multiple regression

*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: Thu, 11 Dec 2008 03:49:03 -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/11/t1228992626egoj6vzguulq72d.htm/, Retrieved Thu, 11 Dec 2008 10:50:36 +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/Dec/11/t1228992626egoj6vzguulq72d.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:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

2648,9 0 2669,6 0 3042,3 0 2604,2 0 2732,1 0 2621,7 0 2483,7 0 2479,3 0 2684,6 0 2834,7 0 2566,1 0 2251,2 0 2350 1 2299,8 1 2542,8 1 2530,2 1 2508,1 1 2616,8 1 2534,1 1 2181,8 1 2578,9 1 2841,9 1 2529,9 1 2103,2 1 2326,2 1 2452,6 1 2782,1 1 2727,3 1 2648,2 1 2760,7 1 2613 1 2225,4 1 2713,9 1 2923,3 1 2707 1 2473,9 1 2521 1 2531,8 1 3068,8 1 2826,9 1 2674,2 1 2966,6 1 2798,8 1 2629,6 1 3124,6 1 3115,7 1 3083 1 2863,9 1 2728,7 1 2789,4 1 3225,7 1 3148,2 1 2836,5 1 3153,5 1 2656,9 1 2834,7 1 3172,5 1 2998,8 1 3103,1 1 2735,6 1 2818,1 1 2874,4 1 3438,5 1 2949,1 1 3306,8 1 3530 1 3003,8 1 3206,4 1 3514,6 1 3522,6 1 3525,5 1 2996,2 1 3231,1 1 3030 1 3541,7 1 3113,2 1 3390,8 1 3424,2 1 3079,8 1 3123,4 1 3317,1 1 3579,9 1 3317,9 1 2668,1 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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Uitvoer_D[t] = + 2201.40380952381 -321.558888888888Euro[t] + 226.961587301585M1[t] + 216.607936507936M2[t] + 630.64M3[t] + 367.943492063493M4[t] + 382.446984126984M5[t] + 508.264761904763M6[t] + 222.625396825397M7[t] + 138.971746031746M8[t] + 471.760952380952M9[t] + 559.564444444445M10[t] + 405.210793650794M11[t] + 13.7250793650794t + 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) 2201.40380952381 66.466234 33.1206 0 0 Euro -321.558888888888 56.01116 -5.741 0 0 M1 226.961587301585 76.439485 2.9692 0.004087 0.002043 M2 216.607936507936 76.347818 2.8371 0.00595 0.002975 M3 630.64 76.264786 8.2691 0 0 M4 367.943492063493 76.190418 4.8293 8e-06 4e-06 M5 382.446984126984 76.124738 5.024 4e-06 2e-06 M6 508.264761904763 76.06777 6.6817 0 0 M7 222.625396825397 76.019533 2.9285 0.004593 0.002296 M8 138.971746031746 75.980043 1.8291 0.071651 0.035825 M9 471.760952380952 75.949315 6.2115 0 0 M10 559.564444444445 75.927359 7.3697 0 0 M11 405.210793650794 75.914182 5.3377 1e-06 1e-06 t 13.7250793650794 0.816659 16.8064 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.930155464214882 R-squared 0.865189187608803 Adjusted R-squared 0.84015289387901 F-TEST (value) 34.5573988285339 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 142.014211249527 Sum Squared Residuals 1411762.53377778 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2648.9 2442.09047619048 206.809523809518 2 2669.6 2445.46190476191 224.138095238094 3 3042.3 2873.21904761904 169.080952380956 4 2604.2 2624.24761904762 -20.0476190476165 5 2732.1 2652.47619047619 79.6238095238104 6 2621.7 2792.01904761905 -170.319047619048 7 2483.7 2520.10476190476 -36.4047619047618 8 2479.3 2450.17619047619 29.1238095238083 9 2684.6 2796.69047619048 -112.090476190476 10 2834.7 2898.21904761905 -63.5190476190474 11 2566.1 2757.59047619047 -191.490476190475 12 2251.2 2366.10476190476 -114.904761904762 13 2350 2285.23253968254 64.7674603174614 14 2299.8 2288.60396825397 11.1960317460319 15 2542.8 2716.36111111111 -173.561111111111 16 2530.2 2467.38968253968 62.8103174603168 17 2508.1 2495.61825396825 12.4817460317458 18 2616.8 2635.16111111111 -18.3611111111108 19 2534.1 2363.24682539683 170.853174603174 20 2181.8 2293.31825396825 -111.518253968254 21 2578.9 2639.83253968254 -60.9325396825397 22 2841.9 2741.36111111111 100.538888888889 23 2529.9 2600.73253968254 -70.83253968254 24 2103.2 2209.24682539683 -106.046825396825 25 2326.2 2449.93349206349 -123.733492063491 26 2452.6 2453.30492063492 -0.704920634920697 27 2782.1 2881.06206349206 -98.962063492064 28 2727.3 2632.09063492064 95.2093650793648 29 2648.2 2660.31920634921 -12.1192063492067 30 2760.7 2799.86206349206 -39.1620634920636 31 2613 2527.94777777778 85.052222222222 32 2225.4 2458.01920634921 -232.619206349206 33 2713.9 2804.53349206349 -90.633492063492 34 2923.3 2906.06206349206 17.2379365079365 35 2707 2765.43349206349 -58.4334920634923 36 2473.9 2373.94777777778 99.9522222222223 37 2521 2614.63444444444 -93.6344444444436 38 2531.8 2618.00587301587 -86.2058730158727 39 3068.8 3045.76301587302 23.0369841269838 40 2826.9 2796.79158730159 30.1084126984123 41 2674.2 2825.02015873016 -150.820158730159 42 2966.6 2964.56301587302 2.03698412698417 43 2798.8 2692.64873015873 106.151269841270 44 2629.6 2622.72015873016 6.87984126984121 45 3124.6 2969.23444444444 155.365555555555 46 3115.7 3070.76301587302 44.9369841269839 47 3083 2930.13444444444 152.865555555555 48 2863.9 2538.64873015873 325.25126984127 49 2728.7 2779.33539682540 -50.6353968253962 50 2789.4 2782.70682539683 6.69317460317478 51 3225.7 3210.46396825397 15.2360317460310 52 3148.2 2961.49253968254 186.707460317460 53 2836.5 2989.72111111111 -153.221111111111 54 3153.5 3129.26396825397 24.2360317460319 55 2656.9 2857.34968253968 -200.449682539683 56 2834.7 2787.42111111111 47.2788888888888 57 3172.5 3133.93539682540 38.5646031746032 58 2998.8 3235.46396825397 -236.663968253968 59 3103.1 3094.8353968254 8.2646031746029 60 2735.6 2703.34968253968 32.2503174603174 61 2818.1 2944.03634920635 -125.936349206348 62 2874.4 2947.40777777778 -73.0077777777776 63 3438.5 3375.16492063492 63.3350793650789 64 2949.1 3126.19349206349 -177.093492063493 65 3306.8 3154.42206349206 152.377936507937 66 3530 3293.96492063492 236.035079365080 67 3003.8 3022.05063492063 -18.2506349206348 68 3206.4 2952.12206349206 254.277936507937 69 3514.6 3298.63634920635 215.963650793651 70 3522.6 3400.16492063492 122.435079365079 71 3525.5 3259.53634920635 265.963650793651 72 2996.2 2868.05063492063 128.149365079365 73 3231.1 3108.7373015873 122.362698412699 74 3030 3112.10873015873 -82.10873015873 75 3541.7 3539.86587301587 1.83412698412645 76 3113.2 3290.89444444445 -177.694444444445 77 3390.8 3319.12301587302 71.6769841269842 78 3424.2 3458.66587301587 -34.4658730158731 79 3079.8 3186.75158730159 -106.951587301587 80 3123.4 3116.82301587302 6.57698412698441 81 3317.1 3463.3373015873 -146.237301587302 82 3579.9 3564.86587301587 15.0341269841272 83 3317.9 3424.2373015873 -106.337301587302 84 2668.1 3032.75158730159 -364.651587301587 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.506811294382964 0.986377411234073 0.493188705617036 18 0.587943902934499 0.824112194131003 0.412056097065501 19 0.66565973031277 0.668680539374461 0.334340269687230 20 0.565214099365742 0.869571801268515 0.434785900634258 21 0.472705927047209 0.945411854094418 0.527294072952791 22 0.458988552297921 0.917977104595842 0.541011447702079 23 0.401664004317992 0.803328008635984 0.598335995682008 24 0.316522429730645 0.63304485946129 0.683477570269355 25 0.235123145537769 0.470246291075539 0.76487685446223 26 0.186179173512081 0.372358347024161 0.81382082648792 27 0.144026913669174 0.288053827338348 0.855973086330826 28 0.163335450175247 0.326670900350493 0.836664549824753 29 0.114760626277251 0.229521252554501 0.88523937372275 30 0.0997838925093681 0.199567785018736 0.900216107490632 31 0.0759212110686243 0.151842422137249 0.924078788931376 32 0.092116506634572 0.184233013269144 0.907883493365428 33 0.0756989443597987 0.151397888719597 0.924301055640201 34 0.052573996581565 0.10514799316313 0.947426003418435 35 0.0498964429361935 0.099792885872387 0.950103557063807 36 0.0688861200713352 0.137772240142670 0.931113879928665 37 0.055253819602594 0.110507639205188 0.944746180397406 38 0.0425168183125392 0.0850336366250784 0.95748318168746 39 0.0347987750657924 0.0695975501315848 0.965201224934208 40 0.0227459163255642 0.0454918326511285 0.977254083674436 41 0.0241900592367839 0.0483801184735678 0.975809940763216 42 0.0227201176880383 0.0454402353760767 0.977279882311962 43 0.0175554058425744 0.0351108116851487 0.982444594157426 44 0.0181391325602460 0.0362782651204921 0.981860867439754 45 0.0265958830802024 0.0531917661604047 0.973404116919798 46 0.0169837608467662 0.0339675216935324 0.983016239153234 47 0.0218721184515816 0.0437442369031631 0.978127881548418 48 0.0777980783745958 0.155596156749192 0.922201921625404 49 0.0608265145826821 0.121653029165364 0.939173485417318 50 0.0426743780706690 0.0853487561413379 0.957325621929331 51 0.0281661679652500 0.0563323359305001 0.97183383203475 52 0.0440236593480444 0.0880473186960889 0.955976340651956 53 0.0653381002925106 0.130676200585021 0.93466189970749 54 0.0490891660458704 0.0981783320917408 0.95091083395413 55 0.0775472563329176 0.155094512665835 0.922452743667082 56 0.067584640893554 0.135169281787108 0.932415359106446 57 0.0474500056307154 0.0949000112614307 0.952549994369285 58 0.212912020109902 0.425824040219803 0.787087979890098 59 0.266261299598019 0.532522599196038 0.733738700401981 60 0.202739620067297 0.405479240134593 0.797260379932703 61 0.478261363471815 0.95652272694363 0.521738636528185 62 0.479548216385095 0.95909643277019 0.520451783614905 63 0.44340885426703 0.88681770853406 0.55659114573297 64 0.55117509714579 0.89764980570842 0.44882490285421 65 0.576018038013696 0.847963923972608 0.423981961986304 66 0.463765762032375 0.92753152406475 0.536234237967625 67 0.48913100554734 0.97826201109468 0.51086899445266 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 7 0.137254901960784 NOK 10% type I error level 16 0.313725490196078 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/105vht1228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/105vht1228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/1oa2u1228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/1oa2u1228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/2gnze1228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/2gnze1228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/35t4k1228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/35t4k1228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/48qi61228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/48qi61228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/55mh01228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/55mh01228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/6swg21228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/6swg21228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/754ln1228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/754ln1228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/8mjp91228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/8mjp91228992538.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/9agkl1228992538.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1228992626egoj6vzguulq72d/9agkl1228992538.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