Regressie prof bachelor 1 juli 2005

*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: Fri, 12 Dec 2008 08:27:30 -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/12/t1229095752i0mvobf72xd3ypv.htm/, Retrieved Fri, 12 Dec 2008 16:29:13 +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/12/t1229095752i0mvobf72xd3ypv.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 «

13363 0 12530 0 11420 0 10948 0 10173 0 10602 0 16094 0 19631 0 17140 0 14345 0 12632 0 12894 0 11808 0 10673 0 9939 0 9890 0 9283 0 10131 0 15864 0 19283 0 16203 0 13919 0 11937 0 11795 0 11268 0 10522 0 9929 0 9725 0 9372 0 10068 0 16230 0 19115 0 18351 0 16265 0 14103 0 14115 0 13327 0 12618 0 12129 0 11775 0 11493 0 12470 0 20792 0 22337 0 21325 0 18581 0 16475 0 16581 0 15745 0 14453 0 13712 0 13766 0 13336 0 15346 0 24446 0 26178 0 24628 0 21282 0 18850 0 18822 0 18060 0 17536 0 16417 0 15842 0 15188 0 16905 0 25430 0 27962 0 26607 0 23364 0 20827 0 20506 0 19181 0 18016 0 17354 0 16256 0 15770 0 17538 0 26899 1 28915 1 25247 1 22856 1 19980 1 19856 1 16994 1 16839 1 15618 1 15883 1 15513 1 17106 1 25272 1 26731 1 22891 1 19583 1 16939 1 16757 1 15435 1 14786 1 13680 1 13208 1 12707 1 14277 1 22436 1 23229 1 18241 1 16145 1 13994 1 14780 1 13100 1 12329 1 12463 1 11532 1 10784 1 13106 1 19491 1 20418 1 16094 1 14491 1 13067 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 9 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation NWWZpb[t] = + 12483.6674186368 -3857.07191842628Dummy[t] -1114.80046221099M1[t] -1996.63418255830M2[t] -2844.66790290561M3[t] -3312.20162325292M4[t] -3916.73534360023M5[t] -2607.66906394754M6[t] + 5234.60440754777M7[t] + 7235.17068720046M8[t] + 4444.03696685315M9[t] + 1770.50324650585M10[t] -516.130473841465M11[t] + 83.93372034731t + 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) 12483.6674186368 1036.110209 12.0486 0 0 Dummy -3857.07191842628 909.361619 -4.2415 4.8e-05 2.4e-05 M1 -1114.80046221099 1225.341142 -0.9098 0.365018 0.182509 M2 -1996.63418255830 1225.017036 -1.6299 0.106123 0.053061 M3 -2844.66790290561 1224.821756 -2.3225 0.022134 0.011067 M4 -3312.20162325292 1224.755364 -2.7044 0.007985 0.003993 M5 -3916.73534360023 1224.817879 -3.1978 0.001831 0.000915 M6 -2607.66906394754 1225.009284 -2.1287 0.035618 0.017809 M7 5234.60440754777 1225.686953 4.2708 4.3e-05 2.1e-05 M8 7235.17068720046 1225.368752 5.9045 0 0 M9 4444.03696685315 1225.179342 3.6273 0.000444 0.000222 M10 1770.50324650585 1225.118782 1.4452 0.151388 0.075694 M11 -516.130473841465 1225.187093 -0.4213 0.674421 0.337211 t 83.93372034731 12.565238 6.6798 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.842742362483911 R-squared 0.710214689524964 Adjusted R-squared 0.674336508228055 F-TEST (value) 19.7951697620232 F-TEST (DF numerator) 13 F-TEST (DF denominator) 105 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2665.32908203119 Sum Squared Residuals 745917807.129727 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13363 11452.8006767731 1910.19932322686 2 12530 10654.9006767731 1875.09932322686 3 11420 9890.80067677315 1529.19932322685 4 10948 9507.20067677315 1440.79932322685 5 10173 8986.60067677315 1186.39932322685 6 10602 10379.6006767731 222.399323226853 7 16094 18305.8078686158 -2211.80786861577 8 19631 20390.3078686158 -759.307868615773 9 17140 17683.1078686158 -543.107868615774 10 14345 15093.5078686158 -748.50786861577 11 12632 12890.8078686158 -258.807868615775 12 12894 13490.8720628045 -596.872062804548 13 11808 12460.0053209409 -652.005320940865 14 10673 11662.1053209409 -989.105320940868 15 9939 10898.0053209409 -959.005320940865 16 9890 10514.4053209409 -624.405320940864 17 9283 9993.80532094086 -710.805320940865 18 10131 11386.8053209409 -1255.80532094086 19 15864 19313.0125127835 -3449.01251278349 20 19283 21397.5125127835 -2114.51251278349 21 16203 18690.3125127835 -2487.31251278349 22 13919 16100.7125127835 -2181.71251278349 23 11937 13898.0125127835 -1961.01251278349 24 11795 14498.0767069723 -2703.07670697227 25 11268 13467.2099651086 -2199.20996510858 26 10522 12669.3099651086 -2147.30996510858 27 9929 11905.2099651086 -1976.20996510858 28 9725 11521.6099651086 -1796.60996510858 29 9372 11001.0099651086 -1629.00996510858 30 10068 12394.0099651086 -2326.00996510858 31 16230 20320.2171569512 -4090.21715695121 32 19115 22404.7171569512 -3289.71715695121 33 18351 19697.5171569512 -1346.51715695121 34 16265 17107.9171569512 -842.917156951213 35 14103 14905.2171569512 -802.217156951212 36 14115 15505.28135114 -1390.28135113999 37 13327 14474.4146092763 -1147.41460927630 38 12618 13676.5146092763 -1058.51460927630 39 12129 12912.4146092763 -783.414609276305 40 11775 12528.8146092763 -753.814609276303 41 11493 12008.2146092763 -515.214609276303 42 12470 13401.2146092763 -931.214609276304 43 20792 21327.4218011189 -535.42180111893 44 22337 23411.9218011189 -1074.92180111893 45 21325 20704.7218011189 620.27819888107 46 18581 18115.1218011189 465.878198881068 47 16475 15912.4218011189 562.578198881068 48 16581 16512.4859953077 68.5140046922939 49 15745 15481.6192534440 263.380746555977 50 14453 14683.7192534440 -230.719253444023 51 13712 13919.6192534440 -207.619253444023 52 13766 13536.0192534440 229.980746555977 53 13336 13015.4192534440 320.580746555977 54 15346 14408.4192534440 937.580746555977 55 24446 22334.6264452867 2111.37355471335 56 26178 24419.1264452867 1758.87355471335 57 24628 21711.9264452867 2916.07355471335 58 21282 19122.3264452866 2159.67355471335 59 18850 16919.6264452867 1930.37355471335 60 18822 17519.6906394754 1302.30936052457 61 18060 16488.8238976117 1571.17610238826 62 17536 15690.9238976117 1845.07610238826 63 16417 14926.8238976117 1490.17610238826 64 15842 14543.2238976117 1298.77610238826 65 15188 14022.6238976117 1165.37610238826 66 16905 15415.6238976117 1489.37610238826 67 25430 23341.8310894544 2088.16891054563 68 27962 25426.3310894544 2535.66891054563 69 26607 22719.1310894544 3887.86891054563 70 23364 20129.5310894544 3234.46891054563 71 20827 17926.8310894544 2900.16891054563 72 20506 18526.8952836431 1979.10471635686 73 19181 17496.0285417795 1684.97145822054 74 18016 16698.1285417795 1317.87145822054 75 17354 15934.0285417795 1419.97145822054 76 16256 15550.4285417795 705.571458220538 77 15770 15029.8285417795 740.171458220537 78 17538 16422.8285417795 1115.17145822054 79 26899 20491.9638151958 6407.03618480418 80 28915 22576.4638151958 6338.53618480419 81 25247 19869.2638151958 5377.73618480419 82 22856 17279.6638151958 5576.33618480418 83 19980 15076.9638151958 4903.03618480419 84 19856 15677.0280093846 4178.97199061541 85 16994 14646.1612675209 2347.83873247909 86 16839 13848.2612675209 2990.73873247909 87 15618 13084.1612675209 2533.83873247910 88 15883 12700.5612675209 3182.43873247909 89 15513 12179.9612675209 3333.03873247910 90 17106 13572.9612675209 3533.03873247910 91 25272 21499.1684593635 3772.83154063647 92 26731 23583.6684593635 3147.33154063647 93 22891 20876.4684593635 2014.53154063647 94 19583 18286.8684593635 1296.13154063647 95 16939 16084.1684593635 854.831540636467 96 16757 16684.2326535523 72.7673464476922 97 15435 15653.3659116886 -218.365911688625 98 14786 14855.4659116886 -69.4659116886235 99 13680 14091.3659116886 -411.365911688624 100 13208 13707.7659116886 -499.765911688625 101 12707 13187.1659116886 -480.165911688624 102 14277 14580.1659116886 -303.165911688625 103 22436 22506.3731035313 -70.373103531251 104 23229 24590.8731035313 -1361.87310353125 105 18241 21883.6731035313 -3642.67310353125 106 16145 19294.0731035313 -3149.07310353125 107 13994 17091.3731035313 -3097.37310353125 108 14780 17691.4372977200 -2911.43729772003 109 13100 16660.5705558563 -3560.57055585634 110 12329 15862.6705558563 -3533.67055585634 111 12463 15098.5705558563 -2635.57055585634 112 11532 14714.9705558563 -3182.97055585634 113 10784 14194.3705558563 -3410.37055585634 114 13106 15587.3705558563 -2481.37055585634 115 19491 23513.5777476990 -4022.57774769897 116 20418 25598.0777476990 -5180.07774769897 117 16094 22890.8777476990 -6796.87774769897 118 14491 20301.2777476990 -5810.27774769897 119 13067 18098.5777476990 -5031.57774769897 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00190886500601283 0.00381773001202567 0.998091134993987 18 0.000747132674759184 0.00149426534951837 0.99925286732524 19 0.000319240033732867 0.000638480067465735 0.999680759966267 20 8.20397326328893e-05 0.000164079465265779 0.999917960267367 21 1.23381462231860e-05 2.46762924463719e-05 0.999987661853777 22 2.60073385489949e-06 5.20146770979898e-06 0.999997399266145 23 3.94477002103068e-07 7.88954004206137e-07 0.999999605522998 24 5.9416196757433e-08 1.18832393514866e-07 0.999999940583803 25 8.02092574769096e-09 1.60418514953819e-08 0.999999991979074 26 1.25845093642548e-09 2.51690187285097e-09 0.99999999874155 27 3.39167485847695e-10 6.78334971695391e-10 0.999999999660833 28 8.75624518675319e-11 1.75124903735064e-10 0.999999999912438 29 4.83798819969427e-11 9.67597639938854e-11 0.99999999995162 30 2.56455876411299e-11 5.12911752822598e-11 0.999999999974354 31 8.77312677307273e-11 1.75462535461455e-10 0.999999999912269 32 3.80932450968513e-11 7.61864901937026e-11 0.999999999961907 33 5.26630702150071e-09 1.05326140430014e-08 0.999999994733693 34 1.33383682735301e-07 2.66767365470603e-07 0.999999866616317 35 4.23553631720444e-07 8.47107263440889e-07 0.999999576446368 36 9.04893014496906e-07 1.80978602899381e-06 0.999999095106985 37 8.63440083377134e-07 1.72688016675427e-06 0.999999136559917 38 9.06866061547026e-07 1.81373212309405e-06 0.999999093133938 39 1.14892643750004e-06 2.29785287500008e-06 0.999998851073562 40 1.21017937094570e-06 2.42035874189141e-06 0.99999878982063 41 1.54886698546416e-06 3.09773397092831e-06 0.999998451133015 42 3.34840255328215e-06 6.69680510656431e-06 0.999996651597447 43 0.000150712327576246 0.000301424655152493 0.999849287672424 44 0.000451957276686011 0.000903914553372022 0.999548042723314 45 0.00151880625599904 0.00303761251199809 0.998481193744 46 0.00332067127304847 0.00664134254609693 0.996679328726952 47 0.0060567094859929 0.0121134189719858 0.993943290514007 48 0.0118674694828716 0.0237349389657433 0.988132530517128 49 0.0156976565374776 0.0313953130749553 0.984302343462522 50 0.0249834150159030 0.0499668300318059 0.975016584984097 51 0.0448885006215635 0.0897770012431269 0.955111499378437 52 0.07811186879994 0.15622373759988 0.92188813120006 53 0.147076290999510 0.294152581999020 0.85292370900049 54 0.333845654154345 0.667691308308689 0.666154345845655 55 0.707506685232502 0.584986629534996 0.292493314767498 56 0.875680318614395 0.248639362771209 0.124319681385605 57 0.92233775216263 0.155324495674739 0.0776622478373695 58 0.952720143692584 0.094559712614832 0.047279856307416 59 0.973854020129866 0.0522919597402689 0.0261459798701344 60 0.988119684900212 0.023760630199576 0.011880315099788 61 0.990542088476663 0.018915823046674 0.009457911523337 62 0.992883051800813 0.0142338963983731 0.00711694819918656 63 0.996313083498957 0.00737383300208685 0.00368691650104343 64 0.9985928897522 0.002814220495601 0.0014071102478005 65 0.999752327554868 0.000495344890263762 0.000247672445131881 66 0.999992233061935 1.55338761291326e-05 7.76693806456629e-06 67 0.999999720573045 5.58853909590853e-07 2.79426954795427e-07 68 0.999999914119712 1.71760575361143e-07 8.58802876805715e-08 69 0.999999969346636 6.13067271579869e-08 3.06533635789935e-08 70 0.99999995425773 9.14845396523973e-08 4.57422698261986e-08 71 0.99999990837857 1.83242858902981e-07 9.16214294514905e-08 72 0.999999790430822 4.1913835533259e-07 2.09569177666295e-07 73 0.999999713261492 5.73477016958044e-07 2.86738508479022e-07 74 0.999999375904435 1.24819112996480e-06 6.24095564982399e-07 75 0.99999882590142 2.34819715873153e-06 1.17409857936577e-06 76 0.999997395213983 5.20957203312668e-06 2.60478601656334e-06 77 0.999994260131463 1.14797370738581e-05 5.73986853692906e-06 78 0.99998681426185 2.63714763017924e-05 1.31857381508962e-05 79 0.99997585348076 4.82930384806374e-05 2.41465192403187e-05 80 0.999950394141045 9.9211717909528e-05 4.9605858954764e-05 81 0.99995711730453 8.57653909383306e-05 4.28826954691653e-05 82 0.999962526583358 7.4946833283213e-05 3.74734166416065e-05 83 0.999933070299802 0.000133859400396863 6.69297001984317e-05 84 0.999877717720676 0.000244564558648871 0.000122282279324436 85 0.99988973632442 0.00022052735115939 0.000110263675579695 86 0.999806156178333 0.000387687643333486 0.000193843821666743 87 0.999825946093801 0.000348107812397569 0.000174053906198784 88 0.9996368050551 0.000726389889800758 0.000363194944900379 89 0.999214403177128 0.00157119364574417 0.000785596822872087 90 0.998406489824718 0.00318702035056434 0.00159351017528217 91 0.99700949187252 0.00598101625495893 0.00299050812747947 92 0.997020478451135 0.00595904309773014 0.00297952154886507 93 0.999872223773199 0.000255552453602189 0.000127776226801095 94 0.999937646701631 0.000124706596737714 6.23532983688571e-05 95 0.999874221201918 0.000251557596164517 0.000125778798082259 96 0.999652659183446 0.000694681633107203 0.000347340816553602 97 0.999203665824625 0.00159266835074928 0.00079633417537464 98 0.998277049924633 0.00344590015073318 0.00172295007536659 99 0.99631973006108 0.00736053987784 0.00368026993892 100 0.989355515324747 0.0212889693505053 0.0106444846752526 101 0.968591131491664 0.0628177370166715 0.0314088685083357 102 0.93479869363945 0.130402612721100 0.0652013063605499 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 67 0.77906976744186 NOK 5% type I error level 75 0.872093023255814 NOK 10% type I error level 79 0.91860465116279 NOK

Charts produced by software:

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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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