dummy goudprijs

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Mon, 24 Nov 2008 06:36:48 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl.htm/, Retrieved Mon, 24 Nov 2008 13:38:57 +0000

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

20.005 0 20.155 0 20.005 0 19.975 0 20.155 0 19.510 0 19.480 0 19.630 0 19.940 0 20.300 0 20.415 0 20.395 0 19.970 0 20.030 0 19.960 0 19.930 0 20.020 0 20.220 0 20.570 0 20.495 0 20.565 0 20.155 0 20.485 0 20.375 0 20.505 0 20.760 0 20.760 0 20.450 0 20.720 0 20.420 0 20.350 0 20.380 0 20.280 0 20.410 0 20.620 0 20.875 0 20.430 0 20.310 0 19.020 0 19.300 0 19.340 0 19.320 0 19.240 0 19.060 0 18.410 0 18.350 0 18.550 0 18.560 0 18.750 0 18.820 0 18.550 0 18.910 0 18.810 0 18.690 0 18.930 0 18.620 0 19.170 0 19.000 0 18.570 0 18.570 0 18.470 0 18.390 0 18.190 0 18.300 0 18.310 0 17.920 0 17.980 0 18.220 0 18.180 0 18.300 0 18.455 0 18.210 0 17.975 0 17.960 0 18.380 0 18.720 0 18.650 0 18.885 0 18.915 0 18.860 0 18.870 0 18.835 0 18.295 0 18.450 0 18.240 0 18.450 0 18.440 0 18.280 0 18.200 0 18.200 0 18.380 0 18.320 0 18.160 0 18.140 0 18.100 0 18.180 0 18.330 0 18.370 0 18.550 0 18.600 0 18.740 0 18.380 0 18.330 0 18.290 0 18.810 0 18.960 0 18.950 0 19.090 0 19.050 0 19.070 0 18.930 0 19.000 0 18.920 0 19.060 0 19.330 0 19.420 0 19.720 0 19.690 0 19.570 0 19.500 0 19.680 0 19.120 0 19.050 0 19.080 0 19.030 0 19.000 0 18.860 0 18.800 0 18.810 0 18.890 0 18.370 0 18.405 0 18.350 0 18.360 0 18.445 0 17.520 0 17.760 0 17.980 0 17.380 0 17.280 0 17.650 0 17.875 0 18.010 0 17.935 0 17.925 0 18.225 0 18.130 0 18.270 0 18.265 0 17.920 0 17.900 0 17.985 0 17.985 0 18.240 0 18.110 0 17.630 0 17.160 0 17.290 0 17.630 0 17.590 0 17.730 0 19.280 0 19.010 0 19.300 0 19.510 0 19.530 0 19.480 0 19.265 0 19.700 0 20.125 0 19.985 1 20.085 1 19.545 1 19.875 1 20.050 1 21.735 1 20.865 1 21.820 1 20.550 1 20.160 1 20.080 1 20.050 1 19.260 1 19.280 1 19.100 1 18.955 1 18.265 1 17.760 1 18.760 1 19.280 1 18.920 1 18.950 1 18.490 1 18.470 1 18.570 1 18.900 1 18.485 1 18.635 1 18.865 1 18.860 1 18.400 1 18.515 1 18.950 1 18.830 1 18.840 1 19.200 1 19.340 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation goudprijs[t] = + 18.9461470588235 + 0.369528616852146dummy[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) 18.9461470588235 0.068311 277.3532 0 0 dummy 0.369528616852146 0.161574 2.2871 0.023214 0.011607 Multiple Linear Regression - Regression Statistics Multiple R 0.157735019172380 R-squared 0.0248803362733112 Adjusted R-squared 0.0201236549868395 F-TEST (value) 5.23060822764659 F-TEST (DF numerator) 1 F-TEST (DF denominator) 205 p-value 0.0232137792889834 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.890660372794549 Sum Squared Residuals 162.621559431638 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 20.005 18.9461470588236 1.05885294117645 2 20.155 18.9461470588235 1.20885294117647 3 20.005 18.9461470588235 1.05885294117647 4 19.975 18.9461470588235 1.02885294117647 5 20.155 18.9461470588235 1.20885294117647 6 19.51 18.9461470588235 0.563852941176472 7 19.48 18.9461470588235 0.533852941176471 8 19.63 18.9461470588235 0.68385294117647 9 19.94 18.9461470588235 0.993852941176472 10 20.3 18.9461470588235 1.35385294117647 11 20.415 18.9461470588235 1.46885294117647 12 20.395 18.9461470588235 1.44885294117647 13 19.97 18.9461470588235 1.02385294117647 14 20.03 18.9461470588235 1.08385294117647 15 19.96 18.9461470588235 1.01385294117647 16 19.93 18.9461470588235 0.98385294117647 17 20.02 18.9461470588235 1.07385294117647 18 20.22 18.9461470588235 1.27385294117647 19 20.57 18.9461470588235 1.62385294117647 20 20.495 18.9461470588235 1.54885294117647 21 20.565 18.9461470588235 1.61885294117647 22 20.155 18.9461470588235 1.20885294117647 23 20.485 18.9461470588235 1.53885294117647 24 20.375 18.9461470588235 1.42885294117647 25 20.505 18.9461470588235 1.55885294117647 26 20.76 18.9461470588235 1.81385294117647 27 20.76 18.9461470588235 1.81385294117647 28 20.45 18.9461470588235 1.50385294117647 29 20.72 18.9461470588235 1.77385294117647 30 20.42 18.9461470588235 1.47385294117647 31 20.35 18.9461470588235 1.40385294117647 32 20.38 18.9461470588235 1.43385294117647 33 20.28 18.9461470588235 1.33385294117647 34 20.41 18.9461470588235 1.46385294117647 35 20.62 18.9461470588235 1.67385294117647 36 20.875 18.9461470588235 1.92885294117647 37 20.43 18.9461470588235 1.48385294117647 38 20.31 18.9461470588235 1.36385294117647 39 19.02 18.9461470588235 0.0738529411764701 40 19.3 18.9461470588235 0.353852941176471 41 19.34 18.9461470588235 0.393852941176470 42 19.32 18.9461470588235 0.373852941176471 43 19.24 18.9461470588235 0.293852941176469 44 19.06 18.9461470588235 0.113852941176469 45 18.41 18.9461470588235 -0.536147058823529 46 18.35 18.9461470588235 -0.596147058823528 47 18.55 18.9461470588235 -0.396147058823529 48 18.56 18.9461470588235 -0.386147058823531 49 18.75 18.9461470588235 -0.196147058823529 50 18.82 18.9461470588235 -0.126147058823529 51 18.55 18.9461470588235 -0.396147058823529 52 18.91 18.9461470588235 -0.0361470588235293 53 18.81 18.9461470588235 -0.136147058823531 54 18.69 18.9461470588235 -0.256147058823528 55 18.93 18.9461470588235 -0.0161470588235297 56 18.62 18.9461470588235 -0.326147058823528 57 19.17 18.9461470588235 0.223852941176472 58 19 18.9461470588235 0.0538529411764705 59 18.57 18.9461470588235 -0.376147058823529 60 18.57 18.9461470588235 -0.376147058823529 61 18.47 18.9461470588235 -0.476147058823531 62 18.39 18.9461470588235 -0.556147058823529 63 18.19 18.9461470588235 -0.756147058823528 64 18.3 18.9461470588235 -0.646147058823529 65 18.31 18.9461470588235 -0.636147058823531 66 17.92 18.9461470588235 -1.02614705882353 67 17.98 18.9461470588235 -0.966147058823529 68 18.22 18.9461470588235 -0.726147058823531 69 18.18 18.9461470588235 -0.76614705882353 70 18.3 18.9461470588235 -0.646147058823529 71 18.455 18.9461470588235 -0.491147058823531 72 18.21 18.9461470588235 -0.736147058823529 73 17.975 18.9461470588235 -0.971147058823528 74 17.96 18.9461470588235 -0.986147058823529 75 18.38 18.9461470588235 -0.56614705882353 76 18.72 18.9461470588235 -0.226147058823531 77 18.65 18.9461470588235 -0.296147058823531 78 18.885 18.9461470588235 -0.0611470588235279 79 18.915 18.9461470588235 -0.0311470588235303 80 18.86 18.9461470588235 -0.08614705882353 81 18.87 18.9461470588235 -0.0761470588235285 82 18.835 18.9461470588235 -0.111147058823529 83 18.295 18.9461470588235 -0.651147058823528 84 18.45 18.9461470588235 -0.49614705882353 85 18.24 18.9461470588235 -0.706147058823531 86 18.45 18.9461470588235 -0.49614705882353 87 18.44 18.9461470588235 -0.506147058823528 88 18.28 18.9461470588235 -0.666147058823528 89 18.2 18.9461470588235 -0.74614705882353 90 18.2 18.9461470588235 -0.74614705882353 91 18.38 18.9461470588235 -0.56614705882353 92 18.32 18.9461470588235 -0.626147058823529 93 18.16 18.9461470588235 -0.78614705882353 94 18.14 18.9461470588235 -0.806147058823529 95 18.1 18.9461470588235 -0.846147058823528 96 18.18 18.9461470588235 -0.76614705882353 97 18.33 18.9461470588235 -0.616147058823531 98 18.37 18.9461470588235 -0.576147058823528 99 18.55 18.9461470588235 -0.396147058823529 100 18.6 18.9461470588235 -0.346147058823528 101 18.74 18.9461470588235 -0.206147058823531 102 18.38 18.9461470588235 -0.56614705882353 103 18.33 18.9461470588235 -0.616147058823531 104 18.29 18.9461470588235 -0.65614705882353 105 18.81 18.9461470588235 -0.136147058823531 106 18.96 18.9461470588235 0.0138529411764714 107 18.95 18.9461470588235 0.00385294117646984 108 19.09 18.9461470588235 0.143852941176470 109 19.05 18.9461470588235 0.103852941176471 110 19.07 18.9461470588235 0.123852941176471 111 18.93 18.9461470588235 -0.0161470588235297 112 19 18.9461470588235 0.0538529411764705 113 18.92 18.9461470588235 -0.0261470588235277 114 19.06 18.9461470588235 0.113852941176469 115 19.33 18.9461470588235 0.383852941176469 116 19.42 18.9461470588235 0.473852941176472 117 19.72 18.9461470588235 0.773852941176469 118 19.69 18.9461470588235 0.743852941176472 119 19.57 18.9461470588235 0.623852941176471 120 19.5 18.9461470588235 0.553852941176471 121 19.68 18.9461470588235 0.73385294117647 122 19.12 18.9461470588235 0.173852941176472 123 19.05 18.9461470588235 0.103852941176471 124 19.08 18.9461470588235 0.133852941176469 125 19.03 18.9461470588235 0.0838529411764717 126 19 18.9461470588235 0.0538529411764705 127 18.86 18.9461470588235 -0.08614705882353 128 18.8 18.9461470588235 -0.146147058823529 129 18.81 18.9461470588235 -0.136147058823531 130 18.89 18.9461470588235 -0.0561470588235289 131 18.37 18.9461470588235 -0.576147058823528 132 18.405 18.9461470588235 -0.541147058823528 133 18.35 18.9461470588235 -0.596147058823528 134 18.36 18.9461470588235 -0.58614705882353 135 18.445 18.9461470588235 -0.501147058823529 136 17.52 18.9461470588235 -1.42614705882353 137 17.76 18.9461470588235 -1.18614705882353 138 17.98 18.9461470588235 -0.966147058823529 139 17.38 18.9461470588235 -1.56614705882353 140 17.28 18.9461470588235 -1.66614705882353 141 17.65 18.9461470588235 -1.29614705882353 142 17.875 18.9461470588235 -1.07114705882353 143 18.01 18.9461470588235 -0.936147058823528 144 17.935 18.9461470588235 -1.01114705882353 145 17.925 18.9461470588235 -1.02114705882353 146 18.225 18.9461470588235 -0.721147058823528 147 18.13 18.9461470588235 -0.81614705882353 148 18.27 18.9461470588235 -0.67614705882353 149 18.265 18.9461470588235 -0.681147058823529 150 17.92 18.9461470588235 -1.02614705882353 151 17.9 18.9461470588235 -1.04614705882353 152 17.985 18.9461470588235 -0.96114705882353 153 17.985 18.9461470588235 -0.96114705882353 154 18.24 18.9461470588235 -0.706147058823531 155 18.11 18.9461470588235 -0.83614705882353 156 17.63 18.9461470588235 -1.31614705882353 157 17.16 18.9461470588235 -1.78614705882353 158 17.29 18.9461470588235 -1.65614705882353 159 17.63 18.9461470588235 -1.31614705882353 160 17.59 18.9461470588235 -1.35614705882353 161 17.73 18.9461470588235 -1.21614705882353 162 19.28 18.9461470588235 0.333852941176472 163 19.01 18.9461470588235 0.0638529411764721 164 19.3 18.9461470588235 0.353852941176471 165 19.51 18.9461470588235 0.563852941176472 166 19.53 18.9461470588235 0.583852941176472 167 19.48 18.9461470588235 0.533852941176471 168 19.265 18.9461470588235 0.318852941176471 169 19.7 18.9461470588235 0.75385294117647 170 20.125 18.9461470588235 1.17885294117647 171 19.985 19.3156756756757 0.669324324324324 172 20.085 19.3156756756757 0.769324324324325 173 19.545 19.3156756756757 0.229324324324326 174 19.875 19.3156756756757 0.559324324324324 175 20.05 19.3156756756757 0.734324324324325 176 21.735 19.3156756756757 2.41932432432432 177 20.865 19.3156756756757 1.54932432432432 178 21.82 19.3156756756757 2.50432432432432 179 20.55 19.3156756756757 1.23432432432432 180 20.16 19.3156756756757 0.844324324324324 181 20.08 19.3156756756757 0.764324324324323 182 20.05 19.3156756756757 0.734324324324325 183 19.26 19.3156756756757 -0.0556756756756742 184 19.28 19.3156756756757 -0.0356756756756746 185 19.1 19.3156756756757 -0.215675675675674 186 18.955 19.3156756756757 -0.360675675675677 187 18.265 19.3156756756757 -1.05067567567568 188 17.76 19.3156756756757 -1.55567567567567 189 18.76 19.3156756756757 -0.555675675675674 190 19.28 19.3156756756757 -0.0356756756756746 191 18.92 19.3156756756757 -0.395675675675674 192 18.95 19.3156756756757 -0.365675675675676 193 18.49 19.3156756756757 -0.825675675675677 194 18.47 19.3156756756757 -0.845675675675677 195 18.57 19.3156756756757 -0.745675675675675 196 18.9 19.3156756756757 -0.415675675675677 197 18.485 19.3156756756757 -0.830675675675676 198 18.635 19.3156756756757 -0.680675675675674 199 18.865 19.3156756756757 -0.450675675675677 200 18.86 19.3156756756757 -0.455675675675676 201 18.4 19.3156756756757 -0.915675675675677 202 18.515 19.3156756756757 -0.800675675675675 203 18.95 19.3156756756757 -0.365675675675676 204 18.83 19.3156756756757 -0.485675675675677 205 18.84 19.3156756756757 -0.475675675675676 206 19.2 19.3156756756757 -0.115675675675676 207 19.34 19.3156756756757 0.0243243243243241 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00211767059626893 0.00423534119253787 0.997882329403731 6 0.0145451728581326 0.0290903457162652 0.985454827141867 7 0.0130589854047794 0.0261179708095587 0.98694101459522 8 0.00558616451608641 0.0111723290321728 0.994413835483914 9 0.00176827008978714 0.00353654017957428 0.998231729910213 10 0.00136769443327383 0.00273538886654766 0.998632305566726 11 0.00136203349474241 0.00272406698948482 0.998637966505258 12 0.00103327633468337 0.00206655266936675 0.998966723665317 13 0.000379534604829508 0.000759069209659016 0.99962046539517 14 0.000135867417882823 0.000271734835765646 0.999864132582117 15 4.701493281403e-05 9.402986562806e-05 0.999952985067186 16 1.6031936510184e-05 3.2063873020368e-05 0.99998396806349 17 5.29700405345268e-06 1.05940081069054e-05 0.999994702995947 18 2.3848152279408e-06 4.7696304558816e-06 0.999997615184772 19 4.84883255961945e-06 9.6976651192389e-06 0.99999515116744 20 5.18555298458125e-06 1.03711059691625e-05 0.999994814447015 21 6.67774429935422e-06 1.33554885987084e-05 0.9999933222557 22 2.79574176172704e-06 5.59148352345407e-06 0.999997204258238 23 2.44169357754971e-06 4.88338715509942e-06 0.999997558306422 24 1.48558721349641e-06 2.97117442699281e-06 0.999998514412787 25 1.32932538185508e-06 2.65865076371016e-06 0.999998670674618 26 3.31321821332286e-06 6.62643642664572e-06 0.999996686781787 27 6.83348154075878e-06 1.36669630815176e-05 0.99999316651846 28 4.94063235387632e-06 9.88126470775265e-06 0.999995059367646 29 7.90863143942994e-06 1.58172628788599e-05 0.99999209136856 30 5.59611033756477e-06 1.11922206751295e-05 0.999994403889662 31 3.64162340313785e-06 7.28324680627571e-06 0.999996358376597 32 2.52406624576151e-06 5.04813249152301e-06 0.999997475933754 33 1.5965207720035e-06 3.193041544007e-06 0.999998403479228 34 1.22757051132876e-06 2.45514102265752e-06 0.999998772429489 35 1.66049716281810e-06 3.32099432563619e-06 0.999998339502837 36 6.17341396517113e-06 1.23468279303423e-05 0.999993826586035 37 5.71085663608403e-06 1.14217132721681e-05 0.999994289143364 38 4.7521693683493e-06 9.5043387366986e-06 0.999995247830632 39 0.000131017966310783 0.000262035932621565 0.99986898203369 40 0.000428264207728593 0.000856528415457187 0.999571735792271 41 0.000968755742042948 0.00193751148408590 0.999031244257957 42 0.00194035948518723 0.00388071897037445 0.998059640514813 43 0.00394645351989197 0.00789290703978395 0.996053546480108 44 0.00959136624657137 0.0191827324931427 0.990408633753429 45 0.0621410097153646 0.124282019430729 0.937858990284635 46 0.195377943048567 0.390755886097133 0.804622056951433 47 0.324035625025888 0.648071250051776 0.675964374974112 48 0.450021255056942 0.900042510113884 0.549978744943058 49 0.524275865848505 0.95144826830299 0.475724134151495 50 0.576654840190666 0.846690319618667 0.423345159809334 51 0.663253740341938 0.673492519316125 0.336746259658062 52 0.686343348203949 0.627313303592102 0.313656651796051 53 0.715382337479366 0.569235325041267 0.284617662520634 54 0.751589550853086 0.496820898293828 0.248410449146914 55 0.760396913126127 0.479206173747746 0.239603086873873 56 0.792893540251459 0.414212919497082 0.207106459748541 57 0.786007198472376 0.427985603055249 0.213992801527624 58 0.785154675830151 0.429690648339699 0.214845324169849 59 0.812793186637107 0.374413626725786 0.187206813362893 60 0.834809971514357 0.330380056971286 0.165190028485643 61 0.859498004782194 0.281003990435613 0.140501995217806 62 0.884073186771027 0.231853626457945 0.115926813228973 63 0.914873035817325 0.170253928365349 0.0851269641826746 64 0.931498281333257 0.137003437333486 0.0685017186667432 65 0.943552334783186 0.112895330433628 0.056447665216814 66 0.96541531208786 0.0691693758242824 0.0345846879121412 67 0.97728708166182 0.0454258366763606 0.0227129183381803 68 0.981378545909466 0.0372429081810674 0.0186214540905337 69 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0.663584001187238 0.672831997625523 0.336415998812761 121 0.684120787016952 0.631758425966097 0.315879212983048 122 0.665113809330728 0.669772381338543 0.334886190669272 123 0.643370629096048 0.713258741807903 0.356629370903952 124 0.623260257568156 0.753479484863687 0.376739742431844 125 0.601393799116456 0.797212401767089 0.398606200883544 126 0.578718359546472 0.842563280907055 0.421281640453528 127 0.551565464347469 0.89686907130506 0.44843453565253 128 0.523117866916939 0.953764266166122 0.476882133083061 129 0.49514817950866 0.99029635901732 0.50485182049134 130 0.470054621766138 0.940109243532276 0.529945378233862 131 0.443081163672955 0.88616232734591 0.556918836327045 132 0.415185559504016 0.830371119008031 0.584814440495984 133 0.388654257548739 0.777308515097478 0.611345742451261 134 0.362011648310533 0.724023296621065 0.637988351689467 135 0.334170871500414 0.668341743000829 0.665829128499586 136 0.364475566783085 0.728951133566169 0.635524433216915 137 0.369627309235714 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0.756485523105591 155 0.226602902304464 0.453205804608927 0.773397097695536 156 0.248591808649865 0.497183617299731 0.751408191350135 157 0.347006576439356 0.694013152878712 0.652993423560644 158 0.453112130241298 0.906224260482597 0.546887869758702 159 0.523093584359388 0.953812831281225 0.476906415640612 160 0.621998929542752 0.756002140914495 0.378001070457248 161 0.718138899136105 0.563722201727791 0.281861100863895 162 0.678345793185114 0.643308413629772 0.321654206814886 163 0.645625279627123 0.708749440745755 0.354374720372877 164 0.603760268736437 0.792479462527126 0.396239731263563 165 0.559220728140907 0.881558543718186 0.440779271859093 166 0.513783569662755 0.97243286067449 0.486216430337245 167 0.468336067115352 0.936672134230704 0.531663932884648 168 0.433083320266026 0.866166640532052 0.566916679733974 169 0.393893332032713 0.787786664065426 0.606106667967287 170 0.359806820572928 0.719613641145857 0.640193179427072 171 0.333126136986457 0.666252273972914 0.666873863013543 172 0.315318696513202 0.630637393026404 0.684681303486798 173 0.273943059323910 0.547886118647821 0.72605694067609 174 0.246203920366435 0.49240784073287 0.753796079633565 175 0.231237183319177 0.462474366638354 0.768762816680823 176 0.563608673249452 0.872782653501097 0.436391326750548 177 0.709552912698459 0.580894174603083 0.290447087301541 178 0.984507464909872 0.0309850701802555 0.0154925350901278 179 0.99708016090403 0.00583967819194076 0.00291983909597038 180 0.999191061090763 0.00161787781847469 0.000808938909237347 181 0.999848669186527 0.000302661626945793 0.000151330813472897 182 0.999989466757722 2.10664845568629e-05 1.05332422784315e-05 183 0.999987723895465 2.45522090709788e-05 1.22761045354894e-05 184 0.999987231348658 2.55373026844934e-05 1.27686513422467e-05 185 0.999979947167004 4.0105665991645e-05 2.00528329958225e-05 186 0.999960493253584 7.90134928315028e-05 3.95067464157514e-05 187 0.999957971701422 8.40565971551015e-05 4.20282985775507e-05 188 0.99999864406601 2.71186797858378e-06 1.35593398929189e-06 189 0.999995903130257 8.19373948590201e-06 4.09686974295100e-06 190 0.999996456524174 7.08695165103292e-06 3.54347582551646e-06 191 0.99998982606117 2.03478776617324e-05 1.01739388308662e-05 192 0.999973104267498 5.37914650050328e-05 2.68957325025164e-05 193 0.99994477724949 0.000110445501019483 5.52227505097415e-05 194 0.999899528959632 0.000200942080736706 0.000100471040368353 195 0.999764339570811 0.000471320858377934 0.000235660429188967 196 0.999280832993241 0.00143833401351749 0.000719167006758743 197 0.998741678562927 0.00251664287414530 0.00125832143707265 198 0.996885371876256 0.00622925624748832 0.00311462812374416 199 0.990619501223458 0.0187609975530838 0.00938049877654192 200 0.973604614869104 0.0527907702617924 0.0263953851308962 201 0.970208343529433 0.0595833129411339 0.0297916564705669 202 0.967954430177635 0.0640911396447294 0.0320455698223647 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 56 0.282828282828283 NOK 5% type I error level 79 0.398989898989899 NOK 10% type I error level 97 0.48989898989899 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/10gjbt1227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/10gjbt1227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/13fsz1227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/13fsz1227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/2ezbe1227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/2ezbe1227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/3d3az1227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/3d3az1227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/41hzj1227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/41hzj1227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/5azj91227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/5azj91227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/682p21227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/682p21227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/7hsj51227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/7hsj51227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/85z2m1227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/85z2m1227533800.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/9mkg71227533800.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227533926281x8c2jjhst4jl/9mkg71227533800.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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