multiple regression Gewest

*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: Sun, 12 Dec 2010 11:08:39 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t1292152054ba6ysfxd0rfqj8s.htm/, Retrieved Sun, 12 Dec 2010 12:07:44 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t1292152054ba6ysfxd0rfqj8s.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

33024 31086 19828 18932 32526 30839 19967 18927 31455 30051 19814 19124 31524 29976 20053 19066 31856 30463 20719 19971 32696 31422 21174 20165 32584 31588 20648 19705 33498 31900 20659 19718 34175 32878 20733 19938 34172 33010 21069 20039 34379 32954 20566 19721 34988 33076 20839 19777 36158 35057 21615 20505 37411 35906 22739 21763 38015 36100 23222 22404 37577 35824 23031 22038 36354 34579 23014 22038 36030 34484 22868 21874 35636 33920 22182 21269 35669 34059 22177 21127 34635 33812 21216 20609 35496 34594 21031 20565 36376 36083 20968 19791 37635 36563 21049 20672 38875 37416 21033 20938 38372 37953 21078 20675 38897 37517 20702 19992 38018 37467 20309 19801 37325 36963 20449 20050 36893 36019 20737 20427 36117 35232 20849 20815 37599 36857 21966 21666 39037 37978 23100 22720 40809 40160 23975 23650 42508 42165 24350 24244 44021 43069 24020 23669 44088 43021 24005 23881 44510 43376 23602 23857 45786 43978 24120 23999 47349 45911 24847 24780 48696 47107 25702 25426 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation MVG[t] = -351.343556830789 + 1.08525691228605VVG[t] + 1.34991956196839MWG[t] -1.4473742694076VWG[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) -351.343556830789 116.053031 -3.0274 0.003091 0.001545 VVG 1.08525691228605 0.024216 44.8163 0 0 MWG 1.34991956196839 0.026425 51.0843 0 0 VWG -1.4473742694076 0.054987 -26.3221 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.99950480804335 R-squared 0.999009861301776 Adjusted R-squared 0.998982100403695 F-TEST (value) 35986.2227217912 F-TEST (DF numerator) 3 F-TEST (DF denominator) 107 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 529.364893669255 Sum Squared Residuals 29984309.3994924 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 33024 32749.4682247779 274.531775222068 2 32526 32676.285457904 -150.285457904011 3 31455 31329.4325869681 125.567413031912 4 31524 31654.6168014827 -130.616801482705 5 31856 31772.3096322231 83.6903677769195 6 32696 33146.4938035359 -450.493803535947 7 32584 33282.3809253076 -698.380925307552 8 33498 33617.0143316202 -119.014331620152 9 34175 34459.8673001519 -284.867300151898 10 34172 34910.5093841849 -738.509384184869 11 34379 34630.9904750984 -251.990475098364 12 34988 35050.8668997278 -62.8668997278086 13 36158 37194.6099549252 -1036.60995492521 14 37411 37812.5058301938 -401.505830193784 15 38015 37747.2899129177 267.710087082261 16 37577 37719.663351394 -142.663351394008 17 36354 36345.5698630444 8.43013695558603 18 36030 36282.7515805127 -252.751580512701 19 35636 35620.2832954647 15.7167045353511 20 35669 35969.9115547184 -300.911554718446 21 34635 35154.3202698853 -519.320269885302 22 35496 35816.9405241828 -320.940524182774 23 36376 38468.1108186942 -2092.11081869418 24 37635 37823.2408897628 -188.240889762823 25 38875 38342.3647672889 532.635232711095 26 38372 39366.5535423293 -994.55354232929 27 38897 39374.3683992779 -477.36839927785 28 38018 39066.0356512668 -1048.03565126682 29 37325 38347.6587130677 -1022.65871306773 30 36893 37166.2929221499 -273.292922149935 31 36117 35901.8055065911 215.194493408875 32 37599 37941.4926365088 -342.492636508784 33 39037 39163.341938498 -126.341938497993 34 40809 41366.4940672794 -557.49406727943 35 42508 43188.913696123 -680.91369612299 36 44021 44556.7526942894 -535.75269428938 37 44088 44177.5682239557 -89.5682239557137 38 44510 44053.5538268098 456.446173190223 39 45786 45200.6096748497 585.39032515027 40 47349 47149.4035034424 199.596496557651 41 48696 48666.5482179821 29.451782017869 42 50598 50564.4721086701 33.5278913299033 43 50066 49522.3539082912 543.646091708798 44 49367 48644.588634794 722.411365205995 45 48784 48655.5251265387 128.474873461320 46 47841 48446.9199321674 -605.919932167405 47 48300 47794.4297493811 505.570250618878 48 47518 46326.3958181333 1191.60418186674 49 46504 45859.8098226337 644.190177366305 50 45147 44788.495547488 358.504452511947 51 44404 43843.6841804956 560.315819504374 52 43455 42661.1416098873 793.85839011267 53 42299 41349.6653626651 949.334637334934 54 42105 41081.1683908663 1023.83160913367 55 40152 38815.2958281501 1336.70417184995 56 39519 39485.9899279790 33.0100720209544 57 39633 38929.6940477566 703.305952243436 58 39376 38719.5422334163 656.45776658366 59 38850 38628.7467252689 221.253274731111 60 39657 38310.3007256983 1346.6992743017 61 34804 34090.3221761978 713.677823802197 62 34372 34210.8215729304 161.178427069602 63 32678 32336.8192694790 341.180730520952 64 28420 28094.6131052027 325.386894797292 65 25420 25334.4128273079 85.5871726920804 66 27683 27946.0860851201 -263.086085120115 67 29904 30173.9881070548 -269.988107054818 68 30546 30116.2530607939 429.746939206128 69 29142 29495.027067201 -353.027067200997 70 27724 27897.1701121426 -173.170112142578 71 27069 27062.6735385311 6.32646146889084 72 26665 27252.354240425 -587.354240424991 73 26004 26517.1406894100 -513.14068941003 74 25767 26272.8165667968 -505.816566796782 75 24915 25192.1963130666 -277.196313066641 76 23689 23784.6165741159 -95.616574115853 77 20915 21378.3579261974 -463.357926197425 78 19414 19493.3825672318 -79.3825672318413 79 17824 17905.5371791963 -81.5371791962544 80 16348 17018.6562595456 -670.656259545597 81 15571 15149.1178545471 421.882145452893 82 13929 13968.8847570182 -39.8847570181579 83 12480 12920.7433554902 -440.743355490184 84 10837 10622.4811464187 214.518853581312 85 9473 9245.92255719018 227.077442809821 86 8051 7868.79447317318 182.205526826820 87 5278 5411.83373418216 -133.833734182161 88 3008 3157.59257210614 -149.592572106140 89 2404 2545.46089455325 -141.460894553248 90 2298 2767.27254522525 -469.272545225245 91 2260 2400.55900913542 -140.559009135422 92 1938 2180.01170533972 -242.011705339718 93 1371 2065.25753403841 -694.257534038408 94 1009 1336.35647262124 -327.356472621243 95 686 738.35016004123 -52.3501600412307 96 493 502.544883263155 -9.5448832631551 97 285 191.583760941844 93.4162390581565 98 192 62.3835237602086 129.616476239791 99 129 -85.8818921774387 214.881892177439 100 60 -212.310329513310 272.310329513310 101 54 -226.725706280545 280.725706280545 102 26 -274.344301349027 300.344301349027 103 11 -337.281536148062 348.281536148062 104 3 -325.764629114056 328.764629114056 105 0 -339.773323948175 339.773323948175 106 2 -348.089976412106 350.089976412106 107 1 -354.963635242946 355.963635242946 108 0 -349.89727772048 349.89727772048 109 0 -349.173043006241 349.173043006241 110 0 -351.343556830809 351.343556830809 111 0 -350.258299918525 350.258299918525 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.182755549285566 0.365511098571131 0.817244450714434 8 0.100451328722150 0.200902657444300 0.89954867127785 9 0.0448263408588698 0.0896526817177396 0.95517365914113 10 0.0213890270461422 0.0427780540922843 0.978610972953858 11 0.00830275240930273 0.0166055048186055 0.991697247590697 12 0.0144929080948066 0.0289858161896131 0.985507091905193 13 0.00930586645323362 0.0186117329064672 0.990694133546766 14 0.0199572252639985 0.0399144505279969 0.980042774736002 15 0.0383364397065941 0.0766728794131883 0.961663560293406 16 0.0257640660115046 0.0515281320230093 0.974235933988495 17 0.0160827276668745 0.032165455333749 0.983917272333126 18 0.00910453473315594 0.0182090694663119 0.990895465266844 19 0.0051727491570723 0.0103454983141446 0.994827250842928 20 0.00285594379168057 0.00571188758336114 0.99714405620832 21 0.00608467527846643 0.0121693505569329 0.993915324721534 22 0.00383645390745504 0.00767290781491008 0.996163546092545 23 0.0455755542443418 0.0911511084886836 0.954424445755658 24 0.0423984476282758 0.0847968952565515 0.957601552371724 25 0.0558423624262149 0.111684724852430 0.944157637573785 26 0.0767278664036433 0.153455732807287 0.923272133596357 27 0.116870216977249 0.233740433954499 0.88312978302275 28 0.137354506554984 0.274709013109969 0.862645493445016 29 0.211332679316796 0.422665358633592 0.788667320683204 30 0.188440325222671 0.376880650445341 0.81155967477733 31 0.156555276939186 0.313110553878372 0.843444723060814 32 0.180656613282721 0.361313226565443 0.819343386717279 33 0.173824875408984 0.347649750817967 0.826175124591016 34 0.272192041136278 0.544384082272556 0.727807958863722 35 0.437871772851259 0.875743545702518 0.562128227148741 36 0.558221184263543 0.883557631472913 0.441778815736457 37 0.612755770077894 0.774488459844212 0.387244229922106 38 0.653257706477604 0.693484587044791 0.346742293522396 39 0.827036891578826 0.345926216842348 0.172963108421174 40 0.839634565974865 0.320730868050271 0.160365434025135 41 0.8535873588562 0.292825282287600 0.146412641143800 42 0.845456935509348 0.309086128981304 0.154543064490652 43 0.848564904724251 0.302870190551498 0.151435095275749 44 0.871140827180623 0.257718345638753 0.128859172819377 45 0.856660162280722 0.286679675438556 0.143339837719278 46 0.955075592924749 0.0898488141505026 0.0449244070752513 47 0.940974495573431 0.118051008853139 0.0590255044265693 48 0.961029715945656 0.0779405681086878 0.0389702840543439 49 0.953674538681231 0.092650922637538 0.046325461318769 50 0.947044471706474 0.105911056587052 0.0529555282935261 51 0.93468077114076 0.130638457718479 0.0653192288592397 52 0.91556048516545 0.168879029669102 0.0844395148345509 53 0.900930490100554 0.198139019798892 0.0990695098994462 54 0.899384946523165 0.20123010695367 0.100615053476835 55 0.929379984264152 0.141240031471695 0.0706200157358476 56 0.967166262844572 0.0656674743108562 0.0328337371554281 57 0.956163485173463 0.0876730296530739 0.0438365148265369 58 0.961783253642554 0.0764334927148921 0.0382167463574461 59 0.997627354439394 0.00474529112121114 0.00237264556060557 60 0.998283685302016 0.0034326293959676 0.0017163146979838 61 0.99859903373365 0.0028019325327002 0.0014009662663501 62 0.997820219941786 0.00435956011642691 0.00217978005821346 63 0.998314394662664 0.003371210674671 0.0016856053373355 64 0.99856834957143 0.00286330085714109 0.00143165042857054 65 0.997996906351561 0.00400618729687708 0.00200309364843854 66 0.999406116565595 0.00118776686881033 0.000593883434405166 67 0.999813683069865 0.000372633860270255 0.000186316930135127 68 0.999986957267347 2.60854653059884e-05 1.30427326529942e-05 69 0.99998703441677 2.59311664602497e-05 1.29655832301248e-05 70 0.999994540560705 1.0918878590194e-05 5.459439295097e-06 71 0.99999997289009 5.42198182870974e-08 2.71099091435487e-08 72 0.999999994490635 1.10187308577017e-08 5.50936542885087e-09 73 0.999999999213561 1.57287715293684e-09 7.86438576468418e-10 74 0.999999999850087 2.9982669695339e-10 1.49913348476695e-10 75 0.999999999964294 7.14128887075184e-11 3.57064443537592e-11 76 0.999999999999987 2.66430679181416e-14 1.33215339590708e-14 77 0.999999999999994 1.19220319197361e-14 5.96101595986805e-15 78 1 1.68964081114190e-16 8.44820405570952e-17 79 1 4.39027249365697e-16 2.19513624682848e-16 80 1 1.68610418014383e-18 8.43052090071916e-19 81 1 1.6049392356303e-18 8.0246961781515e-19 82 1 5.77582918742847e-18 2.88791459371423e-18 83 1 3.24606163176492e-17 1.62303081588246e-17 84 1 2.41336966758574e-16 1.20668483379287e-16 85 1 1.56192328537730e-15 7.80961642688651e-16 86 1 2.74379630312328e-17 1.37189815156164e-17 87 1 1.99434484873535e-16 9.97172424367673e-17 88 1 1.48214323429196e-15 7.41071617145982e-16 89 0.999999999999997 6.23868365648422e-15 3.11934182824211e-15 90 0.999999999999976 4.70600043424645e-14 2.35300021712322e-14 91 1 3.16791550422436e-21 1.58395775211218e-21 92 1 9.069873475705e-20 4.5349367378525e-20 93 1 1.69267057989316e-18 8.46335289946578e-19 94 1 2.37303995075008e-17 1.18651997537504e-17 95 1 1.90982443912177e-22 9.54912219560885e-23 96 1 8.38710404399262e-23 4.19355202199631e-23 97 1 8.56421868982515e-21 4.28210934491258e-21 98 1 1.03281395082917e-18 5.16406975414587e-19 99 1 5.81230069778926e-18 2.90615034889463e-18 100 1 3.25224641238244e-16 1.62612320619122e-16 101 0.999999999999972 5.65522513927545e-14 2.82761256963772e-14 102 0.999999999999976 4.77499530443327e-14 2.38749765221663e-14 103 0.999999999987442 2.51159742339512e-11 1.25579871169756e-11 104 0.999999991618518 1.67629649229507e-08 8.38148246147534e-09 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 48 0.489795918367347 NOK 5% type I error level 57 0.581632653061224 NOK 10% type I error level 68 0.693877551020408 NOK

Charts produced by software:

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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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