Multiple Regression Anxiety

*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: Sat, 11 Dec 2010 07:26:18 +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/11/t12920522956rqa79m35lnfg76.htm/, Retrieved Sat, 11 Dec 2010 08:24:55 +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/11/t12920522956rqa79m35lnfg76.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 «

0 69 26 9 15 6 25 25 1 53 20 9 15 6 25 24 1 43 21 9 14 13 19 21 0 60 31 14 10 8 18 23 1 49 21 8 10 7 18 17 1 62 18 8 12 9 22 19 1 45 26 11 18 5 29 18 1 50 22 10 12 8 26 27 1 75 22 9 14 9 25 23 1 82 29 15 18 11 23 23 0 60 15 14 9 8 23 29 1 59 16 11 11 11 23 21 1 21 24 14 11 12 24 26 1 62 17 6 17 8 30 25 0 54 19 20 8 7 19 25 1 47 22 9 16 9 24 23 1 59 31 10 21 12 32 26 0 37 28 8 24 20 30 20 0 43 38 11 21 7 29 29 1 48 26 14 14 8 17 24 0 79 25 11 7 8 25 23 0 62 25 16 18 16 26 24 1 16 29 14 18 10 26 30 0 38 28 11 13 6 25 22 1 58 15 11 11 8 23 22 0 60 18 12 13 9 21 13 0 67 21 9 13 9 19 24 0 55 25 7 18 11 35 17 1 47 23 13 14 12 19 24 0 59 23 10 12 8 20 21 1 49 19 9 9 7 21 23 0 47 18 9 12 8 21 24 1 57 18 13 8 9 24 24 0 39 26 16 5 4 23 24 1 49 18 12 10 8 19 23 1 26 18 6 11 8 17 26 0 53 28 14 11 8 24 24 0 75 17 14 12 6 15 21 1 65 29 10 12 8 25 23 1 49 12 4 15 4 27 28 0 48 25 12 12 7 29 23 0 45 28 12 16 14 27 22 0 31 20 14 14 10 18 24 1 61 17 9 17 9 25 21 1 49 17 9 13 6 22 23 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Anxiety[t] = + 62.6264784679251 -4.54909193536483Gender[t] -0.361672579691564Mistakes[t] + 0.169699251103092Doubts[t] + 0.788331768369545Expectations[t] -1.10287823451153Critism[t] + 0.108738082842006Pstandards[t] -0.232322040117941Organization[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) 62.6264784679251 9.241389 6.7767 0 0 Gender -4.54909193536483 2.197821 -2.0698 0.040256 0.020128 Mistakes -0.361672579691564 0.241604 -1.497 0.136591 0.068295 Doubts 0.169699251103092 0.441371 0.3845 0.701188 0.350594 Expectations 0.788331768369545 0.402988 1.9562 0.052375 0.026187 Critism -1.10287823451153 0.505322 -2.1825 0.030692 0.015346 Pstandards 0.108738082842006 0.309861 0.3509 0.726157 0.363079 Organization -0.232322040117941 0.316958 -0.733 0.464766 0.232383 Multiple Linear Regression - Regression Statistics Multiple R 0.275657312550536 R-squared 0.0759869539625838 Adjusted R-squared 0.0310696531135426 F-TEST (value) 1.69170792826493 F-TEST (DF numerator) 7 F-TEST (DF denominator) 144 p-value 0.115413556168906 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.2170763953282 Sum Squared Residuals 25155.5196153517 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 69 56.8683928424479 12.1316071575521 2 53 54.7216584253504 -1.72165842535038 3 43 45.8960440590103 -2.89604405901032 4 60 49.4645883889766 10.5354116110234 5 49 50.010837219128 -1.010837219128 6 62 50.4370702770509 11.5629297229491 7 45 58.187779561103 -13.1877795611030 8 50 49.0090327054268 0.990967294573172 9 75 50.1336688341811 24.8663311658189 10 82 49.3502507217298 32.6497492782702 11 60 53.6127760691744 6.38722393082556 12 59 48.3195189549568 10.6804810450432 13 21 43.7794857184743 -22.7794857184743 14 62 54.9798538529239 7.02014614707609 15 54 53.9931635522725 0.0068364477275411 16 47 51.6015942880781 -4.60159428807814 17 59 49.3222030026525 9.6777969973475 18 37 49.3353396789259 -12.3353396789259 19 43 56.0004969349573 -13.0004969349573 20 48 49.5361263025878 -1.53612630258779 21 79 49.5216973886021 29.4783026113979 22 62 50.0952332628144 11.9047667371856 23 16 48.9833896728386 -32.9833896728386 24 38 55.6047487688857 -17.6047487688857 25 58 51.757504198065 6.24249580193499 26 60 57.7384851430633 2.26151485693675 27 67 53.3713510436979 13.6286489563021 28 55 56.6872282018478 -1.68722820184783 29 47 46.2574080181973 0.742591981802723 30 59 53.9379558047557 5.0620441952443 31 49 49.0478318690631 -0.0478318690630564 32 47 54.9883914145986 -7.98839141459862 33 57 47.1881054241825 9.81189457581755 34 39 52.3935722599311 -13.3935722599311 35 49 49.3865795702379 -0.386579570237895 36 26 48.2422735459511 -22.2422735459511 37 53 51.7580443533549 1.24195564664509 38 75 58.4488543421305 16.5511456578695 39 65 47.2978747252156 17.7021252747844 40 49 58.2604872816027 -9.26048728160274 41 48 55.1708860474325 -7.17088604743247 42 45 49.5338936146892 -4.53389361468916 43 31 54.158235329921 -23.1582353299210 44 61 54.7716711179834 6.22832888201661 45 49 54.1361204192779 -5.1361204192779 46 69 49.0850024885426 19.9149975114574 47 54 52.9149325985926 1.08506740140735 48 80 53.7617045525349 26.2382954474651 49 57 56.7644452206825 0.235554779317542 50 34 52.3490862820169 -18.3490862820169 51 69 57.1420760357219 11.8579239642781 52 44 48.7252614958083 -4.72526149580833 53 70 52.6607505984853 17.3392494015147 54 51 55.8650122801255 -4.86501228012553 55 66 51.5512570058816 14.4487429941184 56 18 46.9244344438527 -28.9244344438527 57 74 49.7394531084645 24.2605468915355 58 59 54.6995302279488 4.30046977205118 59 48 55.9314419418487 -7.93144194184867 60 55 49.8045672759454 5.1954327240546 61 44 50.5696397410586 -6.56963974105856 62 56 51.986479526336 4.01352047366402 63 65 55.3351280673687 9.66487193263132 64 77 52.6150190165715 24.3849809834285 65 46 45.3857436450983 0.614256354901673 66 70 54.3410779681867 15.6589220318133 67 39 54.5524444273522 -15.5524444273522 68 55 59.3390187494024 -4.33901874940245 69 44 53.0384156384161 -9.03841563841606 70 45 56.4683324091105 -11.4683324091105 71 45 52.8442834771808 -7.84428347718081 72 49 58.2605569672453 -9.26055696724525 73 65 48.1551418995639 16.8448581004361 74 45 50.9692730454757 -5.9692730454757 75 71 49.3155468247754 21.6844531752246 76 48 50.9040230802883 -2.90402308028826 77 41 46.3865725417958 -5.38657254179578 78 40 54.4234088893673 -14.4234088893673 79 64 52.9991764651861 11.0008235348139 80 56 53.6776831573423 2.32231684265774 81 52 55.1672318876934 -3.16723188769338 82 41 47.1669139630747 -6.16691396307471 83 42 52.7597642203348 -10.7597642203348 84 54 58.1303769171605 -4.13037691716053 85 40 47.8255198689898 -7.8255198689898 86 40 49.4696513449374 -9.46965134493738 87 51 55.4680335780202 -4.46803357802024 88 48 55.6558883643725 -7.65588836437253 89 80 62.4793001427889 17.5206998572111 90 38 57.3483504592595 -19.3483504592595 91 57 57.0216305591598 -0.0216305591597679 92 28 44.8882630066128 -16.8882630066128 93 51 53.8701651397952 -2.87016513979516 94 46 51.490009762044 -5.49000976204398 95 58 53.3339480184928 4.66605198150715 96 67 50.9287780045707 16.0712219954293 97 72 48.5906673900651 23.4093326099349 98 26 48.6181524816782 -22.6181524816782 99 54 53.839081736377 0.160918263622951 100 53 53.3967192630048 -0.396719263004809 101 64 46.8995871145523 17.1004128854477 102 47 48.5957112245472 -1.59571122454718 103 43 54.7710864290161 -11.7710864290161 104 66 47.4292694491622 18.5707305508378 105 54 50.1728990825374 3.82710091746263 106 62 56.7738835878157 5.22611641218431 107 52 50.8934831444521 1.10651685554786 108 64 53.0741070795385 10.9258929204615 109 55 49.7186490580538 5.28135094194622 110 57 54.9531190994082 2.04688090059181 111 74 54.035664947457 19.9643350525430 112 32 49.0143670547102 -17.0143670547102 113 38 52.9323366417668 -14.9323366417668 114 66 52.1031182054533 13.8968817945467 115 37 55.5088808818292 -18.5088808818292 116 26 49.5283945733107 -23.5283945733107 117 64 51.3482231288045 12.6517768711955 118 28 47.38931989405 -19.3893198940500 119 66 60.5866201128332 5.4133798871668 120 65 54.2982054513544 10.7017945486456 121 48 48.4936803092093 -0.493680309209307 122 44 54.7518905242933 -10.7518905242933 123 64 56.7921441833638 7.2078558166362 124 39 48.72029818031 -9.72029818031 125 50 53.3495799354858 -3.3495799354858 126 66 51.6122181741426 14.3877818258574 127 48 52.0982219457131 -4.09822194571311 128 70 55.4479711117546 14.5520288882454 129 66 58.7784256393873 7.22157436061266 130 61 49.7056967811452 11.2943032188548 131 31 48.732352955723 -17.7323529557230 132 61 54.6760347657122 6.32396523428777 133 54 45.0684743181211 8.93152568187892 134 34 45.2149963154671 -11.2149963154671 135 62 49.8284566108289 12.1715433891711 136 47 53.841147467513 -6.84114746751298 137 52 48.1021138553018 3.89788614469821 138 37 59.8945094173258 -22.8945094173258 139 46 46.8097998676881 -0.80979986768813 140 38 53.0809431521072 -15.0809431521072 141 63 51.757504198065 11.242495801935 142 34 54.9949948139028 -20.9949948139028 143 46 47.225674051785 -1.22567405178501 144 40 46.3566514362257 -6.3566514362257 145 30 47.8255198689898 -17.8255198689898 146 35 48.2784881954771 -13.2784881954771 147 51 48.4936803092093 2.50631969079069 148 56 55.089911859418 0.91008814058204 149 68 52.9455708120432 15.0544291879568 150 39 51.4191899341407 -12.4191899341407 151 44 53.9356715056027 -9.93567150560273 152 58 51.4191899341407 6.58081006585934 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.906584485556144 0.186831028887713 0.0934155144438564 12 0.833002869091047 0.333994261817906 0.166997130908953 13 0.954564785331215 0.0908704293375694 0.0454352146687847 14 0.920552515093133 0.158894969813733 0.0794474849068667 15 0.876230730015828 0.247538539968343 0.123769269984172 16 0.871639559504647 0.256720880990706 0.128360440495353 17 0.817158875581931 0.365682248836138 0.182841124418069 18 0.836383348190564 0.327233303618871 0.163616651809436 19 0.872476752495675 0.255046495008650 0.127523247504325 20 0.861312475865695 0.277375048268610 0.138687524134305 21 0.919959732552525 0.160080534894949 0.0800402674474745 22 0.908510319228728 0.182979361542543 0.0914896807712715 23 0.964971575311988 0.070056849376025 0.0350284246880125 24 0.9817162190004 0.036567561999199 0.0182837809995995 25 0.972966234121493 0.0540675317570141 0.0270337658785071 26 0.963585597822903 0.0728288043541939 0.0364144021770970 27 0.95586706852882 0.0882658629423597 0.0441329314711799 28 0.940742266020965 0.118515467958071 0.0592577339790353 29 0.919193971075364 0.161612057849272 0.080806028924636 30 0.893771031724157 0.212457936551686 0.106228968275843 31 0.86872591453739 0.26254817092522 0.13127408546261 32 0.861393315372931 0.277213369254137 0.138606684627069 33 0.833079594073843 0.333840811852313 0.166920405926157 34 0.846211097833152 0.307577804333695 0.153788902166848 35 0.811287820625366 0.377424358749268 0.188712179374634 36 0.87594144853743 0.248117102925139 0.124058551462570 37 0.843594243949252 0.312811512101497 0.156405756050748 38 0.84494065407223 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0.648865642158395 0.70226871568321 0.351134357841605 109 0.612620407214158 0.774759185571683 0.387379592785842 110 0.559973828521083 0.880052342957835 0.440026171478917 111 0.616603479389774 0.766793041220452 0.383396520610226 112 0.643794887666486 0.712410224667028 0.356205112333514 113 0.630787277707196 0.738425444585609 0.369212722292804 114 0.612704538229718 0.774590923540563 0.387295461770282 115 0.628306385397957 0.743387229204086 0.371693614602043 116 0.761440647838666 0.477118704322667 0.238559352161334 117 0.75373712232401 0.492525755351979 0.246262877675989 118 0.828733206132194 0.342533587735612 0.171266793867806 119 0.789261644650961 0.421476710698077 0.210738355349038 120 0.763185336537162 0.473629326925676 0.236814663462838 121 0.710910352882027 0.578179294235946 0.289089647117973 122 0.68031051712088 0.639378965758241 0.319689482879121 123 0.644910476146118 0.710179047707763 0.355089523853882 124 0.636921494127129 0.726157011745742 0.363078505872871 125 0.569473087452574 0.861053825094852 0.430526912547426 126 0.56146731217799 0.877065375644019 0.438532687822010 127 0.48901222505462 0.97802445010924 0.51098777494538 128 0.542883326123665 0.91423334775267 0.457116673876335 129 0.518786319500514 0.962427360998971 0.481213680499486 130 0.595509196014591 0.808981607970819 0.404490803985409 131 0.648863022723052 0.702273954553897 0.351136977276948 132 0.692869486517901 0.614261026964199 0.307130513482099 133 0.615610593001964 0.768778813996073 0.384389406998036 134 0.72675480124932 0.546490397501361 0.273245198750680 135 0.746652578004772 0.506694843990455 0.253347421995228 136 0.787080596528095 0.425838806943811 0.212919403471905 137 0.71429755902708 0.571404881945839 0.285702440972920 138 0.622387633767318 0.755224732465364 0.377612366232682 139 0.490831376076063 0.981662752152126 0.509168623923937 140 0.476367700116323 0.952735400232646 0.523632299883677 141 0.471436632886289 0.942873265772577 0.528563367113711 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 0.00763358778625954 OK 10% type I error level 7 0.0534351145038168 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/10hmtw1292052367.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/10hmtw1292052367.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/1itt71292052366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/1itt71292052366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/2t2as1292052366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/2t2as1292052366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/3t2as1292052366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/3t2as1292052366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/4t2as1292052366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/4t2as1292052366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/53t9d1292052366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/53t9d1292052366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/63t9d1292052366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/63t9d1292052366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/7wkqg1292052366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/7wkqg1292052366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/8wkqg1292052366.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/8wkqg1292052366.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/9hmtw1292052367.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920522956rqa79m35lnfg76/9hmtw1292052367.ps (open in new window)

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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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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