Regressie prof bach zonder 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: Sun, 14 Dec 2008 08:03:49 -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/t1229267634fmo6zai90r3wj9l.htm/, Retrieved Sun, 14 Dec 2008 16:14:04 +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/t1229267634fmo6zai90r3wj9l.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 0 25247 0 22856 0 19980 0 19856 0 16994 0 16839 0 15618 0 15883 0 15513 0 17106 0 25272 0 26731 0 22891 0 19583 0 16939 0 16757 0 15435 0 14786 0 13680 0 13208 0 12707 0 14277 0 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation NWWZPB[t] = + 16469.4642301711 -2119.17807153966Dummy[t] -1429.44642301712M1[t] -2227.34642301710M2[t] -2991.44642301711M3[t] -3375.04642301711M4[t] -3895.64642301711M5[t] -2502.64642301711M6[t] + 5249.77138413685M7[t] + 7334.27138413685M8[t] + 4627.07138413685M9[t] + 2037.47138413686M10[t] -165.228615863145M11[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) 16469.4642301711 1044.803137 15.7632 0 0 Dummy -2119.17807153966 825.989405 -2.5656 0.011697 0.005848 M1 -1429.44642301712 1434.624155 -0.9964 0.321329 0.160665 M2 -2227.34642301710 1434.624155 -1.5526 0.123508 0.061754 M3 -2991.44642301711 1434.624155 -2.0852 0.039455 0.019727 M4 -3375.04642301711 1434.624155 -2.3526 0.02049 0.010245 M5 -3895.64642301711 1434.624155 -2.7154 0.007729 0.003865 M6 -2502.64642301711 1434.624155 -1.7445 0.083977 0.041989 M7 5249.77138413685 1436.472388 3.6546 0.000402 0.000201 M8 7334.27138413685 1436.472388 5.1058 1e-06 1e-06 M9 4627.07138413685 1436.472388 3.2211 0.001696 0.000848 M10 2037.47138413686 1436.472388 1.4184 0.159012 0.079506 M11 -165.228615863145 1436.472388 -0.115 0.908644 0.454322 Multiple Linear Regression - Regression Statistics Multiple R 0.773655826489416 R-squared 0.598543337861021 Adjusted R-squared 0.553095413845287 F-TEST (value) 13.1698719099647 F-TEST (DF numerator) 12 F-TEST (DF denominator) 106 p-value 4.44089209850063e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3122.29336517236 Sum Squared Residuals 1033363880.96913 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13363 15040.0178071540 -1677.01780715405 2 12530 14242.1178071540 -1712.11780715397 3 11420 13478.0178071539 -2058.01780715395 4 10948 13094.4178071540 -2146.41780715398 5 10173 12573.8178071540 -2400.81780715397 6 10602 13966.8178071540 -3364.81780715397 7 16094 21719.2356143079 -5625.23561430794 8 19631 23803.7356143079 -4172.73561430794 9 17140 21096.5356143079 -3956.53561430793 10 14345 18506.9356143079 -4161.93561430793 11 12632 16304.2356143079 -3672.23561430793 12 12894 16469.4642301711 -3575.46423017108 13 11808 15040.0178071540 -3232.01780715396 14 10673 14242.1178071540 -3569.11780715396 15 9939 13478.0178071540 -3539.01780715397 16 9890 13094.4178071540 -3204.41780715396 17 9283 12573.8178071540 -3290.81780715397 18 10131 13966.8178071540 -3835.81780715397 19 15864 21719.2356143079 -5855.23561430793 20 19283 23803.7356143079 -4520.73561430793 21 16203 21096.5356143079 -4893.53561430793 22 13919 18506.9356143079 -4587.93561430793 23 11937 16304.2356143079 -4367.23561430793 24 11795 16469.4642301711 -4674.46423017108 25 11268 15040.0178071540 -3772.01780715395 26 10522 14242.1178071540 -3720.11780715396 27 9929 13478.0178071540 -3549.01780715397 28 9725 13094.4178071540 -3369.41780715396 29 9372 12573.8178071540 -3201.81780715396 30 10068 13966.8178071540 -3898.81780715397 31 16230 21719.2356143079 -5489.23561430793 32 19115 23803.7356143079 -4688.73561430793 33 18351 21096.5356143079 -2745.53561430793 34 16265 18506.9356143079 -2241.93561430793 35 14103 16304.2356143079 -2201.23561430793 36 14115 16469.4642301711 -2354.46423017107 37 13327 15040.0178071540 -1713.01780715396 38 12618 14242.1178071540 -1624.11780715397 39 12129 13478.0178071540 -1349.01780715397 40 11775 13094.4178071540 -1319.41780715396 41 11493 12573.8178071540 -1080.81780715396 42 12470 13966.8178071540 -1496.81780715397 43 20792 21719.2356143079 -927.23561430793 44 22337 23803.7356143079 -1466.73561430793 45 21325 21096.5356143079 228.464385692070 46 18581 18506.9356143079 74.0643856920689 47 16475 16304.2356143079 170.764385692069 48 16581 16469.4642301711 111.535769828924 49 15745 15040.0178071540 704.982192846043 50 14453 14242.1178071540 210.882192846034 51 13712 13478.0178071540 233.982192846033 52 13766 13094.4178071540 671.582192846035 53 13336 12573.8178071540 762.182192846036 54 15346 13966.8178071540 1379.18219284603 55 24446 21719.2356143079 2726.76438569207 56 26178 23803.7356143079 2374.26438569207 57 24628 21096.5356143079 3531.46438569207 58 21282 18506.9356143079 2775.06438569207 59 18850 16304.2356143079 2545.76438569207 60 18822 16469.4642301711 2352.53576982892 61 18060 15040.0178071540 3019.98219284604 62 17536 14242.1178071540 3293.88219284603 63 16417 13478.0178071540 2938.98219284603 64 15842 13094.4178071540 2747.58219284604 65 15188 12573.8178071540 2614.18219284604 66 16905 13966.8178071540 2938.18219284603 67 25430 21719.2356143079 3710.76438569207 68 27962 23803.7356143079 4158.26438569207 69 26607 21096.5356143079 5510.46438569207 70 23364 18506.9356143079 4857.06438569207 71 20827 16304.2356143079 4522.76438569207 72 20506 16469.4642301711 4036.53576982893 73 19181 15040.0178071540 4140.98219284604 74 18016 14242.1178071540 3773.88219284603 75 17354 13478.0178071540 3875.98219284603 76 16256 13094.4178071540 3161.58219284604 77 15770 12573.8178071540 3196.18219284604 78 17538 13966.8178071540 3571.18219284603 79 26899 21719.2356143079 5179.76438569207 80 28915 23803.7356143079 5111.26438569207 81 25247 21096.5356143079 4150.46438569207 82 22856 18506.9356143079 4349.06438569207 83 19980 16304.2356143079 3675.76438569207 84 19856 16469.4642301711 3386.53576982892 85 16994 15040.0178071540 1953.98219284604 86 16839 14242.1178071540 2596.88219284603 87 15618 13478.0178071540 2139.98219284603 88 15883 13094.4178071540 2788.58219284604 89 15513 12573.8178071540 2939.18219284604 90 17106 13966.8178071540 3139.18219284603 91 25272 21719.2356143079 3552.76438569207 92 26731 23803.7356143079 2927.26438569207 93 22891 21096.5356143079 1794.46438569207 94 19583 18506.9356143079 1076.06438569207 95 16939 16304.2356143079 634.764385692069 96 16757 16469.4642301711 287.535769828924 97 15435 15040.0178071540 394.982192846043 98 14786 14242.1178071540 543.882192846034 99 13680 13478.0178071540 201.982192846033 100 13208 13094.4178071540 113.582192846035 101 12707 12573.8178071540 133.182192846036 102 14277 13966.8178071540 310.182192846034 103 22436 19600.0575427683 2835.94245723173 104 23229 21684.5575427683 1544.44245723173 105 18241 18977.3575427683 -736.357542768274 106 16145 16387.7575427683 -242.757542768274 107 13994 14185.0575427683 -191.057542768274 108 14780 14350.2861586314 429.713841368582 109 13100 12920.8397356143 179.160264385701 110 12329 12122.9397356143 206.060264385692 111 12463 11358.8397356143 1104.16026438569 112 11532 10975.2397356143 556.760264385692 113 10784 10454.6397356143 329.360264385692 114 13106 11847.6397356143 1258.36026438569 115 19491 19600.0575427683 -109.057542768273 116 20418 21684.5575427683 -1266.55754276827 117 16094 18977.3575427683 -2883.35754276827 118 14491 16387.7575427683 -1896.75754276827 119 13067 14185.0575427683 -1118.05754276827 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0830382648287223 0.166076529657445 0.916961735171278 17 0.0325548602009425 0.065109720401885 0.967445139799058 18 0.0112686872215247 0.0225373744430494 0.988731312778475 19 0.00399850801062934 0.00799701602125868 0.99600149198937 20 0.00133412524519958 0.00266825049039916 0.9986658747548 21 0.000586131399069735 0.00117226279813947 0.99941386860093 22 0.000207504262360255 0.00041500852472051 0.99979249573764 23 8.23165124524506e-05 0.000164633024904901 0.999917683487548 24 4.65073923434916e-05 9.30147846869831e-05 0.999953492607657 25 3.86934386513920e-05 7.73868773027839e-05 0.999961306561349 26 2.52560623995084e-05 5.05121247990169e-05 0.9999747439376 27 1.28419827489997e-05 2.56839654979995e-05 0.99998715801725 28 6.36620330616772e-06 1.27324066123354e-05 0.999993633796694 29 2.68845847100334e-06 5.37691694200668e-06 0.999997311541529 30 1.33828036938885e-06 2.67656073877769e-06 0.99999866171963 31 1.37790652941587e-06 2.75581305883175e-06 0.99999862209347 32 1.29658177592963e-06 2.59316355185926e-06 0.999998703418224 33 3.53585602014736e-06 7.07171204029473e-06 0.99999646414398 34 1.60454233595579e-05 3.20908467191157e-05 0.99998395457664 35 3.59347979007866e-05 7.18695958015731e-05 0.9999640652021 36 7.61754473215877e-05 0.000152350894643175 0.999923824552678 37 8.59324956725822e-05 0.000171864991345164 0.999914067504327 38 0.000117473686028644 0.000234947372057288 0.999882526313971 39 0.000206262651097654 0.000412525302195309 0.999793737348902 40 0.000314482587597754 0.000628965175195508 0.999685517412402 41 0.000583354427298501 0.00116670885459700 0.999416645572701 42 0.00172529714991817 0.00345059429983633 0.998274702850082 43 0.0540836609159272 0.108167321831854 0.945916339084073 44 0.154213748000408 0.308427496000816 0.845786251999592 45 0.336414226382958 0.672828452765916 0.663585773617042 46 0.505977952975814 0.988044094048371 0.494022047024186 47 0.639766791764239 0.720466416471523 0.360233208235761 48 0.756246214734474 0.487507570531053 0.243753785265526 49 0.815319355754834 0.369361288490331 0.184680644245166 50 0.859894729207975 0.280210541584049 0.140105270792024 51 0.894577483113299 0.210845033773402 0.105422516886701 52 0.919109362796481 0.161781274407037 0.0808906372035187 53 0.937636951688358 0.124726096623284 0.0623630483116421 54 0.965898994201772 0.068202011596456 0.034101005798228 55 0.994446028531697 0.0111079429366069 0.00555397146830345 56 0.998169980700138 0.00366003859972428 0.00183001929986214 57 0.999352017954692 0.00129596409061627 0.000647982045308135 58 0.999610754966087 0.000778490067825253 0.000389245033912626 59 0.99970606996032 0.000587860079361395 0.000293930039680697 60 0.999761353501125 0.000477292997750692 0.000238646498875346 61 0.999803695347441 0.000392609305117388 0.000196304652558694 62 0.99985197644036 0.000296047119281814 0.000148023559640907 63 0.999863024104828 0.000273951790344966 0.000136975895172483 64 0.999855420117189 0.000289159765622549 0.000144579882811274 65 0.999836086792972 0.000327826414056297 0.000163913207028148 66 0.999835806594322 0.000328386811356683 0.000164193405678341 67 0.999886117572745 0.000227764854510721 0.000113882427255361 68 0.999911568703268 0.000176862593464079 8.84312967320397e-05 69 0.99997734591689 4.53081662208595e-05 2.26540831104298e-05 70 0.999989120151673 2.17596966548284e-05 1.08798483274142e-05 71 0.9999933853928 1.32292143982477e-05 6.61460719912383e-06 72 0.999993874639687 1.22507206264998e-05 6.12536031324991e-06 73 0.999995403824465 9.19235107047918e-06 4.59617553523959e-06 74 0.999995076837787 9.84632442510167e-06 4.92316221255084e-06 75 0.999994895834656 1.02083306876851e-05 5.10416534384256e-06 76 0.99999232440933 1.53511813389010e-05 7.67559066945049e-06 77 0.999988583904174 2.28321916527237e-05 1.14160958263618e-05 78 0.999983794905632 3.24101887354721e-05 1.62050943677360e-05 79 0.999983971126275 3.20577474493164e-05 1.60288737246582e-05 80 0.999988745136116 2.25097277676580e-05 1.12548638838290e-05 81 0.999994775377347 1.04492453050846e-05 5.2246226525423e-06 82 0.999998171752224 3.65649555114977e-06 1.82824777557488e-06 83 0.999998872293607 2.25541278567546e-06 1.12770639283773e-06 84 0.999998816076931 2.36784613722021e-06 1.18392306861010e-06 85 0.999997250170779 5.49965844261199e-06 2.74982922130600e-06 86 0.99999512864782 9.74270436199236e-06 4.87135218099618e-06 87 0.999988557534752 2.28849304962878e-05 1.14424652481439e-05 88 0.999982656392727 3.46872145468521e-05 1.73436072734260e-05 89 0.999978364935755 4.32701284897922e-05 2.16350642448961e-05 90 0.999967861709595 6.42765808097887e-05 3.21382904048943e-05 91 0.999937215346586 0.000125569306829005 6.27846534145025e-05 92 0.999914192566373 0.000171614867252875 8.58074336264374e-05 93 0.999961908438605 7.6183122789205e-05 3.80915613946025e-05 94 0.999952887458164 9.4225083672467e-05 4.71125418362335e-05 95 0.999906616897594 0.000186766204812819 9.33831024064093e-05 96 0.999693159660826 0.000613680678348697 0.000306840339174348 97 0.999086225687559 0.0018275486248828 0.0009137743124414 98 0.997529065436519 0.00494186912696282 0.00247093456348141 99 0.993005201215071 0.0139895975698580 0.00699479878492902 100 0.981180869346215 0.0376382613075704 0.0188191306537852 101 0.953938913839563 0.0921221723208738 0.0460610861604369 102 0.89302889076474 0.213942218470521 0.106971109235261 103 0.867151249935435 0.26569750012913 0.132848750064565 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 67 0.761363636363636 NOK 5% type I error level 71 0.806818181818182 NOK 10% type I error level 74 0.840909090909091 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/10bcdf1229267024.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/10bcdf1229267024.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/1a8ul1229267023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/1a8ul1229267023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/202ya1229267023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/202ya1229267023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/3slf11229267023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/3slf11229267023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/4fhgg1229267023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/4fhgg1229267023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/50v441229267023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/50v441229267023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/659lt1229267023.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/659lt1229267023.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/79p0y1229267024.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/79p0y1229267024.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/8561k1229267024.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/8561k1229267024.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/917651229267024.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229267634fmo6zai90r3wj9l/917651229267024.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') }

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