WS 7 Multiple Lineair Regression 1

*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, 22 Nov 2010 21:47:12 +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/Nov/22/t1290463253whxcw9yzr5smxhe.htm/, Retrieved Mon, 22 Nov 2010 23:01:03 +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/Nov/22/t1290463253whxcw9yzr5smxhe.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 «

5,3 6,0 5,5 12 5,6 4,0 3,5 11 3,8 4,0 8,5 14 4,0 4,0 5,0 12 4,0 4,5 6,0 21 3,6 3,5 6,0 12 4,4 2,0 5,5 22 3,6 5,5 5,5 11 4,0 3,5 6,0 10 3,8 3,5 6,5 13 5,1 6,0 7,0 10 6,7 5,0 8,0 8 5,1 5,0 5,5 15 4,0 4,0 5,0 14 3,3 4,0 5,5 10 2,7 2,0 7,5 14 4,7 4,5 4,5 14 3,3 4,0 5,5 11 4,4 3,5 8,5 10 6,9 5,5 8,5 13 6,0 4,5 5,5 7 7,6 5,5 9,0 14 4,7 6,5 7,0 12 6,9 4,0 5,0 14 4,2 4,0 5,5 11 3,6 4,5 7,5 9 4,4 3,0 7,5 11 4,7 4,5 6,5 15 4,9 4,5 8,0 14 3,8 3,0 6,5 13 5,3 3,0 4,5 9 5,6 8,0 9,0 15 5,8 2,5 9,0 10 5,6 3,5 6,0 11 3,8 4,5 8,5 13 7,1 3,0 4,5 8 7,3 3,0 4,5 20 2,9 2,5 6,0 12 7,1 6,0 9,0 10 5,6 3,5 6,0 10 6,4 5,0 9,0 9 4,9 4,5 7,0 14 4,0 4,0 7,5 8 3,8 2,5 8,0 14 4,4 4,0 5,0 11 3,3 4,0 5,5 13 4,4 5,0 7,0 9 7,3 3,0 4,5 11 6,4 4,0 6,0 15 5,1 3,5 8,5 11 5,8 2,0 2,5 10 4,0 4,0 6,0 14 4,4 4,0 6,0 18 2,4 2,0 3,0 14 6,2 10,0 12,0 11 5,8 4,0 6,0 12 4,9 4,0 6,0 13 3,8 3,0 7,0 9 2,7 2,0 3,5 10 3,1 4,0 6,5 15 3,8 4,5 6,0 20 4,7 3,0 6,5 12 4,2 3,5 7,0 12 4,0 4,5 4,0 14 2,2 2,5 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Depression[t] = + 14.1649413765651 -0.229535622522770Concerns[t] -0.0358463929726011Criticism[t] -0.00365780052241122Expectat[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) 14.1649413765651 1.194343 11.86 0 0 Concerns -0.229535622522770 0.212105 -1.0822 0.280854 0.140427 Criticism -0.0358463929726011 0.233418 -0.1536 0.878147 0.439074 Expectat -0.00365780052241122 0.184625 -0.0198 0.984219 0.492109 Multiple Linear Regression - Regression Statistics Multiple R 0.0994157252602019 R-squared 0.00988348642901194 Adjusted R-squared -0.00928005899494266 F-TEST (value) 0.515744149131063 F-TEST (DF numerator) 3 F-TEST (DF denominator) 155 p-value 0.672036190260502 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.15996396263658 Sum Squared Residuals 1547.73269800009 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12 12.7132063164855 -0.713206316485528 2 11 12.7233540167187 -1.72335401671874 3 14 13.1182291346477 0.881770865352333 4 12 13.0851243119716 -1.08512431197155 5 21 13.0635433149628 7.93645668503716 6 12 13.1912039569446 -1.19120395694455 7 22 13.0631739486464 8.93682605135356 8 11 13.1213400712606 -2.12134007126055 9 10 13.0993897079354 -3.09938970793544 10 13 13.1434679321788 -0.14346793217879 11 10 12.7536267402065 -2.75362674020648 12 8 12.4185583366202 -4.41855833662024 13 15 12.7949598339627 2.2050401660373 14 14 13.0851243119716 0.914875688028448 15 10 13.2439703474763 -3.24397034747629 16 14 13.4460689058903 0.553931094109673 17 14 12.9083550799805 1.09164492001948 18 11 13.2439703474763 -2.24397034747629 19 10 12.9984309576203 -2.99843095762031 20 13 12.3528991153682 0.64710088463182 21 7 12.6063009701785 -5.60630097017851 22 14 12.1903952793410 1.80960472065897 23 12 12.8275177927293 -0.827517792729288 24 14 12.4194710066555 1.58052899334448 25 11 13.0373882872058 -2.03738828720579 26 9 13.1498708631883 -4.14987086318833 27 11 13.0200119546290 -2.02001195462902 28 15 12.9010394789357 2.09896052106430 29 14 12.8496456536475 1.15035434635247 30 13 13.1613911286651 -0.161391128665091 31 9 12.8244032959258 -3.82440329592576 32 15 12.5598505419551 2.44014945804493 33 10 12.7110985787998 -2.71109857879982 34 11 12.7321327118990 -1.73213271189901 35 13 13.1003059381614 -0.100305938161367 36 8 12.4112391753848 -4.41123917538477 37 20 12.3653320508802 7.63466794911978 38 12 13.3877252856831 -1.38772528568309 39 10 12.2872398941161 -2.28723989411612 40 10 12.7321327118990 -2.73213271189901 41 9 12.4837612228547 -3.48376122285466 42 14 12.8533034541699 1.14669654583006 43 8 13.0759798106655 -5.07597981066553 44 14 13.1738276243678 0.826172375632225 45 11 12.9933100629624 -1.99331006296244 46 13 13.2439703474763 -0.243970347476285 47 9 12.9501480689450 -3.95014806894502 48 11 12.3653320508802 -1.36533205088022 49 15 12.5305810173945 2.46941898260551 50 11 12.8377560218544 -1.83775602185437 51 10 12.7527974786818 -2.7527974786818 52 14 13.0814665114491 0.918533488550859 53 18 12.9896522624400 5.01034773755997 54 14 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12 12.941003567639 -0.941003567638993 104 11 13.0401334176929 -2.04013341769292 105 16 12.7949598339627 3.2050401660373 106 19 12.7698080051328 6.23019199486718 107 11 12.9423725726919 -1.94237257269191 108 16 12.7453949089357 3.25460509106427 109 15 12.6549532251702 2.34504677482981 110 24 13.1632200289263 10.8367799710737 111 14 13.3518788927105 0.648121107289512 112 15 12.4974797549090 2.50252024509098 113 11 12.8725086870079 -1.87250868700792 114 15 12.6494665243866 2.35053347561343 115 12 13.4940837680806 -1.4940837680806 116 10 13.2252214496559 -3.22522144965595 117 14 13.0264148856386 0.97358511436144 118 13 13.1687067297099 -0.168706729709913 119 9 12.6907996181428 -3.69079961814279 120 15 13.0686606494301 1.93133935056994 121 15 12.6114183046457 2.38858169535428 122 14 13.3059717682059 0.694028231794066 123 11 12.8174570611973 -1.81745706119734 124 8 13.2889612417549 -5.28896124175491 125 11 13.0607981844757 -2.06079818447571 126 11 12.980507761134 -1.98050776113401 127 8 13.3174920336827 -5.31749203368269 128 10 12.9914811627012 -2.99148116270124 129 11 13.0598855144404 -2.05988551444043 130 13 12.3258278573852 0.674172142614793 131 11 12.8192859614585 -1.81928596145854 132 20 13.1732807604582 6.82671923954175 133 10 13.1829721256738 -3.1829721256738 134 15 13.2504602471871 1.74953975281293 135 12 12.9207915756832 -0.920791575683202 136 14 12.6178247958459 1.38217520415409 137 23 13.1173129044217 9.88268709557826 138 14 13.0617144147016 0.938285585298365 139 16 13.1094504394674 2.89054956053261 140 11 12.7852684687471 -1.78526846874715 141 12 12.4492838349350 -0.449283834934979 142 10 12.4203872368814 -2.42038723688144 143 14 12.5460450411995 1.45395495880053 144 12 12.9837056664481 -0.983705666448132 145 12 12.8578774849183 -0.857877484918273 146 11 12.5682563106283 -1.5682563106283 147 12 12.3887455083408 -0.388745508340781 148 13 13.0094043591875 -0.00940435918753933 149 11 13.1553575639719 -2.15535756397195 150 19 12.8353802576836 6.16461974231636 151 12 12.9735579662149 -0.973557966214938 152 17 12.5691725408542 4.43082745914577 153 9 12.0729728664844 -3.07297286648440 154 12 12.9070730436288 -0.907073043628838 155 19 12.8854050779189 6.11459492208111 156 18 13.1613911286651 4.83860887133491 157 15 12.5783170421603 2.42168295783975 158 14 12.8335513574224 1.16644864257757 159 11 12.2596253324143 -1.25962533241427 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.979678820306521 0.0406423593869571 0.0203211796934785 8 0.95622575593217 0.087548488135661 0.0437742440678305 9 0.973408504855963 0.0531829902880748 0.0265914951440374 10 0.957172628586854 0.0856547428262916 0.0428273714131458 11 0.935245850344444 0.129508299311112 0.064754149655556 12 0.929303469402726 0.141393061194549 0.0706965305972745 13 0.922084502787729 0.155830994424542 0.077915497212271 14 0.884172133013607 0.231655733972785 0.115827866986393 15 0.899902197412811 0.200195605174377 0.100097802587189 16 0.872976918118862 0.254046163762276 0.127023081881138 17 0.829255844055818 0.341488311888365 0.170744155944182 18 0.803897515799534 0.392204968400933 0.196102484200466 19 0.783497282970281 0.433005434059437 0.216502717029719 20 0.755523167639414 0.488953664721172 0.244476832360586 21 0.845771259737194 0.308457480525611 0.154228740262806 22 0.838365043747632 0.323269912504735 0.161634956252368 23 0.802108657839811 0.395782684320378 0.197891342160189 24 0.756256274984631 0.487487450030737 0.243743725015369 25 0.721725120443323 0.556549759113354 0.278274879556677 26 0.72176653143144 0.55646693713712 0.27823346856856 27 0.694415559382633 0.611168881234735 0.305584440617367 28 0.674558545464621 0.650882909070758 0.325441454535379 29 0.633981984676767 0.732036030646466 0.366018015323233 30 0.57563672840173 0.84872654319654 0.42436327159827 31 0.628564446163763 0.742871107672474 0.371435553836237 32 0.640119465858258 0.719761068283485 0.359880534141742 33 0.62215258275036 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0.190111226650010 0.904944386674995 104 0.0869464622385325 0.173892924477065 0.913053537761467 105 0.0876850514409576 0.175370102881915 0.912314948559042 106 0.148797864291120 0.297595728582239 0.85120213570888 107 0.131501532453448 0.263003064906896 0.868498467546552 108 0.131685146691815 0.26337029338363 0.868314853308185 109 0.118887995753205 0.237775991506410 0.881112004246795 110 0.51396475922903 0.97207048154194 0.48603524077097 111 0.464315181580657 0.928630363161313 0.535684818419343 112 0.443633486782436 0.887266973564873 0.556366513217564 113 0.404243436241197 0.808486872482395 0.595756563758803 114 0.374864720891147 0.749729441782293 0.625135279108853 115 0.336063774433124 0.672127548866249 0.663936225566875 116 0.352878412633685 0.70575682526737 0.647121587366315 117 0.307900521635744 0.615801043271488 0.692099478364256 118 0.26308332055766 0.52616664111532 0.73691667944234 119 0.282909499504766 0.565818999009532 0.717090500495234 120 0.255642143057877 0.511284286115754 0.744357856942123 121 0.263723563128933 0.527447126257865 0.736276436871067 122 0.221728709525835 0.44345741905167 0.778271290474165 123 0.196695666761989 0.393391333523977 0.803304333238011 124 0.257131312402607 0.514262624805214 0.742868687597393 125 0.230183147571908 0.460366295143816 0.769816852428092 126 0.228605834677433 0.457211669354865 0.771394165322568 127 0.396240023445292 0.792480046890584 0.603759976554708 128 0.418378689283459 0.836757378566919 0.58162131071654 129 0.446681999092015 0.893363998184031 0.553318000907985 130 0.428683608453685 0.857367216907369 0.571316391546315 131 0.400967587916697 0.801935175833393 0.599032412083303 132 0.499363693769705 0.99872738753941 0.500636306230295 133 0.623552680635004 0.752894638729992 0.376447319364996 134 0.56701907239611 0.86596185520778 0.43298092760389 135 0.532231509955278 0.935536980089444 0.467768490044722 136 0.467635964524509 0.935271929049019 0.532364035475491 137 0.824025058502407 0.351949882995186 0.175974941497593 138 0.775138061286718 0.449723877426564 0.224861938713282 139 0.72806702572295 0.5438659485541 0.27193297427705 140 0.689610388889354 0.620779222221292 0.310389611110646 141 0.632630629308515 0.73473874138297 0.367369370691485 142 0.578721933862848 0.842556132274304 0.421278066137152 143 0.509387654662908 0.981224690674184 0.490612345337092 144 0.426080408098394 0.852160816196789 0.573919591901606 145 0.421666048725201 0.843332097450402 0.578333951274799 146 0.333123125906313 0.666246251812627 0.666876874093687 147 0.249252643441651 0.498505286883303 0.750747356558349 148 0.185090032942969 0.370180065885938 0.81490996705703 149 0.324849045209418 0.649698090418837 0.675150954790582 150 0.374415341345407 0.748830682690815 0.625584658654593 151 0.411212034748996 0.822424069497993 0.588787965251004 152 0.489453196330052 0.978906392660103 0.510546803669948 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 1 0.00684931506849315 OK 10% type I error level 4 0.0273972602739726 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/10nhfs1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/10nhfs1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/1ygiz1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/1ygiz1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/2wboq1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/2wboq1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/3wboq1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/3wboq1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/4wboq1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/4wboq1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/5wboq1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/5wboq1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/6khyn1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/6khyn1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/7c8gp1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/7c8gp1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/8c8gp1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/8c8gp1290462421.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/9c8gp1290462421.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290463253whxcw9yzr5smxhe/9c8gp1290462421.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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