Multiple Regression - Totale Productie (B)

*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: Thu, 11 Dec 2008 17:13:51 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do.htm/, Retrieved Fri, 12 Dec 2008 00:15:16 +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/Dec/12/t1229040916g0hd9nbgdz0f4do.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 «

101.5 1 100.7 1 110.6 1 96.8 1 100.0 1 104.8 1 86.8 1 92.0 1 100.2 1 106.6 1 102.1 1 93.7 1 97.6 1 96.9 1 105.6 1 102.8 1 101.7 1 104.2 1 92.7 1 91.9 1 106.5 1 112.3 1 102.8 1 96.5 1 101.0 0 98.9 0 105.1 0 103.0 0 99.0 0 104.3 0 94.6 0 90.4 0 108.9 0 111.4 0 100.8 0 102.5 0 98.2 0 98.7 0 113.3 0 104.6 0 99.3 0 111.8 0 97.3 0 97.7 0 115.6 0 111.9 0 107.0 0 107.1 0 100.6 0 99.2 0 108.4 0 103.0 0 99.8 0 115.0 0 90.8 0 95.9 0 114.4 0 108.2 0 112.6 0 109.1 0 105.0 0 105.0 0 118.5 0 103.7 0 112.5 0 116.6 0 96.6 0 101.9 0 116.5 0 119.3 0 115.4 0 108.5 0 111.5 0 108.8 0 121.8 0 109.6 0 112.2 0 119.6 0 104.1 0 105.3 0 115.0 0 124.1 0 116.8 0 107.5 0 115.6 0 116.2 0 116.3 0 119.0 0 111.9 0 118.6 0 106.9 0 103.2 0

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 Y[t] = + 105.723214285714 -7.58125X[t] + 0.0470982142857072M1[t] -0.777901785714282M2[t] + 8.62209821428571M3[t] + 1.48459821428571M4[t] + 0.722098214285715M5[t] + 8.03459821428571M6[t] -7.60290178571429M7[t] -6.54040178571428M8[t] + 7.45714285714286M9[t] + 9.84285714285714M10[t] + 4.65714285714285M11[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) 105.723214285714 2.032675 52.0119 0 0 X -7.58125 1.257654 -6.0281 0 0 M1 0.0470982142857072 2.739888 0.0172 0.986329 0.493164 M2 -0.777901785714282 2.739888 -0.2839 0.777217 0.388608 M3 8.62209821428571 2.739888 3.1469 0.002328 0.001164 M4 1.48459821428571 2.739888 0.5418 0.589449 0.294725 M5 0.722098214285715 2.739888 0.2636 0.792813 0.396406 M6 8.03459821428571 2.739888 2.9325 0.004398 0.002199 M7 -7.60290178571429 2.739888 -2.7749 0.00689 0.003445 M8 -6.54040178571428 2.739888 -2.3871 0.019372 0.009686 M9 7.45714285714286 2.829364 2.6356 0.010105 0.005053 M10 9.84285714285714 2.829364 3.4788 0.000822 0.000411 M11 4.65714285714285 2.829364 1.646 0.103736 0.051868 Multiple Linear Regression - Regression Statistics Multiple R 0.793150832912932 R-squared 0.629088243750478 Adjusted R-squared 0.572747217484728 F-TEST (value) 11.1657221290785 F-TEST (DF numerator) 12 F-TEST (DF denominator) 79 p-value 1.18460796727504e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.29325548485415 Sum Squared Residuals 2213.46573660714 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.5 98.1890625 3.31093749999996 2 100.7 97.3640625 3.3359375 3 110.6 106.7640625 3.8359375 4 96.8 99.6265625 -2.8265625 5 100 98.8640625 1.13593750000000 6 104.8 106.1765625 -1.37656250000000 7 86.8 90.5390625 -3.7390625 8 92 91.6015625 0.398437499999997 9 100.2 105.599107142857 -5.39910714285714 10 106.6 107.984821428571 -1.38482142857143 11 102.1 102.799107142857 -0.699107142857147 12 93.7 98.1419642857143 -4.44196428571427 13 97.6 98.1890625 -0.589062499999994 14 96.9 97.3640625 -0.464062499999996 15 105.6 106.7640625 -1.1640625 16 102.8 99.6265625 3.1734375 17 101.7 98.8640625 2.83593750000000 18 104.2 106.1765625 -1.97656249999999 19 92.7 90.5390625 2.16093750000000 20 91.9 91.6015625 0.298437500000003 21 106.5 105.599107142857 0.900892857142857 22 112.3 107.984821428571 4.31517857142857 23 102.8 102.799107142857 0.000892857142858823 24 96.5 98.1419642857143 -1.64196428571428 25 101 105.7703125 -4.77031249999999 26 98.9 104.9453125 -6.0453125 27 105.1 114.3453125 -9.2453125 28 103 107.2078125 -4.2078125 29 99 106.4453125 -7.4453125 30 104.3 113.7578125 -9.4578125 31 94.6 98.1203125 -3.5203125 32 90.4 99.1828125 -8.7828125 33 108.9 113.180357142857 -4.28035714285714 34 111.4 115.566071428571 -4.16607142857142 35 100.8 110.380357142857 -9.58035714285715 36 102.5 105.723214285714 -3.22321428571429 37 98.2 105.7703125 -7.57031249999999 38 98.7 104.9453125 -6.2453125 39 113.3 114.3453125 -1.04531250000000 40 104.6 107.2078125 -2.60781250000001 41 99.3 106.4453125 -7.1453125 42 111.8 113.7578125 -1.9578125 43 97.3 98.1203125 -0.8203125 44 97.7 99.1828125 -1.4828125 45 115.6 113.180357142857 2.41964285714285 46 111.9 115.566071428571 -3.66607142857142 47 107 110.380357142857 -3.38035714285714 48 107.1 105.723214285714 1.37678571428571 49 100.6 105.7703125 -5.1703125 50 99.2 104.9453125 -5.7453125 51 108.4 114.3453125 -5.94531249999999 52 103 107.2078125 -4.2078125 53 99.8 106.4453125 -6.6453125 54 115 113.7578125 1.2421875 55 90.8 98.1203125 -7.3203125 56 95.9 99.1828125 -3.28281250000000 57 114.4 113.180357142857 1.21964285714286 58 108.2 115.566071428571 -7.36607142857143 59 112.6 110.380357142857 2.21964285714285 60 109.1 105.723214285714 3.37678571428571 61 105 105.7703125 -0.770312499999992 62 105 104.9453125 0.0546874999999955 63 118.5 114.3453125 4.1546875 64 103.7 107.2078125 -3.50781250000000 65 112.5 106.4453125 6.0546875 66 116.6 113.7578125 2.84218750000000 67 96.6 98.1203125 -1.52031250000000 68 101.9 99.1828125 2.7171875 69 116.5 113.180357142857 3.31964285714286 70 119.3 115.566071428571 3.73392857142857 71 115.4 110.380357142857 5.01964285714286 72 108.5 105.723214285714 2.77678571428571 73 111.5 105.7703125 5.72968750000001 74 108.8 104.9453125 3.85468749999999 75 121.8 114.3453125 7.4546875 76 109.6 107.2078125 2.39218750000000 77 112.2 106.4453125 5.7546875 78 119.6 113.7578125 5.8421875 79 104.1 98.1203125 5.9796875 80 105.3 99.1828125 6.11718749999999 81 115 113.180357142857 1.81964285714285 82 124.1 115.566071428571 8.53392857142856 83 116.8 110.380357142857 6.41964285714286 84 107.5 105.723214285714 1.77678571428571 85 115.6 105.7703125 9.8296875 86 116.2 104.9453125 11.2546875 87 116.3 114.3453125 1.9546875 88 119 107.2078125 11.7921875 89 111.9 106.4453125 5.4546875 90 118.6 113.7578125 4.8421875 91 106.9 98.1203125 8.7796875 92 103.2 99.1828125 4.0171875 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.288610286510185 0.57722057302037 0.711389713489815 17 0.157979477584096 0.315958955168192 0.842020522415904 18 0.0769677286427682 0.153935457285536 0.923032271357232 19 0.0724429441662364 0.144885888332473 0.927557055833764 20 0.0346804159832086 0.0693608319664171 0.965319584016791 21 0.0359765744751113 0.0719531489502227 0.964023425524889 22 0.0325585078554582 0.0651170157109164 0.967441492144542 23 0.0164216372156104 0.0328432744312208 0.98357836278439 24 0.00912854843425288 0.0182570968685058 0.990871451565747 25 0.00442085731886577 0.00884171463773154 0.995579142681134 26 0.00221474987077522 0.00442949974155045 0.997785250129225 27 0.00170654543428251 0.00341309086856501 0.998293454565717 28 0.00111349539069245 0.00222699078138489 0.998886504609308 29 0.000669574531771215 0.00133914906354243 0.999330425468229 30 0.000450988398566164 0.000901976797132329 0.999549011601434 31 0.000432020228832548 0.000864040457665096 0.999567979771167 32 0.000325387813912796 0.000650775627825592 0.999674612186087 33 0.000375779512138108 0.000751559024276216 0.999624220487862 34 0.000188234350531177 0.000376468701062355 0.999811765649469 35 0.000199217516668116 0.000398435033336233 0.999800782483332 36 0.000367353680968083 0.000734707361936165 0.999632646319032 37 0.000358913892282115 0.000717827784564231 0.999641086107718 38 0.000261319223699121 0.000522638447398241 0.9997386807763 39 0.000328605014985619 0.000657210029971238 0.999671394985014 40 0.000222352487642041 0.000444704975284083 0.999777647512358 41 0.000218759966048743 0.000437519932097485 0.999781240033951 42 0.000414206928702953 0.000828413857405905 0.999585793071297 43 0.000385633419052982 0.000771266838105964 0.999614366580947 44 0.000391666824825872 0.000783333649651745 0.999608333175174 45 0.00109340064956693 0.00218680129913386 0.998906599350433 46 0.000701839613479937 0.00140367922695987 0.99929816038652 47 0.000702384027537704 0.00140476805507541 0.999297615972462 48 0.00110917797942303 0.00221835595884605 0.998890822020577 49 0.00123849840874451 0.00247699681748903 0.998761501591256 50 0.00168296283769738 0.00336592567539476 0.998317037162303 51 0.00243580906346947 0.00487161812693893 0.99756419093653 52 0.00235472783894744 0.00470945567789489 0.997645272161053 53 0.00603553411587845 0.0120710682317569 0.993964465884122 54 0.00884959479257376 0.0176991895851475 0.991150405207426 55 0.0247975836412669 0.0495951672825338 0.975202416358733 56 0.0296270114974492 0.0592540229948985 0.97037298850255 57 0.0263462629759997 0.0526925259519995 0.973653737024 58 0.11351488979159 0.22702977958318 0.88648511020841 59 0.143196005517590 0.286392011035181 0.85680399448241 60 0.150666386515966 0.301332773031933 0.849333613484034 61 0.224596972124 0.449193944248 0.775403027876 62 0.303049233706466 0.606098467412932 0.696950766293534 63 0.314390892037228 0.628781784074457 0.685609107962772 64 0.565683924131101 0.868632151737798 0.434316075868899 65 0.606269602828948 0.787460794342105 0.393730397171052 66 0.583571460210321 0.832857079579357 0.416428539789678 67 0.77613586398525 0.4477282720295 0.22386413601475 68 0.74543577888734 0.50912844222532 0.25456422111266 69 0.685050882560944 0.629898234878112 0.314949117439056 70 0.692930573624643 0.614138852750714 0.307069426375357 71 0.637240640595446 0.725518718809108 0.362759359404554 72 0.539304813049445 0.921390373901111 0.460695186950556 73 0.516335177316534 0.967329645366933 0.483664822683466 74 0.605500976071733 0.788998047856533 0.394499023928267 75 0.619576422915225 0.760847154169549 0.380423577084775 76 0.950705984677384 0.0985880306452315 0.0492940153226157 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 28 0.459016393442623 NOK 5% type I error level 33 0.540983606557377 NOK 10% type I error level 39 0.639344262295082 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/10atjs1229040818.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/10atjs1229040818.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/1clow1229040817.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/1clow1229040817.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/2ywit1229040817.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/2ywit1229040817.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/35fkm1229040818.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/35fkm1229040818.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/4qirp1229040818.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/4qirp1229040818.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/5shwo1229040818.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/5shwo1229040818.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/6kevc1229040818.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/6kevc1229040818.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/7vzp61229040818.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/7vzp61229040818.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/8ds1h1229040818.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/8ds1h1229040818.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/98a2p1229040818.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t1229040916g0hd9nbgdz0f4do/98a2p1229040818.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