vraag2

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

Title produced by software: Multiple Regression

Date of computation: Wed, 07 May 2008 06:48:55 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/07/t1210164628656pvbxjt0l5s3f.htm/, Retrieved Wed, 07 May 2008 14:50:41 +0200

User-defined keywords:

s0171333

Dataseries X:

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112 118 132 129 121 135 148 148 136 119 104 118 115 126 141 135 125 149 170 170 158 133 114 140 145 150 178 163 172 178 199 199 184 162 146 166 171 180 193 181 183 218 230 242 209 191 172 194 196 196 236 235 229 243 264 272 237 211 180 201 204 188 235 227 234 264 302 293 259 229 203 229 242 233 267 269 270 315 364 347 312 274 237 278 284 277 317 313 318 374 413 405 355 306 271 306 315 301 356 348 355 422 465 467 404 347 305 336 340 318 362 348 363 435 491 505 404 359 310 337 360 342 406 396 420 472 548 559 463 407 362 405 417 391 419 461 472 535 622 606 508 461 390 432

Text written by user:

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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Sales[t] = + 54.3276515151515 + 9.1802884615385M1[t] -0.230040792540811M2[t] + 32.2762966200467M3[t] + 26.5326340326341M4[t] + 28.6223047785547M5[t] + 65.7953088578089M6[t] + 102.801646270396M7[t] + 99.891317016317M8[t] + 48.5643210955711M9[t] + 10.0706585081584M10[t] -26.3396707459207M11[t] + 2.66032925407925t + 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) 54.3276515151515 8.651184 6.2798 0 0 M1 9.1802884615385 10.765061 0.8528 0.395335 0.197667 M2 -0.230040792540811 10.762324 -0.0214 0.982979 0.49149 M3 32.2762966200467 10.759848 2.9997 0.003236 0.001618 M4 26.5326340326341 10.757631 2.4664 0.01494 0.00747 M5 28.6223047785547 10.755675 2.6611 0.008762 0.004381 M6 65.7953088578089 10.753979 6.1182 0 0 M7 102.801646270396 10.752544 9.5607 0 0 M8 99.891317016317 10.75137 9.291 0 0 M9 48.5643210955711 10.750456 4.5174 1.4e-05 7e-06 M10 10.0706585081584 10.749804 0.9368 0.350574 0.175287 M11 -26.3396707459207 10.749413 -2.4503 0.015592 0.007796 t 2.66032925407925 0.052968 50.225 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.977686415545304 R-squared 0.955870727141825 Adjusted R-squared 0.951828351002145 F-TEST (value) 236.462588861774 F-TEST (DF numerator) 12 F-TEST (DF denominator) 131 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 26.3302560527953 Sum Squared Residuals 90819.9922785548 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 112 66.1682692307692 45.8317307692308 2 118 59.4182692307695 58.5817307692305 3 132 94.5849358974359 37.4150641025641 4 129 91.5016025641026 37.4983974358974 5 121 96.251602564103 24.7483974358971 6 135 136.084935897436 -1.08493589743575 7 148 175.751602564103 -27.7516025641026 8 148 175.501602564103 -27.5016025641027 9 136 126.834935897436 9.16506410256397 10 119 91.0016025641026 27.9983974358974 11 104 57.2516025641024 46.7483974358976 12 118 86.2516025641026 31.7483974358974 13 115 98.0922202797204 16.9077797202796 14 126 91.3422202797202 34.6577797202798 15 141 126.508886946387 14.4911130536130 16 135 123.425553613054 11.5744463869464 17 125 128.175553613054 -3.17555361305357 18 149 168.008886946387 -19.0088869463869 19 170 207.675553613054 -37.6755536130536 20 170 207.425553613054 -37.4255536130536 21 158 158.758886946387 -0.758886946386897 22 133 122.925553613054 10.0744463869464 23 114 89.1755536130536 24.8244463869464 24 140 118.175553613054 21.8244463869464 25 145 130.016171328671 14.9838286713288 26 150 123.266171328671 26.7338286713287 27 178 158.432837995338 19.567162004662 28 163 155.349504662005 7.65049533799537 29 172 160.099504662005 11.9004953379954 30 178 199.932837995338 -21.932837995338 31 199 239.599504662005 -40.5995046620046 32 199 239.349504662005 -40.3495046620047 33 184 190.682837995338 -6.68283799533796 34 162 154.849504662005 7.15049533799535 35 146 121.099504662005 24.9004953379953 36 166 150.099504662005 15.9004953379953 37 171 161.940122377622 9.05987762237771 38 180 155.190122377622 24.8098776223777 39 193 190.356789044289 2.64321095571093 40 181 187.273455710956 -6.2734557109557 41 183 192.023455710956 -9.02345571095565 42 218 231.856789044289 -13.8567890442891 43 230 271.523455710956 -41.5234557109557 44 242 271.273455710956 -29.2734557109557 45 209 222.606789044289 -13.6067890442890 46 191 186.773455710956 4.22654428904431 47 172 153.023455710956 18.9765442890443 48 194 182.023455710956 11.9765442890443 49 196 193.864073426573 2.13592657342668 50 196 187.114073426573 8.88592657342664 51 236 222.28074009324 13.7192599067599 52 235 219.197406759907 15.8025932400933 53 229 223.947406759907 5.05259324009328 54 243 263.78074009324 -20.7807400932401 55 264 303.447406759907 -39.4474067599067 56 272 303.197406759907 -31.1974067599068 57 237 254.53074009324 -17.5307400932401 58 211 218.697406759907 -7.69740675990677 59 180 184.947406759907 -4.94740675990677 60 201 213.947406759907 -12.9474067599068 61 204 225.788024475524 -21.7880244755244 62 188 219.038024475524 -31.0380244755244 63 235 254.204691142191 -19.2046911421911 64 227 251.121357808858 -24.1213578088578 65 234 255.871357808858 -21.8713578088578 66 264 295.704691142191 -31.7046911421912 67 302 335.371357808858 -33.3713578088578 68 293 335.121357808858 -42.1213578088578 69 259 286.454691142191 -27.4546911421911 70 229 250.621357808858 -21.6213578088578 71 203 216.871357808858 -13.8713578088578 72 229 245.871357808858 -16.8713578088579 73 242 257.711975524475 -15.7119755244755 74 233 250.961975524476 -17.9619755244755 75 267 286.128642191142 -19.1286421911422 76 269 283.045308857809 -14.0453088578088 77 270 287.795308857809 -17.7953088578088 78 315 327.628642191142 -12.6286421911422 79 364 367.295308857809 -3.29530885780887 80 347 367.045308857809 -20.0453088578089 81 312 318.378642191142 -6.3786421911422 82 274 282.545308857809 -8.54530885780889 83 237 248.795308857809 -11.7953088578089 84 278 277.795308857809 0.204691142191113 85 284 289.635926573427 -5.63592657342652 86 277 282.885926573427 -5.88592657342655 87 317 318.052593240093 -1.05259324009325 88 313 314.96925990676 -1.96925990675991 89 318 319.71925990676 -1.71925990675988 90 374 359.552593240093 14.4474067599067 91 413 399.21925990676 13.7807400932401 92 405 398.96925990676 6.03074009324011 93 355 350.302593240093 4.69740675990674 94 306 314.46925990676 -8.46925990675994 95 271 280.71925990676 -9.71925990675992 96 306 309.71925990676 -3.71925990675993 97 315 321.559877622378 -6.55987762237756 98 301 314.809877622378 -13.8098776223776 99 356 349.976544289044 6.02345571095569 100 348 346.893210955711 1.10678904428908 101 355 351.643210955711 3.35678904428909 102 422 391.476544289044 30.5234557109557 103 465 431.143210955711 33.8567890442890 104 467 430.893210955711 36.1067890442891 105 404 382.226544289044 21.7734557109557 106 347 346.393210955711 0.606789044289013 107 305 312.643210955711 -7.64321095571099 108 336 341.643210955711 -5.64321095571105 109 340 353.483828671329 -13.4838286713286 110 318 346.733828671329 -28.7338286713287 111 362 381.900495337995 -19.9004953379954 112 348 378.817162004662 -30.817162004662 113 363 383.567162004662 -20.567162004662 114 435 423.400495337995 11.5995046620046 115 491 463.067162004662 27.932837995338 116 505 462.817162004662 42.182837995338 117 404 414.150495337995 -10.1504953379953 118 359 378.317162004662 -19.317162004662 119 310 344.567162004662 -34.567162004662 120 337 373.567162004662 -36.5671620046621 121 360 385.407779720280 -25.4077797202797 122 342 378.657779720280 -36.6577797202797 123 406 413.824446386946 -7.82444638694645 124 396 410.741113053613 -14.7411130536131 125 420 415.491113053613 4.50888694638699 126 472 455.324446386946 16.6755536130536 127 548 494.991113053613 53.008886946387 128 559 494.741113053613 64.258886946387 129 463 446.074446386946 16.9255536130537 130 407 410.241113053613 -3.241113053613 131 362 376.491113053613 -14.4911130536131 132 405 405.491113053613 -0.491113053613119 133 417 417.331730769231 -0.331730769230757 134 391 410.581730769231 -19.5817307692308 135 419 445.748397435898 -26.7483974358975 136 461 442.665064102564 18.3349358974359 137 472 447.415064102564 24.5849358974359 138 535 487.248397435897 47.7516025641025 139 622 526.915064102564 95.084935897436 140 606 526.665064102564 79.3349358974359 141 508 477.998397435897 30.0016025641026 142 461 442.165064102564 18.8349358974360 143 390 408.415064102564 -18.4150641025641 144 432 437.415064102564 -5.4150641025641 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000623424516860495 0.00124684903372099 0.99937657548314 17 5.92474885728363e-05 0.000118494977145673 0.999940752511427 18 5.91990749950453e-05 0.000118398149990091 0.999940800925005 19 0.00021409097886463 0.00042818195772926 0.999785909021135 20 0.000160428406814852 0.000320856813629703 0.999839571593185 21 7.88328030282993e-05 0.000157665606056599 0.999921167196972 22 1.73775560168854e-05 3.47551120337708e-05 0.999982622443983 23 4.07407005744719e-06 8.14814011489437e-06 0.999995925929943 24 2.00695191234328e-06 4.01390382468656e-06 0.999997993048088 25 1.61735263675228e-06 3.23470527350457e-06 0.999998382647363 26 6.20003378279162e-07 1.24000675655832e-06 0.999999379996622 27 2.21029678934956e-06 4.42059357869912e-06 0.99999778970321 28 7.57101273995387e-07 1.51420254799077e-06 0.999999242898726 29 4.12624509153263e-06 8.25249018306526e-06 0.999995873754908 30 1.65263699840193e-06 3.30527399680386e-06 0.999998347363002 31 1.04796458651111e-06 2.09592917302221e-06 0.999998952035414 32 6.27709909227118e-07 1.25541981845424e-06 0.99999937229009 33 2.29028489166292e-07 4.58056978332584e-07 0.999999770971511 34 8.36302512198077e-08 1.67260502439615e-07 0.999999916369749 35 4.26841516862576e-08 8.53683033725153e-08 0.999999957315848 36 1.87241729002386e-08 3.74483458004773e-08 0.999999981275827 37 7.44648368208907e-09 1.48929673641781e-08 0.999999992553516 38 5.1347787837729e-09 1.02695575675458e-08 0.99999999486522 39 1.85337224127325e-09 3.70674448254651e-09 0.999999998146628 40 7.17949180351111e-10 1.43589836070222e-09 0.99999999928205 41 2.13118063661641e-10 4.26236127323282e-10 0.999999999786882 42 9.83982099014542e-10 1.96796419802908e-09 0.999999999016018 43 1.27746991227612e-09 2.55493982455225e-09 0.99999999872253 44 1.01097168745018e-08 2.02194337490035e-08 0.999999989890283 45 3.61532035277831e-09 7.23064070555661e-09 0.99999999638468 46 1.74727529154706e-09 3.49455058309412e-09 0.999999998252725 47 1.37151594103750e-09 2.74303188207499e-09 0.999999998628484 48 9.18052609309558e-10 1.83610521861912e-09 0.999999999081947 49 4.56468423030908e-10 9.12936846061817e-10 0.999999999543532 50 5.5717206076556e-10 1.11434412153112e-09 0.999999999442828 51 1.62853723822144e-09 3.25707447644288e-09 0.999999998371463 52 1.63291837765581e-08 3.26583675531161e-08 0.999999983670816 53 3.31656482964461e-08 6.63312965928923e-08 0.999999966834352 54 2.1179762248791e-08 4.2359524497582e-08 0.999999978820238 55 4.64347021194912e-08 9.28694042389823e-08 0.999999953565298 56 1.19269164238994e-07 2.38538328477989e-07 0.999999880730836 57 5.27295843789275e-08 1.05459168757855e-07 0.999999947270416 58 3.09919596729374e-08 6.19839193458749e-08 0.99999996900804 59 9.05124271801159e-08 1.81024854360232e-07 0.999999909487573 60 1.46704020663494e-07 2.93408041326988e-07 0.99999985329598 61 2.60192394798681e-07 5.20384789597362e-07 0.999999739807605 62 1.02562126228308e-05 2.05124252456615e-05 0.999989743787377 63 8.84343285793995e-06 1.76868657158799e-05 0.999991156567142 64 6.9799745915434e-06 1.39599491830868e-05 0.999993020025408 65 3.75520128648219e-06 7.51040257296439e-06 0.999996244798714 66 3.58794286196063e-06 7.17588572392127e-06 0.999996412057138 67 3.98505942922163e-05 7.97011885844326e-05 0.999960149405708 68 0.000253990816176399 0.000507981632352797 0.999746009183824 69 0.000203850648101464 0.000407701296202928 0.999796149351899 70 0.000135060333262058 0.000270120666524115 0.999864939666738 71 0.000129794143870217 0.000259588287740435 0.99987020585613 72 8.6837036137072e-05 0.000173674072274144 0.999913162963863 73 5.27869341533148e-05 0.000105573868306630 0.999947213065847 74 4.60440740596932e-05 9.20881481193863e-05 0.99995395592594 75 2.71866512556183e-05 5.43733025112367e-05 0.999972813348744 76 1.71879978571726e-05 3.43759957143451e-05 0.999982812002143 77 1.12069497913708e-05 2.24138995827416e-05 0.999988793050209 78 5.92178378909915e-05 0.000118435675781983 0.99994078216211 79 0.00496087988184555 0.0099217597636911 0.995039120118154 80 0.0463530307769774 0.0927060615539548 0.953646969223023 81 0.0523261588452933 0.104652317690587 0.947673841154707 82 0.0418521927249195 0.083704385449839 0.95814780727508 83 0.0356626775837008 0.0713253551674016 0.9643373224163 84 0.0365116974786609 0.0730233949573218 0.96348830252134 85 0.0327281658274845 0.065456331654969 0.967271834172515 86 0.0380367330852738 0.0760734661705476 0.961963266914726 87 0.0423088169567442 0.0846176339134884 0.957691183043256 88 0.0430629196340173 0.0861258392680346 0.956937080365983 89 0.0429046682240021 0.0858093364480042 0.957095331775998 90 0.105791316121280 0.211582632242561 0.89420868387872 91 0.320442259938863 0.640884519877727 0.679557740061137 92 0.598537732006683 0.802924535986635 0.401462267993317 93 0.588908529655842 0.822182940688316 0.411091470344158 94 0.534856289994265 0.93028742001147 0.465143710005735 95 0.522860580240937 0.954278839518126 0.477139419759063 96 0.513356036171479 0.973287927657042 0.486643963828521 97 0.489041515338985 0.97808303067797 0.510958484661015 98 0.513448197177282 0.973103605645435 0.486551802822718 99 0.638683671120523 0.722632657758954 0.361316328879477 100 0.671927433912257 0.656145132175486 0.328072566087743 101 0.672268815137322 0.655462369725355 0.327731184862678 102 0.803840033038988 0.392319933922024 0.196159966961012 103 0.884373879150828 0.231252241698344 0.115626120849172 104 0.930172925286633 0.139654149426733 0.0698270747133667 105 0.95440482246852 0.0911903550629617 0.0455951775314809 106 0.957410961167021 0.0851780776659573 0.0425890388329786 107 0.985432559232988 0.0291348815340242 0.0145674407670121 108 0.99637751041251 0.00724497917498103 0.00362248958749052 109 0.99727951284073 0.00544097431853824 0.00272048715926912 110 0.998543667919737 0.00291266416052501 0.00145633208026250 111 0.999245406774497 0.00150918645100610 0.000754593225503048 112 0.998661092421668 0.00267781515666385 0.00133890757833193 113 0.997660698801486 0.00467860239702769 0.00233930119851384 114 0.996467860056563 0.00706427988687473 0.00353213994343737 115 0.99776673164702 0.00446653670596017 0.00223326835298008 116 0.997489726277193 0.00502054744561406 0.00251027372280703 117 0.995499917202448 0.0090001655951043 0.00450008279755215 118 0.99125370112283 0.0174925977543403 0.00874629887717016 119 0.985839356437192 0.0283212871256159 0.0141606435628079 120 0.977927380113265 0.0441452397734708 0.0220726198867354 121 0.963687956461363 0.0726240870772732 0.0363120435386366 122 0.93852962747981 0.122940745040378 0.0614703725201888 123 0.97394746816686 0.0521050636662807 0.0260525318331403 124 0.961790288643186 0.0764194227136284 0.0382097113568142 125 0.924444923728816 0.151110152542367 0.0755550762711835 126 0.895293902963712 0.209412194072575 0.104706097036288 127 0.966031241343195 0.0679375173136109 0.0339687586568055 128 0.926581185507644 0.146837628984711 0.0734188144923555 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 74 0.654867256637168 NOK 5% type I error level 78 0.690265486725664 NOK 10% type I error level 93 0.823008849557522 NOK

Charts produced by software:

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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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Software written by Ed van Stee & Patrick Wessa

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