multiple linear regression goudprijs

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 06:17:01 -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/27/t1227791934jch1wd8pu35q9ax.htm/, Retrieved Thu, 27 Nov 2008 13:19:04 +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/27/t1227791934jch1wd8pu35q9ax.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

9.103 0 9.155 0 9.308 0 9.394 0 9.948 0 10.177 0 10.002 0 9.728 0 10.002 0 10.063 0 10.018 0 9.96 0 10.236 0 10.893 0 10.756 0 10.94 0 10.997 0 10.827 0 10.166 0 10.186 0 10.457 0 10.368 0 10.244 0 10.511 0 10.812 0 10.738 0 10.171 0 9.721 0 9.897 0 9.828 0 9.924 0 10.371 0 10.846 0 10.413 0 10.709 0 10.662 0 10.57 0 10.297 0 10.635 0 10.872 0 10.296 0 10.383 0 10.431 0 10.574 0 10.653 0 10.805 0 10.872 0 10.625 0 10.407 0 10.463 0 10.556 0 10.646 0 10.702 0 11.353 0 11.346 1 11.451 1 11.964 1 12.574 1 13.031 1 13.812 1 14.544 1 14.931 1 14.886 1 16.005 1 17.064 1 15.168 1 16.05 1 15.839 1 15.137 1 14.954 1 15.648 1 15.305 1 15.579 1 16.348 1 15.928 1 16.171 1 15.937 1 15.713 1 15.594 1 15.683 1 16.438 1 17.032 1 17.696 1 17.745 1 19.394 1 20.148 1 20.108 1 18.584 1 18.441 1 18.391 1 19.178 1 18.079 1 18.483 1 19.644 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation goudprijs[t] = + 10.1468993250844 + 5.86390157480315dummy[t] + 0.234762584364457M1[t] + 0.525762584364453M2[t] + 0.447637584364454M3[t] + 0.445762584364456M4[t] + 0.564387584364456M5[t] + 0.384137584364454M6[t] -0.242475112485938M7[t] -0.339975112485939M8[t] -0.0813501124859381M9[t] + 0.152774887514062M10[t] -0.0574285714285704M11[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) 10.1468993250844 0.612475 16.567 0 0 dummy 5.86390157480315 0.331021 17.7146 0 0 M1 0.234762584364457 0.81605 0.2877 0.774325 0.387163 M2 0.525762584364453 0.81605 0.6443 0.521216 0.260608 M3 0.447637584364454 0.81605 0.5485 0.584829 0.292414 M4 0.445762584364456 0.81605 0.5462 0.5864 0.2932 M5 0.564387584364456 0.81605 0.6916 0.491161 0.24558 M6 0.384137584364454 0.81605 0.4707 0.6391 0.31955 M7 -0.242475112485938 0.8162 -0.2971 0.767167 0.383584 M8 -0.339975112485939 0.8162 -0.4165 0.678121 0.339061 M9 -0.0813501124859381 0.8162 -0.0997 0.920853 0.460427 M10 0.152774887514062 0.8162 0.1872 0.851989 0.425994 M11 -0.0574285714285704 0.842614 -0.0682 0.94583 0.472915 Multiple Linear Regression - Regression Statistics Multiple R 0.891862712448084 R-squared 0.795419097855254 Adjusted R-squared 0.765110816056032 F-TEST (value) 26.2442821115541 F-TEST (DF numerator) 12 F-TEST (DF denominator) 81 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.57638681292758 Sum Squared Residuals 201.284626101729 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.103 10.3816619094488 -1.2786619094488 2 9.155 10.6726619094488 -1.51766190944883 3 9.308 10.5945369094488 -1.28653690944882 4 9.394 10.5926619094488 -1.19866190944882 5 9.948 10.7112869094488 -0.763286909448815 6 10.177 10.5310369094488 -0.35403690944882 7 10.002 9.90442421259843 0.0975757874015746 8 9.728 9.80692421259842 -0.0789242125984247 9 10.002 10.0655492125984 -0.0635492125984253 10 10.063 10.2996742125984 -0.236674212598424 11 10.018 10.0894707536558 -0.0714707536557929 12 9.96 10.1468993250844 -0.186899325084363 13 10.236 10.3816619094488 -0.145661909448821 14 10.893 10.6726619094488 0.220338090551183 15 10.756 10.5945369094488 0.161463090551181 16 10.94 10.5926619094488 0.347338090551180 17 10.997 10.7112869094488 0.28571309055118 18 10.827 10.5310369094488 0.295963090551182 19 10.166 9.90442421259842 0.261575787401575 20 10.186 9.80692421259843 0.379075787401574 21 10.457 10.0655492125984 0.391450787401575 22 10.368 10.2996742125984 0.0683257874015747 23 10.244 10.0894707536558 0.154529246344206 24 10.511 10.1468993250844 0.364100674915635 25 10.812 10.3816619094488 0.430338090551178 26 10.738 10.6726619094488 0.0653380905511822 27 10.171 10.5945369094488 -0.42353690944882 28 9.721 10.5926619094488 -0.87166190944882 29 9.897 10.7112869094488 -0.814286909448819 30 9.828 10.5310369094488 -0.703036909448819 31 9.924 9.90442421259842 0.0195757874015741 32 10.371 9.80692421259843 0.564075787401575 33 10.846 10.0655492125984 0.780450787401574 34 10.413 10.2996742125984 0.113325787401575 35 10.709 10.0894707536558 0.619529246344206 36 10.662 10.1468993250844 0.515100674915637 37 10.57 10.3816619094488 0.188338090551179 38 10.297 10.6726619094488 -0.375661909448817 39 10.635 10.5945369094488 0.0404630905511802 40 10.872 10.5926619094488 0.279338090551181 41 10.296 10.7112869094488 -0.41528690944882 42 10.383 10.5310369094488 -0.148036909448819 43 10.431 9.90442421259842 0.526575787401574 44 10.574 9.80692421259843 0.767075787401574 45 10.653 10.0655492125984 0.587450787401575 46 10.805 10.2996742125984 0.505325787401574 47 10.872 10.0894707536558 0.782529246344207 48 10.625 10.1468993250844 0.478100674915636 49 10.407 10.3816619094488 0.0253380905511783 50 10.463 10.6726619094488 -0.209661909448818 51 10.556 10.5945369094488 -0.0385369094488205 52 10.646 10.5926619094488 0.0533380905511817 53 10.702 10.7112869094488 -0.0092869094488195 54 11.353 10.5310369094488 0.821963090551182 55 11.346 15.7683257874016 -4.42232578740158 56 11.451 15.6708257874016 -4.21982578740158 57 11.964 15.9294507874016 -3.96545078740158 58 12.574 16.1635757874016 -3.58957578740157 59 13.031 15.9533723284589 -2.92237232845894 60 13.812 16.0108008998875 -2.19880089988751 61 14.544 16.2455634842520 -1.70156348425197 62 14.931 16.5365634842520 -1.60556348425197 63 14.886 16.4584384842520 -1.57243848425197 64 16.005 16.4565634842520 -0.451563484251969 65 17.064 16.5751884842520 0.488811515748031 66 15.168 16.3949384842520 -1.22693848425197 67 16.05 15.7683257874016 0.281674212598426 68 15.839 15.6708257874016 0.168174212598426 69 15.137 15.9294507874016 -0.792450787401574 70 14.954 16.1635757874016 -1.20957578740157 71 15.648 15.9533723284589 -0.305372328458943 72 15.305 16.0108008998875 -0.705800899887513 73 15.579 16.2455634842520 -0.66656348425197 74 16.348 16.5365634842520 -0.188563484251967 75 15.928 16.4584384842520 -0.530438484251968 76 16.171 16.4565634842520 -0.285563484251968 77 15.937 16.5751884842520 -0.63818848425197 78 15.713 16.3949384842520 -0.681938484251968 79 15.594 15.7683257874016 -0.174325787401575 80 15.683 15.6708257874016 0.0121742125984253 81 16.438 15.9294507874016 0.508549212598424 82 17.032 16.1635757874016 0.868424212598426 83 17.696 15.9533723284589 1.74262767154106 84 17.745 16.0108008998875 1.73419910011249 85 19.394 16.2455634842520 3.14843651574803 86 20.148 16.5365634842520 3.61143651574803 87 20.108 16.4584384842520 3.64956151574803 88 18.584 16.4565634842520 2.12743651574803 89 18.441 16.5751884842520 1.86581151574803 90 18.391 16.3949384842520 1.99606151574803 91 19.178 15.7683257874016 3.40967421259843 92 18.079 15.6708257874016 2.40817421259843 93 18.483 15.9294507874016 2.55354921259843 94 19.644 16.1635757874016 3.48042421259842 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.314327656813306 0.628655313626612 0.685672343186694 17 0.201319410231666 0.402638820463333 0.798680589768334 18 0.112709150482457 0.225418300964913 0.887290849517543 19 0.0551861205460129 0.110372241092026 0.944813879453987 20 0.0265188744105748 0.0530377488211496 0.973481125589425 21 0.0121499786299029 0.0242999572598058 0.987850021370097 22 0.00512066356364845 0.0102413271272969 0.994879336436352 23 0.00202535295531559 0.00405070591063118 0.997974647044684 24 0.000852348781405411 0.00170469756281082 0.999147651218595 25 0.000636781415941256 0.00127356283188251 0.99936321858406 26 0.000309015981362028 0.000618031962724057 0.999690984018638 27 0.000114011931619453 0.000228023863238905 0.99988598806838 28 4.67560952385985e-05 9.35121904771969e-05 0.99995324390476 29 2.03590224092023e-05 4.07180448184045e-05 0.99997964097759 30 9.38931233570382e-06 1.87786246714076e-05 0.999990610687664 31 3.10307502933647e-06 6.20615005867295e-06 0.99999689692497 32 1.11493462809939e-06 2.22986925619878e-06 0.999998885065372 33 4.66008386582517e-07 9.32016773165033e-07 0.999999533991613 34 1.44308025583295e-07 2.88616051166590e-07 0.999999855691974 35 5.60535005304867e-08 1.12107001060973e-07 0.9999999439465 36 1.89129001450443e-08 3.78258002900886e-08 0.9999999810871 37 6.84600340230841e-09 1.36920068046168e-08 0.999999993153997 38 1.95265987770946e-09 3.90531975541891e-09 0.99999999804734 39 7.20919758186604e-10 1.44183951637321e-09 0.99999999927908 40 3.95309271269926e-10 7.90618542539852e-10 0.99999999960469 41 1.09811182825528e-10 2.19622365651056e-10 0.999999999890189 42 2.89163939591948e-11 5.78327879183897e-11 0.999999999971084 43 8.62036255578048e-12 1.72407251115610e-11 0.99999999999138 44 2.8346291751815e-12 5.669258350363e-12 0.999999999997165 45 7.44509583512648e-13 1.48901916702530e-12 0.999999999999255 46 2.41387364993735e-13 4.82774729987469e-13 0.999999999999759 47 8.70927995227301e-14 1.74185599045460e-13 0.999999999999913 48 2.26835983943272e-14 4.53671967886544e-14 0.999999999999977 49 5.30788669405463e-15 1.06157733881093e-14 0.999999999999995 50 1.21352121072215e-15 2.42704242144430e-15 0.999999999999999 51 2.97582814913398e-16 5.95165629826796e-16 1 52 7.7227371287962e-17 1.54454742575924e-16 1 53 2.09141179857422e-17 4.18282359714844e-17 1 54 1.81884447192535e-17 3.6376889438507e-17 1 55 5.10508866666882e-17 1.02101773333376e-16 1 56 1.33023295492750e-16 2.66046590985501e-16 1 57 3.74520428257021e-16 7.49040856514042e-16 1 58 2.84361456800644e-15 5.68722913601288e-15 0.999999999999997 59 1.14725742282410e-14 2.29451484564820e-14 0.999999999999988 60 1.03024229898989e-13 2.06048459797978e-13 0.999999999999897 61 7.80693527698712e-12 1.56138705539742e-11 0.999999999992193 62 3.42127954605493e-10 6.84255909210985e-10 0.999999999657872 63 4.01580066247472e-09 8.03160132494944e-09 0.9999999959842 64 6.00672386271696e-08 1.20134477254339e-07 0.999999939932761 65 1.20860531602444e-06 2.41721063204888e-06 0.999998791394684 66 1.13061559802609e-06 2.26123119605219e-06 0.999998869384402 67 3.96449802128886e-06 7.92899604257773e-06 0.999996035501979 68 6.03236515514403e-06 1.20647303102881e-05 0.999993967634845 69 6.89319864193642e-06 1.37863972838728e-05 0.999993106801358 70 1.749948622714e-05 3.499897245428e-05 0.999982500513773 71 1.66147242562421e-05 3.32294485124842e-05 0.999983385275744 72 1.54148738241297e-05 3.08297476482594e-05 0.999984585126176 73 4.89413590913824e-05 9.78827181827647e-05 0.999951058640909 74 0.000221225720378729 0.000442451440757458 0.999778774279621 75 0.00149257859595102 0.00298515719190204 0.99850742140405 76 0.00185282725209932 0.00370565450419865 0.9981471727479 77 0.00225622172770083 0.00451244345540165 0.997743778272299 78 0.00326454264117569 0.00652908528235138 0.996735457358824 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 56 0.888888888888889 NOK 5% type I error level 58 0.92063492063492 NOK 10% type I error level 59 0.936507936507937 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/10thjl1227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/10thjl1227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/1mlkz1227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/1mlkz1227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/2t7481227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/2t7481227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/352pz1227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/352pz1227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/4v1mz1227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/4v1mz1227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/5dcao1227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/5dcao1227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/6soty1227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/6soty1227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/7w5ty1227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/7w5ty1227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/8a3h41227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/8a3h41227791816.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/9yxm81227791816.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227791934jch1wd8pu35q9ax/9yxm81227791816.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') }

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