Met 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: Sat, 13 Dec 2008 08:36:57 -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/13/t1229182661g1pv6gzcxsopcaj.htm/, Retrieved Sat, 13 Dec 2008 16:37:51 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj.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 «

32.68 10967.87 31.54 10433.56 32.43 10665.78 26.54 10666.71 25.85 10682.74 27.6 10777.22 25.71 10052.6 25.38 10213.97 28.57 10546.82 27.64 10767.2 25.36 10444.5 25.9 10314.68 26.29 9042.56 21.74 9220.75 19.2 9721.84 19.32 9978.53 19.82 9923.81 20.36 9892.56 24.31 10500.98 25.97 10179.35 25.61 10080.48 24.67 9492.44 25.59 8616.49 26.09 8685.4 28.37 8160.67 27.34 8048.1 24.46 8641.21 27.46 8526.63 30.23 8474.21 32.33 7916.13 29.87 7977.64 24.87 8334.59 25.48 8623.36 27.28 9098.03 28.24 9154.34 29.58 9284.73 26.95 9492.49 29.08 9682.35 28.76 9762.12 29.59 10124.63 30.7 10540.05 30.52 10601.61 32.67 10323.73 33.19 10418.4 37.13 10092.96 35.54 10364.91 37.75 10152.09 41.84 10032.8 42.94 10204.59 49.14 10001.6 44.61 10411.75 40.22 10673.38 44.23 10539.51 45.85 10723.78 53.38 10682.06 53.26 10283.19 51.8 10377.18 55.3 10486.64 57.81 10545.38 63.96 10554.27 63.77 10532.54 59.15 10324.31 56.12 10695.25 57.42 10827.81 63.52 10872.48 61.71 10971.19 63.01 11145.65 68.18 11234.68 72.03 11333.88 69.75 10997.97 74.41 11036.89 74.33 11257.35 64.24 11533.59 60.03 11963.12 59.44 12185.15 62.5 12377.62 55.04 12512.89 58.34 12631.48 61.92 12268.53 67.65 12754.8 67.68 13407.75 70.3 13480.21 75.26 13673.28 71.44 13239.71 76.36 13557.69 81.71 13901.28 92.6 13200.58 90.6 13406.97 92.23 12538.12 94.09 12419.57 102.79 12193.88 109.65 12656.63 124.05 12812.48 132.69 12056.67 135.81 11322.38 116.07 11530.75 101.42 11114.08 75.73 9181.73 55.48 8614.55

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Olieprijs[t] = -24.6359790023053 + 0.00400883150657628DowJones[t] -0.759916263886184M1[t] -3.70663753073985M2[t] -7.40772018370826M3[t] -7.12242507099084M4[t] -6.58460571642925M5[t] -6.0700207531914M6[t] -3.52563125651643M7[t] -2.88889444085642M8[t] -1.17308431965984M9[t] -0.262794230614779M10[t] + 2.06283551975003M11[t] + 0.708215925573804t + 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) -24.6359790023053 11.841256 -2.0805 0.040488 0.020244 DowJones 0.00400883150657628 0.001203 3.3318 0.001279 0.000639 M1 -0.759916263886184 6.169451 -0.1232 0.90226 0.45113 M2 -3.70663753073985 6.171297 -0.6006 0.549688 0.274844 M3 -7.40772018370826 6.167253 -1.2011 0.233034 0.116517 M4 -7.12242507099084 6.378541 -1.1166 0.267302 0.133651 M5 -6.58460571642925 6.367201 -1.0341 0.304002 0.152001 M6 -6.0700207531914 6.361149 -0.9542 0.342671 0.171336 M7 -3.52563125651643 6.351802 -0.5551 0.580311 0.290156 M8 -2.88889444085642 6.354857 -0.4546 0.650559 0.325279 M9 -1.17308431965984 6.362719 -0.1844 0.854164 0.427082 M10 -0.262794230614779 6.354233 -0.0414 0.967108 0.483554 M11 2.06283551975003 6.344029 0.3252 0.745858 0.372929 t 0.708215925573804 0.05905 11.9936 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.902237736752583 R-squared 0.814032933620423 Adjusted R-squared 0.785590911703546 F-TEST (value) 28.6207828683728 F-TEST (DF numerator) 13 F-TEST (DF denominator) 85 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.6874175585917 Sum Squared Residuals 13682.4979660151 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 32.68 19.2806634754151 13.3993365245849 2 31.54 14.9001993718564 16.6398006281436 3 32.43 12.8382634969190 19.5917365030810 4 26.54 13.8355027485113 12.7044972514887 5 25.85 15.1457995976971 10.7042004023029 6 27.6 16.7473548872501 10.8526451127499 7 25.71 17.0950808232036 8.6149191767964 8 25.38 19.0869387046536 6.29306129534637 9 28.57 22.8453043183879 5.72469568161207 10 27.64 25.3472766204261 2.29272337957391 11 25.36 27.0874723691925 -1.72747236919252 12 25.9 25.2124262688326 0.687573731167422 13 26.29 20.0610111943743 6.22898880562567 14 21.74 18.5368395392514 3.20316046074864 15 19.2 17.5527581914870 1.64724180851296 16 19.32 19.5752961892013 -0.255296189201331 17 19.82 20.6019682092969 -0.781968209296856 18 20.36 21.699493113528 -1.33949311352800 19 24.31 27.3911518010079 -3.08115180100792 20 25.97 27.4467440647816 -1.47674406478160 21 25.61 29.4744169404968 -3.86441694049679 22 24.67 28.7355696759885 -4.06556967598854 23 25.59 28.2578793937417 -2.66787939374166 24 26.09 27.1795083786836 -1.08950837868361 25 28.37 25.0242538839254 3.34574611607457 26 27.34 22.3344743799503 5.0055256200497 27 24.46 21.7192857074212 2.74071429257885 28 27.46 22.2534648316889 5.20653516831113 29 30.23 23.2893571642495 6.94064283575048 30 32.33 22.2749093658711 10.0550906341289 31 29.87 25.7740980140894 4.09590198591063 32 24.87 28.5500031615956 -3.68000316159559 33 25.48 32.13165948252 -6.65165948252 34 27.28 35.6530375483654 -8.37303754836543 35 28.24 38.9126205264394 -10.6726205264394 36 29.58 38.0807124724056 -8.50071247240562 37 26.95 38.8618869678995 -11.9118869678995 38 29.08 37.3844983764582 -8.30449837645824 39 28.76 34.7114161383432 -5.95141613834323 40 29.59 37.1581686860834 -7.56816868608341 41 30.7 40.0695527506807 -9.36955275068071 42 30.52 41.5391373070372 -11.0191373070372 43 32.67 43.6777686302386 -11.0077686302386 44 33.19 45.4022374501999 -12.2122374502000 45 37.13 46.5216293714701 -9.39162937147015 46 35.54 49.2303371143024 -13.6903371143024 47 37.75 51.4110232690115 -13.6610232690115 48 41.84 49.5781901644158 -7.73819016441578 49 42.94 50.2151669906181 -7.27516699061813 50 49.14 47.1629089418184 1.97709105818165 51 44.61 45.814264456846 -1.20426445684602 52 40.22 47.8566060822028 -7.63660608220279 53 44.23 48.5659790885528 -4.33597908855282 54 45.85 50.5274873590813 -4.67748735908128 55 53.38 53.6128443308757 -0.232844330875684 56 53.26 53.3587944490814 -0.098794449081426 57 51.8 56.1596105691549 -4.35961056915492 58 55.3 58.2169232804836 -2.91692328048362 59 57.81 61.4862477191185 -3.67624771911851 60 63.96 60.1672666370358 3.79273336296424 61 63.77 60.0284543900855 3.74154560991454 62 59.15 56.9551900641912 2.19480993580877 63 56.12 55.449359295846 0.670640704153959 64 57.42 56.974281038649 0.445718961350994 65 63.52 58.3993908221832 5.12060917781685 66 61.71 60.017903469009 1.69209653099104 67 63.01 63.969889635895 -0.959889635895037 68 68.18 65.6717486461593 2.50825135384067 69 72.03 68.4934507783821 3.53654922161792 70 69.75 68.7653502016269 0.984649798373092 71 74.41 71.9552195998015 2.45478040019854 72 74.33 71.484386999565 2.84561300043494 73 64.24 72.5400862766293 -8.3000862766293 74 60.03 72.0234943323692 -11.9934943323692 75 59.44 69.9207084643797 -10.4807084643797 76 62.5 71.6857993027416 -9.18579930274164 77 55.04 73.4741092207716 -18.4341092207716 78 58.34 75.1723174379482 -16.8323174379481 79 61.92 76.969917464885 -15.0499174648851 80 67.65 80.2642447028217 -12.6142447028217 81 67.68 85.305837281811 -17.6258372818111 82 70.3 87.2148232273965 -16.9148232273965 83 75.26 91.0226540023098 -15.7626540023098 84 71.44 87.9299253318273 -16.4899253318273 85 76.36 89.152953235976 -12.7929532359760 86 81.71 88.2918423120407 -6.58184231204069 87 92.6 82.489987347988 10.1100126520119 88 90.6 84.3108811209216 6.28911887907841 89 92.23 82.0738431465682 10.1561568534318 90 94.09 82.8213970602752 11.2686029397248 91 102.79 85.1692492998048 17.6207507001952 92 109.65 88.3692888207068 21.2807111792932 93 124.05 91.418091257777 32.6319087422229 94 132.69 90.0066823314105 42.6833176685895 95 135.81 90.0968831203852 45.7131168796148 96 116.07 89.5775837472343 26.4924162527657 97 101.42 87.8555235850768 13.5644764149232 98 75.73 77.8705526820642 -2.14055268206423 99 55.48 72.6039569007697 -17.1239569007697 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0109212973890114 0.0218425947780227 0.989078702610989 18 0.00198130827912571 0.00396261655825141 0.998018691720874 19 0.000833460192699568 0.00166692038539914 0.9991665398073 20 0.00040751914116525 0.0008150382823305 0.999592480858835 21 9.42688786517003e-05 0.000188537757303401 0.999905731121348 22 4.34842952811125e-05 8.69685905622249e-05 0.999956515704719 23 6.30201942326391e-05 0.000126040388465278 0.999936979805767 24 3.48182785186753e-05 6.96365570373507e-05 0.999965181721481 25 2.18292088457277e-05 4.36584176914554e-05 0.999978170791154 26 1.62168873140537e-05 3.24337746281075e-05 0.999983783112686 27 6.39298519564626e-06 1.27859703912925e-05 0.999993607014804 28 9.08608424171659e-06 1.81721684834332e-05 0.999990913915758 29 1.89402208066038e-05 3.78804416132075e-05 0.999981059779193 30 2.26296514961578e-05 4.52593029923157e-05 0.999977370348504 31 1.03864099910801e-05 2.07728199821601e-05 0.999989613590009 32 3.44681474676594e-06 6.89362949353188e-06 0.999996553185253 33 1.11076759196834e-06 2.22153518393669e-06 0.999998889232408 34 4.26640742599941e-07 8.53281485199882e-07 0.999999573359257 35 2.31515115377055e-07 4.63030230754110e-07 0.999999768484885 36 1.10799718384312e-07 2.21599436768624e-07 0.999999889200282 37 3.32569204239877e-08 6.65138408479753e-08 0.99999996674308 38 1.29714793924264e-08 2.59429587848528e-08 0.99999998702852 39 5.43851001121955e-09 1.08770200224391e-08 0.99999999456149 40 2.31570510552304e-09 4.63141021104608e-09 0.999999997684295 41 8.49704605417856e-10 1.69940921083571e-09 0.999999999150295 42 2.40135719178803e-10 4.80271438357606e-10 0.999999999759864 43 8.0947984571712e-11 1.61895969143424e-10 0.999999999919052 44 3.38659094692908e-11 6.77318189385817e-11 0.999999999966134 45 2.86715269203169e-11 5.73430538406338e-11 0.999999999971328 46 1.40449215922099e-11 2.80898431844198e-11 0.999999999985955 47 1.06947559600239e-11 2.13895119200479e-11 0.999999999989305 48 1.58178598025603e-11 3.16357196051206e-11 0.999999999984182 49 1.02277779733782e-11 2.04555559467564e-11 0.999999999989772 50 9.16279761623617e-11 1.83255952324723e-10 0.999999999908372 51 1.29282277043571e-10 2.58564554087142e-10 0.999999999870718 52 4.79902577119584e-11 9.59805154239168e-11 0.99999999995201 53 3.05326953769377e-11 6.10653907538754e-11 0.999999999969467 54 1.64766059381874e-11 3.29532118763749e-11 0.999999999983523 55 4.67459872886764e-11 9.34919745773528e-11 0.999999999953254 56 1.46326086245755e-10 2.92652172491511e-10 0.999999999853674 57 1.35290805981591e-10 2.70581611963183e-10 0.99999999986471 58 2.49651034270257e-10 4.99302068540515e-10 0.99999999975035 59 4.05913989938596e-10 8.11827979877192e-10 0.999999999594086 60 1.35560752255872e-09 2.71121504511745e-09 0.999999998644392 61 2.07133677162401e-09 4.14267354324802e-09 0.999999997928663 62 2.10583269493422e-09 4.21166538986844e-09 0.999999997894167 63 2.38686844027488e-09 4.77373688054976e-09 0.999999997613132 64 1.48774564211649e-09 2.97549128423298e-09 0.999999998512254 65 2.7807950991379e-09 5.5615901982758e-09 0.999999997219205 66 2.51186871077253e-09 5.02373742154506e-09 0.999999997488131 67 1.49769470626638e-09 2.99538941253276e-09 0.999999998502305 68 1.56579831216631e-09 3.13159662433262e-09 0.999999998434202 69 2.01724304589373e-09 4.03448609178746e-09 0.999999997982757 70 1.66523906024363e-09 3.33047812048726e-09 0.99999999833476 71 1.78695142650892e-09 3.57390285301785e-09 0.999999998213049 72 4.08603322657101e-09 8.17206645314202e-09 0.999999995913967 73 1.06227327047769e-08 2.12454654095539e-08 0.999999989377267 74 1.42806604480356e-07 2.85613208960712e-07 0.999999857193395 75 5.77207447919561e-06 1.15441489583912e-05 0.999994227925521 76 4.45572332199304e-05 8.91144664398607e-05 0.99995544276678 77 9.24586875234473e-05 0.000184917375046895 0.999907541312476 78 0.000190720613796655 0.00038144122759331 0.999809279386203 79 0.000485476600773336 0.000970953201546672 0.999514523399227 80 0.00700918076391564 0.0140183615278313 0.992990819236084 81 0.0145240859625370 0.0290481719250740 0.985475914037463 82 0.00603204977578515 0.0120640995515703 0.993967950224215 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 62 0.93939393939394 NOK 5% type I error level 66 1 NOK 10% type I error level 66 1 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/107l9p1229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/107l9p1229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/1e9o61229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/1e9o61229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/2y7vw1229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/2y7vw1229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/301dy1229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/301dy1229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/4utni1229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/4utni1229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/5wezf1229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/5wezf1229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/6hmt11229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/6hmt11229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/76zde1229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/76zde1229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/8h3x41229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/8h3x41229182611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/938fp1229182611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229182661g1pv6gzcxsopcaj/938fp1229182611.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') }

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