Home » date » 2007 » Nov » 22 » attachments

Bouwgrond duurder

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

Title produced by software: Multiple Regression

Date of computation: Thu, 22 Nov 2007 13:12:33 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak.htm/, Retrieved Thu, 22 Nov 2007 21:07:12 +0100

User-defined keywords:

grond, Holly

Dataseries X:

» Textbox « » Textfile « » CSV «

4159 0 3497 0 4404 0 3849 0 3734 0 3060 0 3507 0 3287 0 3215 0 3764 0 2734 0 2837 0 2766 0 3851 0 3289 0 3848 0 3348 0 3682 0 4058 0 3655 0 3811 0 3341 0 3032 0 3475 0 3353 0 3186 0 3902 0 4164 0 3499 0 4145 0 3796 0 3711 0 3949 0 3740 0 3243 0 4407 0 4814 0 3908 0 5250 0 3937 0 4004 0 5560 0 3922 0 3759 1 4138 1 4634 1 3996 1 4307 1 4142 1 4429 1 5219 1 4929 1 5754 1 5588 1 4162 1 4947 1 5208 1 4752 1 4487 1 5612 1 5719 1 4994 1 6051 1 4897 1 5337 1 5570 1 4634 1 4733 1 4987 1 5326 1 4186 1 4679 1 4775 1 4266 1 4999 1 4273 1 4137 1 5115 1

Text written by user:

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 compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation y[t] = + 3326.69620534298 + 509.736341138397x[t] + 139.711598663197M1[t] -103.620201910322M2[t] + 593.047997516158M3[t] + 118.287625514067M4[t] + 91.0986820834048M5[t] + 491.195452938457M6[t] -45.4325593236219M7[t] -143.410892944017M8[t] + 44.066830291987M9[t] + 70.3778868613246M10[t] -591.311056569338M11[t] + 15.1889434306624t + 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) 3326.69620534298 241.722727 13.7624 0 0 x 509.736341138397 229.602338 2.2201 0.029962 0.014981 M1 139.711598663197 280.507809 0.4981 0.620144 0.310072 M2 -103.620201910322 280.352568 -0.3696 0.712896 0.356448 M3 593.047997516158 280.288727 2.1158 0.038254 0.019127 M4 118.287625514067 280.316349 0.422 0.674454 0.337227 M5 91.0986820834048 280.435408 0.3248 0.746356 0.373178 M6 491.195452938457 280.645787 1.7502 0.084871 0.042435 M7 -45.4325593236219 291.479174 -0.1559 0.876627 0.438313 M8 -143.410892944017 291.446804 -0.4921 0.624357 0.312178 M9 44.066830291987 291.138746 0.1514 0.880168 0.440084 M10 70.3778868613246 290.918505 0.2419 0.809619 0.404809 M11 -591.311056569338 290.78628 -2.0335 0.046154 0.023077 t 15.1889434306624 5.063454 2.9997 0.003847 0.001924 Multiple Linear Regression - Regression Statistics Multiple R 0.81439666271144 R-squared 0.66324192423553 Adjusted R-squared 0.594837940095873 F-TEST (value) 9.6959545935426 F-TEST (DF numerator) 13 F-TEST (DF denominator) 64 p-value 9.52469214610119e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 503.580248723731 Sum Squared Residuals 16229956.2818979 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4159 3481.59674743684 677.40325256316 2 3497 3253.45389029398 243.546109706018 3 4404 3965.31103315112 438.688966848876 4 3849 3505.73960457970 343.260395420304 5 3734 3493.73960457970 240.260395420304 6 3060 3909.02531886541 -849.02531886541 7 3507 3387.58625003399 119.413749966006 8 3287 3304.79685984426 -17.7968598442613 9 3215 3507.46352651093 -292.463526510928 10 3764 3548.96352651093 215.036473489072 11 2734 2902.46352651093 -168.463526510928 12 2837 3508.96352651093 -671.963526510929 13 2766 3663.86406860479 -897.864068604788 14 3851 3435.72121146193 415.278788538069 15 3289 4147.57835431907 -858.578354319074 16 3848 3688.00692574765 159.993074252355 17 3348 3676.00692574765 -328.006925747645 18 3682 4091.29264003336 -409.292640033360 19 4058 3569.85357120194 488.146428798056 20 3655 3487.06418101221 167.935818987789 21 3811 3689.73084767888 121.269152321123 22 3341 3731.23084767888 -390.230847678877 23 3032 3084.73084767888 -52.7308476788774 24 3475 3691.23084767888 -216.230847678878 25 3353 3846.13138977274 -493.131389772737 26 3186 3617.98853262988 -431.98853262988 27 3902 4329.84567548702 -427.845675487023 28 4164 3870.27424691559 293.725753084405 29 3499 3858.27424691559 -359.274246915595 30 4145 4273.55996120131 -128.559961201309 31 3796 3752.12089236989 43.8791076301071 32 3711 3669.33150218016 41.6684978198397 33 3949 3871.99816884683 77.0018311531732 34 3740 3913.49816884683 -173.498168846827 35 3243 3266.99816884683 -23.9981688468268 36 4407 3873.49816884683 533.501831153173 37 4814 4028.39871094069 785.601289059314 38 3908 3800.25585379783 107.744146202170 39 5250 4512.11299665497 737.887003345027 40 3937 4052.54156808354 -115.541568083544 41 4004 4040.54156808354 -36.5415680835441 42 5560 4455.82728236926 1104.17271763074 43 3922 3934.38821353784 -12.3882135378422 44 3759 4361.33516448651 -602.335164486506 45 4138 4564.00183115317 -426.001831153173 46 4634 4605.50183115317 28.4981688468268 47 3996 3959.00183115317 36.9981688468266 48 4307 4565.50183115317 -258.501831153174 49 4142 4720.40237324703 -578.402373247033 50 4429 4492.25951610418 -63.2595161041762 51 5219 5204.11665896132 14.8833410386808 52 4929 4744.54523038989 184.454769610110 53 5754 4732.54523038989 1021.45476961011 54 5588 5147.8309446756 440.169055324395 55 4162 4626.39187584419 -464.391875844189 56 4947 4543.60248565446 403.397514345544 57 5208 4746.26915232112 461.730847678877 58 4752 4787.76915232112 -35.7691523211226 59 4487 4141.26915232112 345.730847678877 60 5612 4747.76915232112 864.230847678877 61 5719 4902.66969441498 816.330305585017 62 4994 4674.52683727213 319.473162727874 63 6051 5386.38398012927 664.616019870732 64 4897 4926.81255155784 -29.8125515578398 65 5337 4914.81255155784 422.18744844216 66 5570 5330.09826584355 239.901734156446 67 4634 4808.65919701214 -174.659197012138 68 4733 4725.86980682240 7.13019317759478 69 4987 4928.53647348907 58.4635265109281 70 5326 4970.03647348907 355.963526510928 71 4186 4323.53647348907 -137.536473489072 72 4679 4930.03647348907 -251.036473489072 73 4775 5084.93701558293 -309.937015582932 74 4266 4856.79415844008 -590.794158440075 75 4999 5568.65130129722 -569.651301297218 76 4273 5109.07987272579 -836.079872725789 77 4137 5097.07987272579 -960.07987272579 78 5115 5512.3655870115 -397.365587011503

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/1t74b1195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/1t74b1195762344.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/2hu951195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/2hu951195762344.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/3qenm1195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/3qenm1195762344.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/4zctr1195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/4zctr1195762344.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/5nvc31195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/5nvc31195762344.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/6ii6s1195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/6ii6s1195762344.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/7pccx1195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/7pccx1195762344.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/8c2761195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/8c2761195762344.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/9r7gv1195762344.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/22/t11957620317gkwyuy9n1e1pak/9r7gv1195762344.ps (open in new window)

Parameters:

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

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

library(lattice) 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)) 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$resid, main='Residual Normal Q-Q Plot') grid() dev.off() (myerror <- as.ts(mysum$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() 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')

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