Prof bach regressie zonder trend 1 aug 2005

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Sun, 14 Dec 2008 11:30:08 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l.htm/, Retrieved Sun, 14 Dec 2008 19:35:48 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l.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 «

13363 0 12530 0 11420 0 10948 0 10173 0 10602 0 16094 0 19631 0 17140 0 14345 0 12632 0 12894 0 11808 0 10673 0 9939 0 9890 0 9283 0 10131 0 15864 0 19283 0 16203 0 13919 0 11937 0 11795 0 11268 0 10522 0 9929 0 9725 0 9372 0 10068 0 16230 0 19115 0 18351 0 16265 0 14103 0 14115 0 13327 0 12618 0 12129 0 11775 0 11493 0 12470 0 20792 0 22337 0 21325 0 18581 0 16475 0 16581 0 15745 0 14453 0 13712 0 13766 0 13336 0 15346 0 24446 0 26178 0 24628 0 21282 0 18850 0 18822 0 18060 0 17536 0 16417 0 15842 0 15188 0 16905 0 25430 0 27962 0 26607 0 23364 0 20827 0 20506 0 19181 0 18016 0 17354 0 16256 0 15770 0 17538 0 26899 0 28915 1 25247 1 22856 1 19980 1 19856 1 16994 1 16839 1 15618 1 15883 1 15513 1 17106 1 25272 1 26731 1 22891 1 19583 1 16939 1 16757 1 15435 1 14786 1 13680 1 13208 1 12707 1 14277 1 22436 1 23229 1 18241 1 16145 1 13994 1 14780 1 13100 1 12329 1 12463 1 11532 1 10784 1 13106 1 19491 1 20418 1 16094 1 14491 1 13067 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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation NWWZpb[t] = + 15920.3206590621 + 941.038022813692Dummy[t] -1374.53206590622M1[t] -2172.43206590621M2[t] -2936.53206590622M3[t] -3320.13206590620M4[t] -3840.73206590621M5[t] -2447.73206590621M6[t] + 5092.76793409378M7[t] + 7083.16413181242M8[t] + 4375.96413181242M9[t] + 1786.36413181242M10[t] -416.335868187578M11[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) 15920.3206590621 1081.137021 14.7255 0 0 Dummy 941.038022813692 620.755635 1.516 0.132507 0.066254 M1 -1374.53206590622 1462.842406 -0.9396 0.349543 0.174772 M2 -2172.43206590621 1462.842406 -1.4851 0.140492 0.070246 M3 -2936.53206590622 1462.842406 -2.0074 0.04725 0.023625 M4 -3320.13206590620 1462.842406 -2.2696 0.025253 0.012626 M5 -3840.73206590621 1462.842406 -2.6255 0.009931 0.004965 M6 -2447.73206590621 1462.842406 -1.6733 0.097223 0.048611 M7 5092.76793409378 1462.842406 3.4814 0.000726 0.000363 M8 7083.16413181242 1463.281369 4.8406 4e-06 2e-06 M9 4375.96413181242 1463.281369 2.9905 0.003463 0.001732 M10 1786.36413181242 1463.281369 1.2208 0.224873 0.112436 M11 -416.335868187578 1463.281369 -0.2845 0.776565 0.388283 Multiple Linear Regression - Regression Statistics Multiple R 0.76332273674713 R-squared 0.582661600435129 Adjusted R-squared 0.535415743880616 F-TEST (value) 12.3325439081168 F-TEST (DF numerator) 12 F-TEST (DF denominator) 106 p-value 2.55351295663786e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3183.45374933728 Sum Squared Residuals 1074244044.06198 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13363 14545.7885931560 -1182.78859315597 2 12530 13747.8885931559 -1217.88859315590 3 11420 12983.7885931559 -1563.78859315588 4 10948 12600.1885931559 -1652.18859315590 5 10173 12079.5885931559 -1906.58859315587 6 10602 13472.5885931559 -2870.58859315591 7 16094 21013.0885931559 -4919.08859315589 8 19631 23003.4847908745 -3372.48479087454 9 17140 20296.2847908745 -3156.28479087452 10 14345 17706.6847908745 -3361.68479087452 11 12632 15503.9847908745 -2871.98479087452 12 12894 15920.3206590621 -3026.32065906210 13 11808 14545.7885931559 -2737.78859315588 14 10673 13747.8885931559 -3074.88859315589 15 9939 12983.7885931559 -3044.78859315589 16 9890 12600.1885931559 -2710.18859315589 17 9283 12079.5885931559 -2796.58859315589 18 10131 13472.5885931559 -3341.58859315589 19 15864 21013.0885931559 -5149.08859315589 20 19283 23003.4847908745 -3720.48479087452 21 16203 20296.2847908745 -4093.28479087452 22 13919 17706.6847908745 -3787.68479087453 23 11937 15503.9847908745 -3566.98479087453 24 11795 15920.3206590621 -4125.3206590621 25 11268 14545.7885931559 -3277.78859315588 26 10522 13747.8885931559 -3225.88859315589 27 9929 12983.7885931559 -3054.78859315589 28 9725 12600.1885931559 -2875.18859315589 29 9372 12079.5885931559 -2707.58859315589 30 10068 13472.5885931559 -3404.58859315589 31 16230 21013.0885931559 -4783.08859315589 32 19115 23003.4847908745 -3888.48479087452 33 18351 20296.2847908745 -1945.28479087453 34 16265 17706.6847908745 -1441.68479087453 35 14103 15503.9847908745 -1400.98479087453 36 14115 15920.3206590621 -1805.32065906210 37 13327 14545.7885931559 -1218.78859315588 38 12618 13747.8885931559 -1129.88859315589 39 12129 12983.7885931559 -854.788593155894 40 11775 12600.1885931559 -825.188593155894 41 11493 12079.5885931559 -586.588593155895 42 12470 13472.5885931559 -1002.58859315589 43 20792 21013.0885931559 -221.088593155892 44 22337 23003.4847908745 -666.48479087452 45 21325 20296.2847908745 1028.71520912548 46 18581 17706.6847908745 874.315209125475 47 16475 15503.9847908745 971.015209125475 48 16581 15920.3206590621 660.679340937897 49 15745 14545.7885931559 1199.21140684412 50 14453 13747.8885931559 705.111406844108 51 13712 12983.7885931559 728.211406844106 52 13766 12600.1885931559 1165.81140684411 53 13336 12079.5885931559 1256.41140684410 54 15346 13472.5885931559 1873.41140684411 55 24446 21013.0885931559 3432.91140684411 56 26178 23003.4847908745 3174.51520912548 57 24628 20296.2847908745 4331.71520912547 58 21282 17706.6847908745 3575.31520912547 59 18850 15503.9847908745 3346.01520912547 60 18822 15920.3206590621 2901.67934093790 61 18060 14545.7885931559 3514.21140684412 62 17536 13747.8885931559 3788.11140684411 63 16417 12983.7885931559 3433.21140684411 64 15842 12600.1885931559 3241.81140684411 65 15188 12079.5885931559 3108.41140684410 66 16905 13472.5885931559 3432.41140684411 67 25430 21013.0885931559 4416.91140684410 68 27962 23003.4847908745 4958.51520912548 69 26607 20296.2847908745 6310.71520912547 70 23364 17706.6847908745 5657.31520912548 71 20827 15503.9847908745 5323.01520912547 72 20506 15920.3206590621 4585.6793409379 73 19181 14545.7885931559 4635.21140684412 74 18016 13747.8885931559 4268.11140684411 75 17354 12983.7885931559 4370.21140684411 76 16256 12600.1885931559 3655.81140684411 77 15770 12079.5885931559 3690.41140684410 78 17538 13472.5885931559 4065.41140684411 79 26899 21013.0885931559 5885.9114068441 80 28915 23944.5228136882 4970.47718631179 81 25247 21237.3228136882 4009.67718631178 82 22856 18647.7228136882 4208.27718631179 83 19980 16445.0228136882 3534.97718631179 84 19856 16861.3586818758 2994.64131812421 85 16994 15486.8266159696 1507.17338403043 86 16839 14688.9266159696 2150.07338403042 87 15618 13924.8266159696 1693.17338403042 88 15883 13541.2266159696 2341.77338403042 89 15513 13020.6266159696 2492.37338403041 90 17106 14413.6266159696 2692.37338403042 91 25272 21954.1266159696 3317.87338403042 92 26731 23944.5228136882 2786.47718631179 93 22891 21237.3228136882 1653.67718631179 94 19583 18647.7228136882 935.277186311787 95 16939 16445.0228136882 493.977186311786 96 16757 16861.3586818758 -104.358681875793 97 15435 15486.8266159696 -51.8266159695742 98 14786 14688.9266159696 97.0733840304188 99 13680 13924.8266159696 -244.826615969584 100 13208 13541.2266159696 -333.226615969585 101 12707 13020.6266159696 -313.626615969586 102 14277 14413.6266159696 -136.626615969581 103 22436 21954.1266159696 481.873384030419 104 23229 23944.5228136882 -715.52281368821 105 18241 21237.3228136882 -2996.32281368821 106 16145 18647.7228136882 -2502.72281368821 107 13994 16445.0228136882 -2451.02281368821 108 14780 16861.3586818758 -2081.35868187579 109 13100 15486.8266159696 -2386.82661596957 110 12329 14688.9266159696 -2359.92661596958 111 12463 13924.8266159696 -1461.82661596958 112 11532 13541.2266159696 -2009.22661596959 113 10784 13020.6266159696 -2236.62661596959 114 13106 14413.6266159696 -1307.62661596958 115 19491 21954.1266159696 -2463.12661596958 116 20418 23944.5228136882 -3526.52281368821 117 16094 21237.3228136882 -5143.32281368822 118 14491 18647.7228136882 -4156.72281368821 119 13067 16445.0228136882 -3378.02281368822 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0707249701855353 0.141449940371071 0.929275029814465 17 0.0259831007149577 0.0519662014299155 0.974016899285042 18 0.00834372695924672 0.0166874539184934 0.991656273040753 19 0.00263461804183703 0.00526923608367407 0.997365381958163 20 0.000779370413545765 0.00155874082709153 0.999220629586454 21 0.000297500169811257 0.000595000339622514 0.999702499830189 22 8.98213881227925e-05 0.000179642776245585 0.999910178611877 23 2.99824301458857e-05 5.99648602917714e-05 0.999970017569854 24 1.42428780625037e-05 2.84857561250074e-05 0.999985757121937 25 1.00670118582700e-05 2.01340237165400e-05 0.999989932988142 26 5.48297185037026e-06 1.09659437007405e-05 0.99999451702815 27 2.28738851329178e-06 4.57477702658356e-06 0.999997712611487 28 9.22228081990296e-07 1.84445616398059e-06 0.999999077771918 29 3.12079594621253e-07 6.24159189242507e-07 0.999999687920405 30 1.17458927211635e-07 2.34917854423271e-07 0.999999882541073 31 6.55658981978292e-08 1.31131796395658e-07 0.999999934434102 32 3.41483833497277e-08 6.82967666994554e-08 0.999999965851617 33 6.38579973721487e-08 1.27715994744297e-07 0.999999936142003 34 2.16035252372264e-07 4.32070504744529e-07 0.999999783964748 35 3.45393022677313e-07 6.90786045354627e-07 0.999999654606977 36 5.3634308152609e-07 1.07268616305218e-06 0.999999463656918 37 4.34219524337848e-07 8.68439048675695e-07 0.999999565780476 38 4.33731338690846e-07 8.67462677381692e-07 0.999999566268661 39 5.96343416581236e-07 1.19268683316247e-06 0.999999403656583 40 7.03574979852023e-07 1.40714995970405e-06 0.99999929642502 41 1.09157951687510e-06 2.18315903375020e-06 0.999998908420483 42 2.76585199278257e-06 5.53170398556514e-06 0.999997234148007 43 0.000182899454503721 0.000365798909007442 0.999817100545496 44 0.000591193575451711 0.00118238715090342 0.999408806424548 45 0.00257184778007469 0.00514369556014938 0.997428152219925 46 0.00629219673871228 0.0125843934774246 0.993707803261288 47 0.0117641402929971 0.0235282805859942 0.988235859707003 48 0.0217158138357754 0.0434316276715509 0.978284186164225 49 0.0312069819745681 0.0624139639491362 0.968793018025432 50 0.0403750876654911 0.0807501753309823 0.95962491233451 51 0.0511631444299553 0.102326288859911 0.948836855570045 52 0.0649330553917558 0.129866110783512 0.935066944608244 53 0.0810675728274562 0.162135145654912 0.918932427172544 54 0.130974482350925 0.26194896470185 0.869025517649075 55 0.326157945216881 0.652315890433763 0.673842054783119 56 0.466826222692451 0.933652445384901 0.533173777307549 57 0.590237794320783 0.819524411358434 0.409762205679217 58 0.65220940398627 0.695581192027461 0.347790596013731 59 0.68767347170953 0.62465305658094 0.31232652829047 60 0.721376158814987 0.557247682370027 0.278623841185014 61 0.743197409139739 0.513605181720522 0.256802590860261 62 0.769783873600969 0.460432252798062 0.230216126399031 63 0.783918379435096 0.432163241129808 0.216081620564904 64 0.787811157945417 0.424377684109167 0.212188842054583 65 0.787865708904574 0.424268582190853 0.212134291095426 66 0.799828335659885 0.400343328680229 0.200171664340115 67 0.833923458719261 0.332153082561478 0.166076541280739 68 0.856497233178893 0.287005533642213 0.143502766821107 69 0.886923014343432 0.226153971313136 0.113076985656568 70 0.8983971918781 0.203205616243800 0.101602808121900 71 0.902108415577972 0.195783168844056 0.097891584422028 72 0.897718684801725 0.204562630396550 0.102281315198275 73 0.890004392020506 0.219991215958988 0.109995607979494 74 0.87755965743292 0.244880685134160 0.122440342567080 75 0.864277484746412 0.271445030507177 0.135722515253588 76 0.842844248048717 0.314311503902567 0.157155751951283 77 0.819144633345087 0.361710733309826 0.180855366654913 78 0.80099028768242 0.398019424635159 0.199009712317580 79 0.799620921089798 0.400758157820403 0.200379078910202 80 0.828071784371859 0.343856431256282 0.171928215628141 81 0.87803180090748 0.243936398185041 0.121968199092521 82 0.921155931878152 0.157688136243696 0.0788440681218478 83 0.944283327539096 0.111433344921807 0.0557166724609034 84 0.946598052705697 0.106803894588605 0.0534019472943027 85 0.937454469407926 0.125091061184148 0.062545530592074 86 0.931484689710883 0.137030620578233 0.0685153102891165 87 0.916976484104219 0.166047031791563 0.0830235158957816 88 0.91274872795027 0.174502544099461 0.0872512720497305 89 0.913251532022526 0.173496935954949 0.0867484679774744 90 0.908385692034097 0.183228615931806 0.0916143079659029 91 0.922431326584255 0.155137346831491 0.0775686734157455 92 0.950007606930122 0.0999847861397551 0.0499923930698776 93 0.984920791069271 0.0301584178614571 0.0150792089307286 94 0.993137258087222 0.0137254838255553 0.00686274191277763 95 0.99570960304527 0.00858079390945836 0.00429039695472918 96 0.99327697364183 0.0134460527163391 0.00672302635816957 97 0.99076358123842 0.0184728375231587 0.00923641876157937 98 0.987944226633314 0.0241115467333713 0.0120557733666856 99 0.97596783553585 0.0480643289283004 0.0240321644641502 100 0.957848538805628 0.0843029223887441 0.0421514611943721 101 0.931024639364236 0.137950721271528 0.0689753606357638 102 0.866375034203508 0.267249931592985 0.133624965796492 103 0.842026297108987 0.315947405782027 0.157973702891013 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 28 0.318181818181818 NOK 5% type I error level 38 0.431818181818182 NOK 10% type I error level 43 0.488636363636364 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/10pkoz1229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/10pkoz1229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/1gq3q1229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/1gq3q1229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/2lgc11229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/2lgc11229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/3fkbq1229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/3fkbq1229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/4ov8s1229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/4ov8s1229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/5efv41229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/5efv41229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/69zaz1229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/69zaz1229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/786kj1229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/786kj1229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/8difb1229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/8difb1229279399.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/9j8t31229279399.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229279736cc27f28sigxen1l/9j8t31229279399.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