Multiple Linear Regression

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Sun, 23 Nov 2008 06:07:15 -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/Nov/23/t1227446460wtmx32nlsfmwux2.htm/, Retrieved Sun, 23 Nov 2008 13:21:01 +0000

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/Nov/23/t1227446460wtmx32nlsfmwux2.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}, }

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Dataseries X:

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96,5 0 97,3 0 122 0 91 0 107,9 0 114,6 0 98 0 95,5 0 98,7 0 115,9 0 110,4 0 109,5 0 92,3 0 102,1 0 112,8 0 110,2 0 98,9 0 119 0 104,3 0 98,8 0 109,4 1 170,3 1 118 1 116,9 1 111,7 1 116,8 1 116,1 1 114,8 1 110,8 1 122,8 1 104,7 1 86 1 127,2 1 126,1 1 114,6 1 127,8 1 105,2 1 113,1 1 161 1 126,9 1 117,7 1 144,9 1 119,4 1 107,1 1 142,8 1 126,2 1 126,9 1 179,2 1 105,3 1 114,8 1 125,4 1 113,2 1 134,4 1 150 1 100,9 1 101,8 1 137,7 1 138,7 1 135,4 1 153,8 1 119,5 1 123,3 1 166,4 1 137,5 1 142,2 1 167 1 112,3 1 120,6 1 154,9 1 153,4 1 156,2 1 175,8 1 131,7 1 130,1 1 161,1 1 128,2 1 140,3 1 174,9 1 111,8 1 136,6 1 166,1 1 159,4 1 168,2 1 154,6 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 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Productie_Medische_apparatuur[t] = + 116.875474525474 -0.381993006993004Dummy[t] -29.9349275724276M1[t] -25.4925574425575M2[t] -2.19304445554446M3[t] -23.2221028971029M4[t] -19.4797327672328M5[t] + 0.062637362637359M6[t] -35.0807067932068M7[t] -36.3954795204795M8[t] -9.7413961038961M9[t] -2.74188311688312M10[t] -11.9566558441558M11[t] + 0.600487012987013t + 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) 116.875474525474 5.372496 21.7544 0 0 Dummy -0.381993006993004 4.617514 -0.0827 0.934305 0.467152 M1 -29.9349275724276 6.46882 -4.6276 1.7e-05 8e-06 M2 -25.4925574425575 6.464264 -3.9436 0.000188 9.4e-05 M3 -2.19304445554446 6.460719 -0.3394 0.735292 0.367646 M4 -23.2221028971029 6.458185 -3.5958 0.000598 0.000299 M5 -19.4797327672328 6.456664 -3.017 0.003557 0.001779 M6 0.062637362637359 6.456157 0.0097 0.992287 0.496143 M7 -35.0807067932068 6.456664 -5.4333 1e-06 0 M8 -36.3954795204795 6.458185 -5.6356 0 0 M9 -9.7413961038961 6.445266 -1.5114 0.135187 0.067594 M10 -2.74188311688312 6.442726 -0.4256 0.671721 0.33586 M11 -11.9566558441558 6.441201 -1.8563 0.067623 0.033811 t 0.600487012987013 0.080914 7.4213 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.876612420107282 R-squared 0.768449335086346 Adjusted R-squared 0.725447068745238 F-TEST (value) 17.8699729216774 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.0494329899339 Sum Squared Residuals 10163.2184765235 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 96.5 87.541033966034 8.95896603396595 2 97.3 92.5838911088911 4.71610889110891 3 122 116.483891108891 5.51610889110888 4 91 96.0553196803197 -5.05531968031967 5 107.9 100.398176823177 7.50182317682318 6 114.6 120.541033966034 -5.94103396603398 7 98 85.9981768231768 12.0018231768232 8 95.5 85.2838911088911 10.2161088911089 9 98.7 112.538461538462 -13.8384615384616 10 115.9 120.138461538462 -4.23846153846153 11 110.4 111.524175824176 -1.12417582417582 12 109.5 124.081318681319 -14.5813186813187 13 92.3 94.7468781218781 -2.44687812187811 14 102.1 99.7897352647352 2.31026473526475 15 112.8 123.689735264735 -10.8897352647353 16 110.2 103.261163836164 6.93883616383617 17 98.9 107.604020979021 -8.70402097902097 18 119 127.746878121878 -8.74687812187812 19 104.3 93.204020979021 11.0959790209790 20 98.8 92.4897352647352 6.31026473526475 21 109.4 119.362312687313 -9.96231268731268 22 170.3 126.962312687313 43.3376873126873 23 118 118.348026973027 -0.348026973026965 24 116.9 130.905169830170 -14.0051698301698 25 111.7 101.570729270729 10.1292707292707 26 116.8 106.613586413586 10.1864135864136 27 116.1 130.513586413586 -14.4135864135864 28 114.8 110.085014985015 4.71498501498501 29 110.8 114.427872127872 -3.62787212787213 30 122.8 134.570729270729 -11.7707292707293 31 104.7 100.027872127872 4.67212787212788 32 86 99.3135864135864 -13.3135864135864 33 127.2 126.568156843157 0.631843156843158 34 126.1 134.168156843157 -8.06815684315685 35 114.6 125.553871128871 -10.9538711288711 36 127.8 138.111013986014 -10.311013986014 37 105.2 108.776573426573 -3.57657342657341 38 113.1 113.819430569431 -0.719430569430576 39 161 137.719430569431 23.2805694305694 40 126.9 117.290859140859 9.60914085914086 41 117.7 121.633716283716 -3.93371628371628 42 144.9 141.776573426573 3.12342657342658 43 119.4 107.233716283716 12.1662837162837 44 107.1 106.519430569431 0.580569430569429 45 142.8 133.774000999001 9.025999000999 46 126.2 141.374000999001 -15.174000999001 47 126.9 132.759715284715 -5.85971528471529 48 179.2 145.316858141858 33.8831418581419 49 105.3 115.982417582418 -10.6824175824176 50 114.8 121.025274725275 -6.22527472527473 51 125.4 144.925274725275 -19.5252747252747 52 113.2 124.496703296703 -11.2967032967033 53 134.4 128.839560439560 5.56043956043956 54 150 148.982417582418 1.01758241758241 55 100.9 114.439560439560 -13.5395604395604 56 101.8 113.725274725275 -11.9252747252747 57 137.7 140.979845154845 -3.27984515484517 58 138.7 148.579845154845 -9.87984515484518 59 135.4 139.965559440559 -4.56555944055944 60 153.8 152.522702297702 1.27729770229771 61 119.5 123.188261738262 -3.68826173826172 62 123.3 128.231118881119 -4.93111888111889 63 166.4 152.131118881119 14.2688811188811 64 137.5 131.702547452547 5.79745254745255 65 142.2 136.045404595405 6.15459540459539 66 167 156.188261738262 10.8117382617383 67 112.3 121.645404595405 -9.3454045954046 68 120.6 120.931118881119 -0.331118881118888 69 154.9 148.185689310689 6.7143106893107 70 153.4 155.785689310689 -2.38568931068930 71 156.2 147.171403596404 9.0285964035964 72 175.8 159.728546453546 16.0714535464536 73 131.7 130.394105894106 1.30589410589411 74 130.1 135.436963036963 -5.33696303696304 75 161.1 159.336963036963 1.76303696303697 76 128.2 138.908391608392 -10.7083916083916 77 140.3 143.251248751249 -2.95124875124874 78 174.9 163.394105894106 11.5058941058941 79 111.8 128.851248751249 -17.0512487512488 80 136.6 128.136963036963 8.46303696303696 81 166.1 155.391533466533 10.7084665334665 82 159.4 162.991533466533 -3.59153346653345 83 168.2 154.377247752248 13.8227522477522 84 154.6 166.934390609391 -12.3343906093906 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.367518610959830 0.735037221919661 0.63248138904017 18 0.229530844113327 0.459061688226654 0.770469155886673 19 0.133053618761601 0.266107237523201 0.8669463812384 20 0.067638628731271 0.135277257462542 0.93236137126873 21 0.0337157628025035 0.067431525605007 0.966284237197496 22 0.382414152682453 0.764828305364905 0.617585847317547 23 0.437718427679827 0.875436855359654 0.562281572320173 24 0.443401002379271 0.886802004758541 0.556598997620729 25 0.377157784713234 0.754315569426468 0.622842215286766 26 0.320425406713366 0.640850813426733 0.679574593286634 27 0.418175526182759 0.836351052365518 0.581824473817241 28 0.341224275991316 0.682448551982631 0.658775724008684 29 0.284173820588544 0.568347641177087 0.715826179411456 30 0.266770324265750 0.533540648531501 0.73322967573425 31 0.248857678818236 0.497715357636472 0.751142321181764 32 0.358890743465555 0.71778148693111 0.641109256534445 33 0.378574720355999 0.757149440711998 0.621425279644001 34 0.449416171288129 0.898832342576257 0.550583828711871 35 0.40634101307151 0.81268202614302 0.59365898692849 36 0.43654623700205 0.8730924740041 0.56345376299795 37 0.361455324474992 0.722910648949984 0.638544675525008 38 0.293766956157650 0.587533912315299 0.70623304384235 39 0.656905024816885 0.686189950366231 0.343094975183115 40 0.660590108417046 0.678819783165908 0.339409891582954 41 0.595076454331136 0.809847091337728 0.404923545668864 42 0.56418774077467 0.87162451845066 0.43581225922533 43 0.672665896266037 0.654668207467925 0.327334103733963 44 0.606757705175955 0.78648458964809 0.393242294824045 45 0.616324397786585 0.76735120442683 0.383675602213415 46 0.654434251806064 0.691131496387873 0.345565748193936 47 0.603856191116611 0.792287617766778 0.396143808883389 48 0.979189236546012 0.0416215269079767 0.0208107634539883 49 0.972580082178897 0.0548398356422055 0.0274199178211028 50 0.960201922264374 0.0795961554712514 0.0397980777356257 51 0.986051342631095 0.0278973147378111 0.0139486573689055 52 0.980202889817215 0.0395942203655705 0.0197971101827853 53 0.97265116023113 0.0546976795377403 0.0273488397688702 54 0.962227239769853 0.075545520460293 0.0377727602301465 55 0.95066832744899 0.0986633451020194 0.0493316725510097 56 0.951866496174132 0.0962670076517366 0.0481335038258683 57 0.94479765719826 0.110404685603479 0.0552023428017394 58 0.925078800411006 0.149842399177989 0.0749211995889943 59 0.958600901893057 0.0827981962138868 0.0413990981069434 60 0.938062394970874 0.123875210058253 0.0619376050291265 61 0.915539073827771 0.168921852344457 0.0844609261722286 62 0.866576215074504 0.266847569850991 0.133423784925496 63 0.826990037391349 0.346019925217303 0.173009962608651 64 0.793564760259806 0.412870479480388 0.206435239740194 65 0.698519323732766 0.602961352534469 0.301480676267234 66 0.573556186129229 0.852887627741542 0.426443813870771 67 0.420864887795948 0.841729775591896 0.579135112204052 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 3 0.0588235294117647 NOK 10% type I error level 11 0.215686274509804 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/10j84z1227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/10j84z1227445622.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/110ys1227445621.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/110ys1227445621.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/20l0u1227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/20l0u1227445622.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/319eb1227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/319eb1227445622.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/4epfy1227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/4epfy1227445622.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/5wskl1227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/5wskl1227445622.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/6wq3p1227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/6wq3p1227445622.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/7znk01227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/7znk01227445622.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/8r6p31227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/8r6p31227445622.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/9hgi01227445622.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227446460wtmx32nlsfmwux2/9hgi01227445622.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

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

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

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