Multiple Lineair Regression - Collinealiteitstest

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 17 Dec 2010 10:44:51 +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/Dec/17/t1292582626tz9lsoxwnmo8m6u.htm/, Retrieved Fri, 17 Dec 2010 11:43:49 +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/Dec/17/t1292582626tz9lsoxwnmo8m6u.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 «

198563 44164 25943 -7,7 195722 40399 21698 -4,9 202196 36763 20077 -2,4 205816 37903 25673 -3,6 212588 35532 19094 -7 214320 35533 19306 -7 220375 32110 15443 -7,9 204442 33374 15179 -8,8 206903 35462 18288 -14,2 214126 33508 18264 -17,8 226899 36080 16406 -18,2 223532 34560 15678 -22,8 195309 38737 19657 -23,6 186005 38144 18821 -27,6 188906 37594 19493 -29,4 191563 36424 21078 -31,8 189226 36843 19296 -31,4 186413 37246 19985 -27,6 178037 38661 16972 -28,8 166827 40454 16951 -21,9 169362 44928 23126 -13,9 174330 48441 24890 -8 187069 48140 21042 -2,8 186530 45998 20842 -3,3 158114 47369 23904 -1,3 151001 49554 22578 0,5 159612 47510 25452 -1,9 161914 44873 21928 2 164182 45344 25227 1,7 169701 42413 26210 1,9 171297 36912 17436 0,1 166444 43452 21258 2,4 173476 42142 25638 2,3 182516 44382 23516 4,7 202388 43636 23891 5 202300 44167 24617 7,2 168053 44423 26174 8,5 167302 42868 23339 6,8 172608 43908 23660 5,8 178106 42013 26500 3,7 185686 38846 22469 4,8 194581 3508 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation OPENVAC[t] = + 24502.0486346266 -0.0276138848081947NWWZ[t] + 1.0857739331349ONTVANGJOB[t] + 65.9822513835042Producentenvertrouwen[t] -63.5471571335748t + 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) 24502.0486346266 5707.233694 4.2932 4.2e-05 2.1e-05 NWWZ -0.0276138848081947 0.01531 -1.8036 0.074395 0.037197 ONTVANGJOB 1.0857739331349 0.136271 7.9677 0 0 Producentenvertrouwen 65.9822513835042 44.812003 1.4724 0.144144 0.072072 t -63.5471571335748 22.079276 -2.8781 0.004921 0.00246 Multiple Linear Regression - Regression Statistics Multiple R 0.926093106592916 R-squared 0.857648442078918 Adjusted R-squared 0.851778274741966 F-TEST (value) 146.102895002694 F-TEST (DF numerator) 4 F-TEST (DF denominator) 97 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3269.69891642159 Sum Squared Residuals 1037020307.39271 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 44164 46615.5754799891 -2451.57547998911 2 40399 42206.1193273118 -1807.11932731183 3 36763 40368.7159627771 -3605.71596277708 4 37903 46202.0187708006 -8299.01877080055 5 35532 38583.8240249475 -3051.82402494746 6 35533 38702.6336931507 -3169.63369315069 7 32110 34218.1557335582 -2108.15573355822 8 33374 34248.5522584808 -874.552258480842 9 35462 37136.4143314798 -1674.41433147978 10 33508 36609.8174050008 -3101.81740500077 11 36080 34149.7972288941 1930.20277110593 12 34560 33085.2642422234 1474.73575777664 13 38737 38068.5724348684 668.427565131568 14 38144 37090.3088483555 1053.69115164449 15 37594 37557.5258419697 36.4741580302959 16 36424 38983.2028735992 -2559.20287359916 17 36843 37075.7331169693 -232.733116969348 18 37246 38088.6946129885 -842.694612988488 19 38661 34905.8257928127 3755.17420718731 20 40454 35584.3065663293 4869.69343367068 21 44928 42683.270259383 2244.72974061698 22 48441 44787.137823735 3653.86217626502 23 48140 40536.8670005209 7603.13299947907 24 45998 40238.0578149802 5759.94218501976 25 47369 44415.7910945824 2953.2089054176 26 49554 43227.6933172429 6326.30668275705 27 47510 45888.5198785353 1621.4801214647 28 44873 42192.4689986015 2680.53100139846 29 45344 45628.46708072 -284.467080719964 30 42413 46493.0311198783 -4080.03111987827 31 36912 36740.0636607749 171.936339225108 32 43452 41112.1138372391 2339.88616276086 33 42142 45603.4774441269 -3461.47744412685 34 44382 43144.6458855353 1237.35411446465 35 43636 42959.315509834 676.684490166034 36 44167 43831.6312030632 335.36879693684 37 44423 46490.1036996454 -2067.10369964542 38 42868 43256.9556422134 -388.955642213402 39 43908 43329.4403934403 578.559606559654 40 42013 46059.1073398291 -4046.10733982908 41 38846 41482.0726879045 -2636.07268790446 42 35087 42012.2040617962 -6925.20406179617 43 33026 34408.2113830849 -1382.21138308491 44 34646 36450.509681203 -1804.50968120296 45 37135 38656.6608588216 -1521.66085882155 46 37985 37693.9587958421 291.041204157944 47 43121 35947.484985063 7173.51501493701 48 43722 35137.8786956773 8584.1213043227 49 43630 38697.6257169565 4932.37428304354 50 42234 39814.7441290068 2419.25587099317 51 39351 33846.2715742861 5504.72842571394 52 39327 39369.4872589551 -42.4872589550837 53 35704 36128.6176492306 -424.617649230579 54 30466 32858.4368627701 -2392.43686277008 55 28155 29433.4144182857 -1278.41441828566 56 29257 30174.8755251375 -917.875525137522 57 29998 30194.2516744782 -196.251674478202 58 32529 31145.3198342873 1383.6801657127 59 34787 28962.3698738629 5824.63012613713 60 33855 26990.659360267 6864.340639733 61 34556 33921.0019607465 634.998039253474 62 31348 30255.2521715587 1092.74782844125 63 30805 30969.6459683053 -164.645968305259 64 28353 31684.6501758157 -3331.65017581571 65 24514 27980.3304099269 -3466.33040992688 66 21106 26104.6769370512 -4998.67693705122 67 21346 23617.3768815855 -2271.37688158548 68 23335 25502.8890024371 -2167.88900243709 69 24379 26137.0932290354 -1758.09322903544 70 26290 27612.4711534725 -1322.47115347246 71 30084 25634.1236333224 4449.87636667757 72 29429 25451.0542665573 3977.94573344269 73 30632 32450.4485548433 -1818.44855484334 74 27349 26737.0938546585 611.906145341528 75 27264 27758.4004688102 -494.400468810244 76 27474 30271.1457083394 -2797.14570833939 77 24482 26894.35949104 -2412.35949103995 78 21453 26275.9406806996 -4822.94068069965 79 18788 22406.4281588881 -3618.42815888811 80 19282 22993.3237136088 -3711.32371360878 81 19713 23593.6749137336 -3880.67491373363 82 21917 24847.4315708517 -2930.43157085171 83 23812 22046.3965508337 1765.60344916628 84 23785 21723.3275920612 2061.6724079388 85 24696 24636.4468752195 59.5531247805228 86 24562 23878.1799612358 683.820038764213 87 23580 23456.7754067074 123.224593292646 88 24939 24806.3941145964 132.605885403618 89 23899 25726.3502519365 -1827.35025193647 90 21454 24252.8690047615 -2798.86900476152 91 19761 21468.7060151296 -1707.70601512962 92 19815 22083.4346037271 -2268.4346037271 93 20780 25108.7107795026 -4328.71077950256 94 23462 23738.6297489611 -276.629748961055 95 25005 20854.4260887859 4150.57391121415 96 24725 20508.8257776857 4216.1742223143 97 26198 23715.1765035207 2482.82349647927 98 27543 24657.158095184 2885.84190481595 99 26471 24898.690571306 1572.309428694 100 26558 25359.7601159688 1198.23988403123 101 25317 24858.8683914518 458.131608548217 102 22896 23599.7880688334 -703.788068833391 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.0050862698233777 0.0101725396467554 0.994913730176622 9 0.00110120833991343 0.00220241667982687 0.998898791660087 10 0.00263473750222936 0.00526947500445871 0.99736526249777 11 0.0270728617825816 0.0541457235651632 0.972927138217418 12 0.0111772082784847 0.0223544165569694 0.988822791721515 13 0.00693704491317744 0.0138740898263549 0.993062955086823 14 0.00315778438581709 0.00631556877163418 0.996842215614183 15 0.00145098469638436 0.00290196939276873 0.998549015303616 16 0.00167876763929588 0.00335753527859175 0.998321232360704 17 0.000727506586600796 0.00145501317320159 0.9992724934134 18 0.00115382128102081 0.00230764256204161 0.99884617871898 19 0.00297429976162061 0.00594859952324122 0.99702570023838 20 0.00639432203536382 0.0127886440707276 0.993605677964636 21 0.0075867733545157 0.0151735467090314 0.992413226645484 22 0.00753626090646535 0.0150725218129307 0.992463739093535 23 0.011189412263506 0.022378824527012 0.988810587736494 24 0.00763209974837078 0.0152641994967416 0.99236790025163 25 0.00941254636471751 0.018825092729435 0.990587453635282 26 0.00867359941347292 0.0173471988269458 0.991326400586527 27 0.0128046205771364 0.0256092411542728 0.987195379422864 28 0.0216325726308096 0.0432651452616192 0.97836742736919 29 0.0416592343232275 0.0833184686464549 0.958340765676773 30 0.137427088937075 0.274854177874151 0.862572911062925 31 0.243314330257414 0.486628660514828 0.756685669742586 32 0.238647232453938 0.477294464907877 0.761352767546062 33 0.264785241934477 0.529570483868955 0.735214758065523 34 0.229881115247341 0.459762230494683 0.770118884752659 35 0.197569916321781 0.395139832643562 0.802430083678219 36 0.160279076667539 0.320558153335078 0.839720923332461 37 0.156469904923108 0.312939809846216 0.843530095076892 38 0.148397782771973 0.296795565543945 0.851602217228027 39 0.127876027167029 0.255752054334058 0.872123972832971 40 0.139676477072372 0.279352954144744 0.860323522927628 41 0.137089836707772 0.274179673415545 0.862910163292228 42 0.280126412924167 0.560252825848333 0.719873587075833 43 0.260743186963863 0.521486373927726 0.739256813036137 44 0.222070730598679 0.444141461197357 0.777929269401321 45 0.195061720953525 0.390123441907049 0.804938279046475 46 0.219040953178397 0.438081906356794 0.780959046821603 47 0.539009575623854 0.921980848752292 0.460990424376146 48 0.810318224781449 0.379363550437102 0.189681775218551 49 0.839553871501246 0.320892256997508 0.160446128498754 50 0.817054088296453 0.365891823407094 0.182945911703547 51 0.899258828740407 0.201482342519185 0.100741171259593 52 0.878408495606267 0.243183008787466 0.121591504393733 53 0.863282932312255 0.273434135375491 0.136717067687745 54 0.877789457929723 0.244421084140553 0.122210542070277 55 0.873949224972433 0.252101550055134 0.126050775027567 56 0.858976601617952 0.282046796764095 0.141023398382048 57 0.828967483262433 0.342065033475134 0.171032516737567 58 0.792980124869863 0.414039750260273 0.207019875130137 59 0.85388888500648 0.292222229987041 0.146111114993521 60 0.957814788709088 0.0843704225818232 0.0421852112909116 61 0.95845441138376 0.0830911772324785 0.0415455886162393 62 0.972572555916317 0.0548548881673653 0.0274274440836827 63 0.97961980120535 0.0407603975892988 0.0203801987946494 64 0.979171247281955 0.0416575054360909 0.0208287527180455 65 0.979174376156712 0.0416512476865766 0.0208256238432883 66 0.98636722409797 0.0272655518040608 0.0136327759020304 67 0.98268285650462 0.0346342869907619 0.017317143495381 68 0.977154500299174 0.045690999401651 0.0228454997008255 69 0.969933571565229 0.0601328568695417 0.0300664284347709 70 0.959007792896319 0.0819844142073626 0.0409922071036813 71 0.97164587082672 0.0567082583465591 0.0283541291732796 72 0.987643512845155 0.0247129743096902 0.0123564871548451 73 0.98499524262232 0.0300095147553593 0.0150047573776797 74 0.988055083818327 0.0238898323633452 0.0119449161816726 75 0.99136269257974 0.017274614840522 0.008637307420261 76 0.992827636439308 0.0143447271213847 0.00717236356069236 77 0.994296331291204 0.0114073374175925 0.00570366870879625 78 0.992207258340187 0.015585483319627 0.00779274165981352 79 0.98820379483501 0.0235924103299779 0.0117962051649889 80 0.983405819978344 0.0331883600433118 0.0165941800216559 81 0.981143587491703 0.0377128250165931 0.0188564125082965 82 0.971613830274716 0.0567723394505679 0.028386169725284 83 0.962903819546158 0.0741923609076846 0.0370961804538423 84 0.963529760351116 0.0729404792977676 0.0364702396488838 85 0.95045530663135 0.0990893867373022 0.0495446933686511 86 0.93341675077578 0.133166498448439 0.0665832492242197 87 0.920188457903428 0.159623084193143 0.0798115420965717 88 0.988890704922157 0.0222185901556867 0.0111092950778434 89 0.994953496835213 0.0100930063295738 0.0050465031647869 90 0.998614318719928 0.00277136256014311 0.00138568128007156 91 0.996787412007047 0.00642517598590625 0.00321258799295312 92 0.989409968646802 0.0211800627063969 0.0105900313531984 93 0.980061653446063 0.0398766931078749 0.0199383465539374 94 0.995678194827666 0.00864361034466786 0.00432180517233393 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.126436781609195 NOK 5% type I error level 43 0.494252873563218 NOK 10% type I error level 55 0.632183908045977 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/10dw491292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/10dw491292582682.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/1elm21292582681.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/1elm21292582681.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/2zm601292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/2zm601292582682.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/3zm601292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/3zm601292582682.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/4zm601292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/4zm601292582682.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/5sd531292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/5sd531292582682.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/6sd531292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/6sd531292582682.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/73n561292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/73n561292582682.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/83n561292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/83n561292582682.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/9dw491292582682.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292582626tz9lsoxwnmo8m6u/9dw491292582682.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal 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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