Q3

*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: Mon, 24 Nov 2008 06:48:10 -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/24/t1227534563vejagke1sswx7rc.htm/, Retrieved Mon, 24 Nov 2008 13:49:33 +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/24/t1227534563vejagke1sswx7rc.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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1.3322 133.52 7.4545 1.4369 153.2 7.4583 1.4975 163.63 7.4595 1.577 168.45 7.4599 1.5553 166.26 7.4586 1.5557 162.31 7.4609 1.575 161.56 7.4603 1.5527 156.59 7.4561 1.4748 157.97 7.454 1.4718 158.68 7.4505 1.457 163.55 7.4599 1.4684 162.89 7.4543 1.4227 164.95 7.4534 1.3896 159.82 7.4506 1.3622 159.05 7.4429 1.3716 166.76 7.441 1.3419 164.55 7.4452 1.3511 163.22 7.4519 1.3516 160.68 7.453 1.3242 155.24 7.4494 1.3074 157.6 7.4541 1.2999 156.56 7.4539 1.3213 154.82 7.4549 1.2881 151.11 7.4564 1.2611 149.65 7.4555 1.2727 148.99 7.4601 1.2811 148.53 7.4609 1.2684 146.7 7.4602 1.265 145.11 7.4566 1.277 142.7 7.4565 1.2271 143.59 7.4618 1.202 140.96 7.4612 1.1938 140.77 7.4641 1.2103 139.81 7.4613 1.1856 140.58 7.4541 1.1786 139.59 7.4596 1.2015 138.05 7.462 1.2256 136.06 7.4584 1.2292 135.98 7.4596 1.2037 134.75 7.4584 1.2165 132.22 7.4448 1.2694 135.37 7.4443 1.2938 138.84 7.4499 1.3201 138.83 7.4466 1.3014 136.55 7.4427 1.3119 135.63 7.4405 1.3408 139.14 7.4338 1.2991 136.09 7.4313 1.249 135.97 7.4379 1.2218 134.51 7.4381 1.2176 134.54 7.4365 1.2266 134.08 7.4355 1.2138 132.86 7.4342 1.2007 134.48 7.4405 1.1985 129.08 7.4436 1.2262 133.13 7.4493 1.2646 134.78 7.4511 1.2613 134.13 7.4481 1.2286 132.43 7.4419 1.1702 127.84 7.437 1.1692 128.12 7.4301 1.1222 128.94 7.4273 1.1139 132.38 7.4322 1.1372 134.99 7.4332 1.1663 138.05 7.425 1.1582 135.83 7.4246 1.0848 130.12 7.4255 1.0807 128.16 7.4274 1.0773 128.6 7.4317 1.0622 126.12 7.4324 1.0183 124.2 7.4264 1.0014 121.65 7.428 0.9811 121.57 7.4297 0.9808 118.38 7.4271

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Dollar[t] = -10.7013998458890 + 0.00820471163242128Yen[t] + 1.44995962091496DeenseKroon[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) -10.7013998458890 6.289947 -1.7013 0.093253 0.046627 Yen 0.00820471163242128 0.000769 10.6628 0 0 DeenseKroon 1.44995962091496 0.853666 1.6985 0.09379 0.046895 Multiple Linear Regression - Regression Statistics Multiple R 0.871427976632575 R-squared 0.759386718457944 Adjusted R-squared 0.752608879541266 F-TEST (value) 112.039652726090 F-TEST (DF numerator) 2 F-TEST (DF denominator) 71 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0688078191277582 Sum Squared Residuals 0.336150634091398 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.3322 1.20281724538249 0.129382754617515 2 1.4369 1.36979581686802 0.0671041831319826 3 1.4975 1.45711091073927 0.0403890892607317 4 1.577 1.49723760465590 0.0797623953440951 5 1.5553 1.47738433867371 0.0779156613262879 6 1.5557 1.44831063485375 0.107389365146248 7 1.575 1.44128712535689 0.133712874643112 8 1.5527 1.39441987813591 0.158280121864088 9 1.4748 1.40269746498473 0.0721025350152692 10 1.4718 1.40344795157055 0.068352048429452 11 1.457 1.45703451765704 -3.45176570406523e-05 12 1.4684 1.44349963410252 0.0249003658974816 13 1.4227 1.45909637640648 -0.0363963764064831 14 1.3896 1.4129463187936 -0.0233463187935993 15 1.3622 1.39546400175559 -0.0332640017555900 16 1.3716 1.45596740516182 -0.0843674051618196 17 1.3419 1.44392482286201 -0.102024822862011 18 1.3511 1.44272728585102 -0.091627285851022 19 1.3516 1.42348227388768 -0.0718822738876786 20 1.3242 1.37362878797201 -0.0494287879720121 21 1.3074 1.39980671764283 -0.0924067176428275 22 1.2999 1.39098382562093 -0.0910838256209257 23 1.3213 1.37815758700143 -0.0568575870014282 24 1.2881 1.34989304627652 -0.0617930462765178 25 1.2611 1.33660920363436 -0.0755092036343583 26 1.2727 1.33786390821317 -0.0651639082131692 27 1.2811 1.33524970855899 -0.0541497085589872 28 1.2684 1.31922011453702 -0.0508201145370167 29 1.265 1.30095476840617 -0.0359547684061726 30 1.277 1.28103641740995 -0.00403641740994588 31 1.2271 1.29602339675365 -0.0689233967536502 32 1.202 1.27357502938783 -0.0715750293878329 33 1.1938 1.27622101707833 -0.0824210170783267 34 1.2103 1.26428460697264 -0.0539846069726395 35 1.1856 1.26016252565902 -0.0745625256590173 36 1.1786 1.26001463905795 -0.0814146390579518 37 1.2015 1.25085928623422 -0.0493592862342187 38 1.2256 1.22931205545041 -0.003712055450407 39 1.2292 1.23039563006491 -0.00119563006491091 40 1.2037 1.21856388321194 -0.0148638832119351 41 1.2165 1.17808651193747 0.0384134880625345 42 1.2694 1.20320637376914 0.0661936262308647 43 1.2938 1.23979649701076 0.0540035029892386 44 1.3201 1.23492958314542 0.0851704168545826 45 1.3014 1.21056799810193 0.090832001898071 46 1.3119 1.19982975223409 0.112070247765912 47 1.3408 1.21891356060376 0.121886439396244 48 1.2991 1.19026429107258 0.108835708927416 49 1.249 1.19884945917473 0.050150540825268 50 1.2218 1.18716057211558 0.0346394278844194 51 1.2176 1.18508677807109 0.0325132219289118 52 1.2266 1.17986265109926 0.0467373489007395 53 1.2138 1.16796795540052 0.0458320445994837 54 1.2007 1.19039433385680 0.0103056661431967 55 1.1985 1.15058376586656 0.047916234133435 56 1.2262 1.19207761781709 0.0341223821829137 57 1.2646 1.20822531932823 0.0563746806717712 58 1.2613 1.19854237790441 0.0627576220955903 59 1.2286 1.17560461847962 0.0529953815203786 60 1.1702 1.13084018994432 0.0393598100556758 61 1.1692 1.12313278781709 0.0460672121829111 62 1.1222 1.12580076441711 -0.00360076441711151 63 1.1139 1.16112977457512 -0.0472297745751244 64 1.1372 1.18399403155666 -0.0467940315566594 65 1.1663 1.19721078026037 -0.0309107802603654 66 1.1582 1.17841633658802 -0.0202163365880242 67 1.0848 1.13287239682572 -0.0480723968257228 68 1.0807 1.11954608530591 -0.0388460853059142 69 1.0773 1.12939098479411 -0.0520909847941148 70 1.0622 1.11005827168035 -0.0478582716803507 71 1.0183 1.08560546762061 -0.0673054676206118 72 1.0014 1.06700338835140 -0.0656033883514011 73 0.9811 1.06881194277636 -0.0877119427763635 74 0.9808 1.03886901765456 -0.0580690176545605 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.117619711212311 0.235239422424623 0.882380288787689 7 0.114640473616879 0.229280947233758 0.885359526383121 8 0.231422152882692 0.462844305765385 0.768577847117307 9 0.185643389535158 0.371286779070315 0.814356610464842 10 0.126616686122625 0.253233372245250 0.873383313877375 11 0.273495230600485 0.54699046120097 0.726504769399515 12 0.255713594582767 0.511427189165535 0.744286405417233 13 0.344387543208279 0.688775086416559 0.655612456791721 14 0.314685997635795 0.62937199527159 0.685314002364205 15 0.236791316484775 0.473582632969551 0.763208683515225 16 0.170696798655672 0.341393597311344 0.829303201344328 17 0.170715101794462 0.341430203588924 0.829284898205538 18 0.290475462653744 0.580950925307488 0.709524537346256 19 0.381643058646963 0.763286117293926 0.618356941353037 20 0.359546159148159 0.719092318296317 0.640453840851841 21 0.569331543850262 0.861336912299476 0.430668456149738 22 0.7166834753402 0.5666330493196 0.2833165246598 23 0.758492102082917 0.483015795834165 0.241507897917083 24 0.826210649610715 0.347578700778571 0.173789350389285 25 0.88020976196351 0.239580476072982 0.119790238036491 26 0.92479953967528 0.150400920649440 0.0752004603247199 27 0.940377825822253 0.119244348355494 0.0596221741777472 28 0.944177796663927 0.111644406672146 0.0558222033360729 29 0.933627405225784 0.132745189548433 0.0663725947742165 30 0.90926317291553 0.181473654168939 0.0907368270844694 31 0.928008286243785 0.143983427512431 0.0719917137562154 32 0.937886685881207 0.124226628237586 0.062113314118793 33 0.958176339376555 0.0836473212468896 0.0418236606234448 34 0.959090747215283 0.0818185055694333 0.0409092527847166 35 0.97638162824647 0.0472367435070616 0.0236183717535308 36 0.992477031896223 0.0150459362075534 0.00752296810377672 37 0.99686885595024 0.00626228809952232 0.00313114404976116 38 0.997232975233965 0.00553404953206965 0.00276702476603482 39 0.998063827910495 0.00387234417900942 0.00193617208950471 40 0.999427771395538 0.00114445720892374 0.000572228604461871 41 0.999523306380847 0.000953387238306551 0.000476693619153276 42 0.999511447677084 0.00097710464583188 0.00048855232291594 43 0.999529615044779 0.00094076991044244 0.00047038495522122 44 0.999407916383939 0.00118416723212186 0.000592083616060931 45 0.999254536569925 0.00149092686014982 0.00074546343007491 46 0.999486445136478 0.00102710972704371 0.000513554863521855 47 0.999800833290438 0.000398333419124229 0.000199166709562114 48 0.999981376747545 3.72465049106824e-05 1.86232524553412e-05 49 0.999962456555199 7.50868896027206e-05 3.75434448013603e-05 50 0.99991566456211 0.000168670875780404 8.4335437890202e-05 51 0.999825213402036 0.000349573195927901 0.000174786597963950 52 0.99976148740216 0.000477025195681782 0.000238512597840891 53 0.99977104804324 0.000457903913520725 0.000228951956760362 54 0.999516590825768 0.000966818348464826 0.000483409174232413 55 0.999119686455717 0.00176062708856538 0.000880313544282691 56 0.998345879702693 0.00330824059461382 0.00165412029730691 57 0.996912419992487 0.00617516001502573 0.00308758000751287 58 0.993729432709782 0.0125411345804365 0.00627056729021823 59 0.98944091651581 0.0211181669683821 0.0105590834841911 60 0.994334136680898 0.0113317266382044 0.00566586331910221 61 0.999966946623381 6.6106753237574e-05 3.3053376618787e-05 62 0.999997880443805 4.2391123900512e-06 2.1195561950256e-06 63 0.99998871257041 2.25748591793458e-05 1.12874295896729e-05 64 0.999948460382 0.000103079236001545 5.15396180007726e-05 65 0.999785435506852 0.000429128986295374 0.000214564493147687 66 0.998955796542565 0.00208840691486945 0.00104420345743472 67 0.99518704866574 0.00962590266851894 0.00481295133425947 68 0.98216713931437 0.0356657213712609 0.0178328606856304 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 28 0.444444444444444 NOK 5% type I error level 34 0.53968253968254 NOK 10% type I error level 36 0.571428571428571 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/1077mk1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/1077mk1227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/12f5x1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/12f5x1227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/2gm2c1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/2gm2c1227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/3ui861227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/3ui861227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/43tmk1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/43tmk1227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/5t6wx1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/5t6wx1227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/6h1aa1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/6h1aa1227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/7risa1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/7risa1227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/80nwp1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/80nwp1227534485.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/9iw4i1227534485.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227534563vejagke1sswx7rc/9iw4i1227534485.ps (open in new window)

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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