Q3: aangepast 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: Sun, 23 Nov 2008 06:07:02 -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/23/t1227445789sebr1kqg304kg13.htm/, Retrieved Sun, 23 Nov 2008 13:09:49 +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/23/t1227445789sebr1kqg304kg13.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:

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28 30 32 31 32 34 32 35 29 33 33 34 32 27 26 22 22 23 23 19 15 5 1 11 18 20 21 19 20 19 18 16 16 16 15 11 10 6 1 8 10 9 6 8 14 4 13 13 16 18 16 15 13 19 15 17 17 13 12 13 13 16 17 14 8 8 8 9 5 11 10 14 18 17 14 15 13 17 17 17 17 21 20 11 18 20 18 21 21 20 18 17 17 18 11 15 13 16 16 12 10 8 6 8 10

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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation X[t] = + 21.9121621621622 + 2.57757757757759M1[t] + 3.1453953953954M2[t] + 2.26876876876877M3[t] + 1.94769769769770M4[t] + 1.18218218218219M5[t] + 2.19444444444445M6[t] + 0.762262262262266M7[t] + 1.21896896896897M8[t] + 0.67567567567568M9[t] -0.371746746746742M10[t] -0.99837337337337M11[t] -0.123373373373373t + 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) 21.9121621621622 2.729911 8.0267 0 0 M1 2.57757757757759 3.366769 0.7656 0.445878 0.222939 M2 3.1453953953954 3.3661 0.9344 0.352527 0.176263 M3 2.26876876876877 3.36558 0.6741 0.501932 0.250966 M4 1.94769769769770 3.365209 0.5788 0.564156 0.282078 M5 1.18218218218219 3.364986 0.3513 0.726153 0.363076 M6 2.19444444444445 3.364912 0.6522 0.515927 0.257964 M7 0.762262262262266 3.364986 0.2265 0.821294 0.410647 M8 1.21896896896897 3.365209 0.3622 0.718013 0.359007 M9 0.67567567567568 3.36558 0.2008 0.841329 0.420664 M10 -0.371746746746742 3.462754 -0.1074 0.91474 0.45737 M11 -0.99837337337337 3.462538 -0.2883 0.773738 0.386869 t -0.123373373373373 0.022361 -5.5173 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.522001199043027 R-squared 0.272485251802358 Adjusted R-squared 0.177592023776579 F-TEST (value) 2.87149312412823 F-TEST (DF numerator) 12 F-TEST (DF denominator) 92 p-value 0.00211574077367216 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.92493099916933 Sum Squared Residuals 4411.82957957958 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 28 24.3663663663663 3.63363363363368 2 30 24.8108108108108 5.18918918918919 3 32 23.8108108108108 8.18918918918919 4 31 23.3663663663664 7.63363363363363 5 32 22.4774774774775 9.52252252252252 6 34 23.3663663663664 10.6336336336336 7 32 21.8108108108108 10.1891891891892 8 35 22.1441441441441 12.8558558558559 9 29 21.4774774774775 7.52252252252252 10 33 20.3066816816817 12.6933183183183 11 33 19.5566816816817 13.4433183183183 12 34 20.4316816816817 13.5683183183183 13 32 22.8858858858859 9.1141141141141 14 27 23.3303303303303 3.66966966966967 15 26 22.3303303303303 3.66966966966967 16 22 21.8858858858859 0.114114114114112 17 22 20.996996996997 1.003003003003 18 23 21.8858858858859 1.11411411411411 19 23 20.3303303303303 2.66966966966967 20 19 20.6636636636637 -1.66366366366367 21 15 19.996996996997 -4.996996996997 22 5 18.8262012012012 -13.8262012012012 23 1 18.0762012012012 -17.0762012012012 24 11 18.9512012012012 -7.9512012012012 25 18 21.4054054054054 -3.40540540540541 26 20 21.8498498498498 -1.84984984984985 27 21 20.8498498498498 0.150150150150148 28 19 20.4054054054054 -1.40540540540541 29 20 19.5165165165165 0.483483483483482 30 19 20.4054054054054 -1.40540540540541 31 18 18.8498498498498 -0.849849849849851 32 16 19.1831831831832 -3.18318318318318 33 16 18.5165165165165 -2.51651651651652 34 16 17.3457207207207 -1.34572072072072 35 15 16.5957207207207 -1.59572072072072 36 11 17.4707207207207 -6.47072072072072 37 10 19.9249249249249 -9.92492492492493 38 6 20.3693693693694 -14.3693693693694 39 1 19.3693693693694 -18.3693693693694 40 8 18.9249249249249 -10.9249249249249 41 10 18.0360360360360 -8.03603603603604 42 9 18.9249249249249 -9.92492492492493 43 6 17.3693693693694 -11.3693693693694 44 8 17.7027027027027 -9.7027027027027 45 14 17.0360360360360 -3.03603603603603 46 4 15.8652402402402 -11.8652402402402 47 13 15.1152402402402 -2.11524024024024 48 13 15.9902402402402 -2.99024024024023 49 16 18.4444444444444 -2.44444444444445 50 18 18.8888888888889 -0.888888888888889 51 16 17.8888888888889 -1.88888888888889 52 15 17.4444444444444 -2.44444444444444 53 13 16.5555555555556 -3.55555555555556 54 19 17.4444444444444 1.55555555555556 55 15 15.8888888888889 -0.888888888888888 56 17 16.2222222222222 0.777777777777778 57 17 15.5555555555556 1.44444444444444 58 13 14.3847597597598 -1.38475975975976 59 12 13.6347597597598 -1.63475975975976 60 13 14.5097597597598 -1.50975975975975 61 13 16.9639639639640 -3.96396396396397 62 16 17.4084084084084 -1.40840840840841 63 17 16.4084084084084 0.591591591591589 64 14 15.9639639639640 -1.96396396396396 65 8 15.0750750750751 -7.07507507507508 66 8 15.9639639639640 -7.96396396396397 67 8 14.4084084084084 -6.40840840840841 68 9 14.7417417417417 -5.74174174174174 69 5 14.0750750750751 -9.07507507507508 70 11 12.9042792792793 -1.90427927927928 71 10 12.1542792792793 -2.15427927927928 72 14 13.0292792792793 0.970720720720727 73 18 15.4834834834835 2.51651651651651 74 17 15.9279279279279 1.07207207207207 75 14 14.9279279279279 -0.927927927927928 76 15 14.4834834834835 0.516516516516517 77 13 13.5945945945946 -0.594594594594594 78 17 14.4834834834835 2.51651651651652 79 17 12.9279279279279 4.07207207207207 80 17 13.2612612612613 3.73873873873874 81 17 12.5945945945946 4.40540540540541 82 21 11.4237987987988 9.5762012012012 83 20 10.6737987987988 9.3262012012012 84 11 11.5487987987988 -0.548798798798796 85 18 14.003003003003 3.99699699699699 86 20 14.4474474474474 5.55255255255255 87 18 13.4474474474474 4.55255255255255 88 21 13.003003003003 7.996996996997 89 21 12.1141141141141 8.88588588588589 90 20 13.003003003003 6.996996996997 91 18 11.4474474474474 6.55255255255255 92 17 11.7807807807808 5.21921921921922 93 17 11.1141141141141 5.88588588588589 94 18 9.94331831831832 8.05668168168168 95 11 9.19331831831832 1.80668168168168 96 15 10.0683183183183 4.93168168168169 97 13 12.5225225225225 0.477477477477473 98 16 12.9669669669670 3.03303303303304 99 16 11.9669669669670 4.03303303303304 100 12 11.5225225225225 0.477477477477479 101 10 10.6336336336336 -0.633633633633635 102 8 11.5225225225225 -3.52252252252252 103 6 9.96696696696696 -3.96696696696697 104 8 10.3003003003003 -2.3003003003003 105 10 9.63363363363364 0.366366366366363 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.365149788525643 0.730299577051287 0.634850211474356 17 0.332852805014395 0.66570561002879 0.667147194985605 18 0.307417291980895 0.61483458396179 0.692582708019105 19 0.238747131491654 0.477494262983307 0.761252868508346 20 0.346236567589913 0.692473135179827 0.653763432410087 21 0.325508560815023 0.651017121630045 0.674491439184977 22 0.813711823149782 0.372576353700436 0.186288176850218 23 0.979219354520987 0.0415612909580257 0.0207806454790129 24 0.980573379135608 0.0388532417287833 0.0194266208643917 25 0.977666095757292 0.044667808485415 0.0223339042427075 26 0.979868933961402 0.0402621320771955 0.0201310660385978 27 0.982783198037018 0.0344336039259647 0.0172168019629824 28 0.98260003758267 0.0347999248346599 0.0173999624173299 29 0.984976453487157 0.0300470930256851 0.0150235465128425 30 0.982552578269543 0.0348948434609141 0.0174474217304571 31 0.981454577509193 0.0370908449816136 0.0185454224908068 32 0.97578482508063 0.0484303498387401 0.0242151749193701 33 0.974330791190787 0.0513384176184255 0.0256692088092128 34 0.977419023962762 0.0451619520744766 0.0225809760372383 35 0.980034101585371 0.0399317968292574 0.0199658984146287 36 0.970229838009805 0.0595403239803897 0.0297701619901949 37 0.958793713058004 0.0824125738839911 0.0412062869419956 38 0.963881502187872 0.072236995624256 0.036118497812128 39 0.988535007626262 0.0229299847474764 0.0114649923737382 40 0.986147052442291 0.0277058951154176 0.0138529475577088 41 0.979756578940852 0.0404868421182969 0.0202434210591485 42 0.973446482826652 0.053107034346695 0.0265535171733475 43 0.969921565567572 0.060156868864856 0.030078434432428 44 0.963314898803427 0.0733702023931464 0.0366851011965732 45 0.967399038691246 0.0652019226175078 0.0326009613087539 46 0.98135183734334 0.0372963253133213 0.0186481626566607 47 0.986132133222297 0.0277357335554065 0.0138678667777033 48 0.985685618457914 0.0286287630841725 0.0143143815420863 49 0.988492975597048 0.0230140488059032 0.0115070244029516 50 0.992514261118428 0.0149714777631439 0.00748573888157193 51 0.993404685666143 0.0131906286677136 0.0065953143338568 52 0.993021258310557 0.0139574833788869 0.00697874168944345 53 0.990664448399747 0.0186711032005056 0.00933555160025281 54 0.993285084478133 0.0134298310437331 0.00671491552186654 55 0.99245436024584 0.0150912795083201 0.00754563975416006 56 0.993221246010602 0.0135575079787953 0.00677875398939763 57 0.994518525468583 0.0109629490628331 0.00548147453141654 58 0.994003286155756 0.0119934276884872 0.00599671384424361 59 0.992020029605038 0.0159599407899241 0.00797997039496205 60 0.98893109810706 0.0221378037858816 0.0110689018929408 61 0.984751980994053 0.0304960380118948 0.0152480190059474 62 0.980522607892296 0.0389547842154090 0.0194773921077045 63 0.97696060199786 0.0460787960042803 0.0230393980021401 64 0.968954812400368 0.0620903751992636 0.0310451875996318 65 0.96448636929795 0.0710272614040992 0.0355136307020496 66 0.962945842682481 0.0741083146350374 0.0370541573175187 67 0.958876734765136 0.0822465304697283 0.0411232652348642 68 0.954696111716708 0.0906077765665831 0.0453038882832916 69 0.97973498382408 0.0405300323518382 0.0202650161759191 70 0.991684688993252 0.0166306220134963 0.00831531100674813 71 0.994947229895016 0.0101055402099684 0.00505277010498422 72 0.992977732874955 0.0140445342500896 0.00702226712504479 73 0.990358215066977 0.0192835698660466 0.0096417849330233 74 0.989610229292404 0.0207795414151915 0.0103897707075958 75 0.992319302155777 0.0153613956884466 0.00768069784422331 76 0.993899614343968 0.0122007713120630 0.00610038565603151 77 0.997387188672635 0.00522562265473029 0.00261281132736515 78 0.996676975923452 0.00664604815309638 0.00332302407654819 79 0.994832816492766 0.0103343670144679 0.00516718350723397 80 0.992789653362766 0.0144206932744676 0.00721034663723381 81 0.993276516022726 0.0134469679545485 0.00672348397727425 82 0.991165294579067 0.017669410841866 0.008834705420933 83 0.98512118873932 0.0297576225213603 0.0148788112606802 84 0.997788464691085 0.0044230706178295 0.00221153530891475 85 0.995373384265642 0.00925323146871648 0.00462661573435824 86 0.99342975858162 0.0131404828367605 0.00657024141838025 87 0.999130323711562 0.00173935257687576 0.00086967628843788 88 0.996080927245427 0.00783814550914707 0.00391907275457353 89 0.98217410855722 0.0356517828855602 0.0178258914427801 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.0810810810810811 NOK 5% type I error level 54 0.72972972972973 NOK 10% type I error level 67 0.905405405405405 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/10ekwc1227445611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/10ekwc1227445611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/1ewrq1227445610.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/1ewrq1227445610.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/2pfzl1227445610.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/2pfzl1227445610.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/3kf4u1227445611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/3kf4u1227445611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/4j5xa1227445611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/4j5xa1227445611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/5vyao1227445611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/5vyao1227445611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/6hkrf1227445611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/6hkrf1227445611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/789kr1227445611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/789kr1227445611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/8mdp21227445611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/8mdp21227445611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/97iyy1227445611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445789sebr1kqg304kg13/97iyy1227445611.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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