WS7 aanpassing

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 25 Nov 2009 10:21:22 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f.htm/, Retrieved Wed, 25 Nov 2009 18:23:44 +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/2009/Nov/25/t12591698123hise409lpir07f.htm/}, year = {2009}, } @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 = {2009}, 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 «

103.7 114813 116476 106370 106.2 117925 123297 109375 107.7 126466 114813 116476 109.9 131235 117925 123297 111.7 120546 126466 114813 114.9 123791 131235 117925 116 129813 120546 126466 118.3 133463 123791 131235 120.4 122987 129813 120546 126 125418 133463 123791 128.1 130199 122987 129813 130.1 133016 125418 133463 130.8 121454 130199 122987 133.6 122044 133016 125418 134.2 128313 121454 130199 135.5 131556 122044 133016 136.2 120027 128313 121454 139.1 123001 131556 122044 139 130111 120027 128313 139.6 132524 123001 131556 138.7 123742 130111 120027 140.9 124931 132524 123001 141.3 133646 123742 130111 141.8 136557 124931 132524 142 127509 133646 123742 144.5 128945 136557 124931 144.6 137191 127509 133646 145.5 139716 128945 136557 146.8 129083 137191 127509 149.5 131604 139716 128945 149.9 139413 129083 137191 150.1 143125 131604 139716 150.9 133948 139413 129083 152.8 137116 143125 131604 153.1 144864 133948 139413 154 149277 137116 143125 154.9 138796 144864 133948 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation HFCE[t] = + 74688.2221859923 -442.983839249867RPI[t] + 0.630377456292092`HFCE-2`[t] + 0.211331462128754`HFCE-4`[t] -12639.0030914300Q1[t] -11436.7551722576Q2[t] -1206.43033445861Q3[t] + 720.266859665427t + 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) 74688.2221859923 10682.706333 6.9915 0 0 RPI -442.983839249867 79.24004 -5.5904 0 0 `HFCE-2` 0.630377456292092 0.159069 3.9629 0.000163 8.1e-05 `HFCE-4` 0.211331462128754 0.149661 1.4121 0.161906 0.080953 Q1 -12639.0030914300 2487.099896 -5.0818 2e-06 1e-06 Q2 -11436.7551722576 2762.014669 -4.1407 8.7e-05 4.3e-05 Q3 -1206.43033445861 645.142892 -1.87 0.065233 0.032616 t 720.266859665427 119.451131 6.0298 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.998043546349196 R-squared 0.99609092040928 Adjusted R-squared 0.995740105574216 F-TEST (value) 2839.36373507684 F-TEST (DF numerator) 7 F-TEST (DF denominator) 78 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1980.38801473839 Sum Squared Residuals 305911061.735718 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 114813 112735.23404973 2077.76595027006 2 117925 118485.144903508 -560.144903507992 3 126466 124923.803215492 1542.19678450810 4 131235 129279.162510427 1955.83748957257 5 120546 120154.173097504 391.826902496483 6 123791 124323.073189943 -532.073189943426 7 129813 129853.260051969 -40.2600519685351 8 133463 133814.509004378 -351.509004377733 9 122987 122502.717753285 484.282246714847 10 125418 124931.171342398 486.828657602327 11 130199 129620.300810261 578.699189739208 12 133016 132964.837758901 51.1622410988788 13 121454 121535.939060933 -81.9390609332829 14 122044 124507.619168681 -2463.61916868131 15 128313 128914.372133384 -601.372133384241 16 131556 131232.433764512 323.566235487521 17 120027 120512.030753635 -485.030753635457 18 123001 123318.89205206 -317.892052059885 19 130111 128370.997375943 1740.00262405706 20 132524 132591.994753213 -67.9947532132893 21 123742 123117.487264128 624.512735872052 22 124931 126215.038167020 -1284.03816701980 23 133646 132955.028203363 690.971796637425 24 136557 135919.695091510 637.304908490343 25 127509 127550.188723066 -41.1887230659590 26 128945 130451.545787516 -1506.54578751649 27 137191 137495.937568977 -304.937568977185 28 139716 140544.357221269 -828.357221268587 29 129083 131335.707433723 -2252.70743372279 30 131604 133957.340903340 -2353.3409033404 31 139413 139770.574809065 -357.57480906478 32 143125 143731.468744526 -606.468744526318 33 133948 134133.875560732 -185.875560731733 34 137116 138087.448778778 -971.448778777643 35 144864 144770.458795838 93.541204161959 36 149277 149079.968703592 197.031296407523 37 138796 139707.322719899 -911.322719898554 38 143258 144195.223606878 -937.223606877547 39 150034 149511.749594643 522.250405356648 40 154708 154607.917750092 100.082249907925 41 144888 144524.162187966 363.837812033706 42 148762 149007.070663782 -245.070663782258 43 156500 154977.845808218 1522.15419178214 44 161088 160024.299834532 1063.70016546766 45 152772 151218.238088927 1553.76191107349 46 158011 156054.251810869 1956.74818913052 47 163318 163353.609051836 -35.6090518361932 48 169969 169020.861880621 948.138119378625 49 162269 158380.017682861 3888.98231713871 50 165765 164229.088551916 1535.91144808422 51 170600 171314.414753673 -714.414753673445 52 174681 176363.194866438 -1682.19486643813 53 166364 165953.678145304 410.321854695638 54 170240 170257.312052448 -17.3120524476215 55 176150 176942.543681398 -792.543681398274 56 182056 182263.624360908 -207.624360907873 57 172218 172268.476741380 -50.4767413797112 58 177856 177802.855461865 53.1445381350352 59 182253 183534.972381959 -1281.97238195882 60 188090 189555.087147190 -1465.08714719031 61 176863 177886.057827069 -1023.05782706950 62 183273 183749.306539341 -476.306539341452 63 187969 188330.38305437 -361.383054369768 64 194650 195044.059264597 -394.059264596803 65 183036 183314.271786935 -278.271786935468 66 189516 189695.513425381 -179.513425381408 67 193805 193830.031668451 -25.0316684512737 68 200499 200456.109367181 42.8906328194848 69 188142 188565.166524664 -423.166524664422 70 193732 195102.291424166 -1370.29142416612 71 197126 198859.620847824 -1733.62084782451 72 205140 205237.498606936 -97.4986069363634 73 191751 192625.348664677 -874.348664677184 74 196700 199274.90619809 -2574.90619809002 75 199784 201749.560589 -1965.56058899985 76 207360 207559.340089389 -199.340089388571 77 196101 194046.396843587 2054.60315641288 78 200824 200328.683967844 495.316032155527 79 205743 204479.215042721 1263.78495727856 80 212489 209788.175754026 2700.82424597419 81 200810 198015.006306630 2794.99369337044 82 203683 203327.633776398 355.3662236015 83 207286 207025.320561614 260.679438385779 84 210910 213030.403525761 -2120.40352576076 85 194915 202952.502783364 -8037.50278336425 86 217920 207013.588227776 10906.4117722243 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.113073177669745 0.226146355339490 0.886926822330255 12 0.0579507703149179 0.115901540629836 0.942049229685082 13 0.0223412149828266 0.0446824299656531 0.977658785017173 14 0.00811049374063787 0.0162209874812757 0.991889506259362 15 0.00477605397739956 0.00955210795479913 0.9952239460226 16 0.00460524344286830 0.00921048688573661 0.995394756557132 17 0.00176711583955673 0.00353423167911345 0.998232884160443 18 0.00153682717133702 0.00307365434267403 0.998463172828663 19 0.00240144017456847 0.00480288034913693 0.997598559825432 20 0.00116305764280964 0.00232611528561928 0.99883694235719 21 0.000516912283490055 0.00103382456698011 0.99948308771651 22 0.000196616444311713 0.000393232888623426 0.999803383555688 23 9.3338518393699e-05 0.000186677036787398 0.999906661481606 24 3.85954730223443e-05 7.71909460446885e-05 0.999961404526978 25 1.51403647196483e-05 3.02807294392967e-05 0.99998485963528 26 5.60047744306368e-06 1.12009548861274e-05 0.999994399522557 27 2.04870017605542e-06 4.09740035211084e-06 0.999997951299824 28 7.45712585792165e-07 1.49142517158433e-06 0.999999254287414 29 8.01848485735302e-07 1.60369697147060e-06 0.999999198151514 30 3.18796800462823e-07 6.37593600925646e-07 0.9999996812032 31 1.72074146108611e-07 3.44148292217223e-07 0.999999827925854 32 6.60889359225331e-08 1.32177871845066e-07 0.999999933911064 33 4.57107100686876e-08 9.14214201373752e-08 0.99999995428929 34 6.27642329314905e-08 1.25528465862981e-07 0.999999937235767 35 3.18502061387298e-08 6.37004122774595e-08 0.999999968149794 36 1.9861765013917e-08 3.9723530027834e-08 0.999999980138235 37 7.09973856037044e-09 1.41994771207409e-08 0.999999992900261 38 9.93207370174289e-09 1.98641474034858e-08 0.999999990067926 39 7.40515131545105e-09 1.48103026309021e-08 0.999999992594849 40 3.56815344651333e-09 7.13630689302666e-09 0.999999996431847 41 2.45221629833006e-09 4.90443259666012e-09 0.999999997547784 42 4.96853179751971e-09 9.93706359503941e-09 0.999999995031468 43 5.15119093568681e-09 1.03023818713736e-08 0.99999999484881 44 3.04504367912784e-09 6.09008735825569e-09 0.999999996954956 45 2.59364542844847e-09 5.18729085689694e-09 0.999999997406355 46 1.34114427967779e-08 2.68228855935559e-08 0.999999986588557 47 1.13039473784277e-08 2.26078947568555e-08 0.999999988696053 48 4.6087750046188e-09 9.2175500092376e-09 0.999999995391225 49 5.68675936267155e-08 1.13735187253431e-07 0.999999943132406 50 2.49836299052218e-08 4.99672598104436e-08 0.99999997501637 51 4.59745495067858e-08 9.19490990135716e-08 0.99999995402545 52 2.54943167700930e-07 5.09886335401859e-07 0.999999745056832 53 2.33447832053266e-07 4.66895664106532e-07 0.999999766552168 54 1.01321132888002e-07 2.02642265776003e-07 0.999999898678867 55 8.86884895039645e-08 1.77376979007929e-07 0.99999991131151 56 5.84407036040984e-08 1.16881407208197e-07 0.999999941559296 57 3.06795256232356e-08 6.13590512464713e-08 0.999999969320474 58 1.62051504315226e-08 3.24103008630452e-08 0.99999998379485 59 1.78734280458408e-08 3.57468560916816e-08 0.999999982126572 60 4.33518048156874e-08 8.67036096313748e-08 0.999999956648195 61 2.48333521653283e-08 4.96667043306565e-08 0.999999975166648 62 1.53608198601331e-08 3.07216397202663e-08 0.99999998463918 63 7.9240922743439e-09 1.58481845486878e-08 0.999999992075908 64 1.48050709172517e-08 2.96101418345035e-08 0.99999998519493 65 1.71390945758549e-08 3.42781891517098e-08 0.999999982860905 66 8.34203198647446e-08 1.66840639729489e-07 0.99999991657968 67 1.32883450006375e-07 2.65766900012749e-07 0.99999986711655 68 2.9665872629177e-07 5.9331745258354e-07 0.999999703341274 69 3.37298606721484e-07 6.74597213442968e-07 0.999999662701393 70 1.64924718326091e-07 3.29849436652182e-07 0.999999835075282 71 7.7409490203867e-08 1.54818980407734e-07 0.99999992259051 72 5.63556525324159e-08 1.12711305064832e-07 0.999999943644347 73 1.77201105772803e-08 3.54402211545605e-08 0.99999998227989 74 2.75984526279561e-08 5.51969052559121e-08 0.999999972401547 75 9.43391343773175e-09 1.88678268754635e-08 0.999999990566087 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 61 0.938461538461538 NOK 5% type I error level 63 0.96923076923077 NOK 10% type I error level 63 0.96923076923077 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/10dnv81259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/10dnv81259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/1nywu1259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/1nywu1259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/292eq1259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/292eq1259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/3klty1259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/3klty1259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/4hy5f1259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/4hy5f1259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/5j0x01259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/5j0x01259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/6ankb1259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/6ankb1259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/7uv8b1259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/7uv8b1259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/8unuh1259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/8unuh1259169675.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/9cvhp1259169675.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t12591698123hise409lpir07f/9cvhp1259169675.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Quarterly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Quarterly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

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

Privacy Policy

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

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

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

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

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

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

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

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

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

FreeStatistics.org is powered by