workshop 7 berekening 5

*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: Thu, 19 Nov 2009 11:23:59 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix.htm/, Retrieved Thu, 19 Nov 2009 19:25:02 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

5246.24 0 5170.09 4920.10 4926.65 5283.61 0 5246.24 5170.09 4920.10 4979.05 0 5283.61 5246.24 5170.09 4825.20 0 4979.05 5283.61 5246.24 4695.12 0 4825.20 4979.05 5283.61 4711.54 0 4695.12 4825.20 4979.05 4727.22 0 4711.54 4695.12 4825.20 4384.96 0 4727.22 4711.54 4695.12 4378.75 0 4384.96 4727.22 4711.54 4472.93 0 4378.75 4384.96 4727.22 4564.07 0 4472.93 4378.75 4384.96 4310.54 0 4564.07 4472.93 4378.75 4171.38 0 4310.54 4564.07 4472.93 4049.38 0 4171.38 4310.54 4564.07 3591.37 0 4049.38 4171.38 4310.54 3720.46 0 3591.37 4049.38 4171.38 4107.23 0 3720.46 3591.37 4049.38 4101.71 0 4107.23 3720.46 3591.37 4162.34 0 4101.71 4107.23 3720.46 4136.22 0 4162.34 4101.71 4107.23 4125.88 0 4136.22 4162.34 4101.71 4031.48 0 4125.88 4136.22 4162.34 3761.36 0 4031.48 4125.88 4136.22 3408.56 0 3761.36 4031.48 4125.88 3228.47 0 3408.56 3761.36 4031.48 3090.45 0 3228.47 3408.56 3761.36 2741.14 0 3090.45 3228.47 3408.56 2980.44 0 2741.14 3090.45 3228.47 3104.33 0 2980.44 2741.14 3090.45 3181.57 0 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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -96.5555117821981 -360.738224795812X[t] + 1.00748531201206Y1[t] + 0.166310440331208Y2[t] -0.188431146174605Y3[t] + 100.779769951900M1[t] + 36.3284441219571M2[t] -83.1206506007616M3[t] + 92.1137994899325M4[t] + 216.725669303820M5[t] + 77.7633337740983M6[t] + 90.034499607901M7[t] + 29.6711390219347M8[t] + 179.016831664903M9[t] + 107.290835612820M10[t] -19.5422145209566M11[t] + 3.32000008834032t + 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) -96.5555117821981 127.205611 -0.7591 0.449874 0.224937 X -360.738224795812 107.986594 -3.3406 0.001233 0.000617 Y1 1.00748531201206 0.104613 9.6306 0 0 Y2 0.166310440331208 0.149316 1.1138 0.268425 0.134213 Y3 -0.188431146174605 0.106365 -1.7716 0.079971 0.039985 M1 100.779769951900 126.637313 0.7958 0.428306 0.214153 M2 36.3284441219571 126.703162 0.2867 0.775008 0.387504 M3 -83.1206506007616 126.351095 -0.6579 0.512368 0.256184 M4 92.1137994899325 127.515034 0.7224 0.472001 0.236 M5 216.725669303820 128.414482 1.6877 0.09505 0.047525 M6 77.7633337740983 127.448163 0.6102 0.543349 0.271674 M7 90.034499607901 126.673135 0.7108 0.479133 0.239566 M8 29.6711390219347 126.795652 0.234 0.815529 0.407764 M9 179.016831664903 130.306957 1.3738 0.173031 0.086515 M10 107.290835612820 131.790432 0.8141 0.417809 0.208905 M11 -19.5422145209566 130.385552 -0.1499 0.881206 0.440603 t 3.32000008834032 1.515248 2.1911 0.03112 0.01556 Multiple Linear Regression - Regression Statistics Multiple R 0.991885608386146 R-squared 0.983837060123556 Adjusted R-squared 0.980864565433635 F-TEST (value) 330.980258252291 F-TEST (DF numerator) 16 F-TEST (DF denominator) 87 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 259.673901397968 Sum Squared Residuals 5866456.55085003 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5246.24 5106.26368621096 139.976313789040 2 5283.61 5164.66253796491 118.947462035086 3 4979.05 5051.74180723946 -72.6918072394601 4 4825.2 4915.32252016608 -90.122520166081 5 4695.12 4830.55959517543 -135.439595175435 6 4711.54 4595.66529898151 115.874701018495 7 4727.22 4635.15584348757 92.0641565124338 8 4384.96 4621.15179360692 -236.191793606921 9 4378.75 4428.50927173319 -49.7592717331867 10 4472.93 4293.97078030207 178.959219697929 11 4564.07 4328.80235319720 235.267646802804 12 4310.54 4460.32005383141 -149.780053831408 13 4171.38 4306.40316090229 -135.023160902292 14 4049.38 4045.73187854157 3.64812145843335 15 3591.37 3831.31876345487 -239.948763454874 16 3720.46 3554.36707046051 166.092929539488 17 4107.23 3759.17197434758 348.058025652417 18 4101.71 4120.96709703489 -19.2570970348935 19 4162.34 4170.99625638195 -8.6562563819518 20 4136.22 4101.23918231504 34.9808176849629 21 4125.88 4238.71290062076 -112.832900620756 22 4031.48 4144.12089743679 -112.640897436791 23 3761.36 3928.70340552247 -167.343405522471 24 3408.56 3665.67236013525 -257.112360135249 25 3228.47 3387.19543615425 -158.725436154250 26 3090.45 3136.85077842823 -46.4007784282287 27 2741.14 2918.1962222011 -177.056222201099 28 2980.44 2755.80637618127 224.633623818730 29 3104.33 3092.74284813091 11.5871518690903 30 3181.57 3187.53684003621 -5.96684003621169 31 2863.86 3256.45879863122 -392.598798631217 32 2898.01 2868.82836336585 29.1816366341508 33 3112.33 2988.50676777422 123.823232225784 34 3254.33 3201.57098486934 52.7590151306578 35 3513.47 3250.32957905954 263.140420940459 36 3587.61 3517.50305670253 70.1069432974656 37 3727.45 3712.63825252599 14.8117474740144 38 3793.34 3755.89388164262 37.4461183573824 39 3817.58 3715.43456101525 102.145438984755 40 3845.13 3903.01843858982 -57.8884385898188 41 3931.86 4050.32216569016 -118.462165690162 42 4197.52 4002.07331300744 195.44668699256 43 4307.13 4294.54585333152 12.5841466684760 44 4229.43 4375.77235615421 -146.342356154205 45 4362.28 4418.32710921413 -56.0471092141354 46 4217.34 4450.18927780526 -232.849277805261 47 4361.28 4217.38674869256 143.893251307436 48 4327.74 4336.12828612198 -8.38828612197622 49 4417.65 4457.68693390515 -40.0369339051536 50 4557.68 4454.43778121747 103.242218782527 51 4650.35 4500.65980715702 149.690192842980 52 4967.18 4778.92452780723 188.255472192767 53 5123.42 5215.08394422091 -91.6639442209063 54 5290.85 5272.08133642243 18.7686635775745 55 5535.66 5422.6394712896 113.020528710403 56 5514.06 5610.64346477198 -96.5834647719775 57 5493.88 5750.7129068573 -256.832906857296 58 5694.83 5612.25372289099 82.5762771090105 59 5850.41 5691.90881436587 158.501185634134 60 6116.64 5908.73821733236 207.901782667641 61 6175 6269.07014147252 -94.070141472515 62 6513.58 6281.69636934747 231.883630652532 63 6383.78 6466.2215049058 -82.4415049057986 64 6673.66 6559.31690878226 114.343091217743 65 6936.61 6893.91250830375 42.6974916962457 66 7300.68 7095.85686887262 204.823131127381 67 7392.93 7467.35212197099 -74.4221219709918 68 7497.31 7514.24995363125 -16.9399536312489 69 7584.71 7718.81697396314 -134.106973963144 70 7160.79 7738.44190479642 -577.651904796418 71 7196.19 7182.70277073007 13.4872292699298 72 7245.63 7154.25876134373 91.3712386562724 73 7347.51 7393.93572628391 -46.4257262839081 74 7425.75 7436.99892972549 -11.2489297254887 75 7778.51 7407.323157697 371.186842302995 76 7822.33 7935.09289022066 -112.762890220661 77 8181.22 8151.09758454979 30.1224154502077 78 8371.47 8317.84840510718 53.6215948928178 79 8347.71 8576.54375274472 -228.833752744716 80 8672.11 8459.57704845609 212.532951543911 81 8802.79 8899.27041478213 -96.480414782126 82 9138.46 9020.95083026867 117.509169731327 83 9123.29 9196.22675942976 -72.9367594297645 84 9023.21 8874.26844038391 148.941559616092 85 8850.41 8811.76546818173 38.6445318182745 86 8864.58 8562.75483214356 301.825167856441 87 9163.74 8451.02154940031 712.718450599686 88 8516.66 8965.89282651934 -449.232826519343 89 8553.44 8488.984462713 64.4555372870072 90 7555.2 8226.4102156283 -671.210215628304 91 7851.22 7364.33616774957 486.883832250433 92 7442 7432.58037780123 9.41962219877333 93 7992.53 7410.29365505514 582.23634494486 94 8264.04 7772.70160163046 491.338398369546 95 7517.39 8091.39956900253 -574.009569002527 96 7200.4 7303.44082414884 -103.040824148837 97 7193.69 6912.84119436321 280.848805636791 98 6193.58 6932.92301098868 -739.343010988684 99 5104.21 5867.81262692919 -763.602626929185 100 4800.46 4783.77844127283 16.6815587271750 101 4461.61 4612.96491686846 -151.354916868464 102 4398.59 4290.69062490942 107.899375090581 103 4243.63 4243.67173441287 -0.0417344128685802 104 4293.82 4083.87745989745 209.942540102554 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.456487764262263 0.912975528524526 0.543512235737737 21 0.291422352996407 0.582844705992813 0.708577647003593 22 0.173320969549334 0.346641939098668 0.826679030450666 23 0.108075796079291 0.216151592158581 0.89192420392071 24 0.062561502999662 0.125123005999324 0.937438497000338 25 0.0322309247106332 0.0644618494212664 0.967769075289367 26 0.0180759101960211 0.0361518203920423 0.981924089803979 27 0.00925854958576818 0.0185170991715364 0.990741450414232 28 0.00479142564165589 0.00958285128331178 0.995208574358344 29 0.00336221513360233 0.00672443026720465 0.996637784866398 30 0.00145167290838317 0.00290334581676635 0.998548327091617 31 0.00485068889305774 0.00970137778611547 0.995149311106942 32 0.00377256391058799 0.00754512782117597 0.996227436089412 33 0.00236092720321594 0.00472185440643188 0.997639072796784 34 0.00115358364838279 0.00230716729676558 0.998846416351617 35 0.00183716695698327 0.00367433391396655 0.998162833043017 36 0.00266177200596548 0.00532354401193096 0.997338227994035 37 0.002027618142755 0.00405523628551 0.997972381857245 38 0.00112732987902023 0.00225465975804045 0.99887267012098 39 0.00138825950163486 0.00277651900326972 0.998611740498365 40 0.00103722274021454 0.00207444548042907 0.998962777259785 41 0.00053989846196685 0.0010797969239337 0.999460101538033 42 0.000516859112204057 0.00103371822440811 0.999483140887796 43 0.000268902482567204 0.000537804965134407 0.999731097517433 44 0.000153323276909460 0.000306646553818921 0.99984667672309 45 7.55477790420018e-05 0.000151095558084004 0.999924452220958 46 6.92759054298304e-05 0.000138551810859661 0.99993072409457 47 4.54979766963736e-05 9.09959533927471e-05 0.999954502023304 48 2.36515345451589e-05 4.73030690903178e-05 0.999976348465455 49 1.14716920525279e-05 2.29433841050558e-05 0.999988528307947 50 5.48205944314602e-06 1.09641188862920e-05 0.999994517940557 51 4.31587220125221e-06 8.63174440250441e-06 0.9999956841278 52 2.14771625802605e-06 4.29543251605209e-06 0.999997852283742 53 1.10077700015232e-06 2.20155400030465e-06 0.999998899223 54 4.50160445991516e-07 9.00320891983031e-07 0.999999549839554 55 2.5823181397344e-07 5.1646362794688e-07 0.999999741768186 56 1.37922311723677e-07 2.75844623447354e-07 0.999999862077688 57 1.54505632811504e-07 3.09011265623009e-07 0.999999845494367 58 9.16343385747625e-08 1.83268677149525e-07 0.999999908365661 59 3.97702632624459e-08 7.95405265248919e-08 0.999999960229737 60 4.6070535911162e-08 9.2141071822324e-08 0.999999953929464 61 2.71130369707650e-08 5.42260739415299e-08 0.999999972886963 62 2.19039894187291e-08 4.38079788374581e-08 0.99999997809601 63 1.19248270250692e-08 2.38496540501385e-08 0.999999988075173 64 5.02990076142499e-09 1.00598015228500e-08 0.9999999949701 65 1.76251584175015e-09 3.52503168350029e-09 0.999999998237484 66 1.06802449132289e-09 2.13604898264578e-09 0.999999998931975 67 4.49446103846945e-10 8.9889220769389e-10 0.999999999550554 68 1.76704459993446e-10 3.53408919986893e-10 0.999999999823296 69 1.47227990370749e-10 2.94455980741498e-10 0.999999999852772 70 2.54015830053602e-07 5.08031660107205e-07 0.99999974598417 71 1.83538436984950e-07 3.67076873969901e-07 0.999999816461563 72 1.18684498997847e-07 2.37368997995694e-07 0.9999998813155 73 3.38572684866247e-07 6.77145369732495e-07 0.999999661427315 74 3.86943844592037e-07 7.73887689184075e-07 0.999999613056155 75 9.30987004694379e-07 1.86197400938876e-06 0.999999069012995 76 9.02001882648798e-07 1.80400376529760e-06 0.999999097998117 77 4.26422991027671e-07 8.52845982055343e-07 0.99999957357701 78 2.00195270349507e-07 4.00390540699014e-07 0.99999979980473 79 1.16391849366838e-07 2.32783698733677e-07 0.99999988360815 80 7.98080095064588e-08 1.59616019012918e-07 0.99999992019199 81 5.72677547212754e-08 1.14535509442551e-07 0.999999942732245 82 1.14939752107190e-07 2.29879504214381e-07 0.999999885060248 83 3.78977711450038e-08 7.57955422900076e-08 0.999999962102229 84 2.55721635218840e-08 5.11443270437679e-08 0.999999974427837 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 57 0.876923076923077 NOK 5% type I error level 59 0.907692307692308 NOK 10% type I error level 60 0.923076923076923 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/10wdqt1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/10wdqt1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/15szk1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/15szk1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/2b4ex1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/2b4ex1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/3dd1j1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/3dd1j1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/4lh3s1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/4lh3s1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/5p6ta1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/5p6ta1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/61m5y1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/61m5y1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/75sme1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/75sme1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/88p6h1258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/88p6h1258655030.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/9px651258655030.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655090pguan731bdrmkix/9px651258655030.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') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by