*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: Tue, 30 Nov 2010 09:47:16 +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/30/t1291110412gmbydzmv7e1it4e.htm/, Retrieved Tue, 30 Nov 2010 10:47:02 +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/30/t1291110412gmbydzmv7e1it4e.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 «

31.514 0 27.071 0 29.462 0 26.105 0 22.397 0 23.843 0 21.705 0 18.089 0 20.764 0 25.316 0 17.704 0 15.548 0 28.029 0 29.383 0 36.438 0 32.034 0 22.679 0 24.319 0 18.004 0 17.537 0 20.366 0 22.782 0 19.169 0 13.807 0 29.743 0 25.591 0 29.096 0 26.482 0 22.405 0 27.044 0 17.970 0 18.730 0 19.684 0 19.785 0 18.479 0 10.698 0 31.956 0 29.506 0 34.506 0 27.165 0 26.736 0 23.691 0 18.157 0 17.328 0 18.205 0 20.995 0 17.382 0 9.367 0 31.124 0 26.551 0 30.651 0 25.859 0 25.100 0 25.778 0 20.418 0 18.688 0 20.424 0 24.776 0 19.814 0 12.738 0 31.566 0 30.111 0 30.019 0 31.934 0 25.826 0 26.835 0 20.205 0 17.789 0 20.520 1 22.518 1 15.572 1 11.509 1 25.447 1 24.090 1 27.786 1 26.195 1 20.516 1 22.759 1 19.028 1 16.971 1 20.036 1 22.485 1 18.730 1 14.538 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 13.0633214285714 -1.61912500000001X[t] + 17.0792678571429M1[t] + 14.6398392857143M2[t] + 18.3048392857143M3[t] + 15.1356964285714M4[t] + 10.8335535714285M5[t] + 12.0635535714286M6[t] + 6.52326785714285M7[t] + 5.04398214285713M8[t] + 7.39914285714285M9[t] + 10.0645714285714M10[t] + 5.52071428571428M11[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) 13.0633214285714 0.750462 17.407 0 0 X -1.61912500000001 0.547688 -2.9563 0.004224 0.002112 M1 17.0792678571429 1.04093 16.4077 0 0 M2 14.6398392857143 1.04093 14.0642 0 0 M3 18.3048392857143 1.04093 17.5851 0 0 M4 15.1356964285714 1.04093 14.5405 0 0 M5 10.8335535714285 1.04093 10.4076 0 0 M6 12.0635535714286 1.04093 11.5892 0 0 M7 6.52326785714285 1.04093 6.2668 0 0 M8 5.04398214285713 1.04093 4.8456 7e-06 4e-06 M9 7.39914285714285 1.037986 7.1284 0 0 M10 10.0645714285714 1.037986 9.6963 0 0 M11 5.52071428571428 1.037986 5.3187 1e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.949194746949424 R-squared 0.90097066763638 Adjusted R-squared 0.8842333156876 F-TEST (value) 53.829940984303 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.94189343526714 Sum Squared Residuals 267.737458089287 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 31.514 30.1425892857142 1.37141071428583 2 27.071 27.7031607142857 -0.632160714285683 3 29.462 31.3681607142858 -1.9061607142858 4 26.105 28.1990178571430 -2.09401785714296 5 22.397 23.8968750000001 -1.49987500000005 6 23.843 25.126875 -1.283875 7 21.705 19.5865892857143 2.11841071428573 8 18.089 18.1073035714286 -0.0183035714285787 9 20.764 20.4624642857143 0.30153571428572 10 25.316 23.1278928571429 2.18810714285715 11 17.704 18.5840357142857 -0.88003571428572 12 15.548 13.0633214285714 2.48467857142857 13 28.029 30.1425892857143 -2.1135892857143 14 29.383 27.7031607142857 1.67983928571428 15 36.438 31.3681607142857 5.0698392857143 16 32.034 28.1990178571428 3.83498214285716 17 22.679 23.896875 -1.21787499999999 18 24.319 25.126875 -0.807875000000001 19 18.004 19.5865892857143 -1.58258928571429 20 17.537 18.1073035714286 -0.570303571428571 21 20.366 20.4624642857143 -0.0964642857142865 22 22.782 23.1278928571429 -0.345892857142858 23 19.169 18.5840357142857 0.584964285714287 24 13.807 13.0633214285714 0.743678571428565 25 29.743 30.1425892857143 -0.399589285714304 26 25.591 27.7031607142857 -2.11216071428572 27 29.096 31.3681607142857 -2.2721607142857 28 26.482 28.1990178571428 -1.71701785714284 29 22.405 23.896875 -1.49187499999999 30 27.044 25.126875 1.917125 31 17.97 19.5865892857143 -1.61658928571429 32 18.73 18.1073035714286 0.62269642857143 33 19.684 20.4624642857143 -0.778464285714285 34 19.785 23.1278928571429 -3.34289285714286 35 18.479 18.5840357142857 -0.105035714285713 36 10.698 13.0633214285714 -2.36532142857143 37 31.956 30.1425892857143 1.81341071428570 38 29.506 27.7031607142857 1.80283928571428 39 34.506 31.3681607142857 3.1378392857143 40 27.165 28.1990178571428 -1.03401785714284 41 26.736 23.896875 2.83912500000001 42 23.691 25.126875 -1.435875 43 18.157 19.5865892857143 -1.42958928571429 44 17.328 18.1073035714286 -0.779303571428571 45 18.205 20.4624642857143 -2.25746428571429 46 20.995 23.1278928571429 -2.13289285714286 47 17.382 18.5840357142857 -1.20203571428571 48 9.367 13.0633214285714 -3.69632142857143 49 31.124 30.1425892857143 0.981410714285696 50 26.551 27.7031607142857 -1.15216071428572 51 30.651 31.3681607142857 -0.7171607142857 52 25.859 28.1990178571428 -2.34001785714284 53 25.1 23.896875 1.20312500000001 54 25.778 25.126875 0.651124999999998 55 20.418 19.5865892857143 0.831410714285713 56 18.688 18.1073035714286 0.580696428571428 57 20.424 20.4624642857143 -0.0384642857142866 58 24.776 23.1278928571429 1.64810714285714 59 19.814 18.5840357142857 1.22996428571429 60 12.738 13.0633214285714 -0.325321428571436 61 31.566 30.1425892857143 1.42341071428570 62 30.111 27.7031607142857 2.40783928571428 63 30.019 31.3681607142857 -1.3491607142857 64 31.934 28.1990178571428 3.73498214285716 65 25.826 23.896875 1.92912500000001 66 26.835 25.126875 1.708125 67 20.205 19.5865892857143 0.618410714285712 68 17.789 18.1073035714286 -0.318303571428569 69 20.52 18.8433392857143 1.67666071428571 70 22.518 21.5087678571429 1.00923214285714 71 15.572 16.9649107142857 -1.39291071428571 72 11.509 11.4441964285714 0.0648035714285629 73 25.447 28.5234642857143 -3.07646428571431 74 24.09 26.0840357142857 -1.99403571428572 75 27.786 29.7490357142857 -1.96303571428570 76 26.195 26.5798928571428 -0.384892857142838 77 20.516 22.27775 -1.76174999999999 78 22.759 23.50775 -0.748749999999998 79 19.028 17.9674642857143 1.06053571428571 80 16.971 16.4881785714286 0.482821428571429 81 20.036 18.8433392857143 1.19266071428571 82 22.485 21.5087678571429 0.976232142857142 83 18.73 16.9649107142857 1.76508928571429 84 14.538 11.4441964285714 3.09380357142856 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.996452077148394 0.0070958457032111 0.00354792285160555 17 0.991388538875865 0.0172229222482710 0.00861146112413552 18 0.981624836201743 0.0367503275965147 0.0183751637982573 19 0.981091124784194 0.0378177504316111 0.0189088752158056 20 0.965184086533379 0.0696318269332425 0.0348159134666213 21 0.94014138456684 0.119717230866318 0.059858615433159 22 0.922504861198616 0.154990277602769 0.0774951388013844 23 0.889021227912969 0.221957544174062 0.110978772087031 24 0.855952620916544 0.288094758166912 0.144047379083456 25 0.798584199691953 0.402831600616094 0.201415800308047 26 0.79797920393451 0.404041592130979 0.202020796065489 27 0.837919859971912 0.324160280056176 0.162080140028088 28 0.827053913887561 0.345892172224879 0.172946086112439 29 0.791747573420803 0.416504853158394 0.208252426579197 30 0.79479489702622 0.410410205947560 0.205205102973780 31 0.768451037076802 0.463097925846396 0.231548962923198 32 0.710491989340525 0.579016021318951 0.289508010659475 33 0.64890818020313 0.70218363959374 0.35109181979687 34 0.760146344545893 0.479707310908214 0.239853655454107 35 0.696503505810225 0.60699298837955 0.303496494189775 36 0.743224461099177 0.513551077801645 0.256775538900823 37 0.726054222146344 0.547891555707312 0.273945777853656 38 0.708049795362201 0.583900409275598 0.291950204637799 39 0.790174599681153 0.419650800637693 0.209825400318847 40 0.74810434324724 0.503791313505519 0.251895656752759 41 0.800956967524654 0.398086064950691 0.199043032475346 42 0.774743978443535 0.45051204311293 0.225256021556465 43 0.751867495675459 0.496265008649083 0.248132504324541 44 0.699079762558832 0.601840474882336 0.300920237441168 45 0.730722744443732 0.538554511112535 0.269277255556268 46 0.772436835955547 0.455126328088905 0.227563164044453 47 0.75037437209353 0.499251255812939 0.249625627906470 48 0.918036784486678 0.163926431026644 0.081963215513322 49 0.894239485433059 0.211521029133882 0.105760514566941 50 0.877764105225785 0.24447178954843 0.122235894774215 51 0.841689272042906 0.316621455914188 0.158310727957094 52 0.931563338692568 0.136873322614864 0.068436661307432 53 0.905099462385064 0.189801075229873 0.0949005376149363 54 0.867456033996794 0.265087932006411 0.132543966003206 55 0.824190700206719 0.351618599586563 0.175809299793281 56 0.763249642796169 0.473500714407662 0.236750357203831 57 0.782816464911793 0.434367070176413 0.217183535088207 58 0.739691417513658 0.520617164972683 0.260308582486342 59 0.671127437078936 0.657745125842128 0.328872562921064 60 0.796128995997742 0.407742008004515 0.203871004002258 61 0.78055036207199 0.438899275856019 0.219449637928009 62 0.791340317625888 0.417319364748225 0.208659682374112 63 0.726352692751407 0.547294614497186 0.273647307248593 64 0.76114872695177 0.47770254609646 0.23885127304823 65 0.786223290055095 0.427553419889809 0.213776709944905 66 0.790677879610403 0.418644240779194 0.209322120389597 67 0.660526496930659 0.678947006138682 0.339473503069341 68 0.487497997955959 0.974995995911917 0.512502002044041 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0188679245283019 NOK 5% type I error level 4 0.0754716981132075 NOK 10% type I error level 5 0.0943396226415094 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/10zrlb1291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/10zrlb1291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/13inl1291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/13inl1291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/23inl1291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/23inl1291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/33inl1291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/33inl1291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/4e9m61291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/4e9m61291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/5e9m61291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/5e9m61291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/6e9m61291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/6e9m61291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/7p0mq1291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/7p0mq1291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/8zrlb1291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/8zrlb1291110428.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/9zrlb1291110428.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291110412gmbydzmv7e1it4e/9zrlb1291110428.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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