iraq

*Unverified author*

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 06:02:33 -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/t1227445391sm021h6rqr1k1hi.htm/, Retrieved Sun, 23 Nov 2008 13:03:11 +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/t1227445391sm021h6rqr1k1hi.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

31,54 0 32,43 0 26,54 0 25,85 0 27,6 0 25,71 0 25,38 0 28,57 0 27,64 0 25,36 0 25,9 0 26,29 0 21,74 0 19,2 0 19,32 0 19,82 0 20,36 0 24,31 0 25,97 0 25,61 0 24,67 0 25,59 0 26,09 0 28,37 0 27,34 0 24,46 0 27,46 0 30,23 0 32,33 0 29,87 1 24,87 1 25,48 1 27,28 1 28,24 1 29,58 1 26,95 1 29,08 1 28,76 1 29,59 1 30,7 1 30,52 1 32,67 1 33,19 1 37,13 1 35,54 1 37,75 1 41,84 1 42,94 1 49,14 1 44,61 1 40,22 1 44,23 1 45,85 1 53,38 1 53,26 1 51,8 1 55,3 1 57,81 1 63,96 1 63,77 1 59,15 1 56,12 1 57,42 1 63,52 1 61,71 1 63,01 1 68,18 1 72,03 1 69,75 1 74,41 1 74,33 1 64,24 1 60,03 1 59,44 1 62,5 1 55,04 1 58,34 1 61,92 1 67,65 1 67,68 1 70,3 1 75,26 1 71,44 1 76,36 1 81,71 1 92,6 1 90,6 1 92,23 1 94,09 1 102,79 1 109,65 1 124,05 1 132,69 1 135,81 1 116,07 1 101,42 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation bel20[t] = + 6.92128195374315 -22.5174619598296iraq[t] + 1.34707576756611M1[t] -0.0974022790288692M2[t] -1.77438032562385M3[t] -1.95885837221883M4[t] -1.99208641881382M5[t] + 2.4993682795699M6[t] + 3.12989023297492M7[t] + 4.97416218637994M8[t] + 5.14593413978495M9[t] + 6.09770609318998M10[t] + 3.53947804659499M11[t] + 1.18072804659498t + 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) 6.92128195374315 4.453069 1.5543 0.123971 0.061985 iraq -22.5174619598296 4.042953 -5.5696 0 0 M1 1.34707576756611 5.493535 0.2452 0.806906 0.403453 M2 -0.0974022790288692 5.489867 -0.0177 0.985888 0.492944 M3 -1.77438032562385 5.487013 -0.3234 0.747232 0.373616 M4 -1.95885837221883 5.484973 -0.3571 0.721911 0.360955 M5 -1.99208641881382 5.483749 -0.3633 0.717337 0.358669 M6 2.4993682795699 5.489437 0.4553 0.650093 0.325047 M7 3.12989023297492 5.48495 0.5706 0.569809 0.284905 M8 4.97416218637994 5.481276 0.9075 0.366811 0.183405 M9 5.14593413978495 5.478417 0.9393 0.35033 0.175165 M10 6.09770609318998 5.476374 1.1135 0.268766 0.134383 M11 3.53947804659499 5.475148 0.6465 0.519785 0.259893 t 1.18072804659498 0.066905 17.6479 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.929821421679229 R-squared 0.864567876213582 Adjusted R-squared 0.843096929759638 F-TEST (value) 40.2668731007319 F-TEST (DF numerator) 13 F-TEST (DF denominator) 82 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.9494785986818 Sum Squared Residuals 9831.06868980524 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 31.54 9.4490857679042 22.0909142320958 2 32.43 9.18533576790423 23.2446642320958 3 26.54 8.68908576790424 17.8509142320958 4 25.85 9.68533576790423 16.1646642320958 5 27.6 10.8328357679042 16.7671642320958 6 25.71 16.5050185128829 9.20498148711706 7 25.38 18.3162685128829 7.06373148711706 8 28.57 21.3412685128829 7.22873148711708 9 27.64 22.6937685128829 4.94623148711706 10 25.36 24.8262685128829 0.53373148711706 11 25.9 23.4487685128829 2.45123148711706 12 26.29 21.0900185128829 5.19998148711707 13 21.74 23.6178223270440 -1.87782232704403 14 19.2 23.354072327044 -4.154072327044 15 19.32 22.857822327044 -3.53782232704402 16 19.82 23.8540723270440 -4.03407232704402 17 20.36 25.0015723270440 -4.64157232704403 18 24.31 30.6737550720227 -6.36375507202273 19 25.97 32.4850050720227 -6.51500507202272 20 25.61 35.5100050720227 -9.90000507202273 21 24.67 36.8625050720227 -12.1925050720227 22 25.59 38.9950050720227 -13.4050050720227 23 26.09 37.6175050720227 -11.5275050720227 24 28.37 35.2587550720227 -6.88875507202272 25 27.34 37.7865588861838 -10.4465588861838 26 24.46 37.5228088861838 -13.0628088861838 27 27.46 37.0265588861838 -9.5665588861838 28 30.23 38.0228088861838 -7.7928088861838 29 32.33 39.1703088861838 -6.84030888618382 30 29.87 22.3250296713329 7.54497032866708 31 24.87 24.1362796713329 0.733720328667068 32 25.48 27.1612796713329 -1.68127967133293 33 27.28 28.5137796713329 -1.23377967133292 34 28.24 30.6462796713329 -2.40627967133293 35 29.58 29.2687796713329 0.311220328667074 36 26.95 26.9100296713329 0.0399703286670782 37 29.08 29.437833485494 -0.357833485494021 38 28.76 29.174083485494 -0.414083485494008 39 29.59 28.677833485494 0.912166514505983 40 30.7 29.674083485494 1.02591651450598 41 30.52 30.821583485494 -0.301583485494022 42 32.67 36.4937662304727 -3.82376623047272 43 33.19 38.3050162304727 -5.11501623047272 44 37.13 41.3300162304727 -4.20001623047271 45 35.54 42.6825162304727 -7.14251623047271 46 37.75 44.8150162304727 -7.06501623047272 47 41.84 43.4375162304727 -1.59751623047271 48 42.94 41.0787662304727 1.86123376952729 49 49.14 43.6065700446338 5.53342995536621 50 44.61 43.3428200446338 1.26717995536619 51 40.22 42.8465700446338 -2.62657004463380 52 44.23 43.8428200446338 0.387179955366197 53 45.85 44.9903200446338 0.859679955366204 54 53.38 50.6625027896125 2.7174972103875 55 53.26 52.4737527896125 0.786247210387504 56 51.8 55.4987527896125 -3.6987527896125 57 55.3 56.8512527896125 -1.5512527896125 58 57.81 58.9837527896125 -1.1737527896125 59 63.96 57.6062527896125 6.3537472103875 60 63.77 55.2475027896125 8.52249721038751 61 59.15 57.7753066037736 1.37469339622641 62 56.12 57.5115566037736 -1.39155660377359 63 57.42 57.0153066037736 0.40469339622642 64 63.52 58.0115566037736 5.50844339622641 65 61.71 59.1590566037736 2.55094339622641 66 63.01 64.8312393487523 -1.82123934875229 67 68.18 66.6424893487523 1.53751065124772 68 72.03 69.6674893487523 2.36251065124771 69 69.75 71.0199893487523 -1.26998934875229 70 74.41 73.1524893487523 1.25751065124771 71 74.33 71.7749893487523 2.55501065124772 72 64.24 69.4162393487523 -5.17623934875228 73 60.03 71.9440431629134 -11.9140431629134 74 59.44 71.6802931629134 -12.2402931629134 75 62.5 71.1840431629134 -8.68404316291337 76 55.04 72.1802931629134 -17.1402931629134 77 58.34 73.3277931629134 -14.9877931629134 78 61.92 78.999975907892 -17.0799759078921 79 67.65 80.811225907892 -13.1612259078921 80 67.68 83.836225907892 -16.1562259078921 81 70.3 85.188725907892 -14.8887259078921 82 75.26 87.321225907892 -12.0612259078921 83 71.44 85.943725907892 -14.5037259078921 84 76.36 83.584975907892 -7.22497590789206 85 81.71 86.1127797220532 -4.40277972205317 86 92.6 85.8490297220532 6.75097027794683 87 90.6 85.3527797220532 5.24722027794684 88 92.23 86.3490297220532 5.88097027794683 89 94.09 87.4965297220532 6.59347027794685 90 102.79 93.1687124670319 9.62128753296816 91 109.65 94.9799624670319 14.6700375329681 92 124.05 98.0049624670318 26.0450375329681 93 132.69 99.3574624670318 33.3325375329682 94 135.81 101.489962467032 34.3200375329682 95 116.07 100.112462467032 15.9575375329681 96 101.42 97.7537124670318 3.66628753296815 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0183103297826401 0.0366206595652802 0.98168967021736 18 0.0178437245400979 0.0356874490801959 0.982156275459902 19 0.0164439870928887 0.0328879741857774 0.983556012907111 20 0.00628275584363512 0.0125655116872702 0.993717244156365 21 0.00224725550997857 0.00449451101995713 0.997752744490021 22 0.00128937539632503 0.00257875079265006 0.998710624603675 23 0.000649594505629407 0.00129918901125881 0.99935040549437 24 0.000433481970021106 0.000866963940042212 0.999566518029979 25 0.000318678038221965 0.00063735607644393 0.999681321961778 26 0.000128486701242323 0.000256973402484647 0.999871513298758 27 0.000163716206039616 0.000327432412079233 0.99983628379396 28 0.000283304510152646 0.000566609020305292 0.999716695489847 29 0.000374912334474946 0.000749824668949891 0.999625087665525 30 0.000173607961418002 0.000347215922836003 0.999826392038582 31 8.65048609462803e-05 0.000173009721892561 0.999913495139054 32 3.74417524820012e-05 7.48835049640023e-05 0.999962558247518 33 1.38228972525549e-05 2.76457945051099e-05 0.999986177102747 34 5.15944222973537e-06 1.03188844594707e-05 0.99999484055777 35 1.94620932926184e-06 3.89241865852368e-06 0.99999805379067 36 7.13973452900942e-07 1.42794690580188e-06 0.999999286026547 37 2.49210648859984e-07 4.98421297719969e-07 0.999999750789351 38 8.928744189494e-08 1.7857488378988e-07 0.999999910712558 39 3.83297253176259e-08 7.66594506352518e-08 0.999999961670275 40 1.61528928875455e-08 3.23057857750910e-08 0.999999983847107 41 5.37706908323729e-09 1.07541381664746e-08 0.99999999462293 42 2.52289247291354e-09 5.04578494582709e-09 0.999999997477108 43 1.59525737525935e-09 3.19051475051870e-09 0.999999998404743 44 1.99803412345418e-09 3.99606824690837e-09 0.999999998001966 45 1.36164971071193e-09 2.72329942142386e-09 0.99999999863835 46 1.62721572476737e-09 3.25443144953474e-09 0.999999998372784 47 3.98613805317228e-09 7.97227610634456e-09 0.999999996013862 48 1.01078285179238e-08 2.02156570358476e-08 0.999999989892172 49 1.21872354161731e-07 2.43744708323463e-07 0.999999878127646 50 1.98048036798832e-07 3.96096073597665e-07 0.999999801951963 51 1.18301787673869e-07 2.36603575347738e-07 0.999999881698212 52 1.15119579204046e-07 2.30239158408092e-07 0.99999988488042 53 1.12718928095667e-07 2.25437856191334e-07 0.999999887281072 54 4.69524897573204e-07 9.39049795146408e-07 0.999999530475102 55 1.08378131853528e-06 2.16756263707056e-06 0.999998916218681 56 9.40743788536571e-07 1.88148757707314e-06 0.999999059256211 57 1.26384173687125e-06 2.52768347374251e-06 0.999998736158263 58 1.86823747219201e-06 3.73647494438402e-06 0.999998131762528 59 6.5012481727394e-06 1.30024963454788e-05 0.999993498751827 60 2.69267301958462e-05 5.38534603916925e-05 0.999973073269804 61 3.11350613607116e-05 6.22701227214232e-05 0.99996886493864 62 2.19871732840851e-05 4.39743465681703e-05 0.999978012826716 63 1.90098957834388e-05 3.80197915668776e-05 0.999980990104217 64 5.2151197863072e-05 0.000104302395726144 0.999947848802137 65 9.10415883777525e-05 0.000182083176755505 0.999908958411622 66 0.000111925280372277 0.000223850560744553 0.999888074719628 67 0.000205189355876699 0.000410378711753398 0.999794810644123 68 0.000388664581333831 0.000777329162667661 0.999611335418666 69 0.000368102455896205 0.00073620491179241 0.999631897544104 70 0.000509166829494982 0.00101833365898996 0.999490833170505 71 0.00501518793862391 0.0100303758772478 0.994984812061376 72 0.0705060978702065 0.141012195740413 0.929493902129794 73 0.122044541761110 0.244089083522221 0.87795545823889 74 0.103906128754296 0.207812257508592 0.896093871245704 75 0.128446346014006 0.256892692028011 0.871553653985994 76 0.108616701555704 0.217233403111407 0.891383298444296 77 0.0963814453998803 0.192762890799761 0.90361855460012 78 0.0647495618993709 0.129499123798742 0.93525043810063 79 0.0368150147923399 0.0736300295846798 0.96318498520766 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 50 0.793650793650794 NOK 5% type I error level 55 0.873015873015873 NOK 10% type I error level 56 0.888888888888889 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/106vp51227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/106vp51227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/1odco1227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/1odco1227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/2j5fj1227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/2j5fj1227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/3i1os1227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/3i1os1227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/4xh691227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/4xh691227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/5yxan1227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/5yxan1227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/6ygz41227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/6ygz41227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/72z7n1227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/72z7n1227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/8z0pa1227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/8z0pa1227445340.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/9cobq1227445340.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227445391sm021h6rqr1k1hi/9cobq1227445340.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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