paper

*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: Sat, 13 Dec 2008 06:31:25 -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/13/t1229175175e2mlktok46b1shi.htm/, Retrieved Sat, 13 Dec 2008 14:33:05 +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/13/t1229175175e2mlktok46b1shi.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 «

6.392 0 8.686 0 9.245 0 8.183 0 7.451 0 7.989 0 8.244 0 8.843 0 9.093 0 8.247 0 9.312 0 8.341 0 7.117 0 9.636 0 9.815 0 8.611 0 8.298 0 8.715 0 8.920 0 10.086 0 9.512 0 8.991 0 10.311 0 8.895 0 7.450 0 10.084 0 9.859 0 9.100 0 8.921 0 8.503 0 8.600 0 10.394 0 9.290 0 8.742 0 10.217 0 8.639 0 8.140 0 10.779 0 10.428 0 10.349 0 10.036 0 9.492 0 10.639 0 12.055 0 10.325 0 11.817 0 11.009 0 9.997 0 9.420 0 11.959 0 12.595 0 11.891 0 10.872 0 11.836 0 11.542 0 13.094 0 11.180 0 12.036 0 12.112 0 10.875 0 9.897 0 11.672 0 12.386 0 11.406 0 9.831 0 11.025 1 10.854 1 12.253 1 11.839 1 11.669 1 11.601 1 11.178 1 9.516 1 12.103 1 12.989 1 11.610 1 10.206 1 11.356 1 11.307 1 12.649 1 11.947 1 11.714 1 12.193 1 11.269 1 9.097 1 12.640 1 13.040 1 11.687 1 11.192 1 11.392 1 11.793 1 13.933 1 12.778 1 11.810 1 13.698 1 11.957 1 10.724 1 13.939 1 13.980 1 13.807 1 12.974 1 12.510 1 12.934 1 14.908 1 13.772 1 13.013 1 14.050 1 11.817 1 11.593 1 14.466 1 13.616 1 14.734 1 13.881 1 13.528 1 13.584 1 16.170 1 13.261 1 14.742 1 15.487 1 13.155 1 12.621 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 7.03121662553579 -0.98364524613586x[t] -1.07873039016283M1[t] + 1.50284244470947M2[t] + 1.64003174777716M3[t] + 0.920821050844853M4[t] + 0.0875103539125496M5[t] + 0.392564181593832M6[t] + 0.537953484661526M7[t] + 2.07304278772922M8[t] + 0.872532090796915M9[t] + 0.78922139386461M10[t] + 1.44841069693230M11[t] + 0.0617106969323052t + 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) 7.03121662553579 0.199371 35.2671 0 0 x -0.98364524613586 0.191853 -5.1271 1e-06 1e-06 M1 -1.07873039016283 0.230709 -4.6757 9e-06 4e-06 M2 1.50284244470947 0.236456 6.3557 0 0 M3 1.64003174777716 0.236347 6.9391 0 0 M4 0.920821050844853 0.23627 3.8973 0.00017 8.5e-05 M5 0.0875103539125496 0.236224 0.3705 0.711776 0.355888 M6 0.392564181593832 0.236583 1.6593 0.099981 0.049991 M7 0.537953484661526 0.236408 2.2755 0.024867 0.012433 M8 2.07304278772922 0.236266 8.7742 0 0 M9 0.872532090796915 0.236155 3.6947 0.000349 0.000174 M10 0.78922139386461 0.236075 3.3431 0.001142 0.000571 M11 1.44841069693230 0.236028 6.1366 0 0 t 0.0617106969323052 0.002737 22.5441 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.96838338094594 R-squared 0.93776637249229 Adjusted R-squared 0.930205277561446 F-TEST (value) 124.025208130496 F-TEST (DF numerator) 13 F-TEST (DF denominator) 107 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.527738560787787 Sum Squared Residuals 29.800354774033 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.392 6.0141969323052 0.377803067694797 2 8.686 8.65748046410986 0.0285195358901394 3 9.245 8.85638046410986 0.388619535890138 4 8.183 8.19888046410986 -0.0158804641098618 5 7.451 7.42728046410986 0.0237195358901383 6 7.989 7.79404498872344 0.194955011276556 7 8.244 8.00114498872345 0.242855011276552 8 8.843 9.59794498872345 -0.75494498872345 9 9.093 8.45914498872345 0.63385501127655 10 8.247 8.43754498872345 -0.190544988723449 11 9.312 9.15844498872345 0.153555011276549 12 8.341 7.77174498872345 0.569255011276552 13 7.117 6.75472529549293 0.36227470450707 14 9.636 9.39800882729753 0.237991172702473 15 9.815 9.59690882729753 0.218091172702474 16 8.611 8.93940882729752 -0.328408827297525 17 8.298 8.16780882729753 0.130191172702475 18 8.715 8.53457335191111 0.180426648088888 19 8.92 8.74167335191111 0.178326648088889 20 10.086 10.3384733519111 -0.252473351911111 21 9.512 9.19967335191111 0.312326648088889 22 8.991 9.17807335191111 -0.187073351911112 23 10.311 9.8989733519111 0.412026648088888 24 8.895 8.51227335191111 0.382726648088888 25 7.45 7.49525365868059 -0.045253658680591 26 10.084 10.1385371904852 -0.0545371904851882 27 9.859 10.3374371904852 -0.478437190485188 28 9.1 9.6799371904852 -0.579937190485188 29 8.921 8.90833719048519 0.0126628095148117 30 8.503 9.27510171509877 -0.772101715098774 31 8.6 9.48220171509877 -0.882201715098774 32 10.394 11.0790017150988 -0.685001715098773 33 9.29 9.94020171509877 -0.650201715098774 34 8.742 9.91860171509877 -1.17660171509877 35 10.217 10.6395017150988 -0.422501715098773 36 8.639 9.25280171509877 -0.613801715098775 37 8.14 8.23578202186825 -0.095782021868253 38 10.779 10.8790655536729 -0.100065553672850 39 10.428 11.0779655536729 -0.649965553672849 40 10.349 10.4204655536728 -0.0714655536728501 41 10.036 9.64886555367285 0.387134446327149 42 9.492 10.0156300782864 -0.523630078286436 43 10.639 10.2227300782864 0.416269921713563 44 12.055 11.8195300782864 0.235469921713564 45 10.325 10.6807300782864 -0.355730078286437 46 11.817 10.6591300782864 1.15786992171356 47 11.009 11.3800300782864 -0.371030078286436 48 9.997 9.99333007828644 0.00366992171356381 49 9.42 8.97631038505592 0.443689614944084 50 11.959 11.6195939168605 0.339406083139487 51 12.595 11.8184939168605 0.776506083139488 52 11.891 11.1609939168605 0.730006083139488 53 10.872 10.3893939168605 0.482606083139488 54 11.836 10.7561584414741 1.07984155852590 55 11.542 10.9632584414741 0.578741558525901 56 13.094 12.5600584414741 0.5339415585259 57 11.18 11.4212584414741 -0.241258441474098 58 12.036 11.3996584414741 0.6363415585259 59 12.112 12.1205584414741 -0.00855844147409893 60 10.875 10.7338584414741 0.141141558525902 61 9.897 9.71683874824358 0.180161251756422 62 11.672 12.3601222800482 -0.688122280048175 63 12.386 12.5590222800482 -0.173022280048176 64 11.406 11.9015222800482 -0.495522280048174 65 9.831 11.1299222800482 -1.29892228004818 66 11.025 10.5130415585259 0.511958441474099 67 10.854 10.7201415585259 0.133858441474099 68 12.253 12.3169415585259 -0.0639415585259011 69 11.839 11.1781415585259 0.660858441474099 70 11.669 11.1565415585259 0.512458441474099 71 11.601 11.8774415585259 -0.276441558525901 72 11.178 10.4907415585259 0.6872584414741 73 9.516 9.47372186529538 0.0422781347046185 74 12.103 12.1170053971000 -0.0140053970999776 75 12.989 12.3159053971000 0.673094602900022 76 11.61 11.6584053971000 -0.0484053970999786 77 10.206 10.8868053971000 -0.680805397099979 78 11.356 11.2535699217136 0.102430078286435 79 11.307 11.4606699217136 -0.153669921713563 80 12.649 13.0574699217136 -0.408469921713564 81 11.947 11.9186699217136 0.0283300782864355 82 11.714 11.8970699217136 -0.183069921713564 83 12.193 12.6179699217136 -0.424969921713564 84 11.269 11.2312699217136 0.0377300782864366 85 9.097 10.2142502284830 -1.11725022848305 86 12.64 12.8575337602876 -0.217533760287640 87 13.04 13.0564337602876 -0.0164337602876419 88 11.687 12.3989337602876 -0.711933760287642 89 11.192 11.6273337602876 -0.43533376028764 90 11.392 11.9940982849012 -0.602098284901227 91 11.793 12.2011982849012 -0.408198284901227 92 13.933 13.7979982849012 0.135001715098774 93 12.778 12.6591982849012 0.118801715098775 94 11.81 12.6375982849012 -0.827598284901226 95 13.698 13.3584982849012 0.339501715098774 96 11.957 11.9717982849012 -0.0147982849012256 97 10.724 10.9547785916707 -0.230778591670706 98 13.939 13.5980621234753 0.340937876524697 99 13.98 13.7969621234753 0.183037876524697 100 13.807 13.1394621234753 0.667537876524698 101 12.974 12.3678621234753 0.606137876524697 102 12.51 12.7346266480889 -0.224626648088889 103 12.934 12.9417266480889 -0.0077266480888893 104 14.908 14.5385266480889 0.369473351911111 105 13.772 13.3997266480889 0.372273351911112 106 13.013 13.3781266480889 -0.365126648088889 107 14.05 14.0990266480889 -0.0490266480888875 108 11.817 12.7123266480889 -0.895326648088889 109 11.593 11.6953069548584 -0.102306954858368 110 14.466 14.3385904866630 0.127409513337034 111 13.616 14.5374904866630 -0.921490486662966 112 14.734 13.8799904866630 0.854009513337034 113 13.881 13.1083904866630 0.772609513337034 114 13.528 13.4751550112766 0.0528449887234491 115 13.584 13.6822550112766 -0.0982550112765508 116 16.17 15.2790550112766 0.89094498872345 117 13.261 14.1402550112766 -0.879255011276552 118 14.742 14.1186550112766 0.62334498872345 119 15.487 14.8395550112766 0.647444988723448 120 13.155 13.4528550112766 -0.297855011276552 121 12.621 12.4358353180460 0.185164681953969 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0386360275218544 0.0772720550437088 0.961363972478146 18 0.0097372040031338 0.0194744080062676 0.990262795996866 19 0.00224440157537495 0.0044888031507499 0.997755598424625 20 0.00581026145536032 0.0116205229107206 0.99418973854464 21 0.00369615333284724 0.00739230666569447 0.996303846667153 22 0.00116606402638908 0.00233212805277817 0.99883393597361 23 0.000578507906304024 0.00115701581260805 0.999421492093696 24 0.000243944735622032 0.000487889471244065 0.999756055264378 25 0.000251310250499836 0.000502620500999672 0.9997486897495 26 8.69280632506669e-05 0.000173856126501334 0.99991307193675 27 0.000437692285004155 0.00087538457000831 0.999562307714996 28 0.000218127876546468 0.000436255753092936 0.999781872123453 29 9.30964769848536e-05 0.000186192953969707 0.999906903523015 30 0.000482189022338555 0.00096437804467711 0.999517810977661 31 0.00192974010852480 0.00385948021704959 0.998070259891475 32 0.00122211803433520 0.00244423606867040 0.998777881965665 33 0.00295245779831393 0.00590491559662786 0.997047542201686 34 0.00531430816985114 0.0106286163397023 0.99468569183015 35 0.00356666926490055 0.00713333852980109 0.9964333307351 36 0.00475202277463182 0.00950404554926364 0.995247977225368 37 0.00338211466228795 0.0067642293245759 0.996617885337712 38 0.00290894117040935 0.0058178823408187 0.99709105882959 39 0.00207957506066626 0.00415915012133252 0.997920424939334 40 0.00450451845542627 0.00900903691085253 0.995495481544574 41 0.0088369044845483 0.0176738089690966 0.991163095515452 42 0.00682902515152333 0.0136580503030467 0.993170974848477 43 0.0172063698710403 0.0344127397420806 0.98279363012896 44 0.0457906591411795 0.091581318282359 0.95420934085882 45 0.0354415048288435 0.070883009657687 0.964558495171157 46 0.292927312042325 0.58585462408465 0.707072687957675 47 0.259885409621614 0.519770819243228 0.740114590378386 48 0.213386628250568 0.426773256501135 0.786613371749432 49 0.203756583647022 0.407513167294045 0.796243416352978 50 0.187113267145499 0.374226534290997 0.812886732854502 51 0.260322576983435 0.520645153966869 0.739677423016565 52 0.329754537450312 0.659509074900624 0.670245462549688 53 0.313459004722629 0.626918009445259 0.68654099527737 54 0.493472877588771 0.986945755177541 0.506527122411229 55 0.504385200310815 0.99122959937837 0.495614799689185 56 0.521291846731977 0.957416306536047 0.478708153268023 57 0.472560147365206 0.945120294730412 0.527439852634794 58 0.51389715166878 0.97220569666244 0.48610284833122 59 0.462332184052675 0.92466436810535 0.537667815947325 60 0.440346546816352 0.880693093632705 0.559653453183648 61 0.475201269814997 0.950402539629994 0.524798730185003 62 0.483605697283198 0.967211394566396 0.516394302716802 63 0.475214261932066 0.950428523864131 0.524785738067934 64 0.45662610514037 0.91325221028074 0.54337389485963 65 0.570101960154894 0.859796079690212 0.429898039845106 66 0.560303712175003 0.879392575649994 0.439696287824997 67 0.522550700394949 0.954898599210102 0.477449299605051 68 0.46756747751928 0.93513495503856 0.53243252248072 69 0.492446255707982 0.984892511415964 0.507553744292018 70 0.508834828598541 0.982330342802919 0.491165171401459 71 0.4722902673528 0.9445805347056 0.5277097326472 72 0.57426598543861 0.85146802912278 0.42573401456139 73 0.576591255626409 0.846817488747183 0.423408744373591 74 0.521628959398052 0.956742081203896 0.478371040601948 75 0.643340549515239 0.713318900969522 0.356659450484761 76 0.585252615087982 0.829494769824035 0.414747384912018 77 0.620742007840832 0.758515984318335 0.379257992159168 78 0.618927879827948 0.762144240344104 0.381072120172052 79 0.58353845036037 0.83292309927926 0.41646154963963 80 0.560848538193299 0.878302923613402 0.439151461806701 81 0.532472176156184 0.935055647687632 0.467527823843816 82 0.497110947164035 0.99422189432807 0.502889052835965 83 0.454600493131334 0.909200986262668 0.545399506868666 84 0.497930847102947 0.995861694205894 0.502069152897053 85 0.577664879917216 0.844670240165567 0.422335120082784 86 0.511741795674674 0.976516408650652 0.488258204325326 87 0.502065329581037 0.995869340837926 0.497934670418963 88 0.649819918702142 0.700360162595717 0.350180081297858 89 0.711560718124485 0.57687856375103 0.288439281875515 90 0.677879259849384 0.644241480301232 0.322120740150616 91 0.617554281378679 0.764891437242642 0.382445718621321 92 0.573690411374476 0.852619177251048 0.426309588625524 93 0.535782556326565 0.92843488734687 0.464217443673435 94 0.623029190485795 0.753941619028411 0.376970809514205 95 0.549334601949557 0.901330796100886 0.450665398050443 96 0.555374995004939 0.889250009990122 0.444625004995061 97 0.463752701646409 0.927505403292817 0.536247298353591 98 0.391343845723113 0.782687691446227 0.608656154276887 99 0.590082271656232 0.819835456687536 0.409917728343768 100 0.502920582141467 0.994158835717067 0.497079417858533 101 0.403591283164568 0.807182566329137 0.596408716835432 102 0.286931220071552 0.573862440143105 0.713068779928448 103 0.199764957564765 0.399529915129531 0.800235042435235 104 0.119080599741176 0.238161199482352 0.880919400258824 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.227272727272727 NOK 5% type I error level 26 0.295454545454545 NOK 10% type I error level 29 0.329545454545455 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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