*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: Tue, 17 Nov 2009 02:44:54 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6.htm/, Retrieved Tue, 17 Nov 2009 10:51:51 +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/2009/Nov/17/t12584514961c3v9f336kbkdk6.htm/}, year = {2009}, } @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 = {2009}, 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 «

108.8235294 111.7647059 105.8823529 100 111.7647059 108.8235294 111.7647059 105.8823529 117.6470588 111.7647059 108.8235294 111.7647059 111.7647059 117.6470588 111.7647059 108.8235294 120.5882353 111.7647059 117.6470588 111.7647059 102.9411765 120.5882353 111.7647059 117.6470588 114.7058824 102.9411765 120.5882353 111.7647059 114.7058824 114.7058824 102.9411765 120.5882353 117.6470588 114.7058824 114.7058824 102.9411765 111.7647059 117.6470588 114.7058824 114.7058824 97.05882353 111.7647059 117.6470588 114.7058824 94.11764706 97.05882353 111.7647059 117.6470588 82.35294118 94.11764706 97.05882353 111.7647059 82.35294118 82.35294118 94.11764706 97.05882353 85.29411765 82.35294118 82.35294118 94.11764706 85.29411765 85.29411765 82.35294118 82.35294118 73.52941176 85.29411765 85.29411765 82.35294118 61.76470588 73.52941176 85.29411765 85.29411765 32.35294118 61.76470588 73.52941176 85.29411765 20.58823529 32.35294118 61.76470588 73.52941176 50 20.58823529 32.35294118 61.76470588 70.58823529 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation X[t] = + 4.62068255335274 + 0.864369165995954Y0[t] + 0.0466691402331548Y1[t] + 0.000721395614455389Y2[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 4.62068255335274 3.405034 1.357 0.177801 0.088901 Y0 0.864369165995954 0.099795 8.6614 0 0 Y1 0.0466691402331548 0.131785 0.3541 0.723979 0.361989 Y2 0.000721395614455389 0.101452 0.0071 0.994341 0.49717 Multiple Linear Regression - Regression Statistics Multiple R 0.89062266616919 R-squared 0.793208733494317 Adjusted R-squared 0.787066418647613 F-TEST (value) 129.138403564582 F-TEST (DF numerator) 3 F-TEST (DF denominator) 101 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.7653753234986 Sum Squared Residuals 11705.223891435 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 108.8235294 106.240226117071 2.58330328292928 2 111.7647059 103.976731699362 7.78797420063825 3 117.6470588 106.385975302841 11.2610834971586 4 111.7647059 111.605640199809 0.159065700191195 5 120.5882353 106.797761833761 13.7904734662386 6 102.9411765 114.154267733574 -11.2130912335739 7 114.7058824 99.3082372636715 15.3976451363285 8 114.7058824 108.660078484215 6.04580391578452 9 117.6470588 109.196396682838 8.45066211716196 10 111.7647059 111.747145881995 0.0175600180053583 11 97.05882353 106.799883585590 -9.7410600555898 12 94.11764706 93.8161697055636 0.301477354436409 13 82.35294118 90.5833530629802 -8.23041188298021 14 82.35294118 80.2664331171196 2.08650806288038 15 85.29411765 79.7152626567973 5.57885499320272 16 85.29411765 82.249037901991 3.04507974800909 17 73.52941176 82.3863000791198 -8.8568883191198 18 61.76470588 72.2193728125996 -10.4546669325996 19 32.35294118 61.5012750939341 -29.1483339139341 20 20.58823529 35.5211168539759 -14.9328815639759 21 50 23.9709590571328 26.0290409428672 22 70.58823529 48.8233153629346 21.7649199270654 23 76.47058824 67.9832858944015 8.4873023455985 24 79.41176471 74.0498631658642 5.36190154413578 25 73.52941176 76.8815020356571 -3.35209027565709 26 76.47058824 71.9384832029214 4.53210503707855 27 73.52941176 74.2083428610683 -0.678931101068292 28 70.58823529 71.7990992739786 -1.21086398397855 29 64.70588235 69.1216965957762 -4.41581424577616 30 64.70588235 63.8977881619916 0.80809418800839 31 64.70588235 63.621142055927 1.08474029407296 32 61.76470588 63.6168985523134 -1.85219267231344 33 50 61.0746362998926 -11.0746362998926 34 47.05882353 50.7683251130804 -3.70950158308044 35 35.29411765 47.6748924003373 -12.3807747503373 36 20.58823529 37.3600942062979 -16.7718589162979 37 41.17647059 24.0976124752277 17.0788581147723 38 47.05882353 41.1986503574789 5.86017317252109 39 44.11764706 47.2334013436482 -3.11575428364823 40 35.29411765 44.98051570814 -9.68639805813999 41 41.17647059 37.2207102773622 3.95576031263779 42 58.82352941 41.8913264990104 16.9322029109896 43 29.41176471 57.4130591123727 -28.0012944023727 44 55.88235294 32.8182531545514 23.0640997854486 45 55.88235294 54.3387221658908 1.54363077410924 46 64.70588235 55.5528642419827 9.15301810801725 47 70.58823529 63.1987467655064 7.3894885244936 48 64.70588235 68.6950578017347 -3.98917545173471 49 61.76470588 63.8914229065712 -2.12671702657121 50 55.88235294 61.0788798035062 -5.19652686350622 51 73.52941176 55.8528496179221 17.6765621420779 52 61.76470588 70.8297770263825 -9.06507114638247 53 67.64705882 61.4800575758589 6.16700124414111 54 67.64705882 66.0282638830258 1.61879493697422 55 55.88235294 66.2943012300564 -10.4119482900564 56 52.94117647 56.1294957239867 -3.18831925398665 57 55.88235294 53.0381847630503 2.84416817694970 58 55.88235294 55.434697831115 0.44765510888496 59 64.70588235 55.5698382564371 9.13604409356287 60 67.64705882 63.1987467655064 4.4483120544936 61 58.82352941 66.1527955493139 -7.32926613931388 62 41.17647059 58.6696362246007 -17.4931656346007 63 41.17647059 43.0063979304959 -1.82992734049588 64 41.17647059 42.1764596123022 -0.999989022302173 65 44.11764706 42.1637291014614 1.95391795853861 66 32.35294118 44.7059913538822 -12.3530501738822 67 50 34.6742045213278 15.3257954786722 68 47.05882353 49.380851079144 -2.32202754914401 69 58.82352941 47.6536748822693 11.1698545277307 70 70.58823529 57.6981922256645 12.8900430643355 71 67.64705882 68.4141681920566 -0.767109372056546 72 58.82352941 66.4294416553784 -7.60591224537845 73 61.76470588 58.6738797282143 3.09082615178573 74 55.88235294 60.8022336974417 -4.91988075744165 75 67.64705882 55.8486061143085 11.7984527056915 76 67.64705882 65.7452525215408 1.90180629845918 77 67.64705882 66.2900577264428 1.35700109355723 78 67.64705882 66.29854473367 1.34851408633004 79 79.41176471 66.29854473367 13.1132199763300 80 76.47058824 76.467593751997 0.00299448800305158 81 50 74.4743802085584 -24.4743802085584 82 70.58823529 51.4652447582328 19.1229905317672 83 76.47058824 68.0235991787451 8.44698906125492 84 70.58823529 74.0498631658642 -3.46162787586422 85 79.41176471 69.2547152697509 10.1570494402491 86 79.41176471 76.6112211845534 2.80054352544656 87 76.47058824 77.018764212786 -0.548175972785977 88 70.58823529 74.4828672157928 -3.89463192579276 89 67.64705882 69.2610805251785 -1.61402170517853 90 67.64705882 66.4421721662264 1.20488665377355 91 70.58823529 66.3006664854768 4.28756880452325 92 50 68.8408069860908 -18.8408069860908 93 61.76470588 51.1822333962739 10.5824724837261 94 55.88235294 60.3925689178618 -4.5102159778618 95 64.70588235 55.8422408588881 8.8636414911119 96 64.70588235 63.20299026912 1.50289208088000 97 52.94117647 63.610533296893 -10.6693568268930 98 47.05882353 53.4478495426302 -6.38902601263015 99 41.17647059 47.814276329273 -6.63780573927295 100 35.29411765 42.4467404629464 -7.15262281294635 101 41.17647059 37.0834481002333 4.09302248976667 102 47.05882353 41.8892047472036 5.16961878279638 103 23.52941176 47.2440101026894 -23.7145983426894 104 8.823529412 27.1846799325505 -18.3611505205505 105 0 13.3795147582909 -13.3795147582909 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.246614563808330 0.493229127616661 0.75338543619167 8 0.129711024527621 0.259422049055242 0.870288975472379 9 0.0982927872102466 0.196585574420493 0.901707212789753 10 0.0469565435624397 0.0939130871248794 0.95304345643756 11 0.181270374837810 0.362540749675621 0.81872962516219 12 0.252547789514791 0.505095579029582 0.747452210485209 13 0.273453415585033 0.546906831170066 0.726546584414967 14 0.196907548213955 0.393815096427909 0.803092451786045 15 0.151656162901908 0.303312325803817 0.848343837098092 16 0.106395498444483 0.212790996888965 0.893604501555517 17 0.134246140249157 0.268492280498314 0.865753859750843 18 0.143913020253848 0.287826040507696 0.856086979746152 19 0.342645580887375 0.685291161774749 0.657354419112625 20 0.303758346172723 0.607516692345446 0.696241653827277 21 0.91590288529162 0.16819422941676 0.08409711470838 22 0.926684669448695 0.146630661102609 0.0733153305513047 23 0.91155522275961 0.176889554480779 0.0884447772403893 24 0.884851560457119 0.230296879085762 0.115148439542881 25 0.85981427358936 0.28037145282128 0.14018572641064 26 0.823968010248918 0.352063979502163 0.176031989751082 27 0.782785185319073 0.434429629361853 0.217214814680926 28 0.733683588333224 0.532632823333552 0.266316411666776 29 0.692161118287755 0.615677763424489 0.307838881712245 30 0.632536889209829 0.734926221580342 0.367463110790171 31 0.571415768059875 0.85716846388025 0.428584231940125 32 0.51547923152225 0.9690415369555 0.48452076847775 33 0.528781668618164 0.942436662763672 0.471218331381836 34 0.471456577049026 0.942913154098052 0.528543422950974 35 0.500758154020327 0.998483691959345 0.499241845979673 36 0.563802830075104 0.872394339849792 0.436197169924896 37 0.681250494814392 0.637499010371217 0.318749505185608 38 0.642360531597397 0.715278936805205 0.357639468402603 39 0.593231323417352 0.813537353165296 0.406768676582648 40 0.57628014449928 0.84743971100144 0.42371985550072 41 0.529832640782355 0.94033471843529 0.470167359217645 42 0.597181246086644 0.805637507826711 0.402818753913356 43 0.874617884696251 0.250764230607497 0.125382115303749 44 0.956257573895387 0.0874848522092266 0.0437424261046133 45 0.94176913776939 0.116461724461220 0.0582308622306101 46 0.93824538819456 0.123509223610882 0.0617546118054409 47 0.928630399734746 0.142739200530508 0.0713696002652539 48 0.910220460252642 0.179559079494716 0.0897795397473578 49 0.885860716147129 0.228278567705742 0.114139283852871 50 0.864315290046198 0.271369419907604 0.135684709953802 51 0.90677024845806 0.186459503083879 0.0932297515419393 52 0.89893037717833 0.202139245643339 0.101069622821670 53 0.881637808118173 0.236724383763654 0.118362191881827 54 0.850649865544801 0.298700268910397 0.149350134455199 55 0.848047528634352 0.303904942731297 0.151952471365649 56 0.81543749719923 0.36912500560154 0.18456250280077 57 0.776215772638017 0.447568454723966 0.223784227361983 58 0.73072668159594 0.538546636808121 0.269273318404061 59 0.72043152305338 0.559136953893241 0.279568476946620 60 0.680674460561146 0.638651078877709 0.319325539438854 61 0.6489880756207 0.702023848758599 0.351011924379300 62 0.73514538211509 0.529709235769821 0.264854617884911 63 0.687386653441091 0.625226693117818 0.312613346558909 64 0.633098782662968 0.733802434674063 0.366901217337032 65 0.58281844877561 0.83436310244878 0.41718155122439 66 0.583351907243562 0.833296185512876 0.416648092756438 67 0.666032930489052 0.667934139021895 0.333967069510948 68 0.610456377468394 0.779087245063211 0.389543622531606 69 0.681239225794094 0.637521548411813 0.318760774205906 70 0.752941712568264 0.494116574863473 0.247058287431736 71 0.71850644803179 0.56298710393642 0.28149355196821 72 0.680654367566204 0.638691264867592 0.319345632433796 73 0.62281601533733 0.754367969325339 0.377183984662670 74 0.574889286888661 0.850221426222678 0.425110713111339 75 0.615042800568545 0.76991439886291 0.384957199431455 76 0.560244686635777 0.879510626728447 0.439755313364223 77 0.513350438826923 0.973299122346155 0.486649561173078 78 0.447785114086158 0.895570228172317 0.552214885913842 79 0.49321179240427 0.98642358480854 0.50678820759573 80 0.427787553589764 0.855575107179529 0.572212446410236 81 0.708994459340757 0.582011081318486 0.291005540659243 82 0.785019040345312 0.429961919309377 0.214980959654688 83 0.765964202526688 0.468071594946624 0.234035797473312 84 0.703366863919538 0.593266272160925 0.296633136080462 85 0.703669200294653 0.592661599410695 0.296330799705347 86 0.637219175686591 0.725561648626818 0.362780824313409 87 0.557051408928832 0.885897182142336 0.442948591071168 88 0.487831761890471 0.975663523780943 0.512168238109529 89 0.408268215484001 0.816536430968003 0.591731784515999 90 0.324064954522418 0.648129909044836 0.675935045477582 91 0.263678881241517 0.527357762483034 0.736321118758483 92 0.414588045955796 0.829176091911592 0.585411954044204 93 0.437433529867881 0.874867059735762 0.562566470132119 94 0.368449122691902 0.736898245383803 0.631550877308098 95 0.5865325499146 0.8269349001708 0.4134674500854 96 0.454699980949525 0.909399961899051 0.545300019050475 97 0.331237638243343 0.662475276486686 0.668762361756657 98 0.204389691398046 0.408779382796091 0.795610308601954 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.0217391304347826 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/10zfbr1258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/10zfbr1258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/1zs8y1258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/1zs8y1258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/2bwpq1258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/2bwpq1258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/3dnw31258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/3dnw31258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/4574t1258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/4574t1258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/59i831258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/59i831258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/6q1u71258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/6q1u71258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/750ut1258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/750ut1258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/8cgkv1258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/8cgkv1258451089.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/94fz41258451089.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t12584514961c3v9f336kbkdk6/94fz41258451089.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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