paper

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 17 Dec 2008 08:17:46 -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/Dec/17/t1229527236ruka05kygbl5awn.htm/, Retrieved Wed, 17 Dec 2008 16:20:47 +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/2008/Dec/17/t1229527236ruka05kygbl5awn.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

6392.3 0 8686.4 0 9244.7 0 8182.7 0 7451.4 0 7988.8 0 8243.5 0 8843 0 9092.7 0 8246.7 0 9311.7 0 8341.2 0 7116.7 0 9635.7 0 9815.4 0 8611.3 0 8297.8 0 8715.1 0 8919.9 0 10085.8 0 9511.7 0 8991.3 0 10311.2 0 8895.4 0 7449.8 0 10084 0 9859.4 0 9100.1 0 8920.8 0 8502.7 0 8599.6 0 10394.4 0 9290.4 0 8742.2 0 10217.3 0 8639 0 8139.6 0 10779.1 0 10427.7 0 10349.1 0 10036.4 0 9492.1 0 10638.8 0 12054.5 0 10324.7 0 11817.3 0 11008.9 0 9996.6 0 9419.5 0 11958.8 0 12594.6 0 11890.6 0 10871.7 0 11835.7 0 11542.2 0 13093.7 0 11180.2 0 12035.7 0 12112 0 10875.2 0 9897.3 0 11672.1 1 12385.7 1 11405.6 1 9830.9 1 11025.1 1 10853.8 1 12252.6 1 11839.4 1 11669.1 1 11601.4 1 11178.4 1 9516.4 1 12102.8 1 12989 1 11610.2 1 10205.5 1 11356.2 1 11307.1 1 12648.6 1 11947.2 1 11714.1 1 12192.5 1 11268.8 1 9097.4 1 12639.8 1 13040.1 1 11687.3 1 11191.7 1 11391.9 1 11793.1 1 13933.2 1 12778.1 1 11810.3 1 13698.4 1 11956.6 1 10723.8 1 13938.9 1 13979.8 1 13807.4 1 12973.9 1 12509.8 1 12934.1 1 14908.3 1 13772.1 1 13012.6 1 14049.9 1 11816.5 1 11593.2 1 14466.2 1 13615.9 1 14733.9 1 13880.7 1 13527.5 1 13584 1 16170.2 1 13260.6 1 14741.9 1 15486.5 1 13154.5 1 12621.2 1

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 9723.01803278689 + 2757.87863387978x[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) 9723.01803278689 185.125033 52.5214 0 0 x 2757.87863387978 262.894929 10.4904 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.69315249945439 R-squared 0.480460387499869 Adjusted R-squared 0.476094508403229 F-TEST (value) 110.048944751965 F-TEST (DF numerator) 1 F-TEST (DF denominator) 119 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1445.87272923845 Sum Squared Residuals 248775205.949497 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6392.3 9723.01803278682 -3330.71803278682 2 8686.4 9723.01803278689 -1036.61803278689 3 9244.7 9723.01803278689 -478.318032786886 4 8182.7 9723.01803278689 -1540.31803278689 5 7451.4 9723.01803278689 -2271.61803278689 6 7988.8 9723.01803278689 -1734.21803278689 7 8243.5 9723.01803278689 -1479.51803278689 8 8843 9723.01803278689 -880.018032786887 9 9092.7 9723.01803278689 -630.318032786886 10 8246.7 9723.01803278689 -1476.31803278689 11 9311.7 9723.01803278689 -411.318032786886 12 8341.2 9723.01803278689 -1381.81803278689 13 7116.7 9723.01803278689 -2606.31803278689 14 9635.7 9723.01803278689 -87.3180327868858 15 9815.4 9723.01803278689 92.3819672131131 16 8611.3 9723.01803278689 -1111.71803278689 17 8297.8 9723.01803278689 -1425.21803278689 18 8715.1 9723.01803278689 -1007.91803278689 19 8919.9 9723.01803278689 -803.118032786887 20 10085.8 9723.01803278689 362.781967213113 21 9511.7 9723.01803278689 -211.318032786886 22 8991.3 9723.01803278689 -731.718032786887 23 10311.2 9723.01803278689 588.181967213114 24 8895.4 9723.01803278689 -827.618032786887 25 7449.8 9723.01803278689 -2273.21803278689 26 10084 9723.01803278689 360.981967213113 27 9859.4 9723.01803278689 136.381967213113 28 9100.1 9723.01803278689 -622.918032786886 29 8920.8 9723.01803278689 -802.218032786887 30 8502.7 9723.01803278689 -1220.31803278689 31 8599.6 9723.01803278689 -1123.41803278689 32 10394.4 9723.01803278689 671.381967213113 33 9290.4 9723.01803278689 -432.618032786887 34 8742.2 9723.01803278689 -980.818032786886 35 10217.3 9723.01803278689 494.281967213113 36 8639 9723.01803278689 -1084.01803278689 37 8139.6 9723.01803278689 -1583.41803278689 38 10779.1 9723.01803278689 1056.08196721311 39 10427.7 9723.01803278689 704.681967213114 40 10349.1 9723.01803278689 626.081967213114 41 10036.4 9723.01803278689 313.381967213113 42 9492.1 9723.01803278689 -230.918032786886 43 10638.8 9723.01803278689 915.781967213113 44 12054.5 9723.01803278689 2331.48196721311 45 10324.7 9723.01803278689 601.681967213114 46 11817.3 9723.01803278689 2094.28196721311 47 11008.9 9723.01803278689 1285.88196721311 48 9996.6 9723.01803278689 273.581967213114 49 9419.5 9723.01803278689 -303.518032786887 50 11958.8 9723.01803278689 2235.78196721311 51 12594.6 9723.01803278689 2871.58196721311 52 11890.6 9723.01803278689 2167.58196721311 53 10871.7 9723.01803278689 1148.68196721311 54 11835.7 9723.01803278689 2112.68196721311 55 11542.2 9723.01803278689 1819.18196721311 56 13093.7 9723.01803278689 3370.68196721311 57 11180.2 9723.01803278689 1457.18196721311 58 12035.7 9723.01803278689 2312.68196721311 59 12112 9723.01803278689 2388.98196721311 60 10875.2 9723.01803278689 1152.18196721311 61 9897.3 9723.01803278689 174.281967213113 62 11672.1 12480.8966666667 -808.796666666666 63 12385.7 12480.8966666667 -95.1966666666661 64 11405.6 12480.8966666667 -1075.29666666667 65 9830.9 12480.8966666667 -2649.99666666667 66 11025.1 12480.8966666667 -1455.79666666667 67 10853.8 12480.8966666667 -1627.09666666667 68 12252.6 12480.8966666667 -228.296666666666 69 11839.4 12480.8966666667 -641.496666666667 70 11669.1 12480.8966666667 -811.796666666666 71 11601.4 12480.8966666667 -879.496666666667 72 11178.4 12480.8966666667 -1302.49666666667 73 9516.4 12480.8966666667 -2964.49666666667 74 12102.8 12480.8966666667 -378.096666666668 75 12989 12480.8966666667 508.103333333333 76 11610.2 12480.8966666667 -870.696666666666 77 10205.5 12480.8966666667 -2275.39666666667 78 11356.2 12480.8966666667 -1124.69666666667 79 11307.1 12480.8966666667 -1173.79666666667 80 12648.6 12480.8966666667 167.703333333334 81 11947.2 12480.8966666667 -533.696666666666 82 11714.1 12480.8966666667 -766.796666666666 83 12192.5 12480.8966666667 -288.396666666667 84 11268.8 12480.8966666667 -1212.09666666667 85 9097.4 12480.8966666667 -3383.49666666667 86 12639.8 12480.8966666667 158.903333333332 87 13040.1 12480.8966666667 559.203333333334 88 11687.3 12480.8966666667 -793.596666666667 89 11191.7 12480.8966666667 -1289.19666666667 90 11391.9 12480.8966666667 -1088.99666666667 91 11793.1 12480.8966666667 -687.796666666666 92 13933.2 12480.8966666667 1452.30333333333 93 12778.1 12480.8966666667 297.203333333334 94 11810.3 12480.8966666667 -670.596666666667 95 13698.4 12480.8966666667 1217.50333333333 96 11956.6 12480.8966666667 -524.296666666666 97 10723.8 12480.8966666667 -1757.09666666667 98 13938.9 12480.8966666667 1458.00333333333 99 13979.8 12480.8966666667 1498.90333333333 100 13807.4 12480.8966666667 1326.50333333333 101 12973.9 12480.8966666667 493.003333333333 102 12509.8 12480.8966666667 28.9033333333325 103 12934.1 12480.8966666667 453.203333333334 104 14908.3 12480.8966666667 2427.40333333333 105 13772.1 12480.8966666667 1291.20333333333 106 13012.6 12480.8966666667 531.703333333334 107 14049.9 12480.8966666667 1569.00333333333 108 11816.5 12480.8966666667 -664.396666666667 109 11593.2 12480.8966666667 -887.696666666666 110 14466.2 12480.8966666667 1985.30333333333 111 13615.9 12480.8966666667 1135.00333333333 112 14733.9 12480.8966666667 2253.00333333333 113 13880.7 12480.8966666667 1399.80333333333 114 13527.5 12480.8966666667 1046.60333333333 115 13584 12480.8966666667 1103.10333333333 116 16170.2 12480.8966666667 3689.30333333333 117 13260.6 12480.8966666667 779.703333333334 118 14741.9 12480.8966666667 2261.00333333333 119 15486.5 12480.8966666667 3005.60333333333 120 13154.5 12480.8966666667 673.603333333333 121 12621.2 12480.8966666667 140.303333333334 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.517272432352664 0.965455135294672 0.482727567647336 6 0.353139982038707 0.706279964077414 0.646860017961293 7 0.228850192714583 0.457700385429165 0.771149807285417 8 0.170199492003075 0.34039898400615 0.829800507996925 9 0.138069644728162 0.276139289456324 0.861930355271838 10 0.083763085496049 0.167526170992098 0.916236914503951 11 0.0752807559770003 0.150561511954001 0.924719244023 12 0.0450123638897919 0.0900247277795837 0.954987636110208 13 0.0544364296397025 0.108872859279405 0.945563570360297 14 0.0649636073298382 0.129927214659676 0.935036392670162 15 0.0786691993883494 0.157338398776699 0.92133080061165 16 0.0531229702046943 0.106245940409389 0.946877029795306 17 0.0360708963718464 0.0721417927436927 0.963929103628154 18 0.0238400707624113 0.0476801415248225 0.976159929237589 19 0.0161789939625880 0.0323579879251759 0.983821006037412 20 0.024600317040841 0.049200634081682 0.97539968295916 21 0.0208784464996795 0.041756892999359 0.97912155350032 22 0.0142496393184909 0.0284992786369818 0.98575036068151 23 0.0219875505326229 0.0439751010652458 0.978012449467377 24 0.0150703481410427 0.0301406962820855 0.984929651858957 25 0.0207510518979398 0.0415021037958797 0.97924894810206 26 0.0239145775570137 0.0478291551140273 0.976085422442986 27 0.0226193631500473 0.0452387263000946 0.977380636849953 28 0.0165374380944475 0.0330748761888949 0.983462561905553 29 0.0120095295567347 0.0240190591134694 0.987990470443265 30 0.00951361015348044 0.0190272203069609 0.99048638984652 31 0.00743876854027244 0.0148775370805449 0.992561231459728 32 0.0105615997010446 0.0211231994020891 0.989438400298955 33 0.00810211334149323 0.0162042266829865 0.991897886658507 34 0.00649169340891269 0.0129833868178254 0.993508306591087 35 0.00751543695491586 0.0150308739098317 0.992484563045084 36 0.00655829916928068 0.0131165983385614 0.99344170083072 37 0.00797056812876229 0.0159411362575246 0.992029431871238 38 0.0142418131012722 0.0284836262025444 0.985758186898728 39 0.0172977404118752 0.0345954808237503 0.982702259588125 40 0.0192294196005144 0.0384588392010289 0.980770580399486 41 0.0184268082967688 0.0368536165935377 0.98157319170323 42 0.016331111287249 0.032662222574498 0.98366888871275 43 0.0202326664474245 0.040465332894849 0.979767333552576 44 0.0652176363874389 0.130435272774878 0.934782363612561 45 0.0634301950393503 0.126860390078701 0.93656980496065 46 0.113259054907448 0.226518109814896 0.886740945092552 47 0.124610891863547 0.249221783727093 0.875389108136453 48 0.114785143826673 0.229570287653346 0.885214856173327 49 0.113159963357320 0.226319926714641 0.88684003664268 50 0.172141165239764 0.344282330479527 0.827858834760236 51 0.298893535542685 0.59778707108537 0.701106464457315 52 0.35260651004552 0.70521302009104 0.64739348995448 53 0.340857530722813 0.681715061445625 0.659142469277187 54 0.378280363834077 0.756560727668153 0.621719636165923 55 0.389556776952487 0.779113553904974 0.610443223047513 56 0.550715281972571 0.898569436054858 0.449284718027429 57 0.53177438550247 0.93645122899506 0.46822561449753 58 0.564392626632603 0.871214746734794 0.435607373367397 59 0.608644670987836 0.782710658024327 0.391355329012164 60 0.579265657260682 0.841468685478636 0.420734342739318 61 0.527608516004012 0.944782967991975 0.472391483995988 62 0.483386252022367 0.966772504044734 0.516613747977633 63 0.434325721332472 0.868651442664944 0.565674278667528 64 0.400792405093584 0.801584810187169 0.599207594906416 65 0.477603476559289 0.955206953118578 0.522396523440711 66 0.458438733497874 0.916877466995748 0.541561266502126 67 0.450006225106018 0.900012450212035 0.549993774893982 68 0.410792588518352 0.821585177036704 0.589207411481648 69 0.370859652933506 0.741719305867013 0.629140347066494 70 0.334976422263034 0.669952844526069 0.665023577736966 71 0.302455518434575 0.604911036869151 0.697544481565425 72 0.286239230517624 0.572478461035249 0.713760769482376 73 0.420823844451594 0.841647688903188 0.579176155548406 74 0.382986840678355 0.76597368135671 0.617013159321645 75 0.358593271300515 0.71718654260103 0.641406728699485 76 0.330459856293418 0.660919712586837 0.669540143706582 77 0.406374690031632 0.812749380063265 0.593625309968368 78 0.394502673577497 0.789005347154993 0.605497326422503 79 0.388394064712304 0.776788129424607 0.611605935287696 80 0.352616724608891 0.705233449217781 0.64738327539111 81 0.321972232672888 0.643944465345777 0.678027767327112 82 0.300810373102688 0.601620746205377 0.699189626897312 83 0.268555270822712 0.537110541645424 0.731444729177288 84 0.274461093838937 0.548922187677873 0.725538906161064 85 0.618578720452723 0.762842559094554 0.381421279547277 86 0.582613952749578 0.834772094500845 0.417386047250423 87 0.546584555009112 0.906830889981776 0.453415444990888 88 0.548166913365681 0.903666173268638 0.451833086634319 89 0.60534735449648 0.78930529100704 0.39465264550352 90 0.651904057306959 0.696191885386082 0.348095942693041 91 0.668568640849326 0.662862718301348 0.331431359150674 92 0.659764304582288 0.680471390835425 0.340235695417712 93 0.622709731365138 0.754580537269724 0.377290268634862 94 0.6450971896992 0.709805620601601 0.354902810300800 95 0.613267134830252 0.773465730339497 0.386732865169748 96 0.628373435058149 0.743253129883702 0.371626564941851 97 0.831133544570488 0.337732910859023 0.168866455429512 98 0.807736216946393 0.384527566107214 0.192263783053607 99 0.780992618122959 0.438014763754082 0.219007381877041 100 0.743228461890353 0.513543076219294 0.256771538109647 101 0.703153150185525 0.593693699628951 0.296846849814475 102 0.690895233554798 0.618209532890404 0.309104766445202 103 0.652372864727412 0.695254270545176 0.347627135272588 104 0.671802636411664 0.656394727176671 0.328197363588336 105 0.609075731208675 0.78184853758265 0.390924268791325 106 0.553586704075873 0.892826591848253 0.446413295924127 107 0.488316267806116 0.976632535612232 0.511683732193884 108 0.579788104286155 0.84042379142769 0.420211895713845 109 0.776253215413243 0.447493569173513 0.223746784586757 110 0.71666194648597 0.566676107028061 0.283338053514030 111 0.638648545462175 0.72270290907565 0.361351454537825 112 0.574336040679168 0.851327918641663 0.425663959320832 113 0.464069215553379 0.928138431106759 0.53593078444662 114 0.36216697165152 0.72433394330304 0.63783302834848 115 0.261634859333202 0.523269718666404 0.738365140666798 116 0.419245746623718 0.838491493247437 0.580754253376282 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 26 0.232142857142857 NOK 10% type I error level 28 0.25 NOK

Charts produced by software:

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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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Software written by Ed van Stee & Patrick Wessa

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