paper - dummies

*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: Tue, 21 Dec 2010 14:30:00 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut.htm/, Retrieved Tue, 21 Dec 2010 15:28:31 +0100

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/2010/Dec/21/t1292941699f8gy734a8hvywut.htm/}, year = {2010}, } @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 = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

631923 -12 -10.8 654294 -13 -12.2 671833 -16 -14.1 586840 -10 -15.2 600969 -4 -15.8 625568 -9 -15.8 558110 -8 -14.9 630577 -9 -12.6 628654 -3 -9.9 603184 -13 -7.8 656255 -3 -6 600730 -1 -5 670326 -2 -4.5 678423 0 -3.9 641502 0 -2.9 625311 -3 -1.5 628177 0 -0.5 589767 5 0 582471 3 0.5 636248 4 0.9 599885 3 0.8 621694 1 0.1 637406 -1 -1 595994 0 -2 696308 -2 -3 674201 -1 -3.7 648861 2 -4.7 649605 0 -6.4 672392 -6 -7.5 598396 -7 -7.8 613177 -6 -7.7 638104 -4 -6.6 615632 -9 -4.2 634465 -2 -2 638686 -3 -0.7 604243 2 0.1 706669 3 0.9 677185 1 2.1 644328 0 3.5 644825 1 4.9 605707 1 5.7 600136 3 6.2 612166 5 6.5 599659 5 6.5 634210 4 6.3 618234 11 6.2 613576 8 6.4 627200 -1 6.3 668973 4 5.8 651479 4 5.1 619661 4 5.1 644260 6 5.8 579936 6 6.7 601752 6 7.1 595376 6 6.7 588902 4 5.5 634341 1 4.2 594305 6 3 606200 0 2.2 610926 2 2 633685 -2 1.8 639696 0 1.8 659451 1 1.5 593248 -3 0.4 606677 -3 -0.9 599434 -5 -1.7 569578 -7 -2.6 629873 -7 -4.4 613438 -5 - etc...

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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 606667.763126078 + 2747.68791214968X1[t] -3701.9255707074X2[t] + 57698.5014731723M1[t] + 56686.3131057879M2[t] + 48406.8244021041M3[t] + 13929.5635338339M4[t] + 12359.3641249392M5[t] -1.62318935044884M6[t] -23122.5659196615M7[t] + 16085.6814453896M8[t] + 12600.6417769315M9[t] + 5839.14642974934M10[t] + 30555.4742742018M11[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) 606667.763126078 10718.249793 56.6014 0 0 X1 2747.68791214968 953.000814 2.8832 0.005225 0.002613 X2 -3701.9255707074 657.891389 -5.627 0 0 M1 57698.5014731723 14984.642875 3.8505 0.000258 0.000129 M2 56686.3131057879 14987.723173 3.7822 0.000324 0.000162 M3 48406.8244021041 14992.667619 3.2287 0.001894 0.000947 M4 13929.5635338339 15010.453364 0.928 0.356601 0.178301 M5 12359.3641249392 15045.450644 0.8215 0.414169 0.207084 M6 -1.62318935044884 15033.796281 -1e-04 0.999914 0.499957 M7 -23122.5659196615 15034.904828 -1.5379 0.128575 0.064288 M8 16085.6814453896 15106.201988 1.0648 0.290608 0.145304 M9 12600.6417769315 15045.283996 0.8375 0.405154 0.202577 M10 5839.14642974934 15037.401274 0.3883 0.698966 0.349483 M11 30555.4742742018 15025.580093 2.0336 0.045788 0.022894 Multiple Linear Regression - Regression Statistics Multiple R 0.784394954125589 R-squared 0.615275444057685 Adjusted R-squared 0.543826597954112 F-TEST (value) 8.61141190671963 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 4.0369796394657e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 28028.9728030983 Sum Squared Residuals 54993632147.7777 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 631923 671374.805817093 -39451.8058170931 2 654294 672797.62533655 -18503.6253365504 3 671833 663308.731480762 8524.2685192383 4 586840 649389.716213168 -62549.7162131679 5 600969 666526.799619596 -65557.7996195956 6 625568 640427.372744558 -14859.3727445576 7 558110 616722.38491276 -58612.3849127596 8 630577 644668.515553034 -14091.5155530339 9 628654 647674.404316564 -19020.4043165639 10 603184 605661.9861494 -2477.98614939942 11 656255 651191.727088075 5063.27291192455 12 600730 622429.703067466 -21699.7030674656 13 670326 675529.553843134 -5203.55384313442 14 678423 677791.585957625 631.414042375005 15 641502 665810.171683234 -24308.1716832338 16 625311 617907.151279524 7403.84872047581 17 628177 620878.090036371 7298.90996362885 18 589767 620404.579497476 -30637.5794974762 19 582471 589937.298157512 -7466.29815751215 20 636248 630412.46320643 5835.53679357008 21 599885 624549.928182893 -24664.9281828929 22 621694 614884.404910907 6809.59508909345 23 637406 638177.475058838 -771.475058837813 24 595994 614071.614267493 -18077.6142674930 25 696308 669976.665487073 26331.3345129267 26 674201 674303.512931334 -102.512931333818 27 648861 677969.013534806 -29108.0135348065 28 649605 644289.65031244 5315.34968756053 29 672392 630305.441558425 42086.5584415752 30 598396 616307.344003198 -17911.3440031977 31 613177 595563.896627966 17613.1033720344 32 638104 636195.401689538 1908.59831046207 33 615632 610087.301090634 5544.69890936637 34 634465 614415.384872943 20049.6151270570 35 638686 631571.521563326 7114.47843667377 36 604243 611792.946393307 -7549.94639330689 37 706669 669277.595322063 37391.4046779371 38 677185 658327.72044553 18857.2795544697 39 644328 642117.848030707 2210.15196929354 40 644825 605205.579275596 39619.4207244044 41 605707 600673.839410135 5033.16058986502 42 600136 591957.265134791 8178.73486520898 43 612166 573221.120557567 38944.8794424329 44 599659 612429.367922618 -12770.3679226182 45 634210 606937.025456152 27272.9745438481 46 618234 619779.538051088 -1545.53805108827 47 613576 635512.41704495 -21936.4170449502 48 627200 580597.944118472 46602.055881528 49 668973 653885.847937746 15087.1520622537 50 651479 655465.007469857 -3986.00746985716 51 619661 647185.518766173 -27524.5187661734 52 644260 615612.285822707 28647.7141772927 53 579936 610710.353400176 -30774.353400176 54 601752 596868.595857603 4883.40414239659 55 595376 575228.423355575 20147.5766444246 56 588902 613383.605581176 -24481.6055811759 57 634341 606468.005418188 27872.9945818117 58 594305 617887.260316604 -23582.2603166035 59 606200 629079.001144724 -22879.0011447238 60 610926 604759.287808963 6166.71219103717 61 633685 652207.422747678 -18522.4227476778 62 639696 656690.610204593 -16994.6102045928 63 659451 652269.387084271 7181.61291572906 64 593248 610873.49269518 -17625.4926951801 65 606677 614115.796528205 -7438.79652820507 66 599434 599220.973846182 213.02615381803 67 569578 573936.388305208 -4358.38830520824 68 629873 619808.101697533 10064.8983024674 69 613438 636255.947579133 -22817.9475791327 70 604172 630094.694916068 -25922.6949160682 71 658328 672111.245461953 -13783.2454619527 72 612633 649815.032506831 -37182.0325068306 73 707372 723004.108845212 -15632.1088452122 74 739770 719671.93765451 20098.0623454894 75 777535 714510.329420047 63024.6705799528 76 685030 685841.124401385 -811.1244013854 77 730234 680881.679447092 49352.3205529077 78 714154 664020.868916192 50133.1310838079 79 630872 637140.488083412 -6268.48808341194 80 719492 685957.544349672 33534.4556503284 81 677023 671210.387956437 5812.61204356327 82 679272 652602.730782991 26669.2692170090 83 718317 671124.612638134 47192.3873618663 84 645672 613931.471837469 31740.5281625308 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.508002512676865 0.98399497464627 0.491997487323135 18 0.631640575812005 0.736718848375991 0.368359424187995 19 0.5071617365684 0.9856765268632 0.4928382634316 20 0.37230888594984 0.74461777189968 0.62769111405016 21 0.398177742896363 0.796355485792725 0.601822257103637 22 0.322415233675945 0.644830467351889 0.677584766324056 23 0.260279187267406 0.520558374534811 0.739720812732594 24 0.191612705836626 0.383225411673251 0.808387294163374 25 0.264043031318462 0.528086062636923 0.735956968681538 26 0.191276143902733 0.382552287805466 0.808723856097267 27 0.162897898805880 0.325795797611760 0.83710210119412 28 0.207629828555497 0.415259657110995 0.792370171444503 29 0.374299886352342 0.748599772704684 0.625700113647658 30 0.324785176707984 0.649570353415968 0.675214823292016 31 0.352246790924097 0.704493581848194 0.647753209075903 32 0.278797892497557 0.557595784995115 0.721202107502443 33 0.215357479302468 0.430714958604937 0.784642520697532 34 0.199911505023967 0.399823010047934 0.800088494976033 35 0.158434533868911 0.316869067737822 0.841565466131089 36 0.125948896786970 0.251897793573939 0.87405110321303 37 0.153240487486901 0.306480974973803 0.846759512513099 38 0.117964540827066 0.235929081654132 0.882035459172934 39 0.0927432381315857 0.185486476263171 0.907256761868414 40 0.0932527484060226 0.186505496812045 0.906747251593977 41 0.0981762666573115 0.196352533314623 0.901823733342689 42 0.0708444765252116 0.141688953050423 0.929155523474788 43 0.0710327837902601 0.142065567580520 0.92896721620974 44 0.0789872154522498 0.157974430904500 0.92101278454775 45 0.070461762678492 0.140923525356984 0.929538237321508 46 0.0491078406164222 0.0982156812328444 0.950892159383578 47 0.0520534120604323 0.104106824120865 0.947946587939568 48 0.0884138993364754 0.176827798672951 0.911586100663525 49 0.0841075488457969 0.168215097691594 0.915892451154203 50 0.0653563489994908 0.130712697998982 0.93464365100051 51 0.109113664417095 0.21822732883419 0.890886335582905 52 0.117667060924254 0.235334121848508 0.882332939075746 53 0.192375256062010 0.384750512124021 0.80762474393799 54 0.150784131988918 0.301568263977835 0.849215868011082 55 0.127166634411246 0.254333268822493 0.872833365588754 56 0.147908137000846 0.295816274001693 0.852091862999154 57 0.248805438921809 0.497610877843618 0.751194561078191 58 0.242280631288501 0.484561262577001 0.757719368711499 59 0.231739760186611 0.463479520373223 0.768260239813389 60 0.165641606960907 0.331283213921814 0.834358393039093 61 0.151204497501092 0.302408995002185 0.848795502498908 62 0.135590230113949 0.271180460227898 0.864409769886051 63 0.290809692115615 0.58161938423123 0.709190307884385 64 0.229069260840671 0.458138521681341 0.770930739159329 65 0.465139305409148 0.930278610818297 0.534860694590852 66 0.933515374331072 0.132969251337857 0.0664846256689284 67 0.845747783299377 0.308504433401247 0.154252216700623 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 1 0.0196078431372549 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/102x871292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/102x871292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/1vwte1292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/1vwte1292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/2o5sh1292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/2o5sh1292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/3o5sh1292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/3o5sh1292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/4o5sh1292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/4o5sh1292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/5hw911292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/5hw911292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/6hw911292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/6hw911292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/7s5r41292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/7s5r41292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/8s5r41292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/8s5r41292941789.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/92x871292941789.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941699f8gy734a8hvywut/92x871292941789.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