Seatbelt Law

*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: Wed, 26 Nov 2008 13:46:12 -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/26/t1227733339w9rfwwc2pfyhxha.htm/, Retrieved Wed, 26 Nov 2008 21:02:19 +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/26/t1227733339w9rfwwc2pfyhxha.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Q3: with dummies

Dataseries X:

» Textbox « » Textfile « » CSV «

98,5 0 96,7 0 113,1 0 100 0 104,7 0 108,5 0 90,5 0 88,6 0 105,4 0 119,9 0 107,2 0 84,1 0 101,4 0 105,1 0 118,7 0 113,8 0 113,8 0 118,9 0 98,5 0 91 0 120,7 0 127,9 0 112,4 0 93,1 0 107,5 0 107,3 0 114,8 0 120,8 0 112,2 0 123,3 0 100,6 0 86,7 0 123,6 0 125,3 0 111,1 0 98,4 0 102,3 0 105 0 128,2 0 124,7 0 116,1 0 131,2 0 97,7 0 88,8 0 132,8 0 113,9 0 112,6 1 104,3 1 107,5 1 106 1 117,3 1 123,1 1 114,3 1 132 1 92,3 1 93,7 1 121,3 1 113,6 1 116,3 1 98,3 1 111,9 1 109,3 1 133,2 1 118 1 131,6 1 134,1 1 96,7 1 99,8 1 128,3 1 134,9 1 130,7 1 107,3 1 121,6 1 120,6 1 140,5 1 124,8 1 129,9 1 159,4 1 111 1 110,1 1 132,7 1 135 1 118,6 1 94 1 117,9 1 114,7 1 113,6 1 130,6 1 117,1 1 123,2 1 106,1 1 87,9 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 8 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 86.4631868131868 -1.87596153846154X[t] + 12.5862637362637M1[t] + 11.8554258241758M2[t] + 25.9495879120879M3[t] + 22.75625M4[t] + 20.5004120879121M5[t] + 31.6195741758242M6[t] + 1.72623626373626M7[t] -4.36710164835165M8[t] + 26.9334478021978M9[t] + 27.5043956043956M10[t] + 18.7290521978022M11[t] + 0.243337912087912t + 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) 86.4631868131868 3.03081 28.5281 0 0 X -1.87596153846154 2.970788 -0.6315 0.52958 0.26479 M1 12.5862637362637 3.63192 3.4655 0.000864 0.000432 M2 11.8554258241758 3.630886 3.2652 0.001626 0.000813 M3 25.9495879120879 3.630714 7.1472 0 0 M4 22.75625 3.631403 6.2665 0 0 M5 20.5004120879121 3.632953 5.6429 0 0 M6 31.6195741758242 3.635362 8.6978 0 0 M7 1.72623626373626 3.638629 0.4744 0.636526 0.318263 M8 -4.36710164835165 3.642752 -1.1988 0.234217 0.117109 M9 26.9334478021978 3.75943 7.1642 0 0 M10 27.5043956043956 3.762838 7.3095 0 0 M11 18.7290521978022 3.748601 4.9963 3e-06 2e-06 t 0.243337912087912 0.055919 4.3516 4.1e-05 2e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.89006531777351 R-squared 0.79221626990326 Adjusted R-squared 0.75758564822047 F-TEST (value) 22.8761781165759 F-TEST (DF numerator) 13 F-TEST (DF denominator) 78 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.0122100219102 Sum Squared Residuals 3835.34497252747 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.5 99.2927884615385 -0.792788461538534 2 96.7 98.8052884615385 -2.10528846153846 3 113.1 113.142788461538 -0.0427884615384666 4 100 110.192788461538 -10.1927884615385 5 104.7 108.180288461538 -3.48028846153846 6 108.5 119.542788461538 -11.0427884615385 7 90.5 89.8927884615385 0.607211538461545 8 88.6 84.0427884615385 4.55721153846154 9 105.4 115.586675824176 -10.1866758241758 10 119.9 116.400961538462 3.49903846153846 11 107.2 107.868956043956 -0.668956043956028 12 84.1 89.3832417582418 -5.28324175824176 13 101.4 102.212843406593 -0.812843406593395 14 105.1 101.725343406593 3.37465659340658 15 118.7 116.062843406593 2.6371565934066 16 113.8 113.112843406593 0.687156593406594 17 113.8 111.100343406593 2.69965659340659 18 118.9 122.462843406593 -3.56284340659340 19 98.5 92.8128434065934 5.68715659340659 20 91 86.9628434065934 4.0371565934066 21 120.7 118.506730769231 2.19326923076923 22 127.9 119.321016483516 8.57898351648352 23 112.4 110.789010989011 1.61098901098902 24 93.1 92.3032967032967 0.796703296703296 25 107.5 105.132898351648 2.36710164835166 26 107.3 104.645398351648 2.65460164835165 27 114.8 118.982898351648 -4.18289835164835 28 120.8 116.032898351648 4.76710164835165 29 112.2 114.020398351648 -1.82039835164835 30 123.3 125.382898351648 -2.08289835164835 31 100.6 95.7328983516483 4.86710164835165 32 86.7 89.8828983516484 -3.18289835164835 33 123.6 121.426785714286 2.17321428571427 34 125.3 122.241071428571 3.05892857142856 35 111.1 113.709065934066 -2.60906593406594 36 98.4 95.2233516483517 3.17664835164836 37 102.3 108.052953296703 -5.75295329670329 38 105 107.565453296703 -2.56545329670330 39 128.2 121.902953296703 6.2970467032967 40 124.7 118.952953296703 5.74704670329671 41 116.1 116.940453296703 -0.8404532967033 42 131.2 128.302953296703 2.8970467032967 43 97.7 98.6529532967033 -0.952953296703295 44 88.8 92.8029532967033 -4.0029532967033 45 132.8 124.346840659341 8.45315934065935 46 113.9 125.161126373626 -11.2611263736264 47 112.6 114.753159340659 -2.15315934065935 48 104.3 96.267445054945 8.03255494505495 49 107.5 109.097046703297 -1.59704670329669 50 106 108.609546703297 -2.6095467032967 51 117.3 122.947046703297 -5.6470467032967 52 123.1 119.997046703297 3.10295329670330 53 114.3 117.984546703297 -3.6845467032967 54 132 129.347046703297 2.6529532967033 55 92.3 99.6970467032967 -7.3970467032967 56 93.7 93.8470467032967 -0.1470467032967 57 121.3 125.390934065934 -4.09093406593407 58 113.6 126.205219780220 -12.6052197802198 59 116.3 117.673214285714 -1.37321428571429 60 98.3 99.1875 -0.887500000000006 61 111.9 112.017101648352 -0.117101648351631 62 109.3 111.529601648352 -2.22960164835165 63 133.2 125.867101648352 7.33289835164835 64 118 122.917101648352 -4.91710164835164 65 131.6 120.904601648352 10.6953983516483 66 134.1 132.267101648352 1.83289835164834 67 96.7 102.617101648352 -5.91710164835165 68 99.8 96.7671016483517 3.03289835164834 69 128.3 128.310989010989 -0.0109890109889980 70 134.9 129.125274725275 5.77472527472528 71 130.7 120.593269230769 10.1067307692308 72 107.3 102.107554945055 5.19244505494506 73 121.6 114.937156593407 6.66284340659341 74 120.6 114.449656593407 6.1503434065934 75 140.5 128.787156593407 11.7128434065934 76 124.8 125.837156593407 -1.03715659340659 77 129.9 123.824656593407 6.07534340659341 78 159.4 135.187156593407 24.2128434065934 79 111 105.537156593407 5.46284340659341 80 110.1 99.6871565934066 10.4128434065934 81 132.7 131.231043956044 1.46895604395603 82 135 132.045329670330 2.95467032967033 83 118.6 123.513324175824 -4.91332417582418 84 94 105.027609890110 -11.0276098901099 85 117.9 117.857211538462 0.0427884615384749 86 114.7 117.369711538462 -2.66971153846153 87 113.6 131.707211538462 -18.1072115384615 88 130.6 128.757211538462 1.84278846153846 89 117.1 126.744711538462 -9.64471153846155 90 123.2 138.107211538462 -14.9072115384615 91 106.1 108.457211538462 -2.35721153846155 92 87.9 102.607211538462 -14.7072115384615 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.109344165877023 0.218688331754046 0.890655834122977 18 0.0459035210234632 0.0918070420469264 0.954096478976537 19 0.0157682939157273 0.0315365878314545 0.984231706084273 20 0.0128281328579945 0.0256562657159891 0.987171867142005 21 0.0155345526424284 0.0310691052848568 0.984465447357572 22 0.00663777109210143 0.0132755421842029 0.993362228907899 23 0.0030967925278702 0.0061935850557404 0.99690320747213 24 0.00114618642890730 0.00229237285781459 0.998853813571093 25 0.000749552470185909 0.00149910494037182 0.999250447529814 26 0.000525737262486214 0.00105147452497243 0.999474262737514 27 0.00335868042649025 0.00671736085298051 0.99664131957351 28 0.00230061261854098 0.00460122523708196 0.99769938738146 29 0.00204445284471766 0.00408890568943532 0.997955547155282 30 0.00100420149626354 0.00200840299252708 0.998995798503736 31 0.000534956487216493 0.00106991297443299 0.999465043512784 32 0.00183611581966136 0.00367223163932272 0.998163884180339 33 0.000988978691751881 0.00197795738350376 0.999011021308248 34 0.000866225604587472 0.00173245120917494 0.999133774395413 35 0.000673435172869051 0.00134687034573810 0.999326564827131 36 0.000358395547659675 0.00071679109531935 0.99964160445234 37 0.000644560817606852 0.00128912163521370 0.999355439182393 38 0.000532662154066942 0.00106532430813388 0.999467337845933 39 0.000381777679517856 0.000763555359035712 0.999618222320482 40 0.000270985801497855 0.00054197160299571 0.999729014198502 41 0.000142598584968416 0.000285197169936832 0.999857401415032 42 0.000102686412090273 0.000205372824180546 0.99989731358791 43 0.000100332441809728 0.000200664883619455 0.99989966755819 44 0.000107277133395112 0.000214554266790224 0.999892722866605 45 0.000179579965351060 0.000359159930702121 0.999820420034649 46 0.0016788520229157 0.0033577040458314 0.998321147977084 47 0.000978422199282355 0.00195684439856471 0.999021577800718 48 0.000924897859103907 0.00184979571820781 0.999075102140896 49 0.000578964392203313 0.00115792878440663 0.999421035607797 50 0.000380454651211864 0.000760909302423728 0.999619545348788 51 0.000349495293386815 0.00069899058677363 0.999650504706613 52 0.000195184887720413 0.000390369775440826 0.99980481511228 53 0.000131290216141649 0.000262580432283298 0.999868709783858 54 9.31377158575369e-05 0.000186275431715074 0.999906862284142 55 0.000139904126573818 0.000279808253147637 0.999860095873426 56 7.35407161435815e-05 0.000147081432287163 0.999926459283856 57 5.10374520836822e-05 0.000102074904167364 0.999948962547916 58 0.000369838129085236 0.000739676258170473 0.999630161870915 59 0.000276555857722812 0.000553111715445624 0.999723444142277 60 0.000151750956539713 0.000303501913079426 0.99984824904346 61 0.000124017181903389 0.000248034363806779 0.999875982818097 62 0.000117445159711696 0.000234890319423392 0.999882554840288 63 8.96686079923559e-05 0.000179337215984712 0.999910331392008 64 0.000146610072941424 0.000293220145882848 0.999853389927059 65 0.000184467629809861 0.000368935259619722 0.99981553237019 66 0.000268201517703577 0.000536403035407154 0.999731798482296 67 0.00220013184260943 0.00440026368521886 0.99779986815739 68 0.00322751242899448 0.00645502485798895 0.996772487571006 69 0.00495620038475907 0.00991240076951814 0.99504379961524 70 0.00664914331672965 0.0132982866334593 0.99335085668327 71 0.00468965405659423 0.00937930811318846 0.995310345943406 72 0.0022027708633488 0.0044055417266976 0.997797229136651 73 0.00187039860031086 0.00374079720062172 0.99812960139969 74 0.00131196667864151 0.00262393335728303 0.998688033321358 75 0.00130947468126867 0.00261894936253735 0.998690525318731 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 52 0.88135593220339 NOK 5% type I error level 57 0.966101694915254 NOK 10% type I error level 58 0.983050847457627 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/1073vh1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/1073vh1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/1g95d1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/1g95d1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/2c44z1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/2c44z1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/3ddrw1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/3ddrw1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/41u8n1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/41u8n1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/578fm1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/578fm1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/6owwf1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/6owwf1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/7azmw1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/7azmw1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/89rub1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/89rub1227732359.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/9o42k1227732359.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227733339w9rfwwc2pfyhxha/9o42k1227732359.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') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by