no lineair trend and no seisonal dummies

*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, 14 Dec 2008 06:33:27 -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/14/t1229261711k62h2k3mcim0a3x.htm/, Retrieved Sun, 14 Dec 2008 14:35:11 +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/14/t1229261711k62h2k3mcim0a3x.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 «

11554.5 180144 13182.1 173666 14800.1 165688 12150.7 161570 14478.2 156145 13253.9 153730 12036.8 182698 12653.2 200765 14035.4 176512 14571.4 166618 15400.9 158644 14283.2 159585 14485.3 163095 14196.3 159044 15559.1 155511 13767.4 153745 14634 150569 14381.1 150605 12509.9 179612 12122.3 194690 13122.3 189917 13908.7 184128 13456.5 175335 12441.6 179566 12953 181140 13057.2 177876 14350.1 175041 13830.2 169292 13755.5 166070 13574.4 166972 12802.6 206348 11737.3 215706 13850.2 202108 15081.8 195411 13653.3 193111 14019.1 195198 13962 198770 13768.7 194163 14747.1 190420 13858.1 189733 13188 186029 13693.1 191531 12970 232571 11392.8 243477 13985.2 227247 14994.7 217859 13584.7 208679 14257.8 213188 13553.4 216234 14007.3 213586 16535.8 209465 14721.4 204045 13664.6 200237 16805.9 203666 13829.4 241476 13735.6 260307 15870.5 243324 15962.4 244460 15744.1 233575 16083.7 237217 14863.9 235243 15533.1 230354 17473.1 227184 15925.5 221678 15573.7 217142 17495 219452 14155.8 256446 14913.9 265845 17250.4 248624 15879.8 241114 17647.8 229245 17749.9 231805 17111.8 219277 16934.8 219313 20280 212610 16238.2 214771 17896.1 211142 18089.3 211457 15660 240048 16162.4 240636 17850.1 230580 18520.4 208795 18524.7 197922 16843.7 194596

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation invoer[t] = + 10341.3857439156 + 0.0218660314107394werkloosheid[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) 10341.3857439156 1306.710077 7.9141 0 0 werkloosheid 0.0218660314107394 0.006411 3.4105 0.001009 0.000504 Multiple Linear Regression - Regression Statistics Multiple R 0.352454518886459 R-squared 0.124224187883486 Adjusted R-squared 0.113543995052796 F-TEST (value) 11.6312682601134 F-TEST (DF numerator) 1 F-TEST (DF denominator) 82 p-value 0.00100870697995736 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1720.29808344907 Sum Squared Residuals 242672890.665319 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11554.5 14280.4201063718 -2725.92010637183 2 13182.1 14138.7719548930 -956.67195489305 3 14800.1 13964.3247562982 835.775243701831 4 12150.7 13874.2804389487 -1723.58043894874 5 14478.2 13755.6572185455 722.542781454517 6 13253.9 13702.8507526885 -448.950752688548 7 12036.8 14336.2659505948 -2299.46595059485 8 12653.2 14731.3195400927 -2078.11954009267 9 14035.4 14201.002680288 -165.602680288013 10 14571.4 13984.6601655102 586.739834489842 11 15400.9 13810.3004310409 1590.59956895908 12 14283.2 13830.8763665984 452.323633401574 13 14485.3 13907.6261368501 577.673863149877 14 14196.3 13819.0468436052 377.253156394782 15 15559.1 13741.7941546311 1817.30584536893 16 13767.4 13703.1787431597 64.2212568402906 17 14634 13633.7322273992 1000.2677726008 18 14381.1 13634.51940453 746.580595470013 19 12509.9 14268.7873776613 -1758.88737766130 20 12122.3 14598.4833992724 -2476.18339927243 21 13122.3 14494.1168313490 -1371.81683134897 22 13908.7 14367.5343755122 -458.834375512203 23 13456.5 14175.2663613176 -718.766361317572 24 12441.6 14267.7815402164 -1826.18154021641 25 12953 14302.1986736569 -1349.19867365691 26 13057.2 14230.8279471323 -1173.62794713226 27 14350.1 14168.8377480828 181.262251917185 28 13830.2 14043.1299335025 -212.929933502473 29 13755.5 13972.6775802971 -217.177580297072 30 13574.4 13992.4007406296 -418.000740629559 31 12802.6 14853.3975934588 -2050.79759345883 32 11737.3 15058.0199154005 -3320.71991540053 33 13850.2 14760.6856202773 -910.485620277297 34 15081.8 14614.2488079196 467.551192080423 35 13653.3 14563.9569356749 -910.656935674876 36 14019.1 14609.5913432291 -590.491343229088 37 13962 14687.6968074283 -725.69680742825 38 13768.7 14586.9600007190 -818.260000718973 39 14747.1 14505.1154451486 241.984554851424 40 13858.1 14490.0934815694 -631.993481569398 41 13188 14409.1017012240 -1221.10170122402 42 13693.1 14529.4086060459 -836.308606045907 43 12970 15426.7905351427 -2456.79053514265 44 11392.8 15665.2614737082 -4272.46147370818 45 13985.2 15310.3757839119 -1325.17578391187 46 14994.7 15105.0974810279 -110.397481027853 47 13584.7 14904.3673126773 -1319.66731267727 48 14257.8 15002.9612483083 -745.161248308291 49 13553.4 15069.5651799854 -1516.16517998540 50 14007.3 15011.6639288098 -1004.36392880977 51 16535.8 14921.5540133661 1614.24598663389 52 14721.4 14803.0401231199 -81.6401231199005 53 13664.6 14719.7742755078 -1055.17427550780 54 16805.9 14794.7528972152 2011.14710278477 55 13829.4 15621.5075448553 -1792.10754485529 56 13735.6 16033.2667823509 -2297.66678235092 57 15870.5 15661.9159709023 208.584029097668 58 15962.4 15686.7557825849 275.644217415067 59 15744.1 15448.7440306790 295.355969320966 60 16083.7 15528.3801170769 555.319882923054 61 14863.9 15485.2165710721 -621.316571072148 62 15533.1 15378.3135435050 154.786456494958 63 17473.1 15308.998223933 2164.101776067 64 15925.5 15188.6038549855 736.896145014532 65 15573.7 15089.4195365064 484.280463493647 66 17495 15139.9300690652 2355.06993093484 67 14155.8 15948.8420350741 -1793.04203507406 68 14913.9 16154.3608643036 -1240.46086430359 69 17250.4 15777.8059373793 1472.59406262075 70 15879.8 15613.5920414846 266.207958515401 71 17647.8 15354.0641146705 2293.73588532947 72 17749.9 15410.0411550820 2339.85884491798 73 17111.8 15136.1035135683 1975.69648643172 74 16934.8 15136.8906906991 1797.90930930093 75 20280 14990.3226821529 5289.67731784712 76 16238.2 15037.5751760315 1200.62482396851 77 17896.1 14958.2233480419 2937.87665195808 78 18089.3 14965.1111479363 3124.1888520637 79 15660 15590.2828520007 69.7171479992499 80 16162.4 15603.1400784703 559.259921529735 81 17850.1 15383.2552666039 2466.84473339613 82 18520.4 14906.9037723209 3613.49622767909 83 18524.7 14669.1544127919 3855.54558720806 84 16843.7 14596.4279923198 2247.27200768018 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.368948764449247 0.737897528898495 0.631051235550753 6 0.261620332472267 0.523240664944534 0.738379667527733 7 0.154497907836687 0.308995815673375 0.845502092163313 8 0.125424065898045 0.250848131796089 0.874575934101955 9 0.101029093524877 0.202058187049755 0.898970906475123 10 0.0849549478672675 0.169909895734535 0.915045052132732 11 0.0911316196933064 0.182263239386613 0.908868380306694 12 0.0543998155143366 0.108799631028673 0.945600184485663 13 0.0340893221284545 0.0681786442569089 0.965910677871545 14 0.0185466054427418 0.0370932108854835 0.981453394557258 15 0.0166247296215336 0.0332494592430672 0.983375270378466 16 0.0106200202438118 0.0212400404876236 0.989379979756188 17 0.00566486809614084 0.0113297361922817 0.99433513190386 18 0.00296579714161133 0.00593159428322265 0.997034202858389 19 0.00178842957981642 0.00357685915963284 0.998211570420184 20 0.00105165203266030 0.00210330406532060 0.99894834796734 21 0.000634884822970905 0.00126976964594181 0.99936511517703 22 0.000477772039778769 0.000955544079557537 0.999522227960221 23 0.000228693802714116 0.000457387605428232 0.999771306197286 24 0.000158530574959603 0.000317061149919207 0.99984146942504 25 8.10193440992273e-05 0.000162038688198455 0.9999189806559 26 4.07536771759618e-05 8.15073543519236e-05 0.999959246322824 27 2.99827972827524e-05 5.99655945655047e-05 0.999970017202717 28 1.35620895968121e-05 2.71241791936243e-05 0.999986437910403 29 6.01379645653018e-06 1.20275929130604e-05 0.999993986203544 30 2.79352468054690e-06 5.58704936109381e-06 0.99999720647532 31 2.54323904516006e-06 5.08647809032012e-06 0.999997456760955 32 2.56633217247007e-06 5.13266434494014e-06 0.999997433667828 33 4.63878272254273e-06 9.27756544508546e-06 0.999995361217278 34 2.54127187642527e-05 5.08254375285053e-05 0.999974587281236 35 1.81297408992561e-05 3.62594817985122e-05 0.9999818702591 36 1.60000599984670e-05 3.20001199969339e-05 0.999983999940002 37 1.43141900879515e-05 2.8628380175903e-05 0.999985685809912 38 1.09566436415800e-05 2.19132872831601e-05 0.999989043356358 39 1.37422945245421e-05 2.74845890490841e-05 0.999986257705475 40 1.10798445726165e-05 2.2159689145233e-05 0.999988920155427 41 1.42840390632135e-05 2.8568078126427e-05 0.999985715960937 42 1.80269810085475e-05 3.60539620170949e-05 0.999981973018991 43 2.14153951718114e-05 4.28307903436228e-05 0.999978584604828 44 8.93547760008823e-05 0.000178709552001765 0.999910645224 45 0.000161426045944012 0.000322852091888024 0.999838573954056 46 0.000406550688378832 0.000813101376757663 0.999593449311621 47 0.000725364171506076 0.00145072834301215 0.999274635828494 48 0.00117808818925577 0.00235617637851154 0.998821911810744 49 0.00256899505418822 0.00513799010837644 0.997431004945812 50 0.00561989909278165 0.0112397981855633 0.994380100907218 51 0.0230269892963938 0.0460539785927875 0.976973010703606 52 0.0456533316938947 0.0913066633877893 0.954346668306105 53 0.264903719166368 0.529807438332735 0.735096280833632 54 0.448256108661023 0.896512217322047 0.551743891338977 55 0.536521110966516 0.926957778066967 0.463478889033484 56 0.532441042943978 0.935117914112043 0.467558957056022 57 0.558339011703674 0.883321976592653 0.441660988296326 58 0.56671094837165 0.8665781032567 0.43328905162835 59 0.5660850993607 0.8678298012786 0.4339149006393 60 0.555202804799044 0.889594390401912 0.444797195200956 61 0.588614169977937 0.822771660044125 0.411385830022063 62 0.601216267193847 0.797567465612306 0.398783732806153 63 0.659790553555775 0.680418892888451 0.340209446444225 64 0.67842829433498 0.64314341133004 0.32157170566502 65 0.770232145824056 0.459535708351888 0.229767854175944 66 0.7764529045484 0.447094190903199 0.223547095451599 67 0.810907429973651 0.378185140052698 0.189092570026349 68 0.771934256324262 0.456131487351476 0.228065743675738 69 0.772263187934954 0.455473624130093 0.227736812065047 70 0.731252621910795 0.53749475617841 0.268747378089205 71 0.713531459318741 0.572937081362518 0.286468540681259 72 0.704529052952395 0.59094189409521 0.295470947047605 73 0.644771198866965 0.71045760226607 0.355228801133035 74 0.578257867962317 0.843484264075367 0.421742132037683 75 0.915103238658292 0.169793522683415 0.0848967613417077 76 0.916398710416409 0.167202579167183 0.0836012895835914 77 0.860563518798332 0.278872962403336 0.139436481201668 78 0.786220698066418 0.427558603867164 0.213779301933582 79 0.722731369121343 0.554537261757314 0.277268630878657 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 32 0.426666666666667 NOK 5% type I error level 38 0.506666666666667 NOK 10% type I error level 40 0.533333333333333 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/100jnf1229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/100jnf1229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/1sauh1229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/1sauh1229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/2o1hb1229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/2o1hb1229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/3edx11229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/3edx11229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/4h1d51229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/4h1d51229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/5nno81229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/5nno81229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/65cgk1229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/65cgk1229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/7ay3n1229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/7ay3n1229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/8f0w01229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/8f0w01229261596.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/9s6861229261596.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229261711k62h2k3mcim0a3x/9s6861229261596.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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