*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: Mon, 20 Dec 2010 23:12:06 +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/21/t1292886607jzd7km7eaon07du.htm/, Retrieved Tue, 21 Dec 2010 00:10:10 +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/21/t1292886607jzd7km7eaon07du.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 «

9.4 0.5 5.1 -1.0 2504.7 9.4 0.8 5.0 3.0 2661.4 9.5 1.0 5.0 2.0 2880.4 9.5 1.3 5.1 3.0 3064.4 9.4 1.3 5.0 5.0 3141.1 9.4 1.2 4.9 5.0 3327.7 9.3 1.2 4.8 3.0 3565.0 9.4 1.0 4.5 2.0 3403.1 9.4 0.8 4.3 1.0 3149.9 9.2 0.7 4.3 -4.0 3006.8 9.1 0.6 4.2 1.0 3230.7 9.1 0.7 4.0 1.0 3361.1 9.1 1.0 3.8 6.0 3484.7 9.0 1.0 4.1 3.0 3411.1 9.0 1.3 4.2 2.0 3288.2 8.9 1.1 4.0 2.0 3280.4 8.8 0.8 4.3 2.0 3174.0 8.7 0.7 4.7 -8.0 3165.3 8.5 0.7 5.0 0.0 3092.7 8.3 0.9 5.1 -2.0 3053.1 8.1 1.3 5.4 3.0 3182.0 7.9 1.4 5.4 5.0 2999.9 7.8 1.6 5.4 8.0 3249.6 7.6 2.1 5.5 8.0 3210.5 7.4 0.3 5.8 9.0 3030.3 7.2 2.1 5.7 11.0 2803.5 7.0 2.5 5.5 13.0 2767.6 7.0 2.3 5.6 12.0 2882.6 6.8 2.4 5.6 13.0 2863.4 6.8 3.0 5.5 15.0 2897.1 6.7 1.7 5.5 13.0 3012.6 6.8 3.5 5.7 16.0 3143.0 6.7 4.0 5.6 10.0 3032.9 6.7 3.7 5.6 14.0 3045.8 6.7 3.7 5.4 14.0 3110.5 6.5 3.0 5.2 15.0 3013.2 6.3 2.7 5.1 13.0 2987.1 6.3 2.5 5.1 8.0 2995.6 6.3 2.2 5.0 7.0 2833.2 6.5 2.9 5.3 3.0 2849.0 6.6 3.1 5.4 3.0 2794.8 6.5 3.0 5.3 4.0 2845.3 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 11.6439743246579 -0.241949543053011hicp[t] -0.69921918814009rente[t] + 0.020441410400977consumer[t] -4.54384030752964e-05bel20[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) 11.6439743246579 0.539118 21.5982 0 0 hicp -0.241949543053011 0.042087 -5.7487 0 0 rente -0.69921918814009 0.091565 -7.6363 0 0 consumer 0.020441410400977 0.008003 2.5541 0.011649 0.005825 bel20 -4.54384030752964e-05 8.2e-05 -0.5536 0.580676 0.290338 Multiple Linear Regression - Regression Statistics Multiple R 0.663219509583841 R-squared 0.439860117892631 Adjusted R-squared 0.424822805621292 F-TEST (value) 29.2512458314115 F-TEST (DF numerator) 4 F-TEST (DF denominator) 149 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.608830743183158 Sum Squared Residuals 55.2305562028985 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.4 7.82273071503326 1.57726928496674 2 9.4 7.89471321477337 1.50528678522663 3 9.5 7.8159308854883 1.6840691145117 4 9.5 7.68550484799351 1.81449515200649 5 9.4 7.7928244620936 1.6071755379064 6 9.4 7.87846252919906 1.52153747080094 7 9.3 7.89671909416135 1.40328090583865 8 9.4 8.14178982627089 1.25821017372911 9 9.4 8.3210871657672 1.0789128342328 10 9.2 8.24957730354769 0.950422696452308 11 9.1 8.43572757022333 0.664272429776673 12 9.1 8.54545128578503 0.554548714214974 13 9.1 8.70930112588192 0.390698874118081 14 9 8.4415554047033 0.558444595296697 15 9 8.28419159231037 0.715808407689633 16 8.9 8.47277975809297 0.427220241907026 17 8.8 8.34043351065406 0.459566489345938 18 8.7 7.88092199980031 0.819078000199687 19 8.5 7.83798635462937 0.662013645370632 20 8.3 7.68059106716458 0.619408932835416 21 8.1 7.47039553534983 0.629604464650167 22 7.9 7.4953577350465 0.404642264953503 23 7.8 7.49694608839092 0.303053911609076 24 7.6 7.30782603961065 0.292173960389345 25 7.4 7.56219887129919 -0.162198871299191 26 7.2 7.24779986323721 -0.0477998632372123 27 7 7.33337794311638 -0.333377943116384 28 7 7.28617910615834 -0.286179106158341 29 6.8 7.28329797959306 -0.483297979593063 30 6.8 7.24740171919358 -0.447401719193582 31 6.7 7.51580516880534 -0.815805168805344 32 6.8 6.99585121712382 -0.19585121712382 33 6.7 6.82715267018405 -0.127152670184052 34 6.7 6.98091701930419 -0.280917019304191 35 6.7 7.11782099225324 -0.417820992253237 36 6.5 7.45189207703857 -0.951892077038566 37 6.3 7.55470198028679 -1.25470198028679 38 6.3 7.50049861046637 -1.20049861046637 39 6.3 7.62994317845473 -1.32994317845473 40 6.5 7.1683291735031 -0.668329173503099 41 6.6 7.05248010752517 -0.452480107525169 42 6.5 7.16474375169015 -0.664743751690153 43 6.3 7.34981044123442 -1.04981044123443 44 6.3 7.41156940158932 -1.11156940158932 45 6.5 7.48806883358351 -0.988068833583515 46 7 7.56164755239507 -0.561647552395073 47 7.1 7.36341059237278 -0.263410592372785 48 7.3 7.31063686931543 -0.0106368693154297 49 7.3 7.3048401549189 -0.00484015491890292 50 7.4 7.36875470690743 0.0312452930925702 51 7.4 7.17989383257775 0.220106167422253 52 7.3 7.42323420070379 -0.12323420070379 53 7.4 7.4926244120732 -0.092624412073195 54 7.5 7.76828807932272 -0.268288079322723 55 7.7 7.8264788759236 -0.126478875923606 56 7.7 7.88113944041585 -0.181139440415845 57 7.7 8.121879560902 -0.421879560902007 58 7.7 7.96353420087402 -0.263534200874024 59 7.7 8.05679261998552 -0.356792619985515 60 7.8 8.13852025579634 -0.338520255796341 61 8 8.28432594371814 -0.284325943718142 62 8.1 8.18392221082336 -0.0839222108233606 63 8.1 7.92361062232679 0.176389377673209 64 8.2 8.10608905609502 0.0939109439049844 65 8.2 8.56833052094801 -0.368330520948014 66 8.2 8.33055735262961 -0.13055735262961 67 8.1 8.0832124790995 0.016787520900493 68 8.1 8.07153414588855 0.0284658541114533 69 8.2 8.2384510000644 -0.0384510000643996 70 8.3 7.89561099028424 0.404389009715758 71 8.3 7.91044190561268 0.389558094387323 72 8.4 8.07347650883803 0.326523491161967 73 8.5 8.2004017204042 0.299598279595806 74 8.5 8.4254333750083 0.0745666249917008 75 8.4 8.43438086019443 -0.0343808601944338 76 8 8.03189186773689 -0.031891867736889 77 7.9 7.75468503186433 0.145314968135673 78 8.1 7.97197770654685 0.128022293453145 79 8.5 7.93623437907164 0.563765620928355 80 8.8 8.13889770317408 0.661102296825925 81 8.8 8.18057181581015 0.619428184189852 82 8.6 8.01584267266458 0.584157327335423 83 8.3 8.15789994601435 0.142100053985653 84 8.3 8.34176698358881 -0.041766983588808 85 8.3 8.45466684352547 -0.15466684352547 86 8.4 8.29907760380095 0.100922396199052 87 8.4 8.19738101955669 0.202618980443308 88 8.5 8.3720852001227 0.127914799877303 89 8.6 8.41611095702852 0.183889042971478 90 8.6 8.38761727611062 0.212382723889384 91 8.6 8.31476912413856 0.285230875861442 92 8.6 8.42232509606326 0.17767490393674 93 8.6 8.24554290095915 0.354457099040849 94 8.5 8.42107563740313 0.0789243625968748 95 8.4 8.30339573559156 0.0966042644084349 96 8.4 8.39811436321658 0.00188563678341779 97 8.4 8.33218441114106 0.0678155888589393 98 8.5 8.30510445005078 0.194895549949217 99 8.5 8.17441769975503 0.325582300244967 100 8.6 7.95925781937598 0.640742180624018 101 8.6 7.87427447521373 0.725725524786265 102 8.4 7.96919933202993 0.430800667970071 103 8.2 8.05734105514484 0.142658944855161 104 8 8.21407789570396 -0.214077895703957 105 8 8.37588137188765 -0.375881371887653 106 8 8.42846317621027 -0.428463176210274 107 8 8.40257344759889 -0.402573447598888 108 7.9 7.9805006047226 -0.080500604722607 109 7.9 8.07357438145211 -0.173574381452111 110 7.8 8.22743885131626 -0.427438851316263 111 7.8 8.12324994318806 -0.323249943188064 112 8 8.10400603773644 -0.104006037736443 113 7.8 8.01041221361586 -0.210412213615863 114 7.4 7.92348402767337 -0.523484027673369 115 7.2 7.95518581610564 -0.755185816105637 116 7 7.97531081663799 -0.97531081663799 117 7 7.92264969813831 -0.92264969813831 118 7.2 7.81315108640434 -0.613151086404343 119 7.2 7.58561312528925 -0.385613125289254 120 7.2 7.47862247901918 -0.278622479019176 121 7 7.63301584956915 -0.63301584956915 122 6.9 7.67575676924658 -0.775756769246579 123 6.8 7.34447072675946 -0.544470726759461 124 6.8 7.25704371381405 -0.457043713814053 125 6.8 6.76701286329082 0.0329871367091839 126 6.9 6.36048118010217 0.539518819897832 127 7.2 6.52660700000787 0.673392999992135 128 7.2 6.82765411696006 0.372345883039942 129 7.2 6.84819061619719 0.351809383802812 130 7.1 6.88876167227754 0.211238327722456 131 7.2 6.48278356030109 0.717216439698912 132 7.3 7.24854629451128 0.051453705488725 133 7.5 7.56390931519097 -0.0639093151909671 134 7.6 7.67466680507599 -0.0746668050759878 135 7.7 8.20536923848024 -0.505369238480237 136 7.7 8.21322842325936 -0.51322842325936 137 7.7 8.27401724235105 -0.574017242351048 138 7.8 8.62887994505134 -0.828879945051338 139 8 8.95665781101074 -0.956657811010738 140 8.1 8.89716406065304 -0.797164060653038 141 8.1 8.96293770694795 -0.862937706947954 142 8 8.91512519786671 -0.915125197866712 143 8.1 8.74035722798334 -0.640357227983343 144 8.2 8.56393861823246 -0.36393861823246 145 8.3 8.37135161929754 -0.0713516192975436 146 8.4 8.44387715860777 -0.0438771586077672 147 8.4 8.35193436499782 0.0480656350021849 148 8.4 8.40438381245953 -0.00438381245953138 149 8.5 8.49460850777406 0.00539149222593931 150 8.5 8.31727790938168 0.182722090618319 151 8.6 8.50089924776923 0.0991007522307683 152 8.6 8.90991102859212 -0.309911028592122 153 8.5 8.50584978925955 -0.00584978925954814 154 8.5 8.35532726328823 0.144672736711773 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.00151281759252968 0.00302563518505937 0.99848718240747 9 0.000185405721704208 0.000370811443408416 0.999814594278296 10 0.000424745646099935 0.00084949129219987 0.9995752543539 11 0.000304200594885356 0.000608401189770713 0.999695799405115 12 7.90081832547321e-05 0.000158016366509464 0.999920991816745 13 1.80061633006874e-05 3.60123266013749e-05 0.9999819938367 14 2.50070260723591e-05 5.00140521447183e-05 0.999974992973928 15 2.20737431466015e-05 4.4147486293203e-05 0.999977926256853 16 1.16339619051357e-05 2.32679238102715e-05 0.999988366038095 17 0.000143434335808424 0.000286868671616848 0.999856565664192 18 0.00059358165122407 0.00118716330244814 0.999406418348776 19 0.024000142633033 0.0480002852660661 0.975999857366967 20 0.132529938962147 0.265059877924294 0.867470061037853 21 0.50671347696456 0.986573046070879 0.493286523035439 22 0.861083196101472 0.277833607797055 0.138916803898528 23 0.94286053764957 0.114278924700858 0.0571394623504292 24 0.9566544617877 0.0866910764246023 0.0433455382123011 25 0.988311584796088 0.023376830407823 0.0116884152039115 26 0.996029918509388 0.00794016298122303 0.00397008149061152 27 0.997268615649055 0.00546276870188906 0.00273138435094453 28 0.997127501143692 0.00574499771261559 0.0028724988563078 29 0.996852525204052 0.00629494959189667 0.00314747479594834 30 0.995248992806729 0.0095020143865422 0.0047510071932711 31 0.996988851043435 0.00602229791313038 0.00301114895656519 32 0.996019626101997 0.00796074779600553 0.00398037389800276 33 0.99423020234788 0.0115395953042405 0.00576979765212024 34 0.99156327386704 0.0168734522659178 0.00843672613295892 35 0.987893928919862 0.0242121421602768 0.0121060710801384 36 0.989600139510643 0.0207997209787143 0.0103998604893571 37 0.99704542503124 0.00590914993752 0.00295457496876 38 0.999748181668018 0.000503636663963426 0.000251818331981713 39 0.99999234005211 1.53198957802813e-05 7.65994789014067e-06 40 0.999996560254244 6.87949151285109e-06 3.43974575642555e-06 41 0.999995946801769 8.10639646274184e-06 4.05319823137092e-06 42 0.999997094890072 5.81021985608881e-06 2.9051099280444e-06 43 0.999999701198368 5.97603263483823e-07 2.98801631741912e-07 44 0.9999999909204 1.81592003781447e-08 9.07960018907237e-09 45 0.999999999400702 1.19859596483584e-09 5.99297982417919e-10 46 0.999999999422899 1.15420281867935e-09 5.77101409339676e-10 47 0.999999998901872 2.19625578314894e-09 1.09812789157447e-09 48 0.999999997836956 4.32608856379989e-09 2.16304428189995e-09 49 0.999999997008192 5.98361531486352e-09 2.99180765743176e-09 50 0.999999995497297 9.00540505003733e-09 4.50270252501866e-09 51 0.999999994250557 1.14988861312934e-08 5.74944306564669e-09 52 0.999999990432617 1.91347660904015e-08 9.56738304520075e-09 53 0.999999985020733 2.99585334179733e-08 1.49792667089867e-08 54 0.999999980174433 3.96511343556302e-08 1.98255671778151e-08 55 0.999999961587322 7.68253557966879e-08 3.84126778983439e-08 56 0.999999932456697 1.35086605017038e-07 6.75433025085189e-08 57 0.999999911907103 1.76185794920671e-07 8.80928974603354e-08 58 0.9999998824181 2.35163801527166e-07 1.17581900763583e-07 59 0.99999983249179 3.3501642198513e-07 1.67508210992565e-07 60 0.999999775218213 4.49563574871483e-07 2.24781787435742e-07 61 0.999999678166691 6.43666617615998e-07 3.21833308807999e-07 62 0.999999658351703 6.8329659365442e-07 3.4164829682721e-07 63 0.999999704942838 5.90114323439866e-07 2.95057161719933e-07 64 0.999999577924722 8.44150556713285e-07 4.22075278356643e-07 65 0.999999453940019 1.0921199628672e-06 5.46059981433599e-07 66 0.999999145808922 1.70838215538325e-06 8.54191077691627e-07 67 0.999998525143543 2.94971291442164e-06 1.47485645721082e-06 68 0.999997552607026 4.8947859483086e-06 2.4473929741543e-06 69 0.999996471615735 7.05676852951705e-06 3.52838426475852e-06 70 0.99999683774882 6.32450236092065e-06 3.16225118046033e-06 71 0.99999675336574 6.4932685185939e-06 3.24663425929695e-06 72 0.99999601929359 7.96141282075579e-06 3.98070641037789e-06 73 0.99999452231959 1.09553608176361e-05 5.47768040881805e-06 74 0.999990690109385 1.86197812300615e-05 9.30989061503077e-06 75 0.999985000674014 2.99986519723215e-05 1.49993259861608e-05 76 0.999974814335862 5.03713282760038e-05 2.51856641380019e-05 77 0.999965116995125 6.9766009749699e-05 3.48830048748495e-05 78 0.999947643515707 0.000104712968585474 5.23564842927372e-05 79 0.999965012930656 6.99741386875888e-05 3.49870693437944e-05 80 0.99998557880057 2.88423988598708e-05 1.44211994299354e-05 81 0.999995790029412 8.41994117637525e-06 4.20997058818763e-06 82 0.999998004969143 3.99006171410111e-06 1.99503085705055e-06 83 0.999996953703489 6.09259302264063e-06 3.04629651132031e-06 84 0.999995457238159 9.08552368300908e-06 4.54276184150454e-06 85 0.99999325840443 1.34831911387529e-05 6.74159556937645e-06 86 0.999989995469935 2.00090601289056e-05 1.00045300644528e-05 87 0.999986239347749 2.75213045019463e-05 1.37606522509731e-05 88 0.99997880644133 4.238711733888e-05 2.119355866944e-05 89 0.99996587689979 6.82462004178242e-05 3.41231002089121e-05 90 0.999943087900231 0.000113824199537624 5.6912099768812e-05 91 0.99991156055951 0.000176878880981763 8.84394404908814e-05 92 0.999854508277473 0.000290983445054595 0.000145491722527297 93 0.999774486453577 0.000451027092846335 0.000225513546423167 94 0.999683324933555 0.00063335013288895 0.000316675066444475 95 0.999558060331834 0.0008838793363326 0.0004419396681663 96 0.99932368560275 0.00135262879450214 0.000676314397251069 97 0.999011433539602 0.0019771329207953 0.000988566460397649 98 0.99871346724184 0.00257306551632081 0.00128653275816041 99 0.999018599110599 0.00196280177880178 0.00098140088940089 100 0.999710275466844 0.000579449066312719 0.000289724533156359 101 0.99996082189641 7.83562071796633e-05 3.91781035898317e-05 102 0.999985597317735 2.88053645291227e-05 1.44026822645614e-05 103 0.99998828833011 2.34233397793088e-05 1.17116698896544e-05 104 0.999985315382438 2.9369235124048e-05 1.4684617562024e-05 105 0.999985641781128 2.87164377433873e-05 1.43582188716937e-05 106 0.99998677676646 2.64464670808994e-05 1.32232335404497e-05 107 0.999984493008268 3.10139834640279e-05 1.55069917320139e-05 108 0.999987031702552 2.59365948959252e-05 1.29682974479626e-05 109 0.999991470115724 1.70597685512677e-05 8.52988427563387e-06 110 0.999990783797337 1.84324053268499e-05 9.21620266342493e-06 111 0.999990356115219 1.92877695618462e-05 9.6438847809231e-06 112 0.999996429506843 7.14098631468006e-06 3.57049315734003e-06 113 0.99999945515116 1.08969768201447e-06 5.44848841007237e-07 114 0.999999738324857 5.23350286257644e-07 2.61675143128822e-07 115 0.999999758939302 4.82121396060873e-07 2.41060698030436e-07 116 0.99999969519096 6.09618080729122e-07 3.04809040364561e-07 117 0.999999527458425 9.45083149938993e-07 4.72541574969497e-07 118 0.999999211916919 1.57616616180477e-06 7.88083080902387e-07 119 0.999998597763942 2.8044721161559e-06 1.40223605807795e-06 120 0.999998349913345 3.30017331006989e-06 1.65008665503494e-06 121 0.999997010549434 5.97890113116135e-06 2.98945056558068e-06 122 0.999998480795695 3.03840861018528e-06 1.51920430509264e-06 123 0.99999961221191 7.7557617824564e-07 3.8778808912282e-07 124 0.999999829654351 3.40691297178161e-07 1.70345648589081e-07 125 0.999999890169287 2.19661424940168e-07 1.09830712470084e-07 126 0.999999842561828 3.14876344994947e-07 1.57438172497474e-07 127 0.999999693288505 6.13422990115114e-07 3.06711495057557e-07 128 0.99999973387324 5.3225352063515e-07 2.66126760317575e-07 129 0.99999999045594 1.90881205253849e-08 9.54406026269245e-09 130 0.99999999998016 3.9678588954611e-11 1.98392944773055e-11 131 0.999999999951308 9.73831737575585e-11 4.86915868787793e-11 132 0.999999999939083 1.21834761688629e-10 6.09173808443143e-11 133 0.999999999768884 4.62232868598362e-10 2.31116434299181e-10 134 0.999999999107593 1.78481406830263e-09 8.92407034151316e-10 135 0.999999995430393 9.13921415675004e-09 4.56960707837502e-09 136 0.999999994660527 1.06789458751805e-08 5.33947293759027e-09 137 0.999999995671512 8.65697561364473e-09 4.32848780682237e-09 138 0.999999998995416 2.00916863911136e-09 1.00458431955568e-09 139 0.999999993338992 1.33220165358602e-08 6.6610082679301e-09 140 0.999999949494552 1.01010896621492e-07 5.05054483107461e-08 141 0.999999658412517 6.83174965565502e-07 3.41587482782751e-07 142 0.999997598907246 4.80218550780434e-06 2.40109275390217e-06 143 0.999996449088108 7.10182378371427e-06 3.55091189185713e-06 144 0.999997121379322 5.75724135567058e-06 2.87862067783529e-06 145 0.99998240784829 3.51843034191558e-05 1.75921517095779e-05 146 0.999828121220923 0.000343757558152969 0.000171878779076485 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 128 0.920863309352518 NOK 5% type I error level 134 0.964028776978417 NOK 10% type I error level 135 0.971223021582734 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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Software written by Ed van Stee & Patrick Wessa

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