*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: Wed, 17 Dec 2008 11:39:55 -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/Dec/17/t1229539708vipcu7q4zoi50ni.htm/, Retrieved Wed, 17 Dec 2008 19:48:28 +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/2008/Dec/17/t1229539708vipcu7q4zoi50ni.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1721 0 0.44 1476 0 0.09 1842 0 0.2 2171 0 0.82 1670 0 0.5 1540 0 0.2 1266 0 1 897 0 0.47 1266 0 0.49 1519 0 0.82 1074 0 0.39 1435 0 0.6 1385 0 0.59 1440 0 0.72 1883 0 0.97 1822 0 0.58 1661 0 0.27 1774 0 0.84 1133 0 0.51 1361 0 0.13 1688 0 0.65 2216 0 0.51 2896 0 1.06 1382 0 0.81 1330 0 0.54 1419 0 0.85 1662 0 0.93 2040 0 0.29 2126 0 1.01 1649 0 0.65 1610 0 0.88 1952 0 0.45 2102 0 0.74 1749 0 1.08 2091 0 0.27 3036 0 0.24 2414 0 0.27 2097 0 0.25 2705 0 0.69 2431 0 0.73 4192 1 1.04 3990 0 1.04 2854 0 0.3 1966 0 0.59 2431 0 0.72 2763 0 0.22 2831 0 1.12 2023 0 0.93 2934 0 0.99 2489 0 0.56 3252 0 1 3018 0 0.57 3193 0 1 3976 0 0.97 2584 0 0.3 2512 0 0.45 2169 0 0.73 2504 0 1.13 1843 0 0.65 1408 -1 0.64 2179 0 0.68 3690 0 0.41 2372 0 0.98 2494 0 0.3 3872 0 0.37 2786 0 1.12 2312 0 0.4 1599 0 0.5 3167 0 1.23 3433 0 0.94 2648 0 1.08 1978 0 1.12 1947 0 0.83 3113 0 1.22 2856 0 0.55 3174 0 0.38 3507 0 1.26 4174 0 0.49 2978 0 1.13 4428 0 1.07 2832 0 0.86 2930 0 0.94 3681 0 0.45 3253 0 0.66 1660 -1 0.71 2208 0 0.54 3139 0 0.9 3409 0 1.23 3445 0 0.46 2410 0 1.33 3262 0 0.64 2897 0 0.9 2526 0 0.5 3982 0 1.37 4097 0 0.96 3403 0 0.62 3362 0 1.24 2708 0 1.1 3129 0 0.86 3550 0 1.2 2696 0 0.77 2885 0 0.67 2945 0 1.05 3600 0 1.32 3808 0 0.6 3671 0 1.31 4005 0 1.41

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 1289.31020876413 + 1344.84084081993D[t] + 84.7943996269861X[t] -56.5131772164014M1[t] -30.5909616006774M2[t] + 181.617856851149M3[t] + 312.240342762376M4[t] + 388.071705980489M5[t] + 381.063287160659M6[t] -99.2947460441981M7[t] -86.0185223282531M8[t] -25.3256950165499M9[t] + 246.771409676564M10[t] + 280.256148923197M11[t] + 19.6106820481556t + 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) 1289.31020876413 236.229378 5.4579 0 0 D 1344.84084081993 341.437127 3.9388 0.000159 8e-05 X 84.7943996269861 189.529942 0.4474 0.655643 0.327821 M1 -56.5131772164014 266.835558 -0.2118 0.832739 0.41637 M2 -30.5909616006774 270.337991 -0.1132 0.910152 0.455076 M3 181.617856851149 270.281946 0.672 0.503295 0.251648 M4 312.240342762376 270.08535 1.1561 0.250642 0.125321 M5 388.071705980489 278.348516 1.3942 0.166617 0.083309 M6 381.063287160659 270.436126 1.4091 0.162186 0.081093 M7 -99.2947460441981 270.162464 -0.3675 0.714063 0.357031 M8 -86.0185223282531 270.56776 -0.3179 0.751267 0.375634 M9 -25.3256950165499 270.154464 -0.0937 0.925515 0.462758 M10 246.771409676564 272.237815 0.9065 0.367063 0.183531 M11 280.256148923197 270.57866 1.0358 0.303026 0.151513 t 19.6106820481556 1.93931 10.1122 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.800301280676477 R-squared 0.640482139852408 Adjusted R-squared 0.585772900264731 F-TEST (value) 11.7070195944868 F-TEST (DF numerator) 14 F-TEST (DF denominator) 92 p-value 5.55111512312578e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 548.764103764027 Sum Squared Residuals 27705067.8253541 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1721 1289.71724943176 431.282750568238 2 1476 1305.57210722619 170.427892773808 3 1842 1546.71899168514 295.281008314856 4 2171 1749.52468741326 421.475312586742 5 1670 1817.83252479889 -147.832524798892 6 1540 1804.99646813912 -264.996468139121 7 1266 1412.08463668401 -146.084636684008 8 897 1400.03051064581 -503.030510645806 9 1266 1482.02990799820 -216.029907998204 10 1519 1801.71984661638 -282.719846616379 11 1074 1818.35367607156 -744.353676071564 12 1435 1575.51503311819 -140.515033118190 13 1385 1537.76459395367 -152.764593953674 14 1440 1594.32076356906 -154.320763569062 15 1883 1847.33886397579 35.6611360242092 16 1822 1964.50221608065 -142.502216080649 17 1661 2033.65799746255 -372.657997462552 18 1774 2094.59306847826 -320.593068478259 19 1133 1605.86356544465 -472.863565444652 20 1361 1606.52859935050 -245.528599350498 21 1688 1730.92519651639 -42.9251965163893 22 2216 2010.76176730988 205.238232690119 23 2896 2110.49410839951 785.505891600488 24 1382 1828.65004161772 -446.650041617724 25 1330 1768.85305855019 -438.853058550192 26 1419 1840.67222009844 -421.672220098437 27 1662 2079.27527256858 -417.275272568578 28 2040 2175.24002476669 -135.240024766690 29 2126 2331.73403776439 -205.734037764389 30 1649 2313.810317127 -664.810317126998 31 1610 1872.56567788450 -262.565677884504 32 1952 1868.990991809 83.0090081909996 33 2102 1973.88487706069 128.115122939315 34 1749 2294.42275967513 -545.42275967513 35 2091 2278.83471727206 -187.834717272060 36 3036 2015.64541840821 1020.35458159179 37 2414 1981.28675522877 432.713244771227 38 2097 2025.12376490011 71.8762350998874 39 2705 2294.25280123597 410.747198764031 40 2431 2447.87774518043 -16.8777451804310 41 4192 3914.44689515099 277.553104849005 42 3990 2582.20831755939 1407.79168244061 43 2854 2058.71311067872 795.286889321281 44 1966 2116.19039233465 -150.190392334646 45 2431 2207.51717364601 223.482826353987 46 2763 2456.82776057379 306.172239426211 47 2831 2586.23814153287 244.761858467135 48 2023 2309.48173872870 -286.481738728697 49 2934 2277.66690753807 656.33309246193 50 2489 2286.73821336235 202.261786637654 51 3252 2555.8672496982 696.132750301798 52 3018 2669.63882581798 348.36117418202 53 3193 2801.54246292385 391.457537076147 54 3976 2811.60089416337 1164.39910583663 55 2584 2294.04129525659 289.958704743414 56 2512 2339.64736096474 172.352639035265 57 2169 2443.69330222015 -274.69330222015 58 2504 2769.31884881221 -265.318848812214 59 1843 2781.71295828605 -938.712958286049 60 1408 1175.37870659481 232.621293405191 61 2179 2486.70882823157 -307.708828231572 62 3690 2509.34723799616 1180.65276200384 63 2372 2789.49954628353 -417.499546283529 64 2494 2882.07252249656 -388.072522496561 65 3872 2983.45017573672 888.549824263281 66 2786 3059.64823868528 -273.648238685284 67 2312 2537.84891979715 -225.848919797152 68 1599 2579.21526552395 -980.215265523952 69 3167 2721.41868661151 445.58131338849 70 3433 2988.53609746095 444.463902539046 71 2648 3053.50273470352 -405.50273470352 72 1978 2796.24904381356 -818.249043813558 73 1947 2734.75617275349 -787.756172753486 74 3113 2813.35888627189 299.641113728109 75 2856 2988.36613902179 -132.366139021792 76 3174 3124.18425904459 49.8157409554123 77 3507 3294.24537598260 212.754624017396 78 4174 3241.55595149815 932.44404850185 79 2978 2835.07701610272 142.922983897281 80 4428 2862.8762578892 1565.1237421108 81 2832 2925.37294332739 -93.3729433273925 82 2930 3223.86428203882 -293.864282038821 83 3681 3235.41044751639 445.589552483614 84 3253 2992.57180456301 260.428195436987 85 1660 1615.06818855619 44.9318114438138 86 2208 2991.02687910341 -783.026879103408 87 3139 3253.37236346910 -114.372363469105 88 3409 3431.58768330539 -22.5876833053930 89 3445 3461.73804085888 -16.7380408588825 90 2410 3548.11143176269 -1138.11143176269 91 3262 3028.85594486336 233.144055136636 92 2897 3083.78939453048 -186.789394530481 93 2526 3130.17514403954 -604.175144039545 94 3982 3495.65405845629 486.345941543708 95 4097 3513.98377590402 583.016224095984 96 3403 3224.5082131558 178.491786844200 97 3362 3240.17824575629 121.821754243714 98 2708 3273.83992747239 -565.839927472387 99 3129 3485.30877206189 -356.308772061892 100 3550 3664.37203589445 -114.372035894451 101 2696 3723.35248932112 -1027.35248932112 102 2885 3727.47531258674 -842.475312586742 103 2945 3298.94983328830 -353.949833288295 104 3600 3354.73122695168 245.268773048318 105 3808 3373.98276858011 434.017231419889 106 3671 3725.89457905654 -54.8945790565403 107 4005 3787.46944031403 217.530559685972 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.0273156798648725 0.0546313597297451 0.972684320135128 19 0.00778041070278168 0.0155608214055634 0.992219589297218 20 0.0205429438427898 0.0410858876855796 0.97945705615721 21 0.0144204156030435 0.0288408312060870 0.985579584396956 22 0.0201459446294766 0.0402918892589533 0.979854055370523 23 0.241664464817151 0.483328929634302 0.758335535182849 24 0.180632947298619 0.361265894597239 0.81936705270138 25 0.149845631065665 0.29969126213133 0.850154368934335 26 0.114591792587378 0.229183585174755 0.885408207412622 27 0.0910623855463508 0.182124771092702 0.90893761445365 28 0.0586597143727504 0.117319428745501 0.94134028562725 29 0.0390999380311605 0.078199876062321 0.96090006196884 30 0.029518279183153 0.059036558366306 0.970481720816847 31 0.0213907982764782 0.0427815965529564 0.978609201723522 32 0.0231414657005011 0.0462829314010022 0.976858534299499 33 0.0172079940714776 0.0344159881429551 0.982792005928522 34 0.0164450120234165 0.0328900240468329 0.983554987976584 35 0.0104583084014561 0.0209166168029121 0.989541691598544 36 0.0712757333555268 0.142551466711054 0.928724266644473 37 0.0609535458261315 0.121907091652263 0.939046454173869 38 0.0426146135699838 0.0852292271399676 0.957385386430016 39 0.0332824433843416 0.0665648867686831 0.966717556615658 40 0.0225134770132019 0.0450269540264038 0.977486522986798 41 0.0143966938915389 0.0287933877830778 0.985603306108461 42 0.144344855304402 0.288689710608803 0.855655144695598 43 0.164111844997337 0.328223689994673 0.835888155002663 44 0.134695573624982 0.269391147249964 0.865304426375018 45 0.101173115773585 0.202346231547169 0.898826884226415 46 0.0755812513786073 0.151162502757215 0.924418748621393 47 0.0548314136643663 0.109662827328733 0.945168586335634 48 0.0512477230630227 0.102495446126045 0.948752276936977 49 0.0458774004350899 0.0917548008701797 0.95412259956491 50 0.0319585007471915 0.0639170014943831 0.968041499252808 51 0.0283543060130451 0.0567086120260903 0.971645693986955 52 0.0198955362578410 0.0397910725156819 0.98010446374216 53 0.0141993521215918 0.0283987042431836 0.985800647878408 54 0.0309661492558769 0.0619322985117539 0.969033850744123 55 0.0221125249556328 0.0442250499112656 0.977887475044367 56 0.0147921672801478 0.0295843345602956 0.985207832719852 57 0.0141191723638961 0.0282383447277923 0.985880827636104 58 0.0120332246482715 0.024066449296543 0.987966775351728 59 0.0437705197686723 0.0875410395373445 0.956229480231328 60 0.0311151700494828 0.0622303400989656 0.968884829950517 61 0.0306615463478116 0.0613230926956232 0.969338453652188 62 0.0695616351087678 0.139123270217536 0.930438364891232 63 0.0751225361791073 0.150245072358215 0.924877463820893 64 0.0746956082280771 0.149391216456154 0.925304391771923 65 0.100852260840534 0.201704521681068 0.899147739159466 66 0.090754724581108 0.181509449162216 0.909245275418892 67 0.0748804827091313 0.149760965418263 0.925119517290869 68 0.222817389529280 0.445634779058559 0.77718261047072 69 0.190973191119901 0.381946382239802 0.809026808880099 70 0.154628477486677 0.309256954973354 0.845371522513323 71 0.190390264333136 0.380780528666272 0.809609735666864 72 0.340503694426320 0.681007388852641 0.65949630557368 73 0.492145852149356 0.984291704298712 0.507854147850644 74 0.457372653182579 0.914745306365157 0.542627346817421 75 0.39904731029275 0.7980946205855 0.60095268970725 76 0.337355701609443 0.674711403218886 0.662644298390557 77 0.292707607133097 0.585415214266194 0.707292392866903 78 0.624066701099974 0.751866597800053 0.375933298900026 79 0.538921937840218 0.922156124319564 0.461078062159782 80 0.909653335718791 0.180693328562418 0.0903466642812091 81 0.86424590167934 0.271508196641319 0.135754098320660 82 0.862781406200064 0.274437187599872 0.137218593799936 83 0.80575625329636 0.38848749340728 0.19424374670364 84 0.71985600715708 0.56028798568584 0.28014399284292 85 0.61350885557941 0.77298228884118 0.38649114442059 86 0.575621729530783 0.848756540938434 0.424378270469217 87 0.45342221512649 0.90684443025298 0.54657778487351 88 0.317046872106552 0.634093744213104 0.682953127893448 89 0.333018288704538 0.666036577409075 0.666981711295462 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 17 0.236111111111111 NOK 10% type I error level 29 0.402777777777778 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/10ki831229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/10ki831229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/1mtpd1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/1mtpd1229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/2turv1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/2turv1229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/330ct1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/330ct1229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/46gpa1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/46gpa1229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/5gv8l1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/5gv8l1229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/62wij1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/62wij1229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/7vkdq1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/7vkdq1229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/8i2yh1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/8i2yh1229539183.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/9pmfe1229539183.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229539708vipcu7q4zoi50ni/9pmfe1229539183.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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