Toon Wouters

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 25 Nov 2008 04:52:10 -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/25/t1227614041jtzvtujlt3n367b.htm/, Retrieved Tue, 25 Nov 2008 11:54:11 +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/25/t1227614041jtzvtujlt3n367b.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 «

124 0 113 0 109 0 109 0 106 0 101 0 98 0 93 0 91 0 122 1 139 1 140 1 132 1 117 0 114 0 113 0 110 0 107 0 103 0 98 0 98 0 137 1 148 1 147 1 139 1 130 0 128 0 127 0 123 0 118 0 114 0 108 0 111 0 151 1 159 1 158 1 148 1 138 0 137 0 136 0 133 0 126 0 120 0 114 0 116 0 153 1 162 1 161 1 149 1 139 0 135 0 130 0 127 0 122 0 117 0 112 0 113 0 149 1 157 1 157 1 147 1 137 0 132 0 125 0 123 0 117 0 114 0 111 0 112 0 144 1 150 1 149 1 134 1 123 0 116 0 117 0 111 0 105 0 102 0 95 0 93 0 124 1 130 1 124 1 115 1 106 0 105 0 105 0 101 0 95 0 93 0 84 0 87 0 116 1 120 1 117 1 109 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Y[t] = + 134.000000000000 + 15.9553571428573X[t] -9.89203042328045M1[t] -3.87433862433845M2[t] -7.1413690476189M3[t] -8.78339947089932M4[t] -12.1754298941797M5[t] -17.4424603174601M6[t] -21.0844907407406M7[t] -26.726521164021M8[t] -25.8685515873014M9[t] -7.34093915343917M10[t] + 1.39203042328042M11[t] -0.107969576719577t + 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) 134.000000000000 13.896644 9.6426 0 0 X 15.9553571428573 13.416869 1.1892 0.237752 0.118876 M1 -9.89203042328045 6.21494 -1.5917 0.115264 0.057632 M2 -3.87433862433845 14.715624 -0.2633 0.792987 0.396493 M3 -7.1413690476189 14.72206 -0.4851 0.628898 0.314449 M4 -8.78339947089932 14.728637 -0.5963 0.552566 0.276283 M5 -12.1754298941797 14.735356 -0.8263 0.411018 0.205509 M6 -17.4424603174601 14.742216 -1.1832 0.240122 0.120061 M7 -21.0844907407406 14.749218 -1.4295 0.156604 0.078302 M8 -26.726521164021 14.75636 -1.8112 0.07373 0.036865 M9 -25.8685515873014 14.763644 -1.7522 0.083436 0.041718 M10 -7.34093915343917 6.215453 -1.1811 0.240945 0.120472 M11 1.39203042328042 6.21494 0.224 0.823322 0.411661 t -0.107969576719577 0.046138 -2.3401 0.021677 0.010839 Multiple Linear Regression - Regression Statistics Multiple R 0.79025662180139 R-squared 0.624505528300945 Adjusted R-squared 0.565693141167358 F-TEST (value) 10.6186053438443 F-TEST (DF numerator) 13 F-TEST (DF denominator) 83 p-value 7.49511563924443e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.4295365269589 Sum Squared Residuals 12822.9503968254 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 124 124 -8.43769498715119e-15 2 113 129.909722222222 -16.9097222222223 3 109 126.534722222222 -17.5347222222222 4 109 124.784722222222 -15.7847222222223 5 106 121.284722222222 -15.2847222222222 6 101 115.909722222222 -14.9097222222222 7 98 112.159722222222 -14.1597222222222 8 93 106.409722222222 -13.4097222222223 9 91 107.159722222222 -16.1597222222222 10 122 141.534722222222 -19.5347222222223 11 139 150.159722222222 -11.1597222222222 12 140 148.659722222222 -8.65972222222222 13 132 138.659722222222 -6.65972222222221 14 117 128.614087301587 -11.6140873015873 15 114 125.239087301587 -11.2390873015873 16 113 123.489087301587 -10.4890873015873 17 110 119.989087301587 -9.9890873015873 18 107 114.614087301587 -7.6140873015873 19 103 110.864087301587 -7.8640873015873 20 98 105.114087301587 -7.1140873015873 21 98 105.864087301587 -7.86408730158731 22 137 140.239087301587 -3.23908730158729 23 148 148.864087301587 -0.864087301587299 24 147 147.364087301587 -0.364087301587306 25 139 137.364087301587 1.63591269841271 26 130 127.318452380952 2.68154761904762 27 128 123.943452380952 4.05654761904762 28 127 122.193452380952 4.80654761904763 29 123 118.693452380952 4.30654761904762 30 118 113.318452380952 4.68154761904762 31 114 109.568452380952 4.43154761904762 32 108 103.818452380952 4.18154761904762 33 111 104.568452380952 6.4315476190476 34 151 138.943452380952 12.0565476190476 35 159 147.568452380952 11.4315476190476 36 158 146.068452380952 11.9315476190476 37 148 136.068452380952 11.9315476190476 38 138 126.022817460317 11.9771825396825 39 137 122.647817460317 14.3521825396825 40 136 120.897817460317 15.1021825396826 41 133 117.397817460317 15.6021825396825 42 126 112.022817460317 13.9771825396825 43 120 108.272817460317 11.7271825396825 44 114 102.522817460317 11.4771825396825 45 116 103.272817460317 12.7271825396825 46 153 137.647817460317 15.3521825396826 47 162 146.272817460317 15.7271825396825 48 161 144.772817460317 16.2271825396825 49 149 134.772817460317 14.2271825396826 50 139 124.727182539683 14.2728174603175 51 135 121.352182539683 13.6478174603175 52 130 119.602182539683 10.3978174603175 53 127 116.102182539683 10.8978174603175 54 122 110.727182539683 11.2728174603175 55 117 106.977182539683 10.0228174603175 56 112 101.227182539683 10.7728174603175 57 113 101.977182539683 11.0228174603174 58 149 136.352182539683 12.6478174603175 59 157 144.977182539683 12.0228174603175 60 157 143.477182539683 13.5228174603175 61 147 133.477182539683 13.5228174603175 62 137 123.431547619048 13.5684523809524 63 132 120.056547619048 11.9434523809524 64 125 118.306547619048 6.69345238095239 65 123 114.806547619048 8.19345238095238 66 117 109.431547619048 7.56845238095238 67 114 105.681547619048 8.31845238095238 68 111 99.9315476190476 11.0684523809524 69 112 100.681547619048 11.3184523809524 70 144 135.056547619048 8.94345238095239 71 150 143.681547619048 6.31845238095238 72 149 142.181547619048 6.81845238095237 73 134 132.181547619048 1.81845238095239 74 123 122.135912698413 0.864087301587304 75 116 118.760912698413 -2.7609126984127 76 117 117.010912698413 -0.0109126984126943 77 111 113.510912698413 -2.5109126984127 78 105 108.135912698413 -3.1359126984127 79 102 104.385912698413 -2.3859126984127 80 95 98.6359126984127 -3.6359126984127 81 93 99.3859126984127 -6.38591269841271 82 124 133.760912698413 -9.76091269841268 83 130 142.385912698413 -12.3859126984127 84 124 140.885912698413 -16.8859126984127 85 115 130.885912698413 -15.8859126984127 86 106 120.840277777778 -14.8402777777778 87 105 117.465277777778 -12.4652777777778 88 105 115.715277777778 -10.7152777777778 89 101 112.215277777778 -11.2152777777778 90 95 106.840277777778 -11.8402777777778 91 93 103.090277777778 -10.0902777777778 92 84 97.3402777777778 -13.3402777777778 93 87 98.0902777777778 -11.0902777777778 94 116 132.465277777778 -16.4652777777778 95 120 141.090277777778 -21.0902777777778 96 117 139.590277777778 -22.5902777777778 97 109 129.590277777778 -20.5902777777778 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 6.2641703918555e-05 0.00012528340783711 0.999937358296081 18 3.70343312513454e-05 7.40686625026908e-05 0.999962965668749 19 2.72705157862402e-06 5.45410315724804e-06 0.999997272948421 20 2.02094521616298e-07 4.04189043232596e-07 0.999999797905478 21 2.95669508954081e-07 5.91339017908162e-07 0.999999704330491 22 0.000529957649785177 0.00105991529957035 0.999470042350215 23 0.000253680409933842 0.000507360819867685 0.999746319590066 24 9.58513692382124e-05 0.000191702738476425 0.999904148630762 25 3.49499462636003e-05 6.98998925272007e-05 0.999965050053736 26 5.34025932889695e-05 0.000106805186577939 0.99994659740671 27 9.42438345116769e-05 0.000188487669023354 0.999905756165488 28 9.49460059119289e-05 0.000189892011823858 0.999905053994088 29 7.48265972426933e-05 0.000149653194485387 0.999925173402757 30 5.29004494029706e-05 0.000105800898805941 0.999947099550597 31 4.37694205656377e-05 8.75388411312754e-05 0.999956230579434 32 4.7032167318997e-05 9.4064334637994e-05 0.99995296783268 33 9.84162002795716e-05 0.000196832400559143 0.99990158379972 34 0.000791733735523188 0.00158346747104638 0.999208266264477 35 0.00063762292083829 0.00127524584167658 0.999362377079162 36 0.0004688528621542 0.0009377057243084 0.999531147137846 37 0.000370859111639514 0.000741718223279028 0.99962914088836 38 0.000335932174158629 0.000671864348317258 0.999664067825841 39 0.000255175337496514 0.000510350674993028 0.999744824662503 40 0.000165873554471583 0.000331747108943166 0.999834126445528 41 0.000106872989203118 0.000213745978406236 0.999893127010797 42 8.33535612173559e-05 0.000166707122434712 0.999916646438783 43 0.000175226789317748 0.000350453578635496 0.999824773210682 44 0.000511238231176356 0.00102247646235271 0.999488761768824 45 0.000973893819677825 0.00194778763935565 0.999026106180322 46 0.000880085038581775 0.00176017007716355 0.999119914961418 47 0.00110550134201218 0.00221100268402437 0.998894498657988 48 0.00138608132914880 0.00277216265829761 0.998613918670851 49 0.00490154824065984 0.00980309648131969 0.99509845175934 50 0.0094370096094911 0.0188740192189822 0.99056299039051 51 0.0200206323049994 0.0400412646099987 0.979979367695 52 0.102554015696499 0.205108031392998 0.897445984303501 53 0.234702289917181 0.469404579834362 0.765297710082819 54 0.375259144365492 0.750518288730983 0.624740855634508 55 0.620613954180037 0.758772091639926 0.379386045819963 56 0.796929071200816 0.406141857598368 0.203070928799184 57 0.921951471516782 0.156097056966435 0.0780485284832175 58 0.942518656602666 0.114962686794667 0.0574813433973337 59 0.957065704526233 0.0858685909475344 0.0429342954737672 60 0.959547612462774 0.0809047750744523 0.0404523875372262 61 0.96143642470713 0.0771271505857407 0.0385635752928704 62 0.960429645695236 0.0791407086095273 0.0395703543047637 63 0.96007379716299 0.079852405674021 0.0399262028370105 64 0.978589113507144 0.0428217729857127 0.0214108864928564 65 0.979920271089343 0.0401594578213133 0.0200797289106567 66 0.980683058024962 0.0386338839500760 0.0193169419750380 67 0.979526743273375 0.0409465134532501 0.0204732567266251 68 0.970686667973219 0.0586266640535618 0.0293133320267809 69 0.958854249899225 0.0822915002015507 0.0411457501007753 70 0.95941727591016 0.0811654481796798 0.0405827240898399 71 0.973982571655941 0.0520348566881177 0.0260174283440589 72 0.998461053955578 0.00307789208884393 0.00153894604442197 73 0.999701237575306 0.000597524849388126 0.000298762424694063 74 0.99998205410541 3.58917891795488e-05 1.79458945897744e-05 75 0.999962793566485 7.44128670306412e-05 3.72064335153206e-05 76 0.999950008779108 9.99824417840524e-05 4.99912208920262e-05 77 0.999826212971373 0.000347574057254115 0.000173787028627058 78 0.999388701489658 0.00122259702068477 0.000611298510342383 79 0.997069759401763 0.00586048119647441 0.00293024059823721 80 0.994581690202565 0.0108366195948695 0.00541830979743475 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 41 0.640625 NOK 5% type I error level 48 0.75 NOK 10% type I error level 57 0.890625 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/10tqxl1227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/10tqxl1227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/169f41227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/169f41227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/216b91227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/216b91227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/3wzyl1227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/3wzyl1227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/41rol1227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/41rol1227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/5m95r1227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/5m95r1227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/60gdq1227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/60gdq1227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/7j6wz1227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/7j6wz1227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/8hjap1227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/8hjap1227613924.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/9fqkx1227613924.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227614041jtzvtujlt3n367b/9fqkx1227613924.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