 0 6365 0 10095 0 12029 0 12184 0 11331 0 9961 0 9739 0 9080 0 8507 0 8097 0 7772 0 7440 0 7902 0 13539 0 14992 0 15436 0 14156 0 12846 0 12302 0 11691 0 10648 0 10064 0 10016 0 9691 0 10260 0 16882 0 18573 0 18227 0 16346 0 14694 0 14453 0 13949 0 13277 0 12726 0 12279 0 11819 0 12207 0 18637 0 20519 0 19974 0 17802 0 15997 0 15430 0 14452 0 13614 0 13080 0 12290 0 11890 0 12292 0 18700 0 20388 0 19170 0 17530 0 15564 0 15163 0 13406 0 12763 0 12083 0 12054 0 11770 0 12266 0 17549 0 18655 0 17279 0 14788 0 13138 0 12494 0 11767 0 10928 0 10104 0 9760 0 9536 0 9978 0 14846 0 15565 0 13587 0 11804 0 10611 0 10915 0 9988 0 9376 0 9319 0 8852 0 8392 0 9050 0 13250 1 14037 1 12486 1 11182 1 10287 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 9 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation NWWZPB[t] = + 8479.36842105263 -5331.38713450292Dummy[t] -799.043664717349M1[t] -1552.45048732943M2[t] -2086.55730994152M3[t] -2470.36413255361M4[t] -2877.57095516569M5[t] -2580.27777777778M6[t] + 2787.95411306043M7[t] + 4320.74729044834M8[t] + 3410.94046783626M9[t] + 1798.13364522417M10[t] + 361.826822612086M11[t] + 56.1068226120858t + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 8479.36842105263 844.620229 10.0393 0 0 Dummy -5331.38713450292 1130.55081 -4.7157 7e-06 4e-06 M1 -799.043664717349 1036.378078 -0.771 0.442441 0.22122 M2 -1552.45048732943 1036.198807 -1.4982 0.137077 0.068539 M3 -2086.55730994152 1036.059352 -2.0139 0.046574 0.023287 M4 -2470.36413255361 1035.95973 -2.3846 0.018891 0.009446 M5 -2877.57095516569 1035.899952 -2.7778 0.006483 0.003242 M6 -2580.27777777778 1035.880025 -2.4909 0.014308 0.007154 M7 2787.95411306043 1041.813206 2.6761 0.008644 0.004322 M8 4320.74729044834 1041.63487 4.148 6.8e-05 3.4e-05 M9 3410.94046783626 1041.496144 3.275 0.001432 0.000716 M10 1798.13364522417 1041.397042 1.7267 0.087171 0.043585 M11 361.826822612086 1041.337576 0.3475 0.728939 0.364469 t 56.1068226120858 6.425244 8.7322 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.812175892484814 R-squared 0.659629680333504 Adjusted R-squared 0.617488593136699 F-TEST (value) 15.6528871040500 F-TEST (DF numerator) 13 F-TEST (DF denominator) 105 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2254.51906923541 Sum Squared Residuals 533699904.522339 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8310 7736.43157894737 573.568421052627 2 7649 7039.13157894737 609.86842105263 3 7279 6561.13157894737 717.868421052632 4 6857 6233.43157894737 623.568421052631 5 6496 5882.33157894737 613.668421052633 6 6280 6235.73157894737 44.2684210526311 7 8962 11660.0702923977 -2698.07029239766 8 11205 13248.9702923977 -2043.97029239766 9 10363 12395.2702923977 -2032.27029239766 10 9175 10838.5702923977 -1663.57029239766 11 8234 9458.37029239766 -1224.37029239766 12 8121 9152.65029239766 -1031.65029239766 13 7438 8409.7134502924 -971.713450292397 14 6876 7712.4134502924 -836.413450292396 15 6489 7234.4134502924 -745.413450292399 16 6319 6906.7134502924 -587.713450292399 17 5952 6555.6134502924 -603.613450292399 18 6055 6909.0134502924 -854.013450292399 19 9107 12333.3521637427 -3226.35216374269 20 11493 13922.2521637427 -2429.25216374269 21 10213 13068.5521637427 -2855.55216374269 22 9238 11511.8521637427 -2273.85216374269 23 8218 10131.6521637427 -1913.65216374269 24 7995 9825.9321637427 -1830.93216374269 25 7581 9082.99532163743 -1501.99532163743 26 7051 8385.69532163743 -1334.69532163743 27 6668 7907.69532163743 -1239.69532163743 28 6433 7579.99532163743 -1146.99532163743 29 6135 7228.89532163743 -1093.89532163743 30 6365 7582.29532163743 -1217.29532163743 31 10095 13006.6340350877 -2911.63403508772 32 12029 14595.5340350877 -2566.53403508772 33 12184 13741.8340350877 -1557.83403508772 34 11331 12185.1340350877 -854.13403508772 35 9961 10804.9340350877 -843.93403508772 36 9739 10499.2140350877 -760.21403508772 37 9080 9756.27719298246 -676.277192982456 38 8507 9058.97719298245 -551.977192982455 39 8097 8580.97719298246 -483.977192982456 40 7772 8253.27719298246 -481.277192982456 41 7440 7902.17719298246 -462.177192982456 42 7902 8255.57719298246 -353.577192982455 43 13539 13679.9159064327 -140.915906432748 44 14992 15268.8159064327 -276.815906432747 45 15436 14415.1159064327 1020.88409356725 46 14156 12858.4159064327 1297.58409356725 47 12846 11478.2159064327 1367.78409356725 48 12302 11172.4959064327 1129.50409356725 49 11691 10429.5590643275 1261.44093567251 50 10648 9732.25906432749 915.740935672515 51 10064 9254.25906432749 809.740935672515 52 10016 8926.55906432749 1089.44093567251 53 9691 8575.45906432749 1115.54093567251 54 10260 8928.85906432749 1331.14093567251 55 16882 14353.1977777778 2528.80222222222 56 18573 15942.0977777778 2630.90222222222 57 18227 15088.3977777778 3138.60222222222 58 16346 13531.6977777778 2814.30222222222 59 14694 12151.4977777778 2542.50222222222 60 14453 11845.7777777778 2607.22222222222 61 13949 11102.8409356725 2846.15906432749 62 13277 10405.5409356725 2871.45906432749 63 12726 9927.54093567251 2798.45906432749 64 12279 9599.84093567251 2679.15906432749 65 11819 9248.74093567251 2570.25906432749 66 12207 9602.14093567251 2604.85906432749 67 18637 15026.4796491228 3610.52035087719 68 20519 16615.3796491228 3903.62035087719 69 19974 15761.6796491228 4212.32035087719 70 17802 14204.9796491228 3597.02035087719 71 15997 12824.7796491228 3172.22035087719 72 15430 12519.0596491228 2910.94035087719 73 14452 11776.1228070175 2675.87719298246 74 13614 11078.8228070175 2535.17719298246 75 13080 10600.8228070175 2479.17719298246 76 12290 10273.1228070175 2016.87719298246 77 11890 9922.02280701754 1967.97719298246 78 12292 10275.4228070175 2016.57719298246 79 18700 15699.7615204678 3000.23847953216 80 20388 17288.6615204678 3099.33847953216 81 19170 16434.9615204678 2735.03847953216 82 17530 14878.2615204678 2651.73847953216 83 15564 13498.0615204678 2065.93847953216 84 15163 13192.3415204678 1970.65847953216 85 13406 12449.4046783626 956.595321637427 86 12763 11752.1046783626 1010.89532163743 87 12083 11274.1046783626 808.895321637426 88 12054 10946.4046783626 1107.59532163743 89 11770 10595.3046783626 1174.69532163743 90 12266 10948.7046783626 1317.29532163743 91 17549 16373.0433918129 1175.95660818713 92 18655 17961.9433918129 693.056608187134 93 17279 17108.2433918129 170.756608187133 94 14788 15551.5433918129 -763.543391812866 95 13138 14171.3433918129 -1033.34339181287 96 12494 13865.6233918129 -1371.62339181287 97 11767 13122.6865497076 -1355.68654970760 98 10928 12425.3865497076 -1497.38654970760 99 10104 11947.3865497076 -1843.38654970760 100 9760 11619.6865497076 -1859.68654970760 101 9536 11268.5865497076 -1732.58654970760 102 9978 11621.9865497076 -1643.98654970760 103 14846 17046.3252631579 -2200.32526315789 104 15565 18635.2252631579 -3070.22526315789 105 13587 17781.5252631579 -4194.5252631579 106 11804 16224.8252631579 -4420.8252631579 107 10611 14844.6252631579 -4233.62526315790 108 10915 14538.9052631579 -3623.90526315789 109 9988 13795.9684210526 -3807.96842105263 110 9376 13098.6684210526 -3722.66842105263 111 9319 12620.6684210526 -3301.66842105263 112 8852 12292.9684210526 -3440.96842105263 113 8392 11941.8684210526 -3549.86842105263 114 9050 12295.2684210526 -3245.26842105263 115 13250 12388.22 861.78 116 14037 13977.12 59.8800000000003 117 12486 13123.42 -637.42 118 11182 11566.72 -384.72 119 10287 10186.52 100.479999999999 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.000199225585788418 0.000398451171576836 0.999800774414212 18 7.77742697111517e-05 0.000155548539422303 0.999922225730289 19 7.1638446385243e-05 0.000143276892770486 0.999928361553615 20 3.89876906877231e-05 7.79753813754461e-05 0.999961012309312 21 6.46097145029442e-06 1.29219429005888e-05 0.99999353902855 22 1.41020563968245e-06 2.8204112793649e-06 0.99999858979436 23 2.49790305628023e-07 4.99580611256046e-07 0.999999750209694 24 3.72035876595896e-08 7.44071753191791e-08 0.999999962796412 25 5.22370273404926e-09 1.04474054680985e-08 0.999999994776297 26 7.67921992953205e-10 1.53584398590641e-09 0.999999999232078 27 1.0642460521204e-10 2.1284921042408e-10 0.999999999893575 28 1.55321399513577e-11 3.10642799027155e-11 0.999999999984468 29 2.4785551965375e-12 4.957110393075e-12 0.999999999997521 30 9.85921722490493e-13 1.97184344498099e-12 0.999999999999014 31 5.29114335304491e-11 1.05822867060898e-10 0.999999999947089 32 7.19413485531977e-11 1.43882697106395e-10 0.999999999928059 33 7.51568959622795e-09 1.50313791924559e-08 0.99999999248431 34 1.21327385096111e-07 2.42654770192221e-07 0.999999878672615 35 2.61313721081326e-07 5.22627442162652e-07 0.999999738686279 36 3.9147788274918e-07 7.8295576549836e-07 0.999999608522117 37 2.81548657162462e-07 5.63097314324924e-07 0.999999718451343 38 1.96731979817517e-07 3.93463959635034e-07 0.99999980326802 39 1.34201252994280e-07 2.68402505988561e-07 0.999999865798747 40 9.34151972600903e-08 1.86830394520181e-07 0.999999906584803 41 7.1437949438421e-08 1.42875898876842e-07 0.99999992856205 42 9.15669374899125e-08 1.83133874979825e-07 0.999999908433063 43 2.70106854890685e-05 5.40213709781371e-05 0.99997298931451 44 0.000267308779435897 0.000534617558871793 0.999732691220564 45 0.00351912437904973 0.00703824875809946 0.99648087562095 46 0.0126188800146414 0.0252377600292828 0.987381119985359 47 0.0274620915516803 0.0549241831033606 0.97253790844832 48 0.0455574718916208 0.0911149437832417 0.95444252810838 49 0.0569999064650774 0.113999812930155 0.943000093534923 50 0.0741667261779603 0.148333452355921 0.92583327382204 51 0.105049035852222 0.210098071704444 0.894950964147778 52 0.148897074830712 0.297794149661424 0.851102925169288 53 0.227667724683308 0.455335449366616 0.772332275316692 54 0.379139866354949 0.758279732709897 0.620860133645051 55 0.749174097151232 0.501651805697536 0.250825902848768 56 0.911331328600696 0.177337342798608 0.088668671399304 57 0.960315783848358 0.0793684323032835 0.0396842161516418 58 0.9766157931485 0.0467684137030012 0.0233842068515006 59 0.987040533318013 0.0259189333639738 0.0129594666819869 60 0.992580089539659 0.0148398209206822 0.00741991046034112 61 0.993678188274682 0.0126436234506368 0.00632181172531839 62 0.994798518168677 0.0104029636626452 0.00520148183132258 63 0.996145874067425 0.00770825186514915 0.00385412593257458 64 0.997610801344522 0.00477839731095671 0.00238919865547835 65 0.999041248866216 0.00191750226756753 0.000958751133783765 66 0.999839207533178 0.000321584933644708 0.000160792466822354 67 0.999949070337582 0.000101859324836062 5.0929662418031e-05 68 0.99995961555311 8.07688937807676e-05 4.03844468903838e-05 69 0.999950812428894 9.83751422109927e-05 4.91875711054963e-05 70 0.999915911589052 0.000168176821896298 8.40884109481491e-05 71 0.999864643273369 0.000270713453262248 0.000135356726631124 72 0.999795008779745 0.000409982440509390 0.000204991220254695 73 0.999681951368326 0.000636097263348306 0.000318048631674153 74 0.999569774801992 0.000860450396015406 0.000430225198007703 75 0.999456753515537 0.00108649296892580 0.000543246484462902 76 0.999675871310042 0.000648257379916036 0.000324128689958018 77 0.999886113898929 0.000227772202142793 0.000113886101071396 78 0.99998902341921 2.19531615790147e-05 1.09765807895074e-05 79 0.999979663327631 4.06733447377663e-05 2.03366723688832e-05 80 0.999959009506592 8.19809868161441e-05 4.09904934080721e-05 81 0.999947865431388 0.000104269137224006 5.21345686120032e-05 82 0.999962873026335 7.42539473290251e-05 3.71269736645126e-05 83 0.99993956584606 0.000120868307881521 6.04341539407603e-05 84 0.99991230498104 0.000175390037918321 8.76950189591607e-05 85 0.999878687032933 0.000242625934133229 0.000121312967066615 86 0.999812496206048 0.000375007587903286 0.000187503793951643 87 0.999746057271964 0.000507885456071799 0.000253942728035899 88 0.999548945947753 0.00090210810449316 0.00045105405224658 89 0.999165520304515 0.00166895939097068 0.00083447969548534 90 0.998417244427881 0.00316551114423707 0.00158275557211853 91 0.997803199969815 0.00439360006037052 0.00219680003018526 92 0.99853119814611 0.00293760370778163 0.00146880185389082 93 0.999914110575724 0.000171778848552615 8.58894242763074e-05 94 0.99998584768904 2.83046219216867e-05 1.41523109608433e-05 95 0.999992811666378 1.43766672433566e-05 7.18833362167832e-06 96 0.99998203734872 3.59253025619023e-05 1.79626512809511e-05 97 0.99997440302619 5.11939476196912e-05 2.55969738098456e-05 98 0.999947773953453 0.000104452093094059 5.22260465470293e-05 99 0.999779880099584 0.000440239800832948 0.000220119900416474 100 0.998993456698541 0.00201308660291808 0.00100654330145904 101 0.995454292456774 0.00909141508645295 0.00454570754322648 102 0.979290177715348 0.0414196445693048 0.0207098222846524 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 68 0.790697674418605 NOK 5% type I error level 75 0.872093023255814 NOK 10% type I error level 78 0.906976744186046 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

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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