Q3 - 1 peak

*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: Sun, 23 Nov 2008 11:09:24 -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/t12274638630b1nklnnwyk1kh7.htm/, Retrieved Sun, 23 Nov 2008 18:11:12 +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/t12274638630b1nklnnwyk1kh7.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 «

1671 0 1385 0 1632 0 1313 0 1300 0 1431 0 1398 0 1198 0 1292 0 1434 0 1660 0 1837 0 1455 0 1315 0 1642 0 1069 0 1209 0 1586 0 1122 0 1063 0 1125 0 1414 0 1347 0 1403 0 1299 0 1547 0 1515 0 1247 0 1639 0 1296 0 1063 0 1282 0 1365 0 1268 0 1532 0 1455 0 1393 0 1515 0 1510 0 1225 0 1577 0 1417 0 1224 0 1693 0 1633 0 1639 0 1914 0 1586 0 1552 0 2081 0 1500 0 1437 0 1470 0 1849 0 1387 0 1592 0 1589 0 1798 0 1935 0 1887 0 2027 0 2080 0 1556 0 1682 0 1785 0 1869 0 1781 0 2082 0 2570 1 1862 0 1936 0 1504 0 1765 0 1607 0 1577 0 1493 0 1615 0 1700 0 1335 0 1523 0 1621 0 1539 0 1637 0 1523 0 1418 0 1819 0 1594 0 1359 0 1261 0 1722 0 1407 0 1380 0 1642 0 1681 0 1542 0 1704 0 1431 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 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Gebouwen[t] = + 1612.375 + 1103.28571428572Dummy[t] -55.5972222222219M1[t] + 56.2499999999999M2[t] -46.6249999999997M3[t] -259.25M4[t] -130.375000000000M5[t] -3.625M6[t] -272.75M7[t] -135.75M8[t] -145.660714285714M9[t] -33M10[t] + 75.5000000000001M11[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) 1612.375 77.895927 20.6991 0 0 Dummy 1103.28571428572 235.535144 4.6842 1.1e-05 5e-06 M1 -55.5972222222219 107.057712 -0.5193 0.604903 0.302452 M2 56.2499999999999 110.161477 0.5106 0.610961 0.30548 M3 -46.6249999999997 110.161477 -0.4232 0.6732 0.3366 M4 -259.25 110.161477 -2.3534 0.020941 0.01047 M5 -130.375000000000 110.161477 -1.1835 0.239954 0.119977 M6 -3.625 110.161477 -0.0329 0.973827 0.486914 M7 -272.75 110.161477 -2.4759 0.015298 0.007649 M8 -135.75 110.161477 -1.2323 0.221282 0.110641 M9 -145.660714285714 114.027961 -1.2774 0.204976 0.102488 M10 -33 110.161477 -0.2996 0.765253 0.382626 M11 75.5000000000001 110.161477 0.6854 0.495005 0.247503 Multiple Linear Regression - Regression Statistics Multiple R 0.588746858772786 R-squared 0.346622863714822 Adjusted R-squared 0.25328327281694 F-TEST (value) 3.71356741957486 F-TEST (DF numerator) 12 F-TEST (DF denominator) 84 p-value 0.000167106157256791 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 220.322953139438 Sum Squared Residuals 4077545.10912699 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1671 1556.77777777778 114.222222222225 2 1385 1668.62500000000 -283.625000000004 3 1632 1565.75 66.2500000000005 4 1313 1353.125 -40.1250000000007 5 1300 1482 -182.000000000001 6 1431 1608.75 -177.75 7 1398 1339.625 58.3749999999998 8 1198 1476.625 -278.625 9 1292 1466.71428571429 -174.714285714286 10 1434 1579.375 -145.375 11 1660 1687.875 -27.8749999999998 12 1837 1612.375 224.625 13 1455 1556.77777777778 -101.777777777778 14 1315 1668.625 -353.624999999999 15 1642 1565.75 76.25 16 1069 1353.125 -284.125 17 1209 1482 -273 18 1586 1608.75 -22.7499999999999 19 1122 1339.625 -217.625 20 1063 1476.625 -413.625 21 1125 1466.71428571429 -341.714285714286 22 1414 1579.375 -165.375 23 1347 1687.875 -340.875 24 1403 1612.375 -209.375 25 1299 1556.77777777778 -257.777777777778 26 1547 1668.625 -121.625000000000 27 1515 1565.75 -50.75 28 1247 1353.125 -106.125 29 1639 1482 157 30 1296 1608.75 -312.75 31 1063 1339.625 -276.625 32 1282 1476.625 -194.625 33 1365 1466.71428571429 -101.714285714286 34 1268 1579.375 -311.375 35 1532 1687.875 -155.875 36 1455 1612.375 -157.375 37 1393 1556.77777777778 -163.777777777778 38 1515 1668.625 -153.625000000000 39 1510 1565.75 -55.75 40 1225 1353.125 -128.125 41 1577 1482 95 42 1417 1608.75 -191.75 43 1224 1339.625 -115.625 44 1693 1476.625 216.375 45 1633 1466.71428571429 166.285714285714 46 1639 1579.375 59.625 47 1914 1687.875 226.125 48 1586 1612.375 -26.3749999999999 49 1552 1556.77777777778 -4.77777777777812 50 2081 1668.625 412.375000000001 51 1500 1565.75 -65.75 52 1437 1353.125 83.8750000000001 53 1470 1482 -12 54 1849 1608.75 240.25 55 1387 1339.625 47.375 56 1592 1476.625 115.375 57 1589 1466.71428571429 122.285714285714 58 1798 1579.375 218.625 59 1935 1687.875 247.125 60 1887 1612.375 274.625 61 2027 1556.77777777778 470.222222222222 62 2080 1668.625 411.375000000001 63 1556 1565.75 -9.75000000000005 64 1682 1353.125 328.875 65 1785 1482 303 66 1869 1608.75 260.25 67 1781 1339.625 441.375 68 2082 1476.625 605.375 69 2570 2570 9.2192919964873e-13 70 1862 1579.375 282.625 71 1936 1687.875 248.125 72 1504 1612.375 -108.375 73 1765 1556.77777777778 208.222222222222 74 1607 1668.625 -61.6249999999996 75 1577 1565.75 11.2500000000000 76 1493 1353.125 139.875 77 1615 1482 133 78 1700 1608.75 91.2500000000001 79 1335 1339.625 -4.62499999999996 80 1523 1476.625 46.375 81 1621 1466.71428571429 154.285714285714 82 1539 1579.375 -40.3750000000000 83 1637 1687.875 -50.875 84 1523 1612.375 -89.375 85 1418 1556.77777777778 -138.777777777778 86 1819 1668.625 150.375000000000 87 1594 1565.75 28.2500000000000 88 1359 1353.125 5.87500000000004 89 1261 1482 -221 90 1722 1608.75 113.25 91 1407 1339.625 67.375 92 1380 1476.625 -96.625 93 1642 1466.71428571429 175.285714285714 94 1681 1579.375 101.625 95 1542 1687.875 -145.875 96 1704 1612.375 91.625 97 1431 1556.77777777778 -125.777777777778 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.226942807297002 0.453885614594005 0.773057192702998 17 0.126494366128316 0.252988732256631 0.873505633871684 18 0.082613091120242 0.165226182240484 0.917386908879758 19 0.103990475686287 0.207980951372574 0.896009524313713 20 0.0804160426196026 0.160832085239205 0.919583957380397 21 0.0680089797884221 0.136017959576844 0.931991020211578 22 0.0381745924459439 0.0763491848918879 0.961825407554056 23 0.0687064876329065 0.137412975265813 0.931293512367094 24 0.161445217052982 0.322890434105964 0.838554782947018 25 0.189953369614619 0.379906739229239 0.81004663038538 26 0.181781253876072 0.363562507752143 0.818218746123928 27 0.140134338662798 0.280268677325595 0.859865661337202 28 0.102801799822218 0.205603599644436 0.897198200177782 29 0.172519127986850 0.345038255973700 0.82748087201315 30 0.19306877433147 0.38613754866294 0.80693122566853 31 0.203056862440255 0.406113724880511 0.796943137559745 32 0.204309141466123 0.408618282932245 0.795690858533877 33 0.188631063909315 0.377262127818629 0.811368936090685 34 0.214054081453705 0.428108162907410 0.785945918546295 35 0.184305736957272 0.368611473914545 0.815694263042728 36 0.168239731906583 0.336479463813166 0.831760268093417 37 0.147589699605322 0.295179399210644 0.852410300394678 38 0.157018184315638 0.314036368631275 0.842981815684362 39 0.123115982311503 0.246231964623006 0.876884017688497 40 0.107818015402938 0.215636030805877 0.892181984597062 41 0.0991311022266283 0.198262204453257 0.900868897773372 42 0.104249892737837 0.208499785475675 0.895750107262163 43 0.0922838959612199 0.184567791922440 0.90771610403878 44 0.235905198558471 0.471810397116943 0.764094801441529 45 0.2924147056268 0.5848294112536 0.7075852943732 46 0.293708400784784 0.587416801569568 0.706291599215216 47 0.369578262135404 0.739156524270807 0.630421737864596 48 0.310676563769059 0.621353127538119 0.68932343623094 49 0.266658150782133 0.533316301564265 0.733341849217867 50 0.5294838587927 0.9410322824146 0.4705161412073 51 0.4692631063113 0.9385262126226 0.5307368936887 52 0.437446557707565 0.87489311541513 0.562553442292435 53 0.376882875382237 0.753765750764473 0.623117124617763 54 0.41686733356228 0.83373466712456 0.58313266643772 55 0.379208131472771 0.758416262945542 0.620791868527229 56 0.359114174318911 0.718228348637823 0.640885825681089 57 0.323612486035422 0.647224972070843 0.676387513964578 58 0.326835025575877 0.653670051151753 0.673164974424123 59 0.34200529065136 0.68401058130272 0.65799470934864 60 0.38402823898011 0.76805647796022 0.61597176101989 61 0.62949863835945 0.7410027232811 0.37050136164055 62 0.74363862306873 0.512722753862539 0.256361376931269 63 0.68013765416905 0.639724691661901 0.319862345830950 64 0.713120206892635 0.57375958621473 0.286879793107365 65 0.764511004505665 0.470977990988671 0.235488995494335 66 0.748317875195507 0.503364249608986 0.251682124804493 67 0.85548450536579 0.289030989268419 0.144515494634210 68 0.990231242907127 0.0195375141857458 0.0097687570928729 69 0.982358416574988 0.0352831668500241 0.0176415834250121 70 0.985043287521834 0.0299134249563329 0.0149567124781665 71 0.993670084640106 0.0126598307197873 0.00632991535989367 72 0.989295703549992 0.0214085929000153 0.0107042964500077 73 0.996847360637266 0.00630527872546786 0.00315263936273393 74 0.996427363244917 0.00714527351016666 0.00357263675508333 75 0.991441323235008 0.0171173535299844 0.0085586767649922 76 0.985215923044972 0.0295681539100551 0.0147840769550275 77 0.99766812246777 0.00466375506446118 0.00233187753223059 78 0.992693859422542 0.0146122811549162 0.00730614057745808 79 0.980907931925281 0.0381841361494373 0.0190920680747186 80 0.967427848237192 0.0651443035256163 0.0325721517628082 81 0.906861646780273 0.186276706439454 0.093138353219727 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0454545454545455 NOK 5% type I error level 12 0.181818181818182 NOK 10% type I error level 14 0.212121212121212 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/10e4hi1227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/10e4hi1227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/17ulb1227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/17ulb1227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/25nb11227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/25nb11227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/3jbip1227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/3jbip1227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/4aiiw1227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/4aiiw1227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/5yy7a1227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/5yy7a1227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/6m7vl1227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/6m7vl1227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/7npu61227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/7npu61227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/8443e1227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/8443e1227463757.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/9zvx91227463757.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t12274638630b1nklnnwyk1kh7/9zvx91227463757.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