Paper - s0410061

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Mon, 22 Dec 2008 10:28:18 -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/22/t1229966931sgvo9fiftx67lj4.htm/, Retrieved Mon, 22 Dec 2008 18:29:01 +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/2008/Dec/22/t1229966931sgvo9fiftx67lj4.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

101.0 0 98.7 0 105.1 0 98.4 0 101.7 0 102.9 0 92.2 0 94.9 0 92.8 0 98.5 0 94.3 0 87.4 0 103.4 0 101.2 0 109.6 0 111.9 0 108.9 0 105.6 0 107.8 0 97.5 0 102.4 0 105.6 0 99.8 0 96.2 0 113.1 0 107.4 0 116.8 0 112.9 0 105.3 0 109.3 0 107.9 0 101.1 0 114.7 0 116.2 0 108.4 0 113.4 0 108.7 0 112.6 0 124.2 1 114.9 1 110.5 1 121.5 1 118.1 1 111.7 1 132.7 1 119.0 1 116.7 1 120.1 1 113.4 1 106.6 1 116.3 1 112.6 1 111.6 1 125.1 1 110.7 1 109.6 1 114.2 1 113.4 1 116.0 1 109.6 1 117.8 1 115.8 1 125.3 1 113.0 1 120.5 1 116.6 1 111.8 1 115.2 1 118.6 1 122.4 1 116.4 1 114.5 1 119.8 1 115.8 1 127.8 1 118.8 1 119.7 1 118.6 1 120.8 1 115.9 1 109.7 1 114.8 1 116.2 1 112.2 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 96.0952380952381 + 4.43333333333331DUM[t] + 6.0958333333333M1[t] + 3.17976190476191M2[t] + 11.9303571428572M3[t] + 5.65714285714287M4[t] + 4.85535714285714M5[t] + 7.72500000000001M6[t] + 3.20892857142858M7[t] -0.321428571428567M8[t] + 5.09107142857143M9[t] + 5.58928571428572M10[t] + 2.24464285714286M11[t] + 0.187500000000000t + 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) 96.0952380952381 2.451748 39.1946 0 0 DUM 4.43333333333331 2.379974 1.8628 0.066692 0.033346 M1 6.0958333333333 2.900367 2.1017 0.039176 0.019588 M2 3.17976190476191 2.896628 1.0977 0.276077 0.138039 M3 11.9303571428572 2.918577 4.0877 0.000115 5.7e-05 M4 5.65714285714287 2.911555 1.943 0.056039 0.028019 M5 4.85535714285714 2.905344 1.6712 0.099151 0.049576 M6 7.72500000000001 2.899951 2.6638 0.009581 0.00479 M7 3.20892857142858 2.89538 1.1083 0.271529 0.135764 M8 -0.321428571428567 2.891635 -0.1112 0.911809 0.455905 M9 5.09107142857143 2.888719 1.7624 0.082367 0.041184 M10 5.58928571428572 2.886634 1.9363 0.056874 0.028437 M11 2.24464285714286 2.885382 0.7779 0.439228 0.219614 t 0.187500000000000 0.049074 3.8207 0.000285 0.000143 Multiple Linear Regression - Regression Statistics Multiple R 0.822720530353184 R-squared 0.676869071064625 Adjusted R-squared 0.616859041405198 F-TEST (value) 11.2792657311793 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 1.48470125083122e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.39727444443688 Sum Squared Residuals 2039.14 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101 102.378571428572 -1.37857142857155 2 98.7 99.65 -0.949999999999967 3 105.1 108.588095238095 -3.48809523809521 4 98.4 102.502380952381 -4.10238095238093 5 101.7 101.888095238095 -0.188095238095272 6 102.9 104.945238095238 -2.04523809523809 7 92.2 100.616666666667 -8.41666666666666 8 94.9 97.2738095238095 -2.37380952380953 9 92.8 102.873809523810 -10.0738095238095 10 98.5 103.559523809524 -5.05952380952381 11 94.3 100.402380952381 -6.10238095238095 12 87.4 98.3452380952381 -10.9452380952381 13 103.4 104.628571428571 -1.2285714285714 14 101.2 101.9 -0.700000000000013 15 109.6 110.838095238095 -1.23809523809525 16 111.9 104.752380952381 7.14761904761905 17 108.9 104.138095238095 4.76190476190477 18 105.6 107.195238095238 -1.59523809523810 19 107.8 102.866666666667 4.93333333333333 20 97.5 99.5238095238095 -2.02380952380952 21 102.4 105.123809523810 -2.72380952380952 22 105.6 105.809523809524 -0.209523809523815 23 99.8 102.652380952381 -2.85238095238096 24 96.2 100.595238095238 -4.39523809523809 25 113.1 106.878571428571 6.22142857142858 26 107.4 104.15 3.25 27 116.8 113.088095238095 3.71190476190475 28 112.9 107.002380952381 5.89761904761905 29 105.3 106.388095238095 -1.08809523809524 30 109.3 109.445238095238 -0.145238095238096 31 107.9 105.116666666667 2.78333333333334 32 101.1 101.773809523810 -0.673809523809531 33 114.7 107.373809523810 7.32619047619048 34 116.2 108.059523809524 8.14047619047619 35 108.4 104.902380952381 3.49761904761905 36 113.4 102.845238095238 10.5547619047619 37 108.7 109.128571428571 -0.428571428571408 38 112.6 106.4 6.19999999999999 39 124.2 119.771428571429 4.42857142857143 40 114.9 113.685714285714 1.21428571428572 41 110.5 113.071428571429 -2.57142857142856 42 121.5 116.128571428571 5.37142857142858 43 118.1 111.8 6.3 44 111.7 108.457142857143 3.24285714285715 45 132.7 114.057142857143 18.6428571428571 46 119 114.742857142857 4.25714285714286 47 116.7 111.585714285714 5.11428571428572 48 120.1 109.528571428571 10.5714285714286 49 113.4 115.811904761905 -2.41190476190473 50 106.6 113.083333333333 -6.48333333333334 51 116.3 122.021428571429 -5.72142857142858 52 112.6 115.935714285714 -3.3357142857143 53 111.6 115.321428571429 -3.72142857142857 54 125.1 118.378571428571 6.72142857142857 55 110.7 114.05 -3.35 56 109.6 110.707142857143 -1.10714285714286 57 114.2 116.307142857143 -2.10714285714285 58 113.4 116.992857142857 -3.59285714285714 59 116 113.835714285714 2.16428571428571 60 109.6 111.778571428571 -2.17857142857143 61 117.8 118.061904761905 -0.261904761904745 62 115.8 115.333333333333 0.466666666666661 63 125.3 124.271428571429 1.02857142857142 64 113 118.185714285714 -5.18571428571429 65 120.5 117.571428571429 2.92857142857143 66 116.6 120.628571428571 -4.02857142857143 67 111.8 116.3 -4.50000000000001 68 115.2 112.957142857143 2.24285714285714 69 118.6 118.557142857143 0.0428571428571376 70 122.4 119.242857142857 3.15714285714286 71 116.4 116.085714285714 0.314285714285716 72 114.5 114.028571428571 0.471428571428576 73 119.8 120.311904761905 -0.511904761904748 74 115.8 117.583333333333 -1.78333333333334 75 127.8 126.521428571429 1.27857142857142 76 118.8 120.435714285714 -1.63571428571430 77 119.7 119.821428571429 -0.121428571428565 78 118.6 122.878571428571 -4.27857142857144 79 120.8 118.55 2.24999999999999 80 115.9 115.207142857143 0.692857142857145 81 109.7 120.807142857143 -11.1071428571429 82 114.8 121.492857142857 -6.69285714285715 83 116.2 118.335714285714 -2.13571428571429 84 112.2 116.278571428571 -4.07857142857143 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.367739311631916 0.735478623263831 0.632260688368084 18 0.249223586189632 0.498447172379264 0.750776413810368 19 0.394065595888627 0.788131191777254 0.605934404111373 20 0.320502909720141 0.641005819440281 0.67949709027986 21 0.258986502633344 0.517973005266688 0.741013497366656 22 0.177490323654591 0.354980647309181 0.82250967634541 23 0.132782897992820 0.265565795985641 0.86721710200718 24 0.125005770982554 0.250011541965108 0.874994229017446 25 0.0800396341654173 0.160079268330835 0.919960365834583 26 0.0561483826031219 0.112296765206244 0.943851617396878 27 0.0329102860563057 0.0658205721126114 0.967089713943694 28 0.0217654919679976 0.0435309839359953 0.978234508032002 29 0.0708769850157292 0.141753970031458 0.92912301498427 30 0.0598113178555123 0.119622635711025 0.940188682144488 31 0.0383676054213887 0.0767352108427775 0.961632394578611 32 0.0359243732992130 0.0718487465984261 0.964075626700787 33 0.0732976409804145 0.146595281960829 0.926702359019586 34 0.0716953453555494 0.143390690711099 0.92830465464445 35 0.0549313571368012 0.109862714273602 0.945068642863199 36 0.15598250283679 0.31196500567358 0.84401749716321 37 0.249830501635261 0.499661003270522 0.750169498364739 38 0.19571785283655 0.3914357056731 0.80428214716345 39 0.147532670236599 0.295065340473199 0.8524673297634 40 0.125641374916653 0.251282749833305 0.874358625083347 41 0.122773505640573 0.245547011281147 0.877226494359427 42 0.114626879508127 0.229253759016254 0.885373120491873 43 0.101602562147939 0.203205124295879 0.89839743785206 44 0.0748954053127018 0.149790810625404 0.925104594687298 45 0.698178536605804 0.603642926788392 0.301821463394196 46 0.662896130556439 0.674207738887122 0.337103869443561 47 0.609414012396901 0.781171975206199 0.390585987603099 48 0.800454740949738 0.399090518100524 0.199545259050262 49 0.827700615652818 0.344598768694363 0.172299384347181 50 0.916441594502156 0.167116810995688 0.0835584054978442 51 0.96137627711823 0.0772474457635402 0.0386237228817701 52 0.95890050721083 0.0821989855783411 0.0410994927891706 53 0.970214021865163 0.0595719562696745 0.0297859781348373 54 0.987104322248965 0.0257913555020705 0.0128956777510353 55 0.985401847927112 0.0291963041457753 0.0145981520728877 56 0.982508980391946 0.0349820392161071 0.0174910196080535 57 0.974734554278693 0.0505308914426148 0.0252654457213074 58 0.970887789727873 0.0582244205442536 0.0291122102721268 59 0.948635271199713 0.102729457600573 0.0513647288002866 60 0.929158927745382 0.141682144509236 0.0708410722546181 61 0.888435613779275 0.223128772441451 0.111564386220725 62 0.823648019976522 0.352703960046956 0.176351980023478 63 0.746677171718646 0.506645656562707 0.253322828281354 64 0.745345215363193 0.509309569273614 0.254654784636807 65 0.619963496270825 0.76007300745835 0.380036503729175 66 0.513609020486398 0.972781959027204 0.486390979513602 67 0.755851550511594 0.488296898976812 0.244148449488406 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 4 0.0784313725490196 NOK 10% type I error level 12 0.235294117647059 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/10qpln1229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/10qpln1229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/1u0nr1229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/1u0nr1229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/20rl11229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/20rl11229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/3khgp1229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/3khgp1229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/4tpkb1229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/4tpkb1229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/56yw41229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/56yw41229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/6phvl1229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/6phvl1229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/7gb8a1229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/7gb8a1229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/88jyf1229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/88jyf1229966893.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/912n21229966893.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229966931sgvo9fiftx67lj4/912n21229966893.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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