*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: Tue, 14 Dec 2010 17:29:37 +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/14/t129234768429ubdgrhbbcjj49.htm/, Retrieved Tue, 14 Dec 2010 18:28:15 +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/14/t129234768429ubdgrhbbcjj49.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 «

2 73 69 1 58 53 1 68 43 2 69 60 1 62 49 1 68 62 1 65 45 1 65 50 1 81 75 1 73 82 2 64 60 1 68 59 1 51 21 1 68 40 1 61 62 2 77 54 1 69 47 1 73 59 2 61 37 2 62 43 1 63 48 1 69 59 2 47 79 2 66 62 1 58 16 2 63 38 1 69 58 2 59 60 1 59 72 2 63 67 2 65 55 1 65 47 2 71 59 1 60 49 2 66 47 1 67 57 2 81 39 1 62 49 1 63 26 2 73 53 2 55 75 1 59 65 1 64 49 2 63 48 2 74 45 2 67 31 1 64 67 1 73 61 1 54 49 1 76 69 2 74 54 2 63 80 2 73 57 2 67 34 2 68 69 1 66 44 2 62 70 2 71 51 1 68 66 1 63 18 1 75 74 1 77 59 2 62 48 1 74 55 2 67 44 2 56 56 2 60 65 2 58 77 1 65 46 2 49 70 1 61 39 2 66 55 2 64 44 2 65 45 1 46 45 2 81 25 2 65 49 1 72 65 2 65 45 2 74 71 1 69 48 1 59 41 2 58 40 1 71 64 2 79 56 2 68 52 1 66 41 2 62 45 1 69 49 1 60 42 2 63 54 1 62 40 1 61 40 2 65 51 1 64 48 2 67 80 2 56 38 2 56 57 1 48 28 1 74 51 1 69 46 1 62 58 1 73 67 1 64 72 1 57 26 1 57 54 2 60 53 2 61 69 1 72 64 1 57 47 1 51 43 1 63 66 1 54 54 1 72 62 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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Nonverbal[t] = + 57.5799653413098 + 0.680297709218542Gender[t] + 0.117226208142543Anxiety[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) 57.5799653413098 2.622451 21.9565 0 0 Gender 0.680297709218542 1.15872 0.5871 0.557951 0.278976 Anxiety 0.117226208142543 0.042248 2.7747 0.006179 0.00309 Multiple Linear Regression - Regression Statistics Multiple R 0.225398005717431 R-squared 0.050804260981395 Adjusted R-squared 0.0390130095650149 F-TEST (value) 4.30864029502574 F-TEST (DF numerator) 2 F-TEST (DF denominator) 161 p-value 0.0150362759913250 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.19587115061747 Sum Squared Residuals 8336.67042022248 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 73 67.0291691215817 5.97083087841833 2 58 64.473252082083 -6.47325208208304 3 68 63.3009900006576 4.69900999934241 4 69 65.9741332482994 3.02586675170064 5 62 64.0043472495128 -2.00434724951285 6 68 65.5282879553659 2.47171204463409 7 65 63.5354424169427 1.46455758305732 8 65 64.1215734576554 0.878426542344606 9 81 67.052228661219 13.9477713387810 10 73 67.8728121182168 5.12718788178324 11 64 65.9741332482994 -1.97413324829936 12 68 65.1766093309383 2.82339066906172 13 51 60.7220134215217 -9.72201342152166 14 68 62.94931137623 5.05068862377003 15 61 65.5282879553659 -4.52828795536591 16 77 65.2707759994441 11.7292240005559 17 69 63.7698948332278 5.23010516677223 18 73 65.1766093309383 7.82339066906172 19 61 63.2779304610209 -2.27793046102088 20 62 63.9812877098761 -1.98128770987613 21 63 63.8871210413703 -0.887121041370309 22 69 65.1766093309383 3.82339066906172 23 47 68.2014312030077 -21.2014312030077 24 66 66.2085856645844 -0.208585664584444 25 58 60.1358823808089 -2.13588238080894 26 63 63.3951566691634 -0.395156669163421 27 69 65.0593831227957 3.94061687720426 28 59 65.9741332482994 -6.97413324829936 29 59 66.7005500367913 -7.70055003679133 30 63 66.7947167052972 -3.79471670529716 31 65 65.3880022075867 -0.388002207586646 32 65 63.7698948332278 1.23010516677223 33 71 65.8569070401568 5.14309295984318 34 60 64.0043472495128 -4.00434724951285 35 66 64.4501925424463 1.54980745755370 36 67 64.9421569146532 2.05784308534681 37 81 63.512382877306 17.4876171226940 38 62 64.0043472495128 -2.00434724951285 39 63 61.3081444622344 1.69185553776563 40 73 65.1535497913016 7.84645020869844 41 55 67.7325263704375 -12.7325263704375 42 59 65.8799665797935 -6.87996657979353 43 64 64.0043472495128 -0.00434724951285137 44 63 64.5674187505888 -1.56741875058885 45 74 64.2157401261612 9.78425987383878 46 67 62.5745732121656 4.42542678783438 47 64 66.1144189960786 -2.11441899607862 48 73 65.4110617472234 7.58893825277664 49 54 64.0043472495128 -10.0043472495129 50 76 66.3488714123637 9.6511285876363 51 74 65.2707759994441 8.7292240005559 52 63 68.3186574111502 -5.31865741115021 53 73 65.6224546238717 7.37754537612827 54 67 62.9262518365933 4.07374816340675 55 68 67.0291691215822 0.970830878417757 56 66 63.4182162088001 2.58178379119986 57 62 67.1463953297248 -5.14639532972478 58 71 64.9190973750165 6.08090262498353 59 68 65.997192787936 2.00280721206392 60 63 60.370334797094 2.62966520290597 61 75 66.9350024530764 8.06499754692358 62 77 65.1766093309383 11.8233906690617 63 62 64.5674187505888 -2.56741875058885 64 74 64.7077044983681 9.29229550163189 65 67 64.0985139180187 2.90148608198132 66 56 65.5052284157292 -9.50522841572919 67 60 66.560264289012 -6.56026428901207 68 58 67.9669787867226 -9.96697878672258 69 65 63.6526686250852 1.34733137491478 70 49 67.1463953297248 -18.1463953297248 71 61 62.8320851680874 -1.83208516808743 72 66 65.3880022075867 0.611997792413354 73 64 64.0985139180187 -0.0985139180186764 74 65 64.2157401261612 0.78425987383878 75 46 63.5354424169427 -17.5354424169427 76 81 61.8712159633104 19.1287840366896 77 65 64.6846449587314 0.31535504126861 78 72 65.8799665797935 6.12003342020647 79 65 64.2157401261612 0.78425987383878 80 74 67.2636215378673 6.73637846213267 81 69 63.8871210413703 5.11287895862969 82 59 63.0665375843725 -4.06653758437251 83 58 63.6296090854485 -5.62960908544851 84 71 65.762740371651 5.23725962834901 85 79 65.5052284157292 13.4947715842708 86 68 65.036323583159 2.96367641684098 87 66 63.0665375843725 2.93346241562749 88 62 64.2157401261612 -2.21574012616122 89 69 64.0043472495128 4.99565275048715 90 60 63.183763792515 -3.18376379251505 91 63 65.2707759994441 -2.27077599944410 92 62 62.94931137623 -0.949311376229968 93 61 62.94931137623 -1.94931137622997 94 65 64.9190973750165 0.080902624983525 95 64 63.8871210413703 0.112878958629691 96 67 68.3186574111502 -1.31865741115021 97 56 63.3951566691634 -7.39515666916342 98 56 65.6224546238717 -9.62245462387173 99 48 61.5425968785195 -13.5425968785195 100 74 64.238799665798 9.76120033420206 101 69 63.6526686250852 5.34733137491478 102 62 65.0593831227957 -3.05938312279573 103 73 66.1144189960786 6.88558100392138 104 64 66.7005500367913 -2.70055003679133 105 57 61.3081444622344 -4.30814446223437 106 57 64.5904782902256 -7.59047829022556 107 60 65.1535497913016 -5.15354979130156 108 61 67.0291691215822 -6.02916912158224 109 72 65.762740371651 6.23725962834901 110 57 63.7698948332278 -6.76989483322777 111 51 63.3009900006576 -12.3009900006576 112 63 65.997192787936 -2.99719278793608 113 54 64.5904782902256 -10.5904782902256 114 72 65.5282879553659 6.47171204463409 115 62 64.3560258739405 -2.35602587394048 116 68 65.762740371651 2.23725962834901 117 62 64.7077044983681 -2.70770449836811 118 63 65.6224546238717 -2.62245462387173 119 77 66.9350024530764 10.0649975469236 120 57 62.0115017110896 -5.01150171108963 121 57 62.7148589599449 -5.71485895994488 122 61 65.997192787936 -4.99719278793608 123 66 63.2779304610209 2.72206953897912 124 65 61.3081444622344 3.69185553776563 125 63 65.762740371651 -2.76274037165099 126 59 61.5425968785195 -2.54259687851946 127 66 66.6774904971546 -0.677490497154615 128 68 65.8799665797935 2.12003342020647 129 72 63.8871210413703 8.11287895862969 130 68 63.4182162088001 4.58178379119986 131 68 66.4430380808695 1.55696191913047 132 67 62.8320851680874 4.16791483191257 133 59 64.1215734576554 -5.12157345765539 134 56 64.3560258739405 -8.35602587394048 135 62 65.997192787936 -3.99719278793608 136 55 64.5674187505888 -9.56741875058885 137 72 67.1463953297248 4.85360467027522 138 68 66.6774904971546 1.32250950284539 139 67 65.4110617472234 1.58893825277664 140 54 61.8942755029471 -7.89427550294709 141 69 66.0913594564419 2.9086405435581 142 61 64.5904782902256 -3.59047829022556 143 55 62.2459541273747 -7.24595412737471 144 75 66.2085856645844 8.79141433541556 145 55 63.7698948332278 -8.76989483322776 146 49 64.3560258739405 -15.3560258739405 147 54 63.2779304610209 -9.27793046102088 148 51 63.6526686250852 -12.6526686250852 149 66 64.1215734576554 1.87842654234461 150 73 65.4110617472234 7.58893825277664 151 63 67.1463953297248 -4.14639532972478 152 61 63.3951566691634 -2.39515666916342 153 74 65.6455141635085 8.35448583649155 154 81 62.9262518365933 18.0737481634068 155 58 63.6526686250852 -5.65266862508522 156 62 62.94931137623 -0.949311376229968 157 64 61.7770492948045 2.22295070519546 158 62 62.3631803355173 -0.363180335517254 159 85 64.238799665798 20.7612003342021 160 74 64.8249307065107 9.17506929348935 161 51 66.2316452042212 -15.2316452042212 162 66 62.8320851680874 3.16791483191257 163 61 64.0985139180187 -3.09851391801868 164 72 65.0593831227957 6.94061687720426 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.310210909251686 0.620421818503372 0.689789090748314 7 0.172998790267757 0.345997580535515 0.827001209732243 8 0.0873697445444499 0.174739489088900 0.91263025545555 9 0.166990480739183 0.333980961478366 0.833009519260817 10 0.136134865667434 0.272269731334869 0.863865134332566 11 0.106647401292022 0.213294802584044 0.893352598707978 12 0.0624762262255569 0.124952452451114 0.937523773774443 13 0.0402481758679124 0.0804963517358247 0.959751824132088 14 0.0533277866049333 0.106655573209867 0.946672213395067 15 0.0773298852551757 0.154659770510351 0.922670114744824 16 0.140720819925870 0.281441639851741 0.85927918007413 17 0.123873629537169 0.247747259074339 0.876126370462831 18 0.109973715250005 0.219947430500009 0.890026284749995 19 0.0783225308664774 0.156645061732955 0.921677469133523 20 0.055490937123747 0.110981874247494 0.944509062876253 21 0.0374563571235878 0.0749127142471756 0.962543642876412 22 0.0245469995594046 0.0490939991188092 0.975453000440595 23 0.616261800544542 0.767476398910916 0.383738199455458 24 0.550615560102225 0.89876887979555 0.449384439897775 25 0.486306876137384 0.972613752274768 0.513693123862616 26 0.425548726978933 0.851097453957867 0.574451273021066 27 0.369581341611885 0.73916268322377 0.630418658388115 28 0.356718586902994 0.713437173805987 0.643281413097006 29 0.413122366360036 0.826244732720071 0.586877633639964 30 0.363823835379656 0.727647670759312 0.636176164620344 31 0.30987572364427 0.61975144728854 0.69012427635573 32 0.259163075946671 0.518326151893343 0.740836924053329 33 0.247964078379981 0.495928156759962 0.752035921620019 34 0.224007082277735 0.448014164555471 0.775992917722265 35 0.188544316125719 0.377088632251439 0.81145568387428 36 0.153109785036411 0.306219570072823 0.846890214963589 37 0.396833246884658 0.793666493769317 0.603166753115342 38 0.352246207751327 0.704492415502655 0.647753792248673 39 0.303642743615608 0.607285487231215 0.696357256384392 40 0.307147091922165 0.61429418384433 0.692852908077835 41 0.414364243541777 0.828728487083553 0.585635756458223 42 0.41136955285004 0.82273910570008 0.58863044714996 43 0.361397539497226 0.722795078994451 0.638602460502774 44 0.316705785580834 0.633411571161669 0.683294214419166 45 0.34814323253442 0.69628646506884 0.65185676746558 46 0.311450972729754 0.622901945459508 0.688549027270246 47 0.270490447033452 0.540980894066904 0.729509552966548 48 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0.182776318412032 0.365552636824064 0.817223681587968 119 0.211024944000194 0.422049888000388 0.788975055999806 120 0.188346952971884 0.376693905943768 0.811653047028116 121 0.171611675760098 0.343223351520197 0.828388324239901 122 0.152682838057148 0.305365676114297 0.847317161942852 123 0.126971475421588 0.253942950843175 0.873028524578412 124 0.110535213709842 0.221070427419684 0.889464786290158 125 0.090622278565921 0.181244557131842 0.909377721434079 126 0.0720266788960841 0.144053357792168 0.927973321103916 127 0.0562274634867515 0.112454926973503 0.943772536513248 128 0.0435095081782084 0.0870190163564169 0.956490491821792 129 0.0470213054083541 0.0940426108167081 0.952978694591646 130 0.0408663249427894 0.0817326498855788 0.95913367505721 131 0.0303418648828558 0.0606837297657115 0.969658135117144 132 0.0259822594810694 0.0519645189621389 0.97401774051893 133 0.0206295127139618 0.0412590254279236 0.979370487286038 134 0.0204263729300918 0.0408527458601836 0.979573627069908 135 0.0159309305102827 0.0318618610205654 0.984069069489717 136 0.0202822640560161 0.0405645281120322 0.979717735943984 137 0.0149377193119745 0.0298754386239491 0.985062280688025 138 0.0102673033399197 0.0205346066798394 0.98973269666008 139 0.00693287268705037 0.0138657453741007 0.99306712731295 140 0.00613256512265778 0.0122651302453156 0.993867434877342 141 0.00400683922523168 0.00801367845046335 0.995993160774768 142 0.00269772689546089 0.00539545379092178 0.99730227310454 143 0.00234079077861269 0.00468158155722539 0.997659209221387 144 0.00245806631964584 0.00491613263929168 0.997541933680354 145 0.00271670294019517 0.00543340588039034 0.997283297059805 146 0.0106094511570275 0.0212189023140550 0.989390548842973 147 0.0140334196885620 0.0280668393771241 0.985966580311438 148 0.0395481734065344 0.0790963468130687 0.960451826593466 149 0.0259740521512477 0.0519481043024954 0.974025947848752 150 0.0213278919238424 0.0426557838476849 0.978672108076158 151 0.0143065462257398 0.0286130924514797 0.98569345377426 152 0.0127259578367387 0.0254519156734774 0.987274042163261 153 0.0115837287116070 0.0231674574232141 0.988416271288393 154 0.0319528268247626 0.0639056536495251 0.968047173175237 155 0.0297577590696647 0.0595155181393294 0.970242240930335 156 0.0192712436035882 0.0385424872071764 0.980728756396412 157 0.0116863793108050 0.0233727586216100 0.988313620689195 158 0.0185681084816001 0.0371362169632001 0.9814318915184 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.0326797385620915 NOK 5% type I error level 23 0.150326797385621 NOK 10% type I error level 34 0.222222222222222 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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Software written by Ed van Stee & Patrick Wessa

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