paperMR1(werk)

*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: Tue, 21 Dec 2010 12:55:46 +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/t1292936142jmufimnksh9vfe0.htm/, Retrieved Tue, 21 Dec 2010 13:55:53 +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/t1292936142jmufimnksh9vfe0.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 «

6,7 0 6,7 0 6,5 0 6,3 0 6,3 0 6,3 0 6,5 0 6,6 0 6,5 0 6,3 0 6,3 0 6,5 0 7 0 7,1 0 7,3 0 7,3 0 7,4 0 7,4 0 7,3 0 7,4 0 7,5 0 7,7 0 7,7 0 7,7 0 7,7 0 7,7 0 7,8 0 8 0 8,1 0 8,1 0 8,2 0 8,2 0 8,2 0 8,1 0 8,1 0 8,2 0 8,3 0 8,3 0 8,4 0 8,5 0 8,5 0 8,4 0 8 0 7,9 0 8,1 0 8,5 0 8,8 0 8,8 0 8,6 0 8,3 0 8,3 0 8,3 0 8,4 0 8,4 0 8,5 0 8,6 0 8,6 0 8,6 0 8,6 0 8,6 0 8,5 0 8,4 0 8,4 0 8,4 0 8,5 0 8,5 0 8,6 0 8,6 0 8,4 0 8,2 0 8 0 8 0 8 0 8 0 7,9 0 7,9 0 7,8 0 7,8 0 8 0 7,8 0 7,4 0 7,2 0 7 0 7 0 7,2 0 7,2 0 7,2 0 7 0 6,9 0 6,8 0 6,8 0 6,8 0 6,9 0 7,2 0 7,2 0 7,2 0 7,1 0 7,2 1 7,3 1 7,5 1 7,6 1 7,7 1 7,7 1 7,7 1 7,8 1 8 1 8,1 1 8,1 1 8 1 8,1 1 8,2 1 8,3 1 8,4 1 8,4 1 8,4 1 8,5 1 8,5 1 8,6 1 8,6 1 8,5 1 8,5 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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 7.71958762886598 + 0.351245704467354X[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) 7.71958762886598 0.068259 113.0924 0 0 X 0.351245704467354 0.153267 2.2917 0.023679 0.01184 Multiple Linear Regression - Regression Statistics Multiple R 0.205594557624321 R-squared 0.0422691221247402 Adjusted R-squared 0.034220963487133 F-TEST (value) 5.25202397567391 F-TEST (DF numerator) 1 F-TEST (DF denominator) 119 p-value 0.0236794934158778 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.672274246500862 Sum Squared Residuals 53.7823668384879 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.7 7.71958762886593 -1.01958762886593 2 6.7 7.71958762886598 -1.01958762886598 3 6.5 7.71958762886598 -1.21958762886598 4 6.3 7.71958762886598 -1.41958762886598 5 6.3 7.71958762886598 -1.41958762886598 6 6.3 7.71958762886598 -1.41958762886598 7 6.5 7.71958762886598 -1.21958762886598 8 6.6 7.71958762886598 -1.11958762886598 9 6.5 7.71958762886598 -1.21958762886598 10 6.3 7.71958762886598 -1.41958762886598 11 6.3 7.71958762886598 -1.41958762886598 12 6.5 7.71958762886598 -1.21958762886598 13 7 7.71958762886598 -0.71958762886598 14 7.1 7.71958762886598 -0.61958762886598 15 7.3 7.71958762886598 -0.41958762886598 16 7.3 7.71958762886598 -0.41958762886598 17 7.4 7.71958762886598 -0.319587628865979 18 7.4 7.71958762886598 -0.319587628865979 19 7.3 7.71958762886598 -0.41958762886598 20 7.4 7.71958762886598 -0.319587628865979 21 7.5 7.71958762886598 -0.219587628865980 22 7.7 7.71958762886598 -0.0195876288659797 23 7.7 7.71958762886598 -0.0195876288659797 24 7.7 7.71958762886598 -0.0195876288659797 25 7.7 7.71958762886598 -0.0195876288659797 26 7.7 7.71958762886598 -0.0195876288659797 27 7.8 7.71958762886598 0.08041237113402 28 8 7.71958762886598 0.28041237113402 29 8.1 7.71958762886598 0.38041237113402 30 8.1 7.71958762886598 0.38041237113402 31 8.2 7.71958762886598 0.480412371134019 32 8.2 7.71958762886598 0.480412371134019 33 8.2 7.71958762886598 0.480412371134019 34 8.1 7.71958762886598 0.38041237113402 35 8.1 7.71958762886598 0.38041237113402 36 8.2 7.71958762886598 0.480412371134019 37 8.3 7.71958762886598 0.580412371134021 38 8.3 7.71958762886598 0.580412371134021 39 8.4 7.71958762886598 0.68041237113402 40 8.5 7.71958762886598 0.78041237113402 41 8.5 7.71958762886598 0.78041237113402 42 8.4 7.71958762886598 0.68041237113402 43 8 7.71958762886598 0.28041237113402 44 7.9 7.71958762886598 0.180412371134021 45 8.1 7.71958762886598 0.38041237113402 46 8.5 7.71958762886598 0.78041237113402 47 8.8 7.71958762886598 1.08041237113402 48 8.8 7.71958762886598 1.08041237113402 49 8.6 7.71958762886598 0.88041237113402 50 8.3 7.71958762886598 0.580412371134021 51 8.3 7.71958762886598 0.580412371134021 52 8.3 7.71958762886598 0.580412371134021 53 8.4 7.71958762886598 0.68041237113402 54 8.4 7.71958762886598 0.68041237113402 55 8.5 7.71958762886598 0.78041237113402 56 8.6 7.71958762886598 0.88041237113402 57 8.6 7.71958762886598 0.88041237113402 58 8.6 7.71958762886598 0.88041237113402 59 8.6 7.71958762886598 0.88041237113402 60 8.6 7.71958762886598 0.88041237113402 61 8.5 7.71958762886598 0.78041237113402 62 8.4 7.71958762886598 0.68041237113402 63 8.4 7.71958762886598 0.68041237113402 64 8.4 7.71958762886598 0.68041237113402 65 8.5 7.71958762886598 0.78041237113402 66 8.5 7.71958762886598 0.78041237113402 67 8.6 7.71958762886598 0.88041237113402 68 8.6 7.71958762886598 0.88041237113402 69 8.4 7.71958762886598 0.68041237113402 70 8.2 7.71958762886598 0.480412371134019 71 8 7.71958762886598 0.28041237113402 72 8 7.71958762886598 0.28041237113402 73 8 7.71958762886598 0.28041237113402 74 8 7.71958762886598 0.28041237113402 75 7.9 7.71958762886598 0.180412371134021 76 7.9 7.71958762886598 0.180412371134021 77 7.8 7.71958762886598 0.08041237113402 78 7.8 7.71958762886598 0.08041237113402 79 8 7.71958762886598 0.28041237113402 80 7.8 7.71958762886598 0.08041237113402 81 7.4 7.71958762886598 -0.319587628865979 82 7.2 7.71958762886598 -0.51958762886598 83 7 7.71958762886598 -0.71958762886598 84 7 7.71958762886598 -0.71958762886598 85 7.2 7.71958762886598 -0.51958762886598 86 7.2 7.71958762886598 -0.51958762886598 87 7.2 7.71958762886598 -0.51958762886598 88 7 7.71958762886598 -0.71958762886598 89 6.9 7.71958762886598 -0.81958762886598 90 6.8 7.71958762886598 -0.91958762886598 91 6.8 7.71958762886598 -0.91958762886598 92 6.8 7.71958762886598 -0.91958762886598 93 6.9 7.71958762886598 -0.81958762886598 94 7.2 7.71958762886598 -0.51958762886598 95 7.2 7.71958762886598 -0.51958762886598 96 7.2 7.71958762886598 -0.51958762886598 97 7.1 7.71958762886598 -0.61958762886598 98 7.2 8.07083333333333 -0.870833333333333 99 7.3 8.07083333333333 -0.770833333333333 100 7.5 8.07083333333333 -0.570833333333333 101 7.6 8.07083333333333 -0.470833333333333 102 7.7 8.07083333333333 -0.370833333333333 103 7.7 8.07083333333333 -0.370833333333333 104 7.7 8.07083333333333 -0.370833333333333 105 7.8 8.07083333333333 -0.270833333333333 106 8 8.07083333333333 -0.0708333333333332 107 8.1 8.07083333333333 0.0291666666666665 108 8.1 8.07083333333333 0.0291666666666665 109 8 8.07083333333333 -0.0708333333333332 110 8.1 8.07083333333333 0.0291666666666665 111 8.2 8.07083333333333 0.129166666666666 112 8.3 8.07083333333333 0.229166666666668 113 8.4 8.07083333333333 0.329166666666667 114 8.4 8.07083333333333 0.329166666666667 115 8.4 8.07083333333333 0.329166666666667 116 8.5 8.07083333333333 0.429166666666667 117 8.5 8.07083333333333 0.429166666666667 118 8.6 8.07083333333333 0.529166666666666 119 8.6 8.07083333333333 0.529166666666666 120 8.5 8.07083333333333 0.429166666666667 121 8.5 8.07083333333333 0.429166666666667 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0596459032410129 0.119291806482026 0.940354096758987 6 0.026983059820126 0.053966119640252 0.973016940179874 7 0.0088538913065143 0.0177077826130286 0.991146108693486 8 0.00332623583754938 0.00665247167509876 0.99667376416245 9 0.00105223955429161 0.00210447910858321 0.998947760445708 10 0.000594840869017487 0.00118968173803497 0.999405159130982 11 0.000334670292177744 0.000669340584355488 0.999665329707822 12 0.000127795415234186 0.000255590830468372 0.999872204584766 13 0.00121992137580897 0.00243984275161794 0.998780078624191 14 0.00546217674991423 0.0109243534998285 0.994537823250086 15 0.0258130772358856 0.0516261544717713 0.974186922764114 16 0.0552300173330238 0.110460034666048 0.944769982666976 17 0.104907216522057 0.209814433044114 0.895092783477943 18 0.152565494134871 0.305130988269743 0.847434505865129 19 0.172129449460888 0.344258898921776 0.827870550539112 20 0.204744997888182 0.409489995776363 0.795255002111819 21 0.251983277278871 0.503966554557741 0.74801672272113 22 0.345553372612895 0.69110674522579 0.654446627387105 23 0.420640706145156 0.841281412290312 0.579359293854844 24 0.477662523920024 0.955325047840047 0.522337476079976 25 0.519400380114519 0.961199239770962 0.480599619885481 26 0.548848726726058 0.902302546547885 0.451151273273942 27 0.589494457135365 0.82101108572927 0.410505542864635 28 0.663759952109158 0.672480095781684 0.336240047890842 29 0.738509144662078 0.522981710675844 0.261490855337922 30 0.78970603071062 0.42058793857876 0.21029396928938 31 0.840144570470734 0.319710859058531 0.159855429529266 32 0.87363630931918 0.252727381361641 0.126363690680820 33 0.896129606028135 0.207740787943730 0.103870393971865 34 0.903449361959311 0.193101276081377 0.0965506380406887 35 0.907744644796586 0.184510710406829 0.0922553552034144 36 0.916457919515251 0.167084160969498 0.0835420804847491 37 0.92880427985908 0.142391440281841 0.0711957201409206 38 0.937436514790924 0.125126970418152 0.0625634852090761 39 0.948767119648056 0.102465760703887 0.0512328803519436 40 0.96139842575113 0.0772031484977385 0.0386015742488692 41 0.970093945536467 0.059812108927065 0.0299060544635325 42 0.973392093655421 0.0532158126891577 0.0266079063445789 43 0.967584030049564 0.0648319399008711 0.0324159699504355 44 0.959180866432455 0.0816382671350891 0.0408191335675445 45 0.95281028758377 0.09437942483246 0.04718971241623 46 0.95962503197058 0.0807499360588399 0.0403749680294199 47 0.976065562004114 0.047868875991772 0.023934437995886 48 0.986001187616617 0.0279976247667659 0.0139988123833829 49 0.989079541752722 0.0218409164945554 0.0109204582472777 50 0.988056693459133 0.0238866130817335 0.0119433065408667 51 0.986895478882828 0.0262090422343438 0.0131045211171719 52 0.98559099350743 0.028818012985141 0.0144090064925705 53 0.985504063248929 0.0289918735021427 0.0144959367510714 54 0.985399335469435 0.029201329061129 0.0146006645305645 55 0.986786746793952 0.0264265064120958 0.0132132532060479 56 0.989519456226874 0.0209610875462525 0.0104805437731263 57 0.991832694273574 0.0163346114528525 0.00816730572642627 58 0.993784419065423 0.0124311618691549 0.00621558093457745 59 0.995414203374344 0.00917159325131291 0.00458579662565646 60 0.99674962346195 0.00650075307609867 0.00325037653804934 61 0.997402492812782 0.00519501437443648 0.00259750718721824 62 0.997664205198778 0.00467158960244323 0.00233579480122161 63 0.997963455339373 0.00407308932125454 0.00203654466062727 64 0.998290381206966 0.00341923758606746 0.00170961879303373 65 0.998865070439042 0.00226985912191554 0.00113492956095777 66 0.999314041071664 0.00137191785667150 0.000685958928335749 67 0.999720132183373 0.000559735633253183 0.000279867816626591 68 0.99991164554728 0.000176708905442275 8.83544527211373e-05 69 0.999960078960398 7.98420792041492e-05 3.99210396020746e-05 70 0.999973152814168 5.36943716641531e-05 2.68471858320766e-05 71 0.999973665288822 5.2669422355767e-05 2.63347111778835e-05 72 0.999975899463814 4.82010723713217e-05 2.41005361856608e-05 73 0.99997974351121 4.05129775806649e-05 2.02564887903325e-05 74 0.999984703769064 3.05924618714652e-05 1.52962309357326e-05 75 0.999986633053651 2.67338926972501e-05 1.33669463486251e-05 76 0.999989543132512 2.09137349768669e-05 1.04568674884334e-05 77 0.999990511060897 1.89778782068971e-05 9.48893910344856e-06 78 0.999992318879362 1.53622412765165e-05 7.68112063825827e-06 79 0.99999764790772 4.70418455923909e-06 2.35209227961954e-06 80 0.999998837491712 2.32501657673324e-06 1.16250828836662e-06 81 0.999998374528691 3.25094261806854e-06 1.62547130903427e-06 82 0.99999720247128 5.59505744010434e-06 2.79752872005217e-06 83 0.999995278224592 9.44355081537455e-06 4.72177540768727e-06 84 0.999991996358943 1.60072821129942e-05 8.0036410564971e-06 85 0.99998628713595 2.74257281005346e-05 1.37128640502673e-05 86 0.999976884711725 4.62305765491589e-05 2.31152882745795e-05 87 0.999961799578727 7.64008425465194e-05 3.82004212732597e-05 88 0.999935065091862 0.000129869816276652 6.49349081383258e-05 89 0.999896124567453 0.000207750865093121 0.000103875432546560 90 0.999852714497975 0.000294571004049871 0.000147285502024935 91 0.999795661021355 0.000408677957290188 0.000204338978645094 92 0.999728021874753 0.000543956250493619 0.000271978125246809 93 0.999600064786085 0.00079987042782904 0.00039993521391452 94 0.999281786473162 0.00143642705367536 0.000718213526837678 95 0.998733012360391 0.00253397527921713 0.00126698763960857 96 0.997815664710786 0.00436867057842849 0.00218433528921424 97 0.996325424199726 0.00734915160054804 0.00367457580027402 98 0.998559030082616 0.00288193983476786 0.00144096991738393 99 0.999480842026997 0.00103831594600563 0.000519157973002815 100 0.999713269008217 0.000573461983566142 0.000286730991783071 101 0.999818257477662 0.000363485044676833 0.000181742522338417 102 0.999860165774662 0.00027966845067698 0.00013983422533849 103 0.999917716600766 0.000164566798467732 8.22833992338662e-05 104 0.99997047690228 5.9046195441026e-05 2.9523097720513e-05 105 0.99998891034103 2.21793179417611e-05 1.10896589708806e-05 106 0.999987981289477 2.40374210466511e-05 1.20187105233256e-05 107 0.999979532577772 4.09348444564697e-05 2.04674222282348e-05 108 0.999969400735929 6.11985281429363e-05 3.05992640714681e-05 109 0.999988554366222 2.28912675550974e-05 1.14456337775487e-05 110 0.999995435268462 9.12946307689897e-06 4.56473153844948e-06 111 0.999997575343313 4.84931337439563e-06 2.42465668719782e-06 112 0.999997048666365 5.90266726939422e-06 2.95133363469711e-06 113 0.99998641580539 2.71683892200113e-05 1.35841946100057e-05 114 0.999949060290563 0.000101879418873982 5.09397094369912e-05 115 0.999894498793588 0.000211002412823615 0.000105501206411808 116 0.998879145785716 0.00224170842856699 0.00112085421428350 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 64 0.571428571428571 NOK 5% type I error level 78 0.696428571428571 NOK 10% type I error level 87 0.776785714285714 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/10vwsr1292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/10vwsr1292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/16vvf1292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/16vvf1292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/26vvf1292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/26vvf1292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/36vvf1292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/36vvf1292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/4hmu01292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/4hmu01292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/5hmu01292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/5hmu01292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/6hmu01292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/6hmu01292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/7aet31292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/7aet31292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/8l5bo1292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/8l5bo1292936137.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/9l5bo1292936137.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936142jmufimnksh9vfe0/9l5bo1292936137.ps (open in new window)

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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