*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, 27 Dec 2010 12:49:24 +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/27/t12934543466avrbndtrvzes23.htm/, Retrieved Mon, 27 Dec 2010 13:52:37 +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/27/t12934543466avrbndtrvzes23.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 «

1775 0 2197 0 2920 0 4240 0 5415 0 6136 0 6719 0 6234 0 7152 0 3646 0 2165 0 2803 0 1615 0 2350 0 3350 0 3536 0 5834 0 6767 0 5993 0 7276 0 5641 0 3477 0 2247 0 2466 0 1567 0 2237 0 2598 0 3729 0 5715 0 5776 0 5852 0 6878 0 5488 0 3583 0 2054 0 2282 0 1552 0 2261 0 2446 0 3519 0 5161 0 5085 0 5711 0 6057 0 5224 0 3363 0 1899 0 2115 0 1491 0 2061 0 2419 0 3430 0 4778 0 4862 0 6176 0 5664 0 5529 0 3418 0 1941 0 2402 0 1579 0 2146 0 2462 0 3695 0 4831 0 5134 0 6250 0 5760 0 6249 0 2917 0 1741 0 2359 0 1511 0 2059 0 2635 0 2867 0 4403 0 5720 0 4502 0 5749 0 5627 0 2846 0 1762 0 2429 0 1169 0 2154 0 2249 0 2687 0 4359 0 5382 0 4459 0 6398 0 4596 0 3024 0 1887 0 2070 0 1351 0 2218 0 2461 0 3028 0 4784 0 4975 0 4607 1 6249 1 4809 1 3157 1 1910 1 2228 1 1594 1 2467 1 2222 1 3607 1 4685 1 4962 1 5770 1 5480 1 5000 1 3228 1 1993 1 2288 1 1588 1 2105 1 2191 1 3591 1 4668 1 4885 1 5822 1 5599 1 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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Huwelijken[t] = + 2392.63212121212 -126.354545454546Dummy[t] -834.023636363627M1[t] -156.956969696964M2[t] + 191.509696969704M3[t] + 1049.84303030304M4[t] + 2599.30969696970M5[t] + 3105.04303030304M6[t] + 3245.06666666668M7[t] + 3857.73333333334M8[t] + 3231.13333333334M9[t] + 932.733333333333M10[t] -376.666666666665M11[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) 2392.63212121212 118.74813 20.1488 0 0 Dummy -126.354545454546 66.895194 -1.8888 0.060646 0.030323 M1 -834.023636363627 162.08957 -5.1454 1e-06 0 M2 -156.956969696964 162.08957 -0.9683 0.334277 0.167139 M3 191.509696969704 162.08957 1.1815 0.239082 0.119541 M4 1049.84303030304 162.08957 6.4769 0 0 M5 2599.30969696970 162.08957 16.0363 0 0 M6 3105.04303030304 162.08957 19.1563 0 0 M7 3245.06666666668 162.028207 20.0278 0 0 M8 3857.73333333334 162.028207 23.809 0 0 M9 3231.13333333334 162.028207 19.9418 0 0 M10 932.733333333333 162.028207 5.7566 0 0 M11 -376.666666666665 162.028207 -2.3247 0.021292 0.010646 Multiple Linear Regression - Regression Statistics Multiple R 0.966902537180792 R-squared 0.934900516406653 Adjusted R-squared 0.93022270920234 F-TEST (value) 199.858710625095 F-TEST (DF numerator) 12 F-TEST (DF denominator) 167 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 443.732519561854 Sum Squared Residuals 32882057.6690908 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1775 1558.60848484848 216.391515151523 2 2197 2235.67515151514 -38.6751515151388 3 2920 2584.14181818183 335.858181818171 4 4240 3442.47515151514 797.524848484856 5 5415 4991.94181818183 423.058181818171 6 6136 5497.67515151516 638.324848484845 7 6719 5637.69878787879 1081.30121212121 8 6234 6250.36545454547 -16.3654545454715 9 7152 5623.76545454548 1528.23454545452 10 3646 3325.36545454547 320.634545454534 11 2165 2015.96545454547 149.034545454534 12 2803 2392.63212121212 410.367878787885 13 1615 1558.60848484848 56.391515151517 14 2350 2235.67515151515 114.324848484847 15 3350 2584.14181818182 765.858181818183 16 3536 3442.47515151515 93.5248484848487 17 5834 4991.94181818182 842.058181818183 18 6767 5497.67515151515 1269.32484848485 19 5993 5637.69878787879 355.301212121212 20 7276 6250.36545454545 1025.63454545455 21 5641 5623.76545454545 17.2345454545469 22 3477 3325.36545454545 151.634545454547 23 2247 2015.96545454545 231.034545454546 24 2466 2392.63212121212 73.36787878788 25 1567 1558.60848484848 8.39151515151506 26 2237 2235.67515151515 1.32484848484714 27 2598 2584.14181818182 13.8581818181829 28 3729 3442.47515151515 286.524848484849 29 5715 4991.94181818182 723.058181818183 30 5776 5497.67515151515 278.324848484849 31 5852 5637.69878787879 214.301212121212 32 6878 6250.36545454545 627.634545454547 33 5488 5623.76545454545 -135.765454545453 34 3583 3325.36545454545 257.634545454547 35 2054 2015.96545454545 38.0345454545463 36 2282 2392.63212121212 -110.63212121212 37 1552 1558.60848484848 -6.60848484848493 38 2261 2235.67515151515 25.3248484848471 39 2446 2584.14181818182 -138.141818181817 40 3519 3442.47515151515 76.5248484848487 41 5161 4991.94181818182 169.058181818183 42 5085 5497.67515151515 -412.675151515151 43 5711 5637.69878787879 73.301212121212 44 6057 6250.36545454545 -193.365454545453 45 5224 5623.76545454545 -399.765454545453 46 3363 3325.36545454545 37.6345454545465 47 1899 2015.96545454545 -116.965454545454 48 2115 2392.63212121212 -277.63212121212 49 1491 1558.60848484848 -67.6084848484847 50 2061 2235.67515151515 -174.675151515153 51 2419 2584.14181818182 -165.141818181817 52 3430 3442.47515151515 -12.4751515151513 53 4778 4991.94181818182 -213.941818181817 54 4862 5497.67515151515 -635.675151515151 55 6176 5637.69878787879 538.301212121212 56 5664 6250.36545454545 -586.365454545453 57 5529 5623.76545454545 -94.765454545453 58 3418 3325.36545454545 92.6345454545465 59 1941 2015.96545454545 -74.9654545454537 60 2402 2392.63212121212 9.36787878788009 61 1579 1558.60848484848 20.3915151515151 62 2146 2235.67515151515 -89.6751515151528 63 2462 2584.14181818182 -122.141818181817 64 3695 3442.47515151515 252.524848484849 65 4831 4991.94181818182 -160.941818181817 66 5134 5497.67515151515 -363.675151515151 67 6250 5637.69878787879 612.301212121212 68 5760 6250.36545454545 -490.365454545453 69 6249 5623.76545454545 625.234545454547 70 2917 3325.36545454545 -408.365454545453 71 1741 2015.96545454545 -274.965454545454 72 2359 2392.63212121212 -33.6321212121199 73 1511 1558.60848484848 -47.6084848484849 74 2059 2235.67515151515 -176.675151515153 75 2635 2584.14181818182 50.8581818181829 76 2867 3442.47515151515 -575.475151515151 77 4403 4991.94181818182 -588.941818181817 78 5720 5497.67515151515 222.324848484849 79 4502 5637.69878787879 -1135.69878787879 80 5749 6250.36545454545 -501.365454545453 81 5627 5623.76545454545 3.23454545454693 82 2846 3325.36545454545 -479.365454545453 83 1762 2015.96545454545 -253.965454545454 84 2429 2392.63212121212 36.3678787878801 85 1169 1558.60848484848 -389.608484848485 86 2154 2235.67515151515 -81.6751515151528 87 2249 2584.14181818182 -335.141818181817 88 2687 3442.47515151515 -755.475151515151 89 4359 4991.94181818182 -632.941818181817 90 5382 5497.67515151515 -115.675151515151 91 4459 5637.69878787879 -1178.69878787879 92 6398 6250.36545454545 147.634545454547 93 4596 5623.76545454545 -1027.76545454545 94 3024 3325.36545454545 -301.365454545454 95 1887 2015.96545454545 -128.965454545454 96 2070 2392.63212121212 -322.632121212120 97 1351 1558.60848484848 -207.608484848485 98 2218 2235.67515151515 -17.6751515151529 99 2461 2584.14181818182 -123.141818181817 100 3028 3442.47515151515 -414.475151515151 101 4784 4991.94181818182 -207.941818181817 102 4975 5497.67515151515 -522.675151515151 103 4607 5511.34424242424 -904.344242424243 104 6249 6124.01090909091 124.989090909092 105 4809 5497.41090909091 -688.410909090908 106 3157 3199.01090909091 -42.0109090909084 107 1910 1889.61090909091 20.3890909090915 108 2228 2266.27757575757 -38.2775757575743 109 1594 1432.25393939394 161.746060606060 110 2467 2109.32060606061 357.679393939392 111 2222 2457.78727272727 -235.787272727273 112 3607 3316.12060606061 290.879393939393 113 4685 4865.58727272727 -180.587272727273 114 4962 5371.32060606061 -409.320606060606 115 5770 5511.34424242424 258.655757575757 116 5480 6124.01090909091 -644.010909090908 117 5000 5497.41090909091 -497.410909090908 118 3228 3199.01090909091 28.9890909090916 119 1993 1889.61090909091 103.389090909091 120 2288 2266.27757575757 21.7224242424255 121 1588 1432.25393939394 155.746060606060 122 2105 2109.32060606061 -4.32060606060828 123 2191 2457.78727272727 -266.787272727273 124 3591 3316.12060606061 274.879393939393 125 4668 4865.58727272727 -197.587272727273 126 4885 5371.32060606061 -486.320606060606 127 5822 5511.34424242424 310.655757575757 128 5599 6124.01090909091 -525.010909090908 129 5340 5497.41090909091 -157.410909090908 130 3082 3199.01090909091 -117.010909090908 131 2010 1889.61090909091 120.389090909091 132 2301 2266.27757575757 34.7224242424255 133 1507 1432.25393939394 74.7460606060598 134 1992 2109.32060606061 -117.320606060608 135 2487 2457.78727272727 29.2127272727274 136 3490 3316.12060606061 173.879393939393 137 4647 4865.58727272727 -218.587272727273 138 5594 5371.32060606061 222.679393939394 139 5611 5511.34424242424 99.6557575757568 140 5788 6124.01090909091 -336.010909090908 141 6204 5497.41090909091 706.589090909092 142 3013 3199.01090909091 -186.010909090908 143 1931 1889.61090909091 41.3890909090915 144 2549 2266.27757575757 282.722424242425 145 1504 1432.25393939394 71.7460606060598 146 2090 2109.32060606061 -19.3206060606082 147 2702 2457.78727272727 244.212727272728 148 2939 3316.12060606061 -377.120606060607 149 4500 4865.58727272727 -365.587272727273 150 6208 5371.32060606061 836.679393939394 151 6415 5511.34424242424 903.655757575757 152 5657 6124.01090909091 -467.010909090908 153 5964 5497.41090909091 466.589090909092 154 3163 3199.01090909091 -36.0109090909084 155 1997 1889.61090909091 107.389090909091 156 2422 2266.27757575757 155.722424242425 157 1376 1432.25393939394 -56.2539393939403 158 2202 2109.32060606061 92.6793939393917 159 2683 2457.78727272727 225.212727272728 160 3303 3316.12060606061 -13.1206060606068 161 5202 4865.58727272727 336.412727272728 162 5231 5371.32060606061 -140.320606060606 163 4880 5511.34424242424 -631.344242424243 164 7998 6124.01090909091 1873.98909090909 165 4977 5497.41090909091 -520.410909090908 166 3531 3199.01090909091 331.989090909091 167 2025 1889.61090909091 135.389090909091 168 2205 2266.27757575757 -61.2775757575743 169 1442 1432.25393939394 9.74606060605975 170 2238 2109.32060606061 128.679393939392 171 2179 2457.78727272727 -278.787272727273 172 3218 3316.12060606061 -98.1206060606067 173 5139 4865.58727272727 273.412727272728 174 4990 5371.32060606061 -381.320606060606 175 4914 5511.34424242424 -597.344242424243 176 6084 6124.01090909091 -40.0109090909082 177 5672 5497.41090909091 174.589090909092 178 3548 3199.01090909091 348.989090909091 179 1793 1889.61090909091 -96.6109090909085 180 2086 2266.27757575757 -180.277575757574 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.444565328976016 0.889130657952032 0.555434671023984 17 0.383776444286755 0.76755288857351 0.616223555713245 18 0.467681112245597 0.935362224491193 0.532318887754403 19 0.553672054602691 0.892655890794617 0.446327945397309 20 0.788221277808992 0.423557444382017 0.211778722191008 21 0.966555091227119 0.0668898175457629 0.0334449087728814 22 0.947548004090876 0.104903991818247 0.0524519959091236 23 0.921253762886646 0.157492474226709 0.0787462371133544 24 0.895496696584573 0.209006606830854 0.104503303415427 25 0.855193550162634 0.289612899674732 0.144806449837366 26 0.804277517696662 0.391444964606676 0.195722482303338 27 0.801603611403901 0.396792777192198 0.198396388596099 28 0.756629263367534 0.486741473264933 0.243370736632466 29 0.73456499971471 0.53087000057058 0.26543500028529 30 0.778473995013526 0.443052009972948 0.221526004986474 31 0.779019218302973 0.441961563394053 0.220980781697027 32 0.76131358470316 0.47737283059368 0.23868641529684 33 0.843562428719044 0.312875142561912 0.156437571280956 34 0.808684877501146 0.382630244997709 0.191315122498854 35 0.766963428842027 0.466073142315946 0.233036571157973 36 0.737704058894217 0.524591882211566 0.262295941105783 37 0.686964742077576 0.626070515844847 0.313035257922424 38 0.631095640305854 0.737808719388293 0.368904359694146 39 0.632170633673668 0.735658732652665 0.367829366326332 40 0.60236681372267 0.79526637255466 0.39763318627733 41 0.613567256906408 0.772865486187184 0.386432743093592 42 0.797670144125422 0.404659711749156 0.202329855874578 43 0.795087059472102 0.409825881055797 0.204912940527898 44 0.826257645202266 0.347484709595468 0.173742354797734 45 0.872467157585967 0.255065684828066 0.127532842414033 46 0.848508381589177 0.302983236821645 0.151491618410823 47 0.82179529996296 0.356409400074079 0.178204700037039 48 0.804606776693376 0.390786446613249 0.195393223306624 49 0.768457361213877 0.463085277572247 0.231542638786123 50 0.732027954456497 0.535944091087006 0.267972045543503 51 0.712775398109253 0.574449203781494 0.287224601890747 52 0.687324917272975 0.62535016545405 0.312675082727025 53 0.726637964259555 0.546724071480891 0.273362035740445 54 0.832592325812338 0.334815348375325 0.167407674187662 55 0.844960971257646 0.310078057484708 0.155039028742354 56 0.892904584664682 0.214190830670636 0.107095415335318 57 0.878060889553324 0.243878220893353 0.121939110446676 58 0.857737599082267 0.284524801835466 0.142262400917733 59 0.830837891097715 0.338324217804571 0.169162108902285 60 0.800230498785252 0.399539002429496 0.199769501214748 61 0.767226566780304 0.465546866439392 0.232773433219696 62 0.729194633125988 0.541610733748023 0.270805366874012 63 0.69913430044241 0.601731399115179 0.300865699557589 64 0.683807168237286 0.632385663525429 0.316192831762714 65 0.683978609800956 0.632042780398088 0.316021390199044 66 0.686195129532237 0.627609740935526 0.313804870467763 67 0.754199868440693 0.491600263118614 0.245800131559307 68 0.76794251521496 0.46411496957008 0.23205748478504 69 0.830742720525648 0.338514558948703 0.169257279474352 70 0.830185674910296 0.339628650179409 0.169814325089704 71 0.808232785390525 0.38353442921895 0.191767214609475 72 0.779326743179609 0.441346513640782 0.220673256820391 73 0.748469819242189 0.503060361515623 0.251530180757811 74 0.712595220775031 0.574809558449937 0.287404779224969 75 0.688105200308542 0.623789599382917 0.311894799691458 76 0.728847865386204 0.542304269227592 0.271152134613796 77 0.773019326510854 0.453961346978293 0.226980673489146 78 0.770302675571063 0.459394648857873 0.229697324428937 79 0.928166667712368 0.143666664575264 0.0718333322876318 80 0.92538680465515 0.1492263906897 0.07461319534485 81 0.921587093145673 0.156825813708655 0.0784129068543274 82 0.91727432510439 0.165451349791220 0.0827256748956098 83 0.901017174263389 0.197965651473222 0.0989828257366111 84 0.887272361761842 0.225455276476317 0.112727638238158 85 0.873709148981482 0.252581702037037 0.126290851018518 86 0.850567646174865 0.29886470765027 0.149432353825135 87 0.834883879942475 0.330232240115050 0.165116120057525 88 0.86447626921615 0.2710474615677 0.13552373078385 89 0.876157427272612 0.247685145454777 0.123842572727388 90 0.86093756665302 0.27812486669396 0.13906243334698 91 0.943982431400261 0.112035137199478 0.056017568599739 92 0.938888102811768 0.122223794376465 0.0611118971882324 93 0.969225474048478 0.061549051903044 0.030774525951522 94 0.961894916400557 0.0762101671988856 0.0381050835994428 95 0.951593141678799 0.0968137166424022 0.0484068583212011 96 0.94167778889416 0.116644422211680 0.0583222111058402 97 0.927806388800134 0.144387222399732 0.0721936111998659 98 0.912022063063224 0.175955873873553 0.0879779369367763 99 0.896361704060781 0.207276591878438 0.103638295939219 100 0.881992975385615 0.23601404922877 0.118007024614385 101 0.863934345599493 0.272131308801014 0.136065654400507 102 0.8543552959882 0.291289408023598 0.145644704011799 103 0.891310709315137 0.217378581369726 0.108689290684863 104 0.890824923602015 0.218350152795970 0.109175076397985 105 0.90280802910303 0.194383941793939 0.0971919708969697 106 0.888793308539575 0.22241338292085 0.111206691460425 107 0.871464185439129 0.257071629121743 0.128535814560872 108 0.84823152020016 0.30353695959968 0.15176847979984 109 0.827756970488468 0.344486059023064 0.172243029511532 110 0.820369016852922 0.359261966294156 0.179630983147078 111 0.793058779876688 0.413882440246623 0.206941220123312 112 0.775366150504433 0.449267698991134 0.224633849495567 113 0.740684962247996 0.518630075504009 0.259315037752004 114 0.726269029210602 0.547461941578796 0.273730970789398 115 0.700251726882544 0.599496546234912 0.299748273117456 116 0.739841472017299 0.520317055965403 0.260158527982701 117 0.751789199437784 0.496421601124432 0.248210800562216 118 0.71338538178081 0.573229236438381 0.286614618219190 119 0.673991902041056 0.652016195917888 0.326008097958944 120 0.629379027117687 0.741241945764625 0.370620972882313 121 0.588269842591792 0.823460314816416 0.411730157408208 122 0.539281917874294 0.921436164251413 0.460718082125706 123 0.504857118383147 0.990285763233707 0.495142881616853 124 0.479289166150559 0.958578332301118 0.520710833849441 125 0.435164538614492 0.870329077228984 0.564835461385508 126 0.446454680784661 0.892909361569322 0.553545319215339 127 0.423873623395849 0.847747246791698 0.576126376604151 128 0.46952285801772 0.93904571603544 0.53047714198228 129 0.436864899123087 0.873729798246173 0.563135100876913 130 0.392846985396891 0.785693970793782 0.607153014603109 131 0.346127641311915 0.692255282623829 0.653872358688085 132 0.298767260731192 0.597534521462383 0.701232739268808 133 0.255109160352922 0.510218320705843 0.744890839647078 134 0.216700962593220 0.433401925186439 0.78329903740678 135 0.17913067372463 0.35826134744926 0.82086932627537 136 0.155952055548921 0.311904111097841 0.844047944451079 137 0.131990990369391 0.263981980738781 0.86800900963061 138 0.107960072749517 0.215920145499034 0.892039927250483 139 0.0865045883006612 0.173009176601322 0.913495411699339 140 0.100768130112730 0.201536260225461 0.89923186988727 141 0.121831304769244 0.243662609538489 0.878168695230756 142 0.105134062805018 0.210268125610036 0.894865937194982 143 0.0803803813982667 0.160760762796533 0.919619618601733 144 0.0663546181762547 0.132709236352509 0.933645381823745 145 0.0490979011329266 0.0981958022658532 0.950902098867073 146 0.0355686455921168 0.0711372911842336 0.964431354407883 147 0.0269355896982871 0.0538711793965743 0.973064410301713 148 0.0208503495828417 0.0417006991656833 0.979149650417158 149 0.0208633812053086 0.0417267624106173 0.979136618794691 150 0.046955542315639 0.093911084631278 0.95304445768436 151 0.214282154429128 0.428564308858255 0.785717845570872 152 0.528447965004832 0.943104069990336 0.471552034995168 153 0.562003835823922 0.875992328352155 0.437996164176078 154 0.517424661071597 0.965150677856806 0.482575338928403 155 0.434946485292244 0.869892970584488 0.565053514707756 156 0.370443660299623 0.740887320599246 0.629556339700377 157 0.289144605260437 0.578289210520873 0.710855394739563 158 0.214931135010318 0.429862270020635 0.785068864989682 159 0.182883435125452 0.365766870250905 0.817116564874548 160 0.123450649020814 0.246901298041628 0.876549350979186 161 0.078487095808326 0.156974191616652 0.921512904191674 162 0.0472744920307762 0.0945489840615524 0.952725507969224 163 0.0258726311656107 0.0517452623312214 0.97412736883439 164 0.724372676991168 0.551254646017663 0.275627323008832 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 2 0.0134228187919463 OK 10% type I error level 12 0.0805369127516778 OK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

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

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

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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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