fd

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 05:34:45 -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/Nov/27/t12277893169xjjwulyi9elbze.htm/, Retrieved Thu, 27 Nov 2008 12:35:27 +0000

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/Nov/27/t12277893169xjjwulyi9elbze.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}, }

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

7,5 0 7,2 0 6,9 0 6,7 0 6,4 0 6,3 0 6,8 0 7,3 0 7,1 0 7,1 0 6,8 0 6,5 0 6,3 0 6,1 0 6,1 0 6,3 0 6,3 0 6 0 6,2 0 6,4 0 6,8 0 7,5 0 7,5 0 7,6 0 7,6 0 7,4 0 7,3 0 7,1 0 6,9 0 6,8 0 7,5 0 7,6 0 7,8 0 8,0 0 8,1 0 8,2 0 8,3 0 8,2 0 8,0 0 7,9 0 7,6 0 7,6 0 8,2 0 8,3 0 8,4 0 8,4 0 8,4 0 8,6 0 8,9 0 8,8 0 8,3 0 7,5 0 7,2 0 7,5 0 8,8 0 9,3 0 9,3 0 8,7 1 8,2 1 8,3 1 8,5 1 8,6 1 8,6 1 8,2 1 8,1 1 8,0 1 8,6 1 8,7 1 8,8 1 8,5 1 8,4 1 8,5 1 8,7 1 8,7 1 8,6 1 8,5 1 8,3 1 8,1 1 8,2 1 8,1 1 8,1 1 7,9 1 7,9 1 7,9 1 8,0 1 8,0 1 7,9 1 8,0 1 7,7 1 7,2 1 7,5 1 7,3 1 7,0 1 7,0 1 7,0 1 7,2 1 7,3 1 7,1 1 6,8 1 6,6 1 6,2 1 6,2 1 6,8 1 6,9 1 6.8 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation w[t] = + 7.41527777777778 -0.118472222222222d[t] + 0.0891550925925951M1[t] -0.0311033950617277M2[t] -0.218028549382717M3[t] -0.416064814814815M4[t] -0.658545524691358M5[t] -0.778804012345679M6[t] -0.243506944444444M7[t] -0.108209876543209M8[t] -0.0951350308641968M9[t] + 0.0557947530864199M10[t] -0.0533526234567892M11[t] + 0.00914737654320988t + 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) 7.41527777777778 0.322124 23.0199 0 0 d -0.118472222222222 0.309809 -0.3824 0.703053 0.351527 M1 0.0891550925925951 0.383647 0.2324 0.816758 0.408379 M2 -0.0311033950617277 0.38354 -0.0811 0.935544 0.467772 M3 -0.218028549382717 0.383501 -0.5685 0.571081 0.285541 M4 -0.416064814814815 0.38353 -1.0848 0.280862 0.140431 M5 -0.658545524691358 0.383626 -1.7166 0.089448 0.044724 M6 -0.778804012345679 0.38379 -2.0292 0.045355 0.022677 M7 -0.243506944444444 0.384023 -0.6341 0.527609 0.263805 M8 -0.108209876543209 0.384322 -0.2816 0.77892 0.38946 M9 -0.0951350308641968 0.384689 -0.2473 0.80523 0.402615 M10 0.0557947530864199 0.394652 0.1414 0.887884 0.443942 M11 -0.0533526234567892 0.394553 -0.1352 0.892734 0.446367 t 0.00914737654320988 0.005101 1.7932 0.076259 0.03813 Multiple Linear Regression - Regression Statistics Multiple R 0.427982147905556 R-squared 0.183168718925853 Adjusted R-squared 0.0664785359152604 F-TEST (value) 1.56970118822443 F-TEST (DF numerator) 13 F-TEST (DF denominator) 91 p-value 0.108650123530041 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.78903966168285 Sum Squared Residuals 56.6551064814815 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.5 7.51358024691357 -0.0135802469135677 2 7.2 7.40246913580247 -0.202469135802471 3 6.9 7.22469135802469 -0.324691358024693 4 6.7 7.0358024691358 -0.335802469135804 5 6.4 6.80246913580247 -0.402469135802467 6 6.3 6.69135802469136 -0.391358024691359 7 6.8 7.2358024691358 -0.435802469135802 8 7.3 7.38024691358025 -0.080246913580246 9 7.1 7.40246913580247 -0.30246913580247 10 7.1 7.5625462962963 -0.462546296296297 11 6.8 7.4625462962963 -0.662546296296297 12 6.5 7.5250462962963 -1.02504629629629 13 6.3 7.6233487654321 -1.3233487654321 14 6.1 7.51223765432099 -1.41223765432099 15 6.1 7.33445987654321 -1.23445987654321 16 6.3 7.14557098765432 -0.845570987654321 17 6.3 6.91223765432099 -0.612237654320989 18 6 6.80112654320988 -0.801126543209877 19 6.2 7.34557098765432 -1.14557098765432 20 6.4 7.49001543209877 -1.09001543209877 21 6.8 7.51223765432099 -0.712237654320988 22 7.5 7.67231481481481 -0.172314814814815 23 7.5 7.57231481481482 -0.072314814814815 24 7.6 7.63481481481481 -0.0348148148148148 25 7.6 7.73311728395062 -0.133117283950619 26 7.4 7.6220061728395 -0.222006172839506 27 7.3 7.44422839506173 -0.144228395061729 28 7.1 7.25533950617284 -0.155339506172840 29 6.9 7.02200617283951 -0.122006172839506 30 6.8 6.9108950617284 -0.110895061728395 31 7.5 7.45533950617284 0.0446604938271603 32 7.6 7.59978395061728 0.000216049382715459 33 7.8 7.6220061728395 0.177993827160493 34 8 7.78208333333333 0.217916666666666 35 8.1 7.68208333333333 0.417916666666666 36 8.2 7.74458333333333 0.455416666666666 37 8.3 7.84288580246914 0.457114197530863 38 8.2 7.73177469135802 0.468225308641975 39 8 7.55399691358025 0.446003086419753 40 7.9 7.36510802469136 0.534891975308642 41 7.6 7.13177469135803 0.468225308641974 42 7.6 7.02066358024691 0.579336419753086 43 8.2 7.56510802469136 0.634891975308641 44 8.3 7.7095524691358 0.590447530864198 45 8.4 7.73177469135802 0.668225308641976 46 8.4 7.89185185185185 0.508148148148148 47 8.4 7.79185185185185 0.608148148148148 48 8.6 7.85435185185185 0.745648148148148 49 8.9 7.95265432098766 0.947345679012345 50 8.8 7.84154320987654 0.958456790123458 51 8.3 7.66376543209877 0.636234567901236 52 7.5 7.47487654320988 0.0251234567901237 53 7.2 7.24154320987654 -0.0415432098765432 54 7.5 7.13043209876543 0.369567901234568 55 8.8 7.67487654320988 1.12512345679012 56 9.3 7.81932098765432 1.48067901234568 57 9.3 7.84154320987654 1.45845679012346 58 8.7 7.88314814814815 0.81685185185185 59 8.2 7.78314814814815 0.416851851851851 60 8.3 7.84564814814815 0.454351851851853 61 8.5 7.94395061728395 0.556049382716048 62 8.6 7.83283950617284 0.76716049382716 63 8.6 7.65506172839506 0.944938271604938 64 8.2 7.46617283950617 0.733827160493827 65 8.1 7.23283950617284 0.86716049382716 66 8 7.12172839506173 0.878271604938271 67 8.6 7.66617283950617 0.933827160493827 68 8.7 7.81061728395062 0.889382716049382 69 8.8 7.83283950617284 0.96716049382716 70 8.5 7.99291666666667 0.507083333333333 71 8.4 7.89291666666667 0.507083333333334 72 8.5 7.95541666666667 0.544583333333334 73 8.7 8.05371913580247 0.646280864197529 74 8.7 7.94260802469136 0.757391975308642 75 8.6 7.76483024691358 0.83516975308642 76 8.5 7.57594135802469 0.924058641975309 77 8.3 7.34260802469136 0.957391975308642 78 8.1 7.23149691358025 0.868503086419753 79 8.2 7.77594135802469 0.424058641975308 80 8.1 7.92038580246914 0.179614197530864 81 8.1 7.94260802469136 0.157391975308642 82 7.9 8.10268518518519 -0.202685185185185 83 7.9 8.00268518518518 -0.102685185185185 84 7.9 8.06518518518518 -0.165185185185184 85 8 8.16348765432099 -0.163487654320989 86 8 8.05237654320988 -0.0523765432098761 87 7.9 7.8745987654321 0.0254012345679019 88 8 7.68570987654321 0.314290123456791 89 7.7 7.45237654320988 0.247623456790124 90 7.2 7.34126543209876 -0.141265432098765 91 7.5 7.88570987654321 -0.385709876543209 92 7.3 8.03015432098765 -0.730154320987654 93 7 8.05237654320988 -1.05237654320988 94 7 8.2124537037037 -1.21245370370370 95 7 8.1124537037037 -1.11245370370370 96 7.2 8.1749537037037 -0.974953703703703 97 7.3 8.2732561728395 -0.973256172839508 98 7.1 8.1621450617284 -1.06214506172839 99 6.8 7.98436728395062 -1.18436728395062 100 6.6 7.79547839506173 -1.19547839506173 101 6.2 7.5621450617284 -1.36214506172839 102 6.2 7.45103395061728 -1.25103395061728 103 6.8 7.99547839506173 -1.19547839506173 104 6.9 8.13992283950617 -1.23992283950617 105 6.8 8.1621450617284 -1.36214506172839 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.151585144786521 0.303170289573042 0.848414855213479 18 0.0881437344944234 0.176287468988847 0.911856265505577 19 0.0447972958640183 0.0895945917280367 0.955202704135982 20 0.0270910150305235 0.054182030061047 0.972908984969477 21 0.0194156041873198 0.0388312083746396 0.98058439581268 22 0.055181939721467 0.110363879442934 0.944818060278533 23 0.136735212048949 0.273470424097898 0.863264787951051 24 0.318612728494636 0.637225456989273 0.681387271505364 25 0.425356665968091 0.850713331936183 0.574643334031909 26 0.506277500715142 0.987444998569715 0.493722499284858 27 0.566464192411252 0.867071615177497 0.433535807588748 28 0.583231577900499 0.833536844199003 0.416768422099501 29 0.59615596904592 0.80768806190816 0.40384403095408 30 0.635863609553686 0.728272780892627 0.364136390446314 31 0.721456115155505 0.55708776968899 0.278543884844495 32 0.778720575975944 0.442558848048112 0.221279424024056 33 0.825355876104633 0.349288247790734 0.174644123895367 34 0.812479640560808 0.375040718878383 0.187520359439192 35 0.804286526944307 0.391426946111387 0.195713473055693 36 0.816377352931465 0.367245294137071 0.183622647068535 37 0.819215720507429 0.361568558985142 0.180784279492571 38 0.8380996234401 0.3238007531198 0.1619003765599 39 0.851374763885152 0.297250472229696 0.148625236114848 40 0.849191562372128 0.301616875255745 0.150808437627872 41 0.847877106907051 0.304245786185898 0.152122893092949 42 0.84941297575506 0.301174048489879 0.150587024244940 43 0.860808109963881 0.278383780072237 0.139191890036119 44 0.86565462093524 0.268690758129520 0.134345379064760 45 0.859363947817064 0.281272104365873 0.140636052182936 46 0.82376252255398 0.352474954892039 0.176237477446020 47 0.778654773147274 0.442690453705452 0.221345226852726 48 0.732290191695654 0.535419616608691 0.267709808304346 49 0.68901543380249 0.62196913239502 0.31098456619751 50 0.646799993074065 0.706400013851869 0.353200006925935 51 0.593285536627855 0.81342892674429 0.406714463372145 52 0.728300848177229 0.543398303645542 0.271699151822771 53 0.909686291182709 0.180627417634582 0.0903137088172912 54 0.966631079217883 0.0667378415642334 0.0333689207821167 55 0.968379537969797 0.063240924060405 0.0316204620302025 56 0.966946453222484 0.0661070935550317 0.0330535467775159 57 0.960443900438982 0.0791121991220356 0.0395560995610178 58 0.946724385815333 0.106551228369333 0.0532756141846667 59 0.960538378235054 0.0789232435298916 0.0394616217649458 60 0.97390590353028 0.0521881929394418 0.0260940964697209 61 0.983798493103602 0.0324030137927958 0.0162015068963979 62 0.987666704674057 0.0246665906518852 0.0123332953259426 63 0.98855260100954 0.0228947979809204 0.0114473989904602 64 0.997190282699057 0.00561943460188682 0.00280971730094341 65 0.999216590796074 0.00156681840785303 0.000783409203926513 66 0.99977924848677 0.000441503026459424 0.000220751513229712 67 0.999828529139304 0.000342941721391756 0.000171470860695878 68 0.999777520194101 0.000444959611798428 0.000222479805899214 69 0.999548526564705 0.000902946870588947 0.000451473435294474 70 0.99929713565299 0.00140572869402232 0.000702864347011158 71 0.999071062020645 0.00185787595870941 0.000928937979354707 72 0.9987705566887 0.00245888662259995 0.00122944331129997 73 0.998055447254462 0.00388910549107566 0.00194455274553783 74 0.996403564599355 0.00719287080128997 0.00359643540064498 75 0.993173977412649 0.0136520451747025 0.00682602258735126 76 0.987419724269496 0.0251605514610084 0.0125802757305042 77 0.97844793767628 0.0431041246474407 0.0215520623237203 78 0.966677440435817 0.0666451191283668 0.0333225595641834 79 0.955282679586529 0.0894346408269418 0.0447173204134709 80 0.952866466266667 0.0942670674666652 0.0471335337333326 81 0.93934113855267 0.121317722894659 0.0606588614473294 82 0.926241895910755 0.147516208178491 0.0737581040892454 83 0.8980375204885 0.203924959022999 0.101962479511499 84 0.862355252730716 0.275289494538568 0.137644747269284 85 0.8148571313767 0.370285737246598 0.185142868623299 86 0.727875751018087 0.544248497963827 0.272124248981913 87 0.619979758795087 0.760040482409826 0.380020241204913 88 0.575729097058612 0.848541805882777 0.424270902941388 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.152777777777778 NOK 5% type I error level 18 0.25 NOK 10% type I error level 29 0.402777777777778 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/102e3o1227789280.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/102e3o1227789280.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/1b5zg1227789279.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/1b5zg1227789279.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/27l221227789279.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/27l221227789279.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/38h2b1227789279.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/38h2b1227789279.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/4icuf1227789279.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/4icuf1227789279.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/54o0l1227789279.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/54o0l1227789279.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/6euvj1227789279.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/6euvj1227789279.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/7sy5a1227789280.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/7sy5a1227789280.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/8cy8u1227789280.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/8cy8u1227789280.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/9t2q01227789280.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277893169xjjwulyi9elbze/9t2q01227789280.ps (open in new window)

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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