Werkloosheid België versus consumentenvertrouwen

*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: Mon, 29 Nov 2010 10:05:04 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6.htm/, Retrieved Mon, 29 Nov 2010 11:04:50 +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/2010/Nov/29/t1291025079pr9znwexljyetd6.htm/}, year = {2010}, } @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 = {2010}, 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 «

235.1 1 280.7 1 264.6 2 240.7 0 201.4 1 240.8 0 241.1 -1 223.8 -3 206.1 -3 174.7 -3 203.3 -4 220.5 -8 299.5 -9 347.4 -13 338.3 -18 327.7 -11 351.6 -9 396.6 -10 438.8 -13 395.6 -11 363.5 -5 378.8 -15 357 -6 369 -6 464.8 -3 479.1 -1 431.3 -3 366.5 -4 326.3 -6 355.1 0 331.6 -4 261.3 -2 249 -2 205.5 -6 235.6 -7 240.9 -6 264.9 -6 253.8 -3 232.3 -2 193.8 -5 177 -11 213.2 -11 207.2 -11 180.6 -10 188.6 -14 175.4 -8 199 -9 179.6 -5 225.8 -1 234 -2 200.2 -5 183.6 -4 178.2 -6 203.2 -2 208.5 -2 191.8 -2 172.8 -2 148 2 159.4 1 154.5 -8 213.2 -1 196.4 1 182.8 -1 176.4 2 153.6 2 173.2 1 171 -1 151.2 -2 161.9 -2 157.2 -1 201.7 -8 236.4 -4 356.1 -6 398.3 -3 403.7 -3 384.6 -7 365.8 -9 368.1 -11 367.9 -13 347 -11 343.3 -9 292.9 -17 311.5 -22 300.9 -25 366.9 -20 356.9 -24 329.7 -24 316.2 -22 269 -19 289.3 -18 266.2 -17 253.6 -11 233.8 -11 228.4 -12 253.6 -10 260.1 -15 306.6 -15 309.2 -15 309.5 -13 271 -8 279.9 -13 317.9 -9 298.4 etc...

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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 235.631767332067 -4.50586331058313X[t] + 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) 235.631767332067 10.877472 21.6624 0 0 X -4.50586331058313 1.131043 -3.9838 0.000126 6.3e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.363866557825529 R-squared 0.132398871903799 Adjusted R-squared 0.124056553364412 F-TEST (value) 15.8707523908017 F-TEST (DF numerator) 1 F-TEST (DF denominator) 104 p-value 0.000126018845868048 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 75.1284511785672 Sum Squared Residuals 587005.554354997 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 235.1 231.125904021486 3.97409597851359 2 280.7 231.125904021484 49.5740959785156 3 264.6 226.620040710901 37.9799592890988 4 240.7 235.631767332067 5.0682326679325 5 201.4 231.125904021484 -29.7259040214844 6 240.8 235.631767332067 5.16823266793252 7 241.1 240.137630642651 0.962369357349377 8 223.8 249.149357263817 -25.3493572638169 9 206.1 249.149357263817 -43.0493572638169 10 174.7 249.149357263817 -74.4493572638169 11 203.3 253.6552205744 -50.3552205744 12 220.5 271.678673816732 -51.1786738167325 13 299.5 276.184537127316 23.3154628726844 14 347.4 294.207990369648 53.1920096303518 15 338.3 316.737306922564 21.5626930774363 16 327.7 285.196263748482 42.5037362515181 17 351.6 276.184537127316 75.4154628726844 18 396.6 280.690400437899 115.909599562101 19 438.8 294.207990369648 144.592009630352 20 395.6 285.196263748482 110.403736251518 21 363.5 258.161083884983 105.338916115017 22 378.8 303.219716990814 75.5802830091856 23 357 262.666947195566 94.3330528044337 24 369 262.666947195566 106.333052804434 25 464.8 249.149357263817 215.650642736183 26 479.1 240.137630642651 238.962369357349 27 431.3 249.149357263817 182.150642736183 28 366.5 253.6552205744 112.8447794256 29 326.3 262.666947195566 63.6330528044338 30 355.1 235.631767332067 119.468232667933 31 331.6 253.6552205744 77.9447794256 32 261.3 244.643493953234 16.6565060467663 33 249 244.643493953234 4.35650604676626 34 205.5 262.666947195566 -57.1669471955663 35 235.6 267.172810506149 -31.5728105061494 36 240.9 262.666947195566 -21.7669471955662 37 264.9 262.666947195566 2.23305280443373 38 253.8 249.149357263817 4.65064273618314 39 232.3 244.643493953234 -12.3434939532337 40 193.8 258.161083884983 -64.3610838849831 41 177 285.196263748482 -108.196263748482 42 213.2 285.196263748482 -71.9962637484819 43 207.2 285.196263748482 -77.9962637484819 44 180.6 280.690400437899 -100.090400437899 45 188.6 298.713853680231 -110.113853680231 46 175.4 271.678673816732 -96.2786738167325 47 199 276.184537127316 -77.1845371273156 48 179.6 258.161083884983 -78.5610838849831 49 225.8 240.137630642651 -14.3376306426506 50 234 244.643493953234 -10.6434939532337 51 200.2 258.161083884983 -57.9610838849831 52 183.6 253.6552205744 -70.0552205744 53 178.2 262.666947195566 -84.4669471955663 54 203.2 244.643493953234 -41.4434939532338 55 208.5 244.643493953234 -36.1434939532337 56 191.8 244.643493953234 -52.8434939532337 57 172.8 244.643493953234 -71.8434939532337 58 148 226.620040710901 -78.6200407109012 59 159.4 231.125904021484 -71.7259040214844 60 154.5 271.678673816732 -117.178673816732 61 213.2 240.137630642651 -26.9376306426506 62 196.4 231.125904021484 -34.7259040214844 63 182.8 240.137630642651 -57.3376306426506 64 176.4 226.620040710901 -50.2200407109012 65 153.6 226.620040710901 -73.0200407109012 66 173.2 231.125904021484 -57.9259040214844 67 171 240.137630642651 -69.1376306426506 68 151.2 244.643493953234 -93.4434939532338 69 161.9 244.643493953234 -82.7434939532338 70 157.2 240.137630642651 -82.9376306426506 71 201.7 271.678673816732 -69.9786738167325 72 236.4 253.6552205744 -17.2552205744000 73 356.1 262.666947195566 93.4330528044338 74 398.3 249.149357263817 149.150642736183 75 403.7 249.149357263817 154.550642736183 76 384.6 267.172810506149 117.427189493851 77 365.8 276.184537127316 89.6154628726844 78 368.1 285.196263748482 82.9037362515181 79 367.9 294.207990369648 73.6920096303518 80 347 285.196263748482 61.8037362515181 81 343.3 276.184537127316 67.1154628726844 82 292.9 312.231443611981 -19.3314436119807 83 311.5 334.760760164896 -23.2607601648963 84 300.9 348.278350096646 -47.3783500966457 85 366.9 325.74903354373 41.1509664562699 86 356.9 343.772486786063 13.1275132139375 87 329.7 343.772486786063 -14.0724867860625 88 316.2 334.760760164896 -18.5607601648963 89 269 321.243170233147 -52.2431702331469 90 289.3 316.737306922564 -27.4373069225637 91 266.2 312.231443611981 -46.0314436119807 92 253.6 285.196263748482 -31.5962637484819 93 233.8 285.196263748482 -51.3962637484819 94 228.4 289.702127059065 -61.302127059065 95 253.6 280.690400437899 -27.0904004378988 96 260.1 303.219716990814 -43.1197169908144 97 306.6 303.219716990814 3.38028300918563 98 309.2 303.219716990814 5.9802830091856 99 309.5 294.207990369648 15.2920096303519 100 271 271.678673816732 -0.678673816732501 101 279.9 294.207990369648 -14.3079903696482 102 317.9 276.184537127316 41.7154628726843 103 298.4 267.172810506149 31.2271894938506 104 246.7 253.6552205744 -6.95522057440001 105 227.3 253.6552205744 -26.3552205744 106 209.1 244.643493953234 -35.5434939532337 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0984807877742278 0.196961575548456 0.901519212225772 6 0.0349128709147215 0.0698257418294429 0.965087129085278 7 0.0117254142043963 0.0234508284087926 0.988274585795604 8 0.00346077450729410 0.00692154901458821 0.996539225492706 9 0.00112904053542727 0.00225808107085454 0.998870959464573 10 0.000893736339207853 0.00178747267841571 0.999106263660792 11 0.000275543005429394 0.000551086010858787 0.99972445699457 12 0.000309490244722149 0.000618980489444299 0.999690509755278 13 0.00312464956048102 0.00624929912096205 0.996875350439519 14 0.00827197307823975 0.0165439461564795 0.99172802692176 15 0.00434998338484944 0.00869996676969887 0.99565001661515 16 0.00292746685212461 0.00585493370424922 0.997072533147875 17 0.00402272263795045 0.0080454452759009 0.99597727736205 18 0.012165229799199 0.024330459598398 0.9878347702008 19 0.0330522246863660 0.0661044493727321 0.966947775313634 20 0.0371661754522276 0.0743323509044553 0.962833824547772 21 0.0525483646079552 0.105096729215910 0.947451635392045 22 0.0377934347465358 0.0755868694930717 0.962206565253464 23 0.040721071324449 0.081442142648898 0.95927892867555 24 0.0500252350216086 0.100050470043217 0.94997476497839 25 0.368730361930999 0.737460723861997 0.631269638069001 26 0.858934325046338 0.282131349907324 0.141065674953662 27 0.955019070156753 0.0899618596864936 0.0449809298432468 28 0.964744676383767 0.0705106472324667 0.0352553236162333 29 0.95884235493012 0.082315290139759 0.0411576450698795 30 0.974541353579112 0.0509172928417769 0.0254586464208884 31 0.974674386958555 0.0506512260828899 0.0253256130414449 32 0.96835283673069 0.0632943265386218 0.0316471632693109 33 0.961162170501115 0.0776756589977703 0.0388378294988852 34 0.968628318899569 0.0627433622008614 0.0313716811004307 35 0.967325070032851 0.0653498599342972 0.0326749299671486 36 0.962814433888808 0.074371132222383 0.0371855661111915 37 0.954108526306874 0.0917829473862522 0.0458914736931261 38 0.943195372222821 0.113609255554357 0.0568046277771787 39 0.931468369934285 0.137063260131431 0.0685316300657154 40 0.938602132526104 0.122795734947792 0.0613978674738961 41 0.969390024839992 0.0612199503200150 0.0306099751600075 42 0.973883914672162 0.0522321706556758 0.0261160853278379 43 0.978007200556738 0.0439855988865231 0.0219927994432615 44 0.985308945435652 0.0293821091286957 0.0146910545643478 45 0.991282742755776 0.0174345144884471 0.00871725724422353 46 0.993579209068156 0.0128415818636883 0.00642079093184416 47 0.993818745727234 0.0123625085455324 0.00618125427276621 48 0.994205683706486 0.0115886325870283 0.00579431629351414 49 0.991944160757789 0.0161116784844229 0.00805583924221145 50 0.988786068255724 0.0224278634885515 0.0112139317442758 51 0.986953011812657 0.0260939763746856 0.0130469881873428 52 0.986207195215038 0.0275856095699239 0.0137928047849619 53 0.987228361088138 0.0255432778237237 0.0127716389118619 54 0.983521192350693 0.0329576152986142 0.0164788076493071 55 0.978363299160214 0.0432734016795716 0.0216367008397858 56 0.9738500771989 0.0522998456022007 0.0261499228011004 57 0.972211257124275 0.0555774857514493 0.0277887428757247 58 0.971846711007155 0.0563065779856898 0.0281532889928449 59 0.96980036696293 0.0603992660741387 0.0301996330370694 60 0.981522923508608 0.0369541529827849 0.0184770764913925 61 0.974696235708485 0.0506075285830311 0.0253037642915156 62 0.966648651471363 0.0667026970572746 0.0333513485286373 63 0.961411916914761 0.0771761661704774 0.0385880830852387 64 0.953921032539823 0.0921579349203538 0.0460789674601769 65 0.954559783354985 0.0908804332900305 0.0454402166450153 66 0.951266856121127 0.097466287757746 0.048733143878873 67 0.95420588319186 0.0915882336162807 0.0457941168081404 68 0.970431578696113 0.0591368426077743 0.0295684213038871 69 0.98144622827264 0.0371075434547207 0.0185537717273604 70 0.991786711014382 0.0164265779712351 0.00821328898561757 71 0.994713185087064 0.0105736298258712 0.0052868149129356 72 0.99429767689637 0.0114046462072579 0.00570232310362896 73 0.994040326831783 0.0119193463364349 0.00595967316821747 74 0.997810455635297 0.00437908872940507 0.00218954436470253 75 0.999626708487657 0.000746583024686149 0.000373291512343074 76 0.99990613560236 0.000187728795277668 9.3864397638834e-05 77 0.999960451130915 7.9097738169271e-05 3.95488690846355e-05 78 0.99998593787389 2.81242522199263e-05 1.40621261099631e-05 79 0.999995299943725 9.40011255080505e-06 4.70005627540253e-06 80 0.999997955227325 4.08954534958307e-06 2.04477267479153e-06 81 0.999999625966502 7.48066995776547e-07 3.74033497888274e-07 82 0.999998970371816 2.05925636748107e-06 1.02962818374053e-06 83 0.999997292402461 5.41519507728363e-06 2.70759753864181e-06 84 0.999995675521268 8.6489574641448e-06 4.3244787320724e-06 85 0.999997390480769 5.21903846278061e-06 2.60951923139030e-06 86 0.999996211318279 7.57736344179065e-06 3.78868172089533e-06 87 0.999990321320148 1.93573597050362e-05 9.67867985251808e-06 88 0.999974725825888 5.05483482239593e-05 2.52741741119797e-05 89 0.999949638762333 0.000100722475334257 5.03612376671285e-05 90 0.999862762017916 0.000274475964167282 0.000137237982083641 91 0.999743541729955 0.000512916540089529 0.000256458270044765 92 0.999413689409927 0.00117262118014494 0.000586310590072468 93 0.999254274360133 0.00149145127973352 0.000745725639866761 94 0.999570579800688 0.000858840398624452 0.000429420199312226 95 0.999056184916857 0.00188763016628616 0.000943815083143079 96 0.99946043685124 0.00107912629751862 0.000539563148759312 97 0.998286874029767 0.00342625194046548 0.00171312597023274 98 0.994970833499983 0.0100583330000337 0.00502916650001684 99 0.984100702890376 0.0317985942192488 0.0158992971096244 100 0.95373802587718 0.0925239482456408 0.0462619741228204 101 0.996795857267365 0.00640828546527091 0.00320414273263545 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 34 0.350515463917526 NOK 5% type I error level 58 0.597938144329897 NOK 10% type I error level 89 0.917525773195876 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/102bvb1291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/102bvb1291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/1vsyh1291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/1vsyh1291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/2o2x21291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/2o2x21291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/3o2x21291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/3o2x21291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/4o2x21291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/4o2x21291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/5ztfn1291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/5ztfn1291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/6ztfn1291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/6ztfn1291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/7r2eq1291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/7r2eq1291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/8r2eq1291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/8r2eq1291025095.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/92bvb1291025095.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/29/t1291025079pr9znwexljyetd6/92bvb1291025095.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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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