Multiple Regression Model 4 zonder lineaire trend

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 31 Dec 2010 09:55:50 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4.htm/, Retrieved Fri, 31 Dec 2010 10:56:07 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

4831 0 3695 2462 2146 1579 5134 0 4831 3695 2462 2146 6250 0 5134 4831 3695 2462 5760 0 6250 5134 4831 3695 6249 0 5760 6250 5134 4831 2917 0 6249 5760 6250 5134 1741 0 2917 6249 5760 6250 2359 0 1741 2917 6249 5760 1511 1 2359 1741 2917 6249 2059 0 1511 2359 1741 2917 2635 0 2059 1511 2359 1741 2867 0 2635 2059 1511 2359 4403 0 2867 2635 2059 1511 5720 0 4403 2867 2635 2059 4502 0 5720 4403 2867 2635 5749 0 4502 5720 4403 2867 5627 0 5749 4502 5720 4403 2846 0 5627 5749 4502 5720 1762 0 2846 5627 5749 4502 2429 0 1762 2846 5627 5749 1169 0 2429 1762 2846 5627 2154 1 1169 2429 1762 2846 2249 0 2154 1169 2429 1762 2687 0 2249 2154 1169 2429 4359 0 2687 2249 2154 1169 5382 0 4359 2687 2249 2154 4459 0 5382 4359 2687 2249 6398 0 4459 5382 4359 2687 4596 0 6398 4459 5382 4359 3024 0 4596 6398 4459 5382 1887 0 3024 4596 6398 4459 2070 0 1887 3024 4596 6398 1351 0 2070 1887 3024 4596 2218 0 1351 2070 1887 3024 2461 1 2218 1351 2070 1887 3028 0 2461 2218 1351 2070 4784 0 3028 2461 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 5 seconds R Server 'Gwilym Jenkins' @ www.wessa.org Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2811.22887588795 + 536.65294179354X[t] -0.270735639007487Y1[t] + 0.146972260964833Y2[t] + 0.38234645812074Y3[t] + 0.0456948838664329Y4[t] + 1482.41945217729M1[t] + 2216.79549815919M2[t] + 1849.07312409255M3[t] + 1917.71663306403M4[t] + 1195.64039723629M5[t] -1357.72571856797M6[t] -3420.23672889271M7[t] -2823.19225751594M8[t] -2512.03180419436M9[t] -1523.91827487921M10[t] -1046.44162180443M11[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) 2811.22887588795 391.292489 7.1845 0 0 X 536.65294179354 124.386049 4.3144 3.8e-05 1.9e-05 Y1 -0.270735639007487 0.091772 -2.9501 0.003965 0.001983 Y2 0.146972260964833 0.088892 1.6534 0.101423 0.050711 Y3 0.38234645812074 0.089338 4.2798 4.3e-05 2.2e-05 Y4 0.0456948838664329 0.092882 0.492 0.623833 0.311916 M1 1482.41945217729 211.375743 7.0132 0 0 M2 2216.79549815919 311.199938 7.1234 0 0 M3 1849.07312409255 449.310673 4.1154 8e-05 4e-05 M4 1917.71663306403 547.120738 3.5051 0.000687 0.000344 M5 1195.64039723629 631.957718 1.892 0.061418 0.030709 M6 -1357.72571856797 668.486055 -2.031 0.044931 0.022465 M7 -3420.23672889271 653.961571 -5.23 1e-06 0 M8 -2823.19225751594 604.44105 -4.6707 9e-06 5e-06 M9 -2512.03180419436 397.395117 -6.3212 0 0 M10 -1523.91827487921 221.860113 -6.8688 0 0 M11 -1046.44162180443 190.099113 -5.5047 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.979897654723466 R-squared 0.96019941373255 Adjusted R-squared 0.953766995749931 F-TEST (value) 149.275034104935 F-TEST (DF numerator) 16 F-TEST (DF denominator) 99 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 350.48446385522 Sum Squared Residuals 12161096.5809843 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4831 4547.79356918019 283.206430819806 2 5134 5302.56120693764 -168.561206937636 3 6250 5505.63918887246 744.360811127543 4 5760 5807.3616880164 -47.3616880164031 5 6249 5549.72732342193 699.272676578067 6 2917 3232.49926934452 -315.49926934452 7 1741 2007.5945697203 -266.594569720301 8 2359 2597.88950396155 -238.889503961549 9 1511 1853.91529502778 -342.915295027784 10 2059 2023.89377391106 35.106226088936 11 2635 2410.92774720325 224.072252796752 12 2867 3085.97608169117 -218.976081691165 13 4403 4761.01748546589 -358.017485465894 14 5720 5358.91351071248 361.08648928752 15 4502 4975.40632430604 -473.406324306041 16 5749 6165.2536820098 -416.253682009801 17 5627 5500.29551744841 126.704482551588 18 2846 2757.71573508724 88.2842649127623 19 1762 1851.31958573186 -89.3195857318558 20 2429 2343.44688434026 85.5531156597398 21 1169 1245.82845969249 -76.828459692487 22 2154 2668.21130137873 -514.211301378728 23 2249 2362.66719287721 -113.667192877213 24 2687 3076.87855633307 -389.878556333072 25 4359 4773.71387099397 -414.713870993968 26 5382 5201.12615298785 180.873847012154 27 4459 4973.98760317394 -514.987603173944 28 6398 6102.17036702774 295.82963297226 29 4596 5187.02460277613 -591.024602776131 30 3024 3100.34340782409 -76.343407824087 31 1887 1897.77821224789 -10.7782122478856 32 2070 1971.2227732229 98.7772267771035 33 1351 1382.34033099598 -31.3403309959778 34 2218 2085.44842819276 132.551571807237 35 2461 2777.19148728784 -316.191487287836 36 3028 3082.07141763516 -54.0714176351594 37 4784 4745.33777960038 38.6622203996229 38 4975 5220.16296908772 -245.16296908772 39 4607 5286.7076767589 -679.707676758901 40 6249 6180.36298234171 68.6370176582872 41 4809 5112.92142479913 -303.921424799135 42 3157 3058.76730789996 98.2326921000364 43 1910 1842.86868439764 67.1313156023588 44 2228 2059.17442211767 168.825577882334 45 1594 1403.52955122859 190.470448771405 46 2467 2057.7526732374 409.2473267626 47 2222 2270.30135350789 -48.3013535078871 48 3607 3820.15625110597 -213.156251105973 49 4685 4659.76359909605 25.2364009039458 50 4962 5252.05995903998 -290.059959039985 51 5770 5486.13450823831 283.865491761689 52 5480 5852.19183318817 -372.191833188173 53 5000 5482.55157323965 -482.551573239646 54 3228 3338.11002947173 -110.110029471735 55 1993 1610.8368795142 382.163120485796 56 2288 2085.02720241632 202.972797583678 57 1580 1435.35843189329 144.641568106712 58 2111 2105.34040061993 5.65959938006733 59 2192 2391.3590921892 -199.359092189204 60 3601 3236.69209619747 364.307903802532 61 4665 5056.87671942958 -391.876719429584 62 4876 5228.85506585427 -352.855065854273 63 5813 5502.81340270894 310.186597291058 64 5589 5819.98948780226 -230.989487802262 65 5331 5425.56550273361 -94.5655027336064 66 3075 3277.0276470921 -202.027647092102 67 2002 1744.54789460313 257.452105396873 68 2306 2191.64124571704 114.358754282959 69 1507 1388.43393920715 118.566060792847 70 1992 2124.19940385637 -132.199403856369 71 2487 2420.14114838163 66.8588516183671 72 3490 3112.24660010223 377.753399897774 73 4647 4544.79729551188 102.20270448812 74 5594 5861.42184214837 -267.421842148368 75 5611 5276.81924709339 334.180752906606 76 5788 5968.24980189918 -180.249801899177 77 6204 5615.70296287732 588.297037122684 78 3013 3025.49785624628 -12.4978562462758 79 1931 1956.4968666689 -25.4968666688975 80 2549 2544.63293573558 4.36706426442032 81 1504 1328.39630161174 175.60369838826 82 2090 2130.74618886155 -40.7461888615537 83 2702 2482.83399154483 219.166008455172 84 2939 3078.39853669535 -139.398536695351 85 4500 4762.90453695668 -262.904536956678 86 6208 5370.26791061217 837.732089387828 87 6415 5424.78708578126 990.212914218735 88 5657 5759.43650601541 -102.436506015411 89 5964 5997.37860676079 -33.3786067607902 90 3163 3406.68425444474 -243.684254444744 91 1997 1867.26447880101 129.735521198991 92 2422 2451.06104297033 -29.0610429703297 93 1376 1418.86509357954 -42.8650935795374 94 2202 2178.82397232791 23.1760276720877 95 2683 2388.15701272634 294.842987273659 96 3303 3045.25981017406 257.740189825938 97 5202 4698.53814957424 503.461850425761 98 5231 5231.56263930886 -0.562639308860696 99 4880 5394.12329845784 -514.123298457836 100 7998 6853.11690605095 1144.88309394905 101 4977 4796.50937415352 180.490625846483 102 3531 3386.41667831097 144.583321689033 103 2025 2447.50355379964 -422.503553799642 104 2205 2227.16200607933 -22.1620060793274 105 1442 1577.33259676344 -135.33259676344 106 2238 2156.58385761428 81.4161423857224 107 2179 2306.42097428181 -127.42097428181 108 3218 3202.32065006552 15.679349934477 109 5139 4664.25699419113 474.743005808868 110 4990 5045.06874331066 -55.0687433106602 111 4914 5394.58166460891 -480.581664608911 112 6084 6243.86674564837 -159.866745648371 113 5672 5761.32311178951 -89.3231117895135 114 3548 2918.93781427837 629.062185721631 115 1793 1814.78927451544 -21.7892745154363 116 2086 2470.74198343903 -384.741983439028 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.987985023966643 0.0240299520667149 0.0120149760333574 21 0.972295001770015 0.0554099964599699 0.027704998229985 22 0.956027057389628 0.0879458852207434 0.0439729426103717 23 0.929822211666415 0.14035557666717 0.0701777883335852 24 0.896304814721268 0.207390370557463 0.103695185278732 25 0.869024307147714 0.261951385704572 0.130975692852286 26 0.821527543984295 0.35694491203141 0.178472456015705 27 0.842762776983308 0.314474446033384 0.157237223016692 28 0.860552024595202 0.278895950809597 0.139447975404798 29 0.902515517219313 0.194968965561374 0.097484482780687 30 0.868199709298383 0.263600581403235 0.131800290701617 31 0.85421693575372 0.29156612849256 0.14578306424628 32 0.829257708942057 0.341484582115886 0.170742291057943 33 0.776583092006754 0.446833815986491 0.223416907993246 34 0.718640652016718 0.562718695966565 0.281359347983282 35 0.689345211592509 0.621309576814982 0.310654788407491 36 0.642426469060872 0.715147061878256 0.357573530939128 37 0.581826360802714 0.836347278394572 0.418173639197286 38 0.549658315236131 0.900683369527739 0.450341684763869 39 0.70194363263884 0.596112734722321 0.298056367361161 40 0.64157120237687 0.716857595246261 0.358428797623131 41 0.619296509772204 0.761406980455591 0.380703490227796 42 0.56122885289851 0.87754229420298 0.43877114710149 43 0.523222750966583 0.953554498066834 0.476777249033417 44 0.472896530337585 0.94579306067517 0.527103469662415 45 0.414798097245385 0.82959619449077 0.585201902754615 46 0.409587517994187 0.819175035988374 0.590412482005813 47 0.353263750268682 0.706527500537365 0.646736249731318 48 0.355294566868076 0.710589133736152 0.644705433131924 49 0.304405116932971 0.608810233865943 0.695594883067029 50 0.289803475557418 0.579606951114837 0.710196524442582 51 0.271939810672559 0.543879621345119 0.72806018932744 52 0.259704452128625 0.51940890425725 0.740295547871375 53 0.337815002621244 0.675630005242488 0.662184997378756 54 0.30444478066265 0.6088895613253 0.69555521933735 55 0.320728413404149 0.641456826808298 0.679271586595851 56 0.281142457183776 0.562284914367552 0.718857542816224 57 0.232654042035619 0.465308084071238 0.767345957964381 58 0.18947842650787 0.378956853015741 0.81052157349213 59 0.169172826830327 0.338345653660653 0.830827173169673 60 0.164534389964771 0.329068779929543 0.835465610035229 61 0.230755873857442 0.461511747714885 0.769244126142558 62 0.223344652933362 0.446689305866723 0.776655347066638 63 0.211322510248585 0.422645020497169 0.788677489751415 64 0.193684356021953 0.387368712043906 0.806315643978047 65 0.162635457371259 0.325270914742518 0.837364542628741 66 0.158808095270116 0.317616190540232 0.841191904729884 67 0.130774659183078 0.261549318366156 0.869225340816922 68 0.100578794166731 0.201157588333462 0.899421205833269 69 0.0755135718215402 0.15102714364308 0.92448642817846 70 0.060307834758814 0.120615669517628 0.939692165241186 71 0.0435676775731899 0.0871353551463798 0.95643232242681 72 0.0417045683497369 0.0834091366994737 0.958295431650263 73 0.0318403205189363 0.0636806410378725 0.968159679481064 74 0.0741523286245456 0.148304657249091 0.925847671375454 75 0.0968950938483093 0.193790187696619 0.90310490615169 76 0.076815511781309 0.153631023562618 0.92318448821869 77 0.185825271467603 0.371650542935206 0.814174728532397 78 0.152615108547283 0.305230217094566 0.847384891452717 79 0.117651077105772 0.235302154211544 0.882348922894228 80 0.093880773964123 0.187761547928246 0.906119226035877 81 0.0725724078822413 0.145144815764483 0.927427592117759 82 0.0521056478366521 0.104211295673304 0.947894352163348 83 0.037640494197202 0.075280988394404 0.962359505802798 84 0.0285376831966281 0.0570753663932561 0.971462316803372 85 0.0490613213125147 0.0981226426250295 0.950938678687485 86 0.247377146783711 0.494754293567422 0.752622853216289 87 0.490904291087423 0.981808582174846 0.509095708912577 88 0.692394469489413 0.615211061021175 0.307605530510587 89 0.952160233091696 0.0956795338166077 0.0478397669083038 90 0.972931755617546 0.054136488764909 0.0270682443824545 91 0.986925010288077 0.0261499794238451 0.0130749897119226 92 0.9762356822122 0.0475286355755975 0.0237643177877988 93 0.952630988715474 0.0947380225690513 0.0473690112845257 94 0.90025286798438 0.19949426403124 0.09974713201562 95 0.976855812304509 0.046288375390983 0.0231441876954915 96 0.957585232908644 0.0848295341827115 0.0424147670913558 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 4 0.051948051948052 NOK 10% type I error level 16 0.207792207792208 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/10igpx1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/10igpx1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/1pj5o1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/1pj5o1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/2dfhy1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/2dfhy1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/330da1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/330da1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/4c9kz1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/4c9kz1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/565tu1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/565tu1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/67ydo1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/67ydo1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/7pwco1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/7pwco1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/8pspo1293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/8pspo1293789345.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/9keh91293789345.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/31/t1293789357amdpmz7u1yulzm4/9keh91293789345.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by