W8

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 16 Dec 2010 13:53:57 +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/16/t12925075357smi0j5lktyypbh.htm/, Retrieved Thu, 16 Dec 2010 14:52:29 +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/16/t12925075357smi0j5lktyypbh.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 «

1856 1834 2095 2164 2368 2072 2521 1823 1947 2226 1754 1786 2072 1846 2137 2466 2154 2289 2628 2074 2798 2194 2442 2565 2063 2069 2539 1898 2139 2408 2725 2201 2311 2548 2276 2351 2280 2057 2479 2379 2295 2456 2546 2844 2260 2981 2678 3440 2842 2450 2669 2570 2540 2318 2930 2947 2799 2695 2498 2260 2160 2058 2533 2150 2172 2155 3016 2333 2355 2825 2214 2360 2299 1746 2069 2267 1878 2266 2282 2085 2277 2251 1828 1954 1851 1570 1852 2187 1855 2218 2253 2028 2169 1997 2034 1791 1627 1631 2319 1707 1747 2397 2059 2251 2558 2406 2049 2074 1734

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation vergunningen[t] = + 2413.39057239057 -218.92884399551M1[t] -389.991021324355M2[t] -6.65858585858594M3[t] -104.992817059483M4[t] -173.993714927048M5[t] -12.8835016835020M6[t] + 253.782267115600M7[t] -7.88529741863053M8[t] + 92.8915824915827M9[t] + 167.112906846240M10[t] -91.8879910213241M11[t] -2.11021324354658t + 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) 2413.39057239057 118.621974 20.3452 0 0 M1 -218.92884399551 143.319426 -1.5276 0.12991 0.064955 M2 -389.991021324355 147.270204 -2.6481 0.009463 0.004731 M3 -6.65858585858594 147.211556 -0.0452 0.964017 0.482008 M4 -104.992817059483 147.159061 -0.7135 0.477289 0.238645 M5 -173.993714927048 147.112727 -1.1827 0.23984 0.11992 M6 -12.8835016835020 147.072559 -0.0876 0.930377 0.465189 M7 253.782267115600 147.038562 1.726 0.087572 0.043786 M8 -7.88529741863053 147.01074 -0.0536 0.957335 0.478668 M9 92.8915824915827 146.989098 0.632 0.528914 0.264457 M10 167.112906846240 146.973637 1.137 0.258357 0.129179 M11 -91.8879910213241 146.964359 -0.6252 0.533297 0.266649 t -2.11021324354658 0.95341 -2.2133 0.029243 0.014622 Multiple Linear Regression - Regression Statistics Multiple R 0.515437731669643 R-squared 0.265676055228747 Adjusted R-squared 0.173885562132341 F-TEST (value) 2.89437442012334 F-TEST (DF numerator) 12 F-TEST (DF denominator) 96 p-value 0.00188088213908377 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 311.751924658146 Sum Squared Residuals 9330169.2026936 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1856 2192.35151515151 -336.351515151507 2 1834 2019.17912457912 -185.179124579124 3 2095 2400.40134680135 -305.401346801347 4 2164 2299.95690235690 -135.956902356902 5 2368 2228.84579124579 139.154208754207 6 2072 2387.84579124579 -315.845791245792 7 2521 2652.40134680135 -131.401346801346 8 1823 2388.62356902357 -565.623569023569 9 1947 2487.29023569024 -540.290235690236 10 2226 2559.40134680135 -333.401346801347 11 1754 2298.29023569024 -544.290235690236 12 1786 2388.06801346801 -602.068013468014 13 2072 2167.02895622896 -95.0289562289578 14 1846 1993.85656565657 -147.856565656566 15 2137 2375.07878787879 -238.078787878788 16 2466 2274.63434343434 191.365656565656 17 2154 2203.52323232323 -49.5232323232325 18 2289 2362.52323232323 -73.5232323232325 19 2628 2627.07878787879 0.921212121211632 20 2074 2363.30101010101 -289.301010101010 21 2798 2461.96767676768 336.032323232323 22 2194 2534.07878787879 -340.078787878788 23 2442 2272.96767676768 169.032323232323 24 2565 2362.74545454545 202.254545454545 25 2063 2141.7063973064 -78.7063973063984 26 2069 1968.53400673401 100.465993265993 27 2539 2349.75622895623 189.243771043771 28 1898 2249.31178451178 -351.311784511785 29 2139 2178.20067340067 -39.2006734006735 30 2408 2337.20067340067 70.7993265993265 31 2725 2601.75622895623 123.243771043771 32 2201 2337.97845117845 -136.978451178451 33 2311 2436.64511784512 -125.645117845118 34 2548 2508.75622895623 39.243771043771 35 2276 2247.64511784512 28.3548821548819 36 2351 2337.42289562290 13.5771043771043 37 2280 2116.38383838384 163.616161616160 38 2057 1943.21144781145 113.788552188552 39 2479 2324.43367003367 154.566329966330 40 2379 2223.98922558923 155.010774410775 41 2295 2152.87811447811 142.121885521885 42 2456 2311.87811447811 144.121885521886 43 2546 2576.43367003367 -30.4336700336703 44 2844 2312.65589225589 531.344107744107 45 2260 2411.32255892256 -151.322558922559 46 2981 2483.43367003367 497.56632996633 47 2678 2222.32255892256 455.677441077441 48 3440 2312.10033670034 1127.89966329966 49 2842 2091.06127946128 750.938720538719 50 2450 1917.88888888889 532.111111111111 51 2669 2299.11111111111 369.888888888889 52 2570 2198.66666666667 371.333333333333 53 2540 2127.55555555556 412.444444444445 54 2318 2286.55555555556 31.4444444444445 55 2930 2551.11111111111 378.888888888889 56 2947 2287.33333333333 659.666666666666 57 2799 2386 413 58 2695 2458.11111111111 236.888888888889 59 2498 2197 301 60 2260 2286.77777777778 -26.7777777777777 61 2160 2065.73872053872 94.2612794612785 62 2058 1892.56632996633 165.43367003367 63 2533 2273.78855218855 259.211447811448 64 2150 2173.34410774411 -23.3441077441075 65 2172 2102.23299663300 69.7670033670034 66 2155 2261.23299663300 -106.232996632997 67 3016 2525.78855218855 490.211447811448 68 2333 2262.01077441077 70.9892255892255 69 2355 2360.67744107744 -5.67744107744105 70 2825 2432.78855218855 392.211447811448 71 2214 2171.67744107744 42.3225589225589 72 2360 2261.45521885522 98.5447811447813 73 2299 2040.41616161616 258.583838383837 74 1746 1867.24377104377 -121.243771043771 75 2069 2248.46599326599 -179.465993265993 76 2267 2148.02154882155 118.978451178452 77 1878 2076.91043771044 -198.910437710437 78 2266 2235.91043771044 30.0895622895625 79 2282 2500.46599326599 -218.465993265993 80 2085 2236.68821548822 -151.688215488215 81 2277 2335.35488215488 -58.354882154882 82 2251 2407.46599326599 -156.465993265993 83 1828 2146.35488215488 -318.354882154882 84 1954 2236.13265993266 -282.132659932660 85 1851 2015.09360269360 -164.093602693604 86 1570 1841.92121212121 -271.921212121212 87 1852 2223.14343434343 -371.143434343434 88 2187 2122.69898989899 64.3010101010105 89 1855 2051.58787878788 -196.587878787878 90 2218 2210.58787878788 7.41212121212147 91 2253 2475.14343434343 -222.143434343434 92 2028 2211.36565656566 -183.365656565656 93 2169 2310.03232323232 -141.032323232323 94 1997 2382.14343434343 -385.143434343434 95 2034 2121.03232323232 -87.0323232323231 96 1791 2210.8101010101 -419.810101010101 97 1627 1989.77104377104 -362.771043771044 98 1631 1816.59865319865 -185.598653198653 99 2319 2197.82087542088 121.179124579125 100 1707 2097.37643097643 -390.376430976431 101 1747 2026.26531986532 -279.265319865319 102 2397 2185.26531986532 211.734680134681 103 2059 2449.82087542088 -390.820875420875 104 2251 2186.04309764310 64.9569023569026 105 2558 2284.70976430976 273.290235690236 106 2406 2356.82087542087 49.179124579125 107 2049 2095.70976430976 -46.7097643097641 108 2074 2185.48754208754 -111.487542087542 109 1734 1964.44848484849 -230.448484848485 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0528531496479013 0.105706299295803 0.947146850352099 17 0.0910536893600996 0.182107378720199 0.9089463106399 18 0.0479584193179421 0.0959168386358842 0.952041580682058 19 0.0194851994505993 0.0389703989011987 0.9805148005494 20 0.0119107756006673 0.0238215512013346 0.988089224399333 21 0.178327407972798 0.356654815945596 0.821672592027202 22 0.161477825852337 0.322955651704673 0.838522174147663 23 0.227087509620839 0.454175019241677 0.772912490379161 24 0.309583763115593 0.619167526231185 0.690416236884407 25 0.298855787932989 0.597711575865978 0.701144212067011 26 0.233447200525868 0.466894401051736 0.766552799474132 27 0.17582676439845 0.3516535287969 0.82417323560155 28 0.466325718111297 0.932651436222594 0.533674281888703 29 0.472194328666593 0.944388657333187 0.527805671333407 30 0.404716084719421 0.809432169438842 0.595283915280579 31 0.336123044437038 0.672246088874075 0.663876955562962 32 0.327507802553858 0.655015605107717 0.672492197446142 33 0.340542703889125 0.68108540777825 0.659457296110875 34 0.316408260333021 0.632816520666042 0.683591739666979 35 0.280762585373938 0.561525170747876 0.719237414626062 36 0.254995165859622 0.509990331719243 0.745004834140379 37 0.214819743279490 0.429639486558981 0.78518025672051 38 0.182399482652279 0.364798965304558 0.817600517347721 39 0.147622451313703 0.295244902627406 0.852377548686297 40 0.118202662211109 0.236405324422219 0.88179733778889 41 0.0989373057551087 0.197874611510217 0.901062694244891 42 0.0791049789566515 0.158209957913303 0.920895021043348 43 0.0965543464717458 0.193108692943492 0.903445653528254 44 0.180283987067465 0.360567974134930 0.819716012932535 45 0.278179612759351 0.556359225518702 0.721820387240649 46 0.308085071872153 0.616170143744306 0.691914928127847 47 0.285459216263893 0.570918432527785 0.714540783736107 48 0.835782008011266 0.328435983977468 0.164217991988734 49 0.88859333880181 0.222813322396380 0.111406661198190 50 0.88820244684569 0.223595106308618 0.111797553154309 51 0.862077126057398 0.275845747885204 0.137922873942602 52 0.834709444031575 0.330581111936851 0.165290555968425 53 0.829334829495419 0.341330341009163 0.170665170504581 54 0.845865498045946 0.308269003908108 0.154134501954054 55 0.822903667232402 0.354192665535195 0.177096332767598 56 0.88187514084255 0.236249718314901 0.118124859157451 57 0.860473876757973 0.279052246484054 0.139526123242027 58 0.832168417775693 0.335663164448615 0.167831582224307 59 0.815850693611207 0.368298612777586 0.184149306388793 60 0.862844848252672 0.274310303494656 0.137155151747328 61 0.867791872174628 0.264416255650745 0.132208127825372 62 0.870860565599867 0.258278868800267 0.129139434400133 63 0.866687917514534 0.266624164970933 0.133312082485466 64 0.870728553355495 0.25854289328901 0.129271446644505 65 0.874150905920603 0.251698188158794 0.125849094079397 66 0.89493536836568 0.210129263268642 0.105064631634321 67 0.96273183953576 0.0745363209284785 0.0372681604642393 68 0.9541513432365 0.091697313526998 0.045848656763499 69 0.945640300184602 0.108719399630797 0.0543596998153983 70 0.969737594206653 0.0605248115866941 0.0302624057933471 71 0.965488176294674 0.0690236474106523 0.0345118237053261 72 0.97539785521856 0.0492042895628794 0.0246021447814397 73 0.994186088823337 0.0116278223533263 0.00581391117666314 74 0.994390088658841 0.0112198226823172 0.0056099113411586 75 0.993295765948931 0.0134084681021372 0.00670423405106861 76 0.995464918399726 0.00907016320054791 0.00453508160027395 77 0.995002398893746 0.00999520221250907 0.00499760110625454 78 0.991341734279957 0.0173165314400870 0.00865826572004349 79 0.991472121802698 0.0170557563946047 0.00852787819730233 80 0.986875270408193 0.026249459183614 0.013124729591807 81 0.978201330445477 0.0435973391090464 0.0217986695545232 82 0.970806560948915 0.05838687810217 0.029193439051085 83 0.962684626191244 0.0746307476175124 0.0373153738087562 84 0.950770083236816 0.098459833526368 0.049229916763184 85 0.953826278994837 0.0923474420103257 0.0461737210051629 86 0.929690693682873 0.140618612634254 0.070309306317127 87 0.937226933458173 0.125546133083654 0.0627730665418269 88 0.98603978762745 0.0279204247450993 0.0139602123725496 89 0.98440287133053 0.0311942573389407 0.0155971286694704 90 0.963027032316658 0.0739459353666848 0.0369729676833424 91 0.987131997972696 0.0257360040546085 0.0128680020273042 92 0.96134910201372 0.0773017959725602 0.0386508979862801 93 0.924060165321722 0.151879669356556 0.0759398346782779 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0256410256410256 NOK 5% type I error level 15 0.192307692307692 NOK 10% type I error level 26 0.333333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/10usgs1292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/10usgs1292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/1y0011292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/1y0011292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/2y0011292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/2y0011292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/3y0011292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/3y0011292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/49rzm1292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/49rzm1292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/59rzm1292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/59rzm1292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/69rzm1292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/69rzm1292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/71jg71292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/71jg71292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/8usgs1292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/8usgs1292507622.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/9usgs1292507622.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t12925075357smi0j5lktyypbh/9usgs1292507622.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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