multiple linear regression rentevoet

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 05:49:29 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2.htm/, Retrieved Thu, 27 Nov 2008 12:55:28 +0000

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2.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}, }

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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4,75 1 4,75 1 4,75 1 4,75 1 4,58 1 4,50 1 4,50 1 4,49 1 4,03 0 3,75 0 3,39 0 3,25 0 3,25 1 3,25 1 3,25 1 3,25 0 3,25 0 3,25 0 3,25 0 3,25 0 3,25 0 3,25 0 3,25 0 2,85 0 2,75 0 2,75 0 2,55 0 2,50 0 2,50 0 2,10 0 2,00 0 2,00 0 2,00 0 2,00 0 2,00 0 2,00 0 2,00 0 2,00 0 2,00 0 2,00 0 2,00 1 2,00 0 2,00 1 2,00 0 2,00 0 2,00 1 2,00 1 2,00 0 2,00 0 2,00 1 2,00 1 2,00 1 2,00 1 2,00 1 2,00 1 2,00 1 2,00 1 2,00 1 2,00 1 2,21 1 2,25 1 2,25 1 2,45 1 2,50 1 2,50 1 2,64 1 2,75 1 2,93 1 3,00 0 3,17 0 3,25 0 3,39 1 3,50 0 3,50 0 3,65 0 3,75 0 3,75 0 3,90 0 4,00 0 4,00 0 4,00 0 4,00 1 4,00 1 4,00 1 4,00 1 4,00 1 4,00 1 4,00 1 4,00 1 4,00 1 4,18 1 4,25 1 4,25 1 3,95 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation rentevoet%[t] = + 2.67337920837125 + 0.328781847133759dummy[t] + 0.224729868061874M1[t] + 0.183632137170154M2[t] + 0.202382137170153M3[t] + 0.255979868061873M4[t] + 0.193632137170153M5[t] + 0.210979868061873M6[t] + 0.206132137170153M7[t] + 0.277229868061873M8[t] + 0.310675329845312M9[t] + 0.177229868061873M10[t] + 0.0271428571428556M11[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) 2.67337920837125 0.373058 7.1661 0 0 dummy 0.328781847133759 0.202684 1.6221 0.10866 0.05433 M1 0.224729868061874 0.497001 0.4522 0.652354 0.326177 M2 0.183632137170154 0.498383 0.3685 0.713494 0.356747 M3 0.202382137170153 0.498383 0.4061 0.685756 0.342878 M4 0.255979868061873 0.497001 0.515 0.607921 0.303961 M5 0.193632137170153 0.498383 0.3885 0.69865 0.349325 M6 0.210979868061873 0.497001 0.4245 0.672322 0.336161 M7 0.206132137170153 0.498383 0.4136 0.680259 0.340129 M8 0.277229868061873 0.497001 0.5578 0.578516 0.289258 M9 0.310675329845312 0.498107 0.6237 0.534569 0.267284 M10 0.177229868061873 0.497001 0.3566 0.722319 0.36116 M11 0.0271428571428556 0.513082 0.0529 0.957941 0.47897 Multiple Linear Regression - Regression Statistics Multiple R 0.202141867402353 R-squared 0.0408613345569104 Adjusted R-squared -0.101233282545770 F-TEST (value) 0.28756426802138 F-TEST (DF numerator) 12 F-TEST (DF denominator) 81 p-value 0.989824486603742 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.959889297728986 Sum Squared Residuals 74.6323845754664 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.75 3.22689092356687 1.52310907643313 2 4.75 3.18579319267516 1.56420680732484 3 4.75 3.20454319267516 1.54545680732484 4 4.75 3.25814092356688 1.49185907643312 5 4.58 3.19579319267516 1.38420680732484 6 4.5 3.21314092356688 1.28685907643312 7 4.5 3.20829319267516 1.29170680732484 8 4.49 3.27939092356688 1.21060907643312 9 4.03 2.98405453821656 1.04594546178344 10 3.75 2.85060907643312 0.89939092356688 11 3.39 2.7005220655141 0.689477934485896 12 3.25 2.67337920837125 0.576620791628752 13 3.25 3.22689092356688 0.0231090764331209 14 3.25 3.18579319267516 0.0642068073248405 15 3.25 3.20454319267516 0.045456807324841 16 3.25 2.92935907643312 0.320640923566879 17 3.25 2.8670113455414 0.382988654458599 18 3.25 2.88435907643312 0.365640923566879 19 3.25 2.8795113455414 0.370488654458599 20 3.25 2.95060907643312 0.299390923566879 21 3.25 2.98405453821656 0.26594546178344 22 3.25 2.85060907643312 0.399390923566879 23 3.25 2.7005220655141 0.549477934485896 24 2.85 2.67337920837125 0.176620791628752 25 2.75 2.89810907643312 -0.148109076433121 26 2.75 2.8570113455414 -0.107011345541402 27 2.55 2.8757613455414 -0.325761345541401 28 2.5 2.92935907643312 -0.429359076433121 29 2.5 2.8670113455414 -0.367011345541401 30 2.1 2.88435907643312 -0.784359076433121 31 2 2.8795113455414 -0.8795113455414 32 2 2.95060907643312 -0.950609076433122 33 2 2.98405453821656 -0.98405453821656 34 2 2.85060907643312 -0.850609076433121 35 2 2.7005220655141 -0.700522065514104 36 2 2.67337920837125 -0.673379208371248 37 2 2.89810907643312 -0.898109076433121 38 2 2.8570113455414 -0.857011345541402 39 2 2.8757613455414 -0.875761345541401 40 2 2.92935907643312 -0.929359076433122 41 2 3.19579319267516 -1.19579319267516 42 2 2.88435907643312 -0.884359076433121 43 2 3.20829319267516 -1.20829319267516 44 2 2.95060907643312 -0.950609076433122 45 2 2.98405453821656 -0.98405453821656 46 2 3.17939092356688 -1.17939092356688 47 2 3.02930391264786 -1.02930391264786 48 2 2.67337920837125 -0.673379208371248 49 2 2.89810907643312 -0.898109076433121 50 2 3.18579319267516 -1.18579319267516 51 2 3.20454319267516 -1.20454319267516 52 2 3.25814092356688 -1.25814092356688 53 2 3.19579319267516 -1.19579319267516 54 2 3.21314092356688 -1.21314092356688 55 2 3.20829319267516 -1.20829319267516 56 2 3.27939092356688 -1.27939092356688 57 2 3.31283638535032 -1.31283638535032 58 2 3.17939092356688 -1.17939092356688 59 2 3.02930391264786 -1.02930391264786 60 2.21 3.00216105550501 -0.792161055505006 61 2.25 3.22689092356688 -0.97689092356688 62 2.25 3.18579319267516 -0.93579319267516 63 2.45 3.20454319267516 -0.754543192675159 64 2.5 3.25814092356688 -0.758140923566879 65 2.5 3.19579319267516 -0.695793192675159 66 2.64 3.21314092356688 -0.573140923566879 67 2.75 3.20829319267516 -0.458293192675159 68 2.93 3.27939092356688 -0.349390923566879 69 3 2.98405453821656 0.0159454617834397 70 3.17 2.85060907643312 0.319390923566879 71 3.25 2.7005220655141 0.549477934485896 72 3.39 3.00216105550501 0.387838944494994 73 3.5 2.89810907643312 0.601890923566879 74 3.5 2.8570113455414 0.642988654458598 75 3.65 2.8757613455414 0.774238654458599 76 3.75 2.92935907643312 0.820640923566879 77 3.75 2.8670113455414 0.882988654458599 78 3.9 2.88435907643312 1.01564092356688 79 4 2.8795113455414 1.12048865445860 80 4 2.95060907643312 1.04939092356688 81 4 2.98405453821656 1.01594546178344 82 4 3.17939092356688 0.820609076433121 83 4 3.02930391264786 0.970696087352138 84 4 3.00216105550501 0.997838944494994 85 4 3.22689092356688 0.773109076433121 86 4 3.18579319267516 0.81420680732484 87 4 3.20454319267516 0.795456807324841 88 4 3.25814092356688 0.741859076433121 89 4 3.19579319267516 0.80420680732484 90 4 3.21314092356688 0.786859076433121 91 4.18 3.20829319267516 0.97170680732484 92 4.25 3.27939092356688 0.97060907643312 93 4.25 3.31283638535032 0.937163614649682 94 3.95 3.17939092356688 0.770609076433121 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.754676775207794 0.490646449584412 0.245323224792206 17 0.613773386252600 0.772453227494799 0.386226613747400 18 0.471474666054759 0.942949332109517 0.528525333945241 19 0.341865352019836 0.683730704039672 0.658134647980164 20 0.234409625244526 0.468819250489052 0.765590374755474 21 0.18612732786942 0.37225465573884 0.81387267213058 22 0.130901815749893 0.261803631499785 0.869098184250107 23 0.0833345010208873 0.166669002041775 0.916665498979113 24 0.0530974723688889 0.106194944737778 0.946902527631111 25 0.0303577971667488 0.0607155943334976 0.969642202833251 26 0.0166419110436122 0.0332838220872244 0.983358088956388 27 0.00890735215057098 0.0178147043011420 0.99109264784943 28 0.008050342441176 0.016100684882352 0.991949657558824 29 0.00626390000033295 0.0125278000006659 0.993736099999667 30 0.0076544698372481 0.0153089396744962 0.992345530162752 31 0.00990459912015638 0.0198091982403128 0.990095400879844 32 0.0118078189165470 0.0236156378330939 0.988192181083453 33 0.0274305315328679 0.0548610630657357 0.972569468467132 34 0.0436036757902138 0.0872073515804275 0.956396324209786 35 0.0526809282863828 0.105361856572766 0.947319071713617 36 0.0526206306223668 0.105241261244734 0.947379369377633 37 0.041634788572113 0.083269577144226 0.958365211427887 38 0.0328776599592647 0.0657553199185293 0.967122340040735 39 0.0260565682583887 0.0521131365167773 0.973943431741611 40 0.0271696668969989 0.0543393337939978 0.972830333103001 41 0.114674237465771 0.229348474931543 0.885325762534229 42 0.112186138642314 0.224372277284628 0.887813861357686 43 0.216200858302757 0.432401716605515 0.783799141697243 44 0.224889738310380 0.449779476620761 0.77511026168962 45 0.252336097577814 0.504672195155628 0.747663902422186 46 0.388911277035644 0.777822554071288 0.611088722964356 47 0.462931875968443 0.925863751936886 0.537068124031557 48 0.486675744765531 0.973351489531062 0.513324255234469 49 0.5114375906714 0.9771248186572 0.4885624093286 50 0.557489362802847 0.885021274394307 0.442510637197154 51 0.599577589076891 0.800844821846218 0.400422410923109 52 0.654504389783967 0.690991220432066 0.345495610216033 53 0.684149563694056 0.631700872611887 0.315850436305944 54 0.722051129543646 0.555897740912708 0.277948870456354 55 0.75970566520608 0.48058866958784 0.24029433479392 56 0.810069726625112 0.379860546749775 0.189930273374888 57 0.850288080418591 0.299423839162818 0.149711919581409 58 0.8891163368559 0.221767326288201 0.110883663144101 59 0.911025569428819 0.177948861142362 0.0889744305711811 60 0.927062942401459 0.145874115197083 0.0729370575985413 61 0.943048253514582 0.113903492970836 0.0569517464854181 62 0.959316245484034 0.081367509031933 0.0406837545159665 63 0.969926516090703 0.0601469678185942 0.0300734839092971 64 0.98086338364825 0.0382732327035015 0.0191366163517508 65 0.99045545985206 0.0190890802958803 0.00954454014794013 66 0.99639303542365 0.00721392915270027 0.00360696457635013 67 0.999491678777996 0.00101664244400817 0.000508321222004084 68 0.999986845552528 2.63088949431352e-05 1.31544474715676e-05 69 0.999999381484483 1.23703103300837e-06 6.18515516504183e-07 70 0.999999426919445 1.14616110928226e-06 5.7308055464113e-07 71 0.999999334253377 1.33149324661814e-06 6.65746623309072e-07 72 0.999999984814358 3.03712840877811e-08 1.51856420438905e-08 73 0.999999967412202 6.51755954328964e-08 3.25877977164482e-08 74 0.999999987054025 2.58919491757661e-08 1.29459745878831e-08 75 0.999999955168958 8.96620845716028e-08 4.48310422858014e-08 76 0.999999183701402 1.63259719562458e-06 8.16298597812292e-07 77 0.99998657450769 2.68509846183028e-05 1.34254923091514e-05 78 0.999949227606294 0.000101544787411891 5.07723937059456e-05 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.206349206349206 NOK 5% type I error level 22 0.349206349206349 NOK 10% type I error level 31 0.492063492063492 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/10akda1227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/10akda1227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/11eym1227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/11eym1227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/26ry21227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/26ry21227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/3iesp1227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/3iesp1227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/4btdg1227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/4btdg1227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/5a9f31227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/5a9f31227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/6mhs81227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/6mhs81227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/7f55x1227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/7f55x1227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/8416q1227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/8416q1227790164.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/9nbmo1227790164.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227790518lg1yey1d49jzxt2/9nbmo1227790164.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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