Paper Multiple Regression Dummy, Lin, Y1

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Sat, 19 Dec 2009 12:25:45 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg.htm/, Retrieved Sat, 19 Dec 2009 20:43:58 +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/2009/Dec/19/t1261251824ikdt6yo34utshzg.htm/}, year = {2009}, } @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 = {2009}, 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:

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100.36 0 100.21 100.62 0 100.36 100.78 0 100.62 100.93 0 100.78 100.70 0 100.93 100.00 0 100.70 100.20 0 100.00 99.68 0 100.20 99.56 0 99.68 100.06 0 99.56 100.50 0 100.06 99.30 0 100.50 99.37 0 99.30 99.20 0 99.37 98.11 0 99.20 97.60 0 98.11 97.76 0 97.60 98.06 0 97.76 98.25 0 98.06 98.50 0 98.25 97.39 0 98.50 98.09 0 97.39 97.78 0 98.09 98.12 0 97.78 97.50 0 98.12 97.30 0 97.50 97.64 0 97.30 96.88 0 97.64 97.40 0 96.88 98.27 0 97.40 97.94 0 98.27 98.61 0 97.94 98.72 0 98.61 98.62 0 98.72 98.56 0 98.62 98.06 0 98.56 97.40 0 98.06 97.76 0 97.40 97.05 0 97.76 97.85 0 97.05 97.40 0 97.85 97.27 0 97.40 97.93 0 97.27 98.60 0 97.93 98.70 0 98.60 98.88 0 98.70 98.27 0 98.88 97.85 0 98.27 97.70 0 97.85 96.97 0 97.70 97.72 0 96.97 97.66 0 97.72 99.00 0 97.66 98.86 0 99.00 99.56 0 98.86 100.19 0 99.56 100.37 0 100.19 100.01 0 100.37 99.68 0 100.01 99.78 0 99.68 99.36 0 99.78 99.21 0 99.36 99.26 0 99.21 99.26 0 99.26 100.43 0 99.26 101.50 0 100.43 102.27 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Huizenprijs_Pacific[t] = + 0.411545470519684 -5.21336097465012Dummy_Crisis[t] + 0.986466746559604Y1[t] + 0.0252112021076391t + 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) 0.411545470519684 0.325567 1.2641 0.207593 0.103797 Dummy_Crisis -5.21336097465012 0.383016 -13.6113 0 0 Y1 0.986466746559604 0.004117 239.6208 0 0 t 0.0252112021076391 0.004152 6.0726 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.99966552251567 R-squared 0.999331156906527 Adjusted R-squared 0.999321647289084 F-TEST (value) 105086.367871553 F-TEST (DF numerator) 3 F-TEST (DF denominator) 211 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.4715230595416 Sum Squared Residuals 456.895204214924 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100.36 99.2905893453655 1.06941065463454 2 100.62 99.4637705594568 1.15622944054317 3 100.78 99.7454631156706 1.03453688432944 4 100.93 99.9285089972271 1.00149100277288 5 100.7 100.101690211319 0.598309788681292 6 100 99.9000140617176 0.099985938282363 7 100.2 99.2346985412336 0.965301458766452 8 99.68 99.4572030926531 0.222796907346897 9 99.56 98.9694515865498 0.590548413450244 10 100.06 98.8762867790702 1.18371322092975 11 100.5 99.3947313544577 1.10526864554231 12 99.3 99.8539879250516 -0.553987925051554 13 99.37 98.6954390312877 0.674560968712345 14 99.2 98.7897029056545 0.410297094345526 15 98.11 98.647214760847 -0.537214760846981 16 97.6 97.5971772092047 0.0028227907953413 17 97.76 97.1192903705669 0.64070962943311 18 98.06 97.302336252124 0.757663747875927 19 98.25 97.6234874781996 0.626512521800408 20 98.5 97.8361273621535 0.663872637846448 21 97.39 98.107955250901 -0.717955250901089 22 98.09 97.0381883643276 1.05181163567243 23 97.78 97.753926289027 0.0260737109730600 24 98.12 97.473332799701 0.646667200298901 25 97.5 97.833942695639 -0.333942695639005 26 97.3 97.2475445148797 0.0524554851203145 27 97.64 97.0754623676754 0.564537632324599 28 96.88 97.4360722636133 -0.556072263613315 29 97.4 96.7115687383356 0.68843126166436 30 98.27 97.2497426486543 1.02025735134571 31 97.94 98.1331799202688 -0.193179920268777 32 98.61 97.8328570960118 0.777142903988255 33 98.72 98.5190010183143 0.200998981685676 34 98.62 98.6527235625435 -0.0327235625435074 35 98.56 98.5792880899952 -0.019288089995199 36 98.06 98.5453112873093 -0.48531128730926 37 97.4 98.077289116137 -0.677289116137091 38 97.76 97.4514322655154 0.308567734484603 39 97.05 97.8317714963845 -0.7817714963845 40 97.85 97.1565913084348 0.693408691565184 41 97.4 97.9709759077901 -0.570975907790127 42 97.27 97.552277073946 -0.282277073945963 43 97.93 97.4492475990008 0.480752400999165 44 98.6 98.1255268538378 0.474473146162167 45 98.7 98.8116707761404 -0.111670776140383 46 98.88 98.935528652904 -0.0555286529040001 47 98.27 99.1383038693924 -0.86830386939236 48 97.85 98.5617703560986 -0.711770356098645 49 97.7 98.1726655246513 -0.472665524651243 50 96.97 98.049906714775 -1.07990671477495 51 97.72 97.354997191894 0.365002808105922 52 97.66 98.1200584539214 -0.460058453921416 53 99 98.0860816512355 0.913918348764526 54 98.86 99.433158293733 -0.573158293732987 55 99.56 99.3202641513223 0.239735848677720 56 100.19 100.036002076022 0.153997923978350 57 100.37 100.682687328462 -0.312687328461822 58 100.01 100.885462544950 -0.875462544950196 59 99.68 100.555545718296 -0.875545718296378 60 99.78 100.255222894039 -0.475222894039352 61 99.36 100.379080770803 -1.01908077080296 62 99.21 99.9899759393556 -0.77997593935556 63 99.26 99.8672171294793 -0.607217129479246 64 99.26 99.9417516689149 -0.681751668914878 65 100.43 99.9669628710225 0.463037128977485 66 101.5 101.146340166605 0.353659833395101 67 102.27 102.227070787531 0.0429292124686908 68 102.69 103.011861384490 -0.321861384489837 69 103.47 103.451388620153 0.0186113798474917 70 104.02 104.246043884577 -0.22604388457664 71 103.55 104.813811797292 -1.26381179729205 72 103.77 104.375383628517 -0.605383628516682 73 104.19 104.617617514867 -0.427617514867433 74 103.64 105.05714475053 -1.41714475053011 75 103.68 104.53979924203 -0.85979924202996 76 105.39 104.604469114 0.785530886000002 77 106.61 106.316538452725 0.293461547275455 78 108.12 107.545239085635 0.574760914365097 79 109.22 109.060015075048 0.159984924952452 80 110.17 110.170339698371 -0.000339698370740757 81 110.31 111.13269430971 -0.822694309710011 82 111.06 111.296010856336 -0.236010856335992 83 111.14 112.061072118363 -0.921072118363334 84 111.39 112.165200660196 -0.77520066019574 85 112.51 112.437028548943 0.0729714510567261 86 111.28 113.567082507198 -2.28708250719768 87 112.22 112.378939611037 -0.158939611037005 88 113.19 113.331429554911 -0.141429554910670 89 114.32 114.313513501181 0.00648649881887221 90 115.34 115.453432126901 -0.113432126901102 91 116.61 116.484839410500 0.125160589500452 92 117.83 117.762863380738 0.0671366192621172 93 117.7 118.991564013648 -1.29156401364823 94 118.51 118.888534538703 -0.378534538703124 95 118.82 119.712783805524 -0.892783805524058 96 119.49 120.043799699065 -0.553799699065159 97 119.57 120.729943621368 -1.15994362136774 98 120 120.8340721632 -0.834072163200136 99 121.96 121.283464066328 0.676535933671585 100 121.45 123.242150091693 -1.79215009169286 101 123.41 122.764263253055 0.64573674694488 102 124.44 124.722949278420 -0.282949278419571 103 126.25 125.764221229484 0.4857787705164 104 127.41 127.574937242864 -0.164937242864126 105 127.63 128.744449870981 -1.11444987098090 106 129.19 128.986683757332 0.203316242668349 107 129.82 130.550783084072 -0.730783084072278 108 130.45 131.197468336512 -0.747468336512467 109 132.02 131.844153588953 0.175846411047371 110 132.72 133.418117583159 -0.698117583158879 111 132.96 134.133855507858 -1.17385550785822 112 135.06 134.395818729140 0.664181270859823 113 137.04 136.492610099023 0.547389900977013 114 137.83 138.471025459319 -0.64102545931861 115 139.17 139.275545391208 -0.105545391208381 116 140.35 140.622622033706 -0.272622033705856 117 141.01 141.811863996754 -0.801863996753837 118 141.89 142.488143251591 -0.598143251590815 119 143.28 143.381445190671 -0.101445190670886 120 142.9 144.777845170496 -1.87784517049638 121 143.37 144.428199008911 -1.05819900891138 122 145.03 144.917049581902 0.112950418097966 123 146.05 146.579795583299 -0.5297955832986 124 147.39 147.611202866897 -0.221202866897069 125 149.58 148.958279509395 0.621720490605475 126 151.02 151.143852886468 -0.123852886467722 127 153.57 152.589576203621 0.980423796378795 128 155.6 155.130277609456 0.469722390544187 129 157.18 157.158016307079 0.0219836929205655 130 158.77 158.741844968751 0.0281550312487439 131 159.95 160.335538297889 -0.385538297888689 132 161.34 161.524780260937 -0.184780260936623 133 161.95 162.921180240762 -0.971180240762142 134 163.36 163.548136158271 -0.188136158271097 135 165 164.964265473028 0.0357345269721856 136 166.65 166.607282139493 0.0427178605068191 137 168.65 168.260163473424 0.389836526575827 138 170.29 170.258308168651 0.0316918313489712 139 172.7 171.901324835116 0.798675164883592 140 173.79 174.303920896433 -0.513920896432687 141 176.45 175.404380852290 1.04561914770970 142 177.58 178.053593600246 -0.473593600246458 143 179.19 179.193512225966 -0.00351222596648489 144 181.01 180.806934890035 0.203065109964925 145 184.08 182.627515570881 1.45248442911884 146 185.63 185.681179684927 -0.0511796849268203 147 188.51 187.235414344202 1.27458565579817 148 190.18 190.101649776401 0.078350223598895 149 192.19 191.774260445263 0.415739554736697 150 193.47 193.782269807956 -0.31226980795574 151 196.73 195.070158445660 1.65984155434032 152 200.39 198.311251241552 2.07874875844838 153 203.24 201.946930736067 1.29306926393262 154 205.53 204.78357216587 0.74642783413008 155 208.21 207.067792217599 1.14220778240097 156 208.88 209.736734300486 -0.856734300486432 157 212.85 210.422878222789 2.42712177721101 158 216.41 214.364362408738 2.04563759126175 159 216.23 217.901395228598 -1.67139522859808 160 219.27 217.749042416325 1.52095758367503 161 222.02 220.773112527974 1.24688747202618 162 224.89 223.511107283120 1.37889271687960 163 230.37 226.367478047854 4.00252195214596 164 232.29 231.798527021108 0.491472978891657 165 235.53 233.717754376610 1.81224562338961 166 236.92 236.939117837571 -0.0191178375711753 167 242.37 238.335517817397 4.03448218260337 168 242.75 243.736972788254 -0.986972788254127 169 244.19 244.137041354054 0.0529586459455905 170 247.94 245.582764671208 2.35723532879212 171 248.8 249.307226172914 -0.507226172914017 172 250.18 250.180798777063 -0.000798777062941902 173 251.55 251.567334089423 -0.0173340894227999 174 254.4 252.944004734317 1.45599526568288 175 255.72 255.780646164120 -0.0606461641196299 176 257.69 257.107993471686 0.582006528314064 177 258.37 259.076544164516 -0.706544164515982 178 258.22 259.772552754284 -1.55255275428412 179 258.59 259.649793944408 -1.05979394440791 180 257.45 260.039997842743 -2.58999784274254 181 257.45 258.940636953772 -1.49063695377226 182 256.73 258.96584815588 -2.23584815587986 183 258.82 258.280803300465 0.539196699535368 184 257.99 260.367730002882 -2.37773000288180 185 262.85 259.574173805345 3.27582619465502 186 262.58 264.393613395732 -1.81361339573233 187 261.55 264.152478576269 -2.60247857626881 188 261.25 263.16162902942 -1.91162902942011 189 259.78 257.677539232910 2.10246076709021 190 256.26 256.252644317575 0.00735568242523145 191 254.29 252.805492571793 1.48450742820738 192 248.5 250.887364283178 -2.38736428317783 193 241.88 245.200933022705 -3.32093302270539 194 238.53 238.695734362588 -0.165734362588442 195 232.24 235.416281963721 -3.17628196372141 196 232.46 229.236617329969 3.22338267003084 197 225.79 229.47885121632 -3.68885121631992 198 221.63 222.924329218875 -1.29432921887499 199 219.62 218.845838755295 0.774161244705324 200 215.94 216.888251796818 -0.94825179681753 201 211.81 213.283265371586 -1.47326537158582 202 205.57 209.234368910402 -3.66436891040231 203 201.25 203.104027613978 -1.85402761397801 204 194.7 198.867702470948 -4.16770247094819 205 187.94 192.431556483090 -4.4915564830904 206 185.61 185.788252478455 -0.17825247845512 207 181.15 183.514996161079 -2.36499616107890 208 186.5 179.140565673531 7.35943432646928 209 183.21 184.443373969732 -1.23337396973222 210 182.61 181.223109575659 1.38689042434124 211 187.09 180.656440729831 6.43355927016935 212 189.1 185.101022956525 3.99897704347469 213 191.25 187.109032319218 4.14096768078225 214 190.74 189.255147026429 1.48485297357147 215 190.79 188.777260187791 2.01273981220921 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.0189090428804070 0.0378180857608140 0.981090957119593 8 0.00531683756112849 0.0106336751222570 0.994683162438871 9 0.00106067021822726 0.00212134043645453 0.998939329781773 10 0.000754659708890541 0.00150931941778108 0.99924534029111 11 0.000565200976515834 0.00113040195303167 0.999434799023484 12 0.000491900628397231 0.000983801256794463 0.999508099371603 13 0.000134983580421806 0.000269967160843612 0.999865016419578 14 3.65344699131193e-05 7.30689398262386e-05 0.999963465530087 15 7.79407014088137e-05 0.000155881402817627 0.99992205929859 16 4.21203741995463e-05 8.42407483990926e-05 0.9999578796258 17 1.30985339493673e-05 2.61970678987346e-05 0.99998690146605 18 5.02874242688969e-06 1.00574848537794e-05 0.999994971257573 19 2.03718333250836e-06 4.07436666501672e-06 0.999997962816668 20 1.12036603403118e-06 2.24073206806237e-06 0.999998879633966 21 7.29992485455051e-07 1.45998497091010e-06 0.999999270007515 22 6.25917947522626e-07 1.25183589504525e-06 0.999999374082053 23 1.94498243384280e-07 3.88996486768559e-07 0.999999805501757 24 1.28409588583953e-07 2.56819177167906e-07 0.999999871590411 25 3.95049911250955e-08 7.9009982250191e-08 0.999999960495009 26 1.17000589783399e-08 2.34001179566797e-08 0.999999988299941 27 6.3137830515087e-09 1.26275661030174e-08 0.999999993686217 28 2.27319864278637e-09 4.54639728557274e-09 0.999999997726801 29 1.47304911768730e-09 2.94609823537461e-09 0.99999999852695 30 7.53982063354006e-09 1.50796412670801e-08 0.99999999246018 31 2.97880717108745e-09 5.95761434217491e-09 0.999999997021193 32 6.62241120333214e-09 1.32448224066643e-08 0.999999993377589 33 3.58744979994121e-09 7.17489959988243e-09 0.99999999641255 34 1.34399721816604e-09 2.68799443633209e-09 0.999999998656003 35 4.87599696006426e-10 9.75199392012853e-10 0.9999999995124 36 1.72579721323678e-10 3.45159442647356e-10 0.99999999982742 37 7.5468198791712e-11 1.50936397583424e-10 0.999999999924532 38 3.60528679719127e-11 7.21057359438254e-11 0.999999999963947 39 1.76613100002011e-11 3.53226200004023e-11 0.999999999982339 40 1.99378754921417e-11 3.98757509842834e-11 0.999999999980062 41 6.97899032116006e-12 1.39579806423201e-11 0.99999999999302 42 2.25426198960879e-12 4.50852397921757e-12 0.999999999997746 43 2.02841823306566e-12 4.05683646613131e-12 0.999999999997972 44 2.34779461718931e-12 4.69558923437863e-12 0.999999999997652 45 9.63921139038023e-13 1.92784227807605e-12 0.999999999999036 46 4.13881227802911e-13 8.27762455605822e-13 0.999999999999586 47 1.65145078134798e-13 3.30290156269595e-13 0.999999999999835 48 5.77733645978928e-14 1.15546729195786e-13 0.999999999999942 49 1.83482321150924e-14 3.66964642301848e-14 0.999999999999982 50 1.06219495804033e-14 2.12438991608065e-14 0.99999999999999 51 8.94249968916993e-15 1.78849993783399e-14 0.99999999999999 52 2.85399442265280e-15 5.70798884530559e-15 0.999999999999997 53 2.41041619967871e-14 4.82083239935742e-14 0.999999999999976 54 8.17445519287645e-15 1.63489103857529e-14 0.999999999999992 55 8.67289405054712e-15 1.73457881010942e-14 0.999999999999991 56 7.09096535502166e-15 1.41819307100433e-14 0.999999999999993 57 2.69881698023686e-15 5.39763396047373e-15 0.999999999999997 58 9.8353837451359e-16 1.96707674902718e-15 0.999999999999999 59 3.46677866788544e-16 6.93355733577088e-16 1 60 1.18857148548995e-16 2.37714297097991e-16 1 61 4.49578802361307e-17 8.99157604722614e-17 1 62 1.44304755065130e-17 2.88609510130259e-17 1 63 4.60547908185137e-18 9.21095816370274e-18 1 64 1.43347377350298e-18 2.86694754700596e-18 1 65 4.25079034977453e-18 8.50158069954906e-18 1 66 8.14436251785225e-18 1.62887250357045e-17 1 67 5.80660050462645e-18 1.16132010092529e-17 1 68 2.1954367608001e-18 4.3908735216002e-18 1 69 1.22073891653754e-18 2.44147783307507e-18 1 70 4.50966604860271e-19 9.01933209720542e-19 1 71 3.00211860757881e-19 6.00423721515762e-19 1 72 9.6333476121954e-20 1.92666952243908e-19 1 73 3.28824360595117e-20 6.57648721190234e-20 1 74 2.28041910489687e-20 4.56083820979375e-20 1 75 7.26511711183335e-21 1.45302342236667e-20 1 76 6.37832296404267e-20 1.27566459280853e-19 1 77 6.41492313972663e-20 1.28298462794533e-19 1 78 9.23551561320309e-20 1.84710312264062e-19 1 79 4.22022106152357e-20 8.44044212304714e-20 1 80 1.54236533892508e-20 3.08473067785015e-20 1 81 8.36260653080287e-21 1.67252130616057e-20 1 82 2.81529441724927e-21 5.63058883449854e-21 1 83 1.49334503305972e-21 2.98669006611944e-21 1 84 5.79447624361171e-22 1.15889524872234e-21 1 85 2.65505219984824e-22 5.31010439969648e-22 1 86 5.94047057533944e-21 1.18809411506789e-20 1 87 2.57420099999853e-21 5.14840199999707e-21 1 88 1.08561267638737e-21 2.17122535277474e-21 1 89 5.1596348292238e-22 1.03192696584476e-21 1 90 2.01192430482270e-22 4.02384860964539e-22 1 91 9.68430400488446e-23 1.93686080097689e-22 1 92 3.98716612432616e-23 7.97433224865232e-23 1 93 3.9408917332984e-23 7.8817834665968e-23 1 94 1.28727717894054e-23 2.57455435788109e-23 1 95 5.35301773540537e-24 1.07060354708107e-23 1 96 1.72137362296566e-24 3.44274724593132e-24 1 97 9.44826175833562e-25 1.88965235166712e-24 1 98 3.29416650294174e-25 6.58833300588347e-25 1 99 6.92555023409842e-25 1.38511004681968e-24 1 100 1.71171296974407e-24 3.42342593948815e-24 1 101 3.04740230666499e-24 6.09480461332997e-24 1 102 1.03599925399335e-24 2.07199850798670e-24 1 103 8.7626859296776e-25 1.75253718593552e-24 1 104 2.94020115211382e-25 5.88040230422765e-25 1 105 1.91032247255103e-25 3.82064494510205e-25 1 106 9.03052781567792e-26 1.80610556313558e-25 1 107 3.48184813363317e-26 6.96369626726634e-26 1 108 1.32144585026466e-26 2.64289170052933e-26 1 109 5.89309189419095e-27 1.17861837883819e-26 1 110 2.18909253128461e-27 4.37818506256921e-27 1 111 1.52363578642027e-27 3.04727157284054e-27 1 112 1.72540716406277e-27 3.45081432812554e-27 1 113 1.18147582864738e-27 2.36295165729476e-27 1 114 4.59291212068017e-28 9.18582424136035e-28 1 115 1.44755223181811e-28 2.89510446363621e-28 1 116 4.48430723322116e-29 8.96861446644233e-29 1 117 2.12355470850563e-29 4.24710941701127e-29 1 118 7.66628865803288e-30 1.53325773160658e-29 1 119 2.39244061356357e-30 4.78488122712713e-30 1 120 1.77595431574463e-29 3.55190863148927e-29 1 121 1.06923880158645e-29 2.13847760317290e-29 1 122 4.54648475320508e-30 9.09296950641015e-30 1 123 1.59588647472912e-30 3.19177294945824e-30 1 124 5.35970897039906e-31 1.07194179407981e-30 1 125 4.38908232531003e-31 8.77816465062006e-31 1 126 1.43087652782208e-31 2.86175305564416e-31 1 127 1.94111824108660e-31 3.88223648217319e-31 1 128 8.04777670687523e-32 1.60955534137505e-31 1 129 2.47149995534393e-32 4.94299991068786e-32 1 130 7.5378794706911e-33 1.50757589413822e-32 1 131 2.96836624165109e-33 5.93673248330217e-33 1 132 9.88245495654124e-34 1.97649099130825e-33 1 133 1.14060411208027e-33 2.28120822416053e-33 1 134 3.96069021954117e-34 7.92138043908234e-34 1 135 1.30077764121211e-34 2.60155528242423e-34 1 136 4.2927515391874e-35 8.5855030783748e-35 1 137 1.52220632229816e-35 3.04441264459632e-35 1 138 5.0999645891342e-36 1.01999291782684e-35 1 139 2.47055425160856e-36 4.94110850321711e-36 1 140 1.7494301024246e-36 3.4988602048492e-36 1 141 1.14235865774455e-36 2.2847173154891e-36 1 142 8.85208450365737e-37 1.77041690073147e-36 1 143 3.58677482620762e-37 7.17354965241524e-37 1 144 1.32323357099292e-37 2.64646714198584e-37 1 145 1.60479527525667e-37 3.20959055051335e-37 1 146 8.2660038919205e-38 1.65320077838410e-37 1 147 5.12579591295231e-38 1.02515918259046e-37 1 148 2.53594115049281e-38 5.07188230098562e-38 1 149 9.44197091694484e-39 1.88839418338897e-38 1 150 1.21774044964249e-38 2.43548089928497e-38 1 151 1.32109666018799e-38 2.64219332037598e-38 1 152 2.65849545055715e-38 5.31699090111431e-38 1 153 9.13817149689407e-39 1.82763429937881e-38 1 154 2.98639493053114e-39 5.97278986106229e-39 1 155 8.66785436083633e-40 1.73357087216727e-39 1 156 2.44538060630453e-38 4.89076121260906e-38 1 157 5.50874314664832e-38 1.10174862932966e-37 1 158 3.26498097089981e-38 6.52996194179963e-38 1 159 8.03440413117926e-35 1.60688082623585e-34 1 160 2.98199028595380e-35 5.96398057190759e-35 1 161 9.66317245722715e-36 1.93263449144543e-35 1 162 2.98520751477961e-36 5.97041502955921e-36 1 163 5.359013229174e-34 1.0718026458348e-33 1 164 3.67749271088627e-34 7.35498542177254e-34 1 165 1.16704138257723e-34 2.33408276515446e-34 1 166 2.33739614244462e-34 4.67479228488924e-34 1 167 3.25273495515206e-32 6.50546991030412e-32 1 168 1.0982021361828e-30 2.1964042723656e-30 1 169 1.21701444626302e-30 2.43402889252603e-30 1 170 1.64269900412856e-30 3.28539800825712e-30 1 171 5.49294380063057e-30 1.09858876012611e-29 1 172 5.5374709203062e-30 1.10749418406124e-29 1 173 5.18527303443553e-30 1.03705460688711e-29 1 174 5.96610154910662e-30 1.19322030982132e-29 1 175 8.31230213057881e-30 1.66246042611576e-29 1 176 1.26418657107415e-29 2.52837314214831e-29 1 177 4.28224585505308e-29 8.56449171010616e-29 1 178 4.16817071507778e-28 8.33634143015555e-28 1 179 1.11038705033421e-27 2.22077410066843e-27 1 180 4.23662812437193e-26 8.47325624874386e-26 1 181 8.18031290897905e-26 1.63606258179581e-25 1 182 4.36100864615848e-25 8.72201729231697e-25 1 183 3.76659166077362e-25 7.53318332154724e-25 1 184 1.84875668268447e-24 3.69751336536894e-24 1 185 2.24244548314593e-21 4.48489096629185e-21 1 186 3.57426691596147e-21 7.14853383192294e-21 1 187 1.19381109868915e-20 2.38762219737830e-20 1 188 1.25809486431054e-20 2.51618972862107e-20 1 189 1.17721138877447e-19 2.35442277754895e-19 1 190 1.70920989093077e-19 3.41841978186154e-19 1 191 2.24801481647049e-18 4.49602963294098e-18 1 192 5.31811410111186e-18 1.06362282022237e-17 1 193 1.33874790894111e-17 2.67749581788222e-17 1 194 1.61173236912504e-17 3.22346473825009e-17 1 195 1.62449157905560e-17 3.24898315811120e-17 1 196 3.62123814185335e-13 7.2424762837067e-13 0.999999999999638 197 4.39604656690124e-13 8.79209313380248e-13 0.99999999999956 198 3.18642023774705e-13 6.3728404754941e-13 0.999999999999681 199 1.47701928926325e-11 2.95403857852650e-11 0.99999999998523 200 1.27155445336515e-10 2.54310890673030e-10 0.999999999872845 201 2.85130005850421e-09 5.70260011700842e-09 0.9999999971487 202 9.07320021276846e-09 1.81464004255369e-08 0.9999999909268 203 1.40578089006114e-06 2.81156178012228e-06 0.99999859421911 204 3.27347595466444e-05 6.54695190932887e-05 0.999967265240453 205 7.07419921002572e-05 0.000141483984200514 0.9999292580079 206 0.00139348509395546 0.00278697018791092 0.998606514906045 207 0.000644947293935835 0.00128989458787167 0.999355052706064 208 0.0574121034892519 0.114824206978504 0.942587896510748 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 199 0.985148514851485 NOK 5% type I error level 201 0.995049504950495 NOK 10% type I error level 201 0.995049504950495 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/10mt9q1261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/10mt9q1261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/1ju101261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/1ju101261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/24m8k1261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/24m8k1261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/3dohb1261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/3dohb1261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/43dch1261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/43dch1261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/5uxab1261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/5uxab1261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/6b8031261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/6b8031261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/7yxrs1261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/7yxrs1261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/8wtwi1261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/8wtwi1261250737.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/9cstz1261250737.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261251824ikdt6yo34utshzg/9cstz1261250737.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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