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R Software Module: rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Wed, 16 Jan 2008 14:48:26 -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/Jan/16/t12005199411kdlxxb6i7osqy2.htm/, Retrieved Wed, 16 Jan 2008 22:45:42 +0100
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
281 0.88 295 0.87 294 0.88 302 0.89 314 0.92 321 0.96 313 0.99 310 0.98 319 0.98 316 0.98 319 1.00 333 1.02 356 1.06 358 1.08 340 1.08 328 1.08 355 1.16 356 1.17 351 1.14 359 1.11 378 1.12 378 1.17 389. 1.17 407 1.23 413 1.26 404 1.26 406 1.23 402 1.20 383 1.20 392 1.21 398 1.23 400 1.22 405 1.22 420 1.25 439 1.30 441 1.34 424 1.31 423 1.30 434 1.32 429 1.29 421 1.27 430 1.22 424 1.20 437 1.23 456 1.23 469 1.20 476 1.18 510 1.19 549 1.21 554 1.19 557 1.20 610 1.23 675 1.28 596 1.27 633 1.27 632 1.28 596 1.27 585 1.26 627 1.29 629 1.32
 
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Multiple Linear Regression - Estimated Regression Equation
Goud[t] = + 464.535125285788 -201.335459150576Dollar[t] + 0.188379650585503M1[t] -5.22513259929208M2[t] -12.2306320942663M3[t] -11.8441443441440M4[t] + 2.38507809879669M5[t] -15.0230923144787M6[t] -17.0312627277541M7[t] -20.4421040593307M8[t] -24.0502744726061M9[t] -26.4477612126768M10[t] -13.6345642795430M11[t] + 6.8081704132754t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)464.53512528578872.4470086.412100
Dollar-201.33545915057669.74492-2.88670.0059110.002956
M10.18837965058550323.929250.00790.9937530.496876
M2-5.2251325992920823.921849-0.21840.8280640.414032
M3-12.230632094266323.907015-0.51160.6113840.305692
M4-11.844144344144023.935706-0.49480.6230740.311537
M52.3850780987966923.8115460.10020.9206490.460324
M6-15.023092314478723.807123-0.6310.5311420.265571
M7-17.031262727754123.813765-0.71520.4781090.239054
M8-20.442104059330723.84392-0.85730.3957070.197854
M9-24.050274472606123.875125-1.00730.3190430.159521
M10-26.447761212676823.852521-1.10880.2732790.136639
M11-13.634564279543023.785193-0.57320.5692760.284638
t6.80817041327540.51326513.264400


Multiple Linear Regression - Regression Statistics
Multiple R0.947398294138828
R-squared0.897563527737161
Adjusted R-squared0.86861408992375
F-TEST (value)31.0045235946295
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation37.4965170883353
Sum Squared Residuals64675.4845127679


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1281294.356471297143-13.356471297143
2295297.764484052046-2.76448405204596
3294295.553800378841-1.55380037884134
4302300.7351039507331.26489604926680
5314315.732433032432-1.73243303243211
6321297.07901466640923.9209853335909
7313295.83895089189217.1610491081082
8310301.2496345650968.75036543490361
9319304.44963456509614.5503654349036
10316308.8603182383017.139681761699
11319324.454976401699-5.4549764016987
12333340.871001911506-7.87100191150561
13356339.81413360934316.1858663906565
14358337.18208258973020.8179174102703
15340336.9847535080313.01524649196910
16328344.179411671429-16.1794116714286
17355349.1099677955995.89003220440132
18356336.49661320409319.5033867959071
19351347.336676978613.6633230213898
20359356.7740698348262.2259301651737
21378357.96071524332120.0392847566795
22378352.30462595899625.6953740410036
23389371.92599330540617.0740066945944
24407380.28860044918926.7113995508106
25413381.24508673853331.754913261467
26404382.63974490193121.3602550980691
27406388.48247959474917.5175204052507
28402401.7172015326640.282798467335687
29383422.75459438888-39.7545943888804
30392410.141239797375-18.1412397973747
31398410.914530614363-12.9145306143631
32400416.325214287568-16.3252142875677
33405419.525214287568-14.5252142875677
34420417.8958341862552.10416581374494
35439427.45042857513511.5495714248645
36441439.8397449019311.16025509806913
37424452.876358740309-28.876358740309
38423456.284371495213-33.2843714952126
39434452.060333230502-18.0603332305022
40429465.295055168417-36.2950551684173
41421490.359157207645-69.3591572076449
42430489.825930165174-59.8259301651737
43424498.652639348185-74.6526393481852
44437496.009904655367-59.0099046553668
45456499.209904655367-43.2099046553668
46469509.660652103089-40.6606521030887
47476533.308728632509-57.3087286325095
48510551.738108733822-41.7381087338221
49549554.707949614672-5.7079496146715
50554560.129316961081-6.12931696108084
51557557.918633287876-0.9186332878762
52610559.07322767675750.9267723232434
53675570.043847575444104.956152424556
54596561.4572021669534.5427978330503
55633566.2572021669566.7427978330503
56632567.64117665714364.3588233428572
57596572.85453124864923.1454687513515
58585579.2785695133595.7214304866411
59627592.85987308525134.1401269147492
60629607.26254400355221.737455996448
 
Charts produced by software:
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Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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)
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')
 





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