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Aanslagen Irak Brent-olie

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

Title produced by software: Multiple Regression

Date of computation: Tue, 18 Dec 2007 15:21:13 -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/Dec/18/t1198015509rwycdc22831zz81.htm/, Retrieved Tue, 18 Dec 2007 23:05:20 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

25.62 0 27.5 0 24.5 0 25.66 0 28.31 0 27.85 0 24.61 0 25.68 0 25.62 0 20.54 0 18.8 0 18.71 0 19.46 0 20.12 0 23.54 0 25.6 0 25.39 0 24.09 0 25.69 0 26.56 0 28.33 0 27.5 0 24.23 0 28.23 0 31.29 0 32.72 0 30.46 0 24.89 0 25.68 0 27.52 1 28.4 1 29.71 1 26.85 1 29.62 1 28.69 1 29.76 0 31.3 1 30.86 1 33.46 0 33.15 1 37.99 1 35.24 1 38.24 1 43.16 1 43.33 1 49.67 1 43.17 1 39.56 1 44.36 1 45.22 1 53.1 1 52.1 1 48.52 1 54.84 1 57.57 1 64.14 1 62.85 1 58.75 1 55.33 1 57.03 1 63.18 1 60.19 1 62.12 1 70.12 1 69.75 1 68.56 0 73.77 1 73.23 1 61.96 0 57.81 0 58.76 0 62.47 1 53.68 1 57.56 1 62.05 1 67.49 1 67.21 1 71.05 1 76.93 1 70.76 0

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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Brent[t] = + 9.9078334843451 -2.14671247357294Aanslagen_Irak[t] + 2.89891471070457M1[t] + 2.92798956580517M2[t] + 4.04753406753823M3[t] + 5.02613927600639M4[t] + 4.8494998453927M5[t] + 5.0242889862076M6[t] + 6.90003705181862M7[t] + 7.01529583926594M8[t] + 4.37229924422201M9[t] + 2.80542171837023M10[t] -0.404789140814893M11[t] + 0.725210859185112t + 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)	9.9078334843451	3.038515	3.2607	0.00176	0.00088
Aanslagen_Irak	-2.14671247357294	1.980771	-1.0838	0.282407	0.141204
M1	2.89891471070457	3.712202	0.7809	0.437644	0.218822
M2	2.92798956580517	3.708969	0.7894	0.432685	0.216342
M3	4.04753406753823	3.699913	1.094	0.277952	0.138976
M4	5.02613927600639	3.703954	1.357	0.179415	0.089707
M5	4.8494998453927	3.702174	1.3099	0.194769	0.097384
M6	5.0242889862076	3.700879	1.3576	0.179217	0.089608
M7	6.90003705181862	3.719555	1.8551	0.068053	0.034027
M8	7.01529583926594	3.699748	1.8962	0.062318	0.031159
M9	4.37229924422201	3.839887	1.1387	0.258964	0.129482
M10	2.80542171837023	3.838714	0.7308	0.467474	0.233737
M11	-0.404789140814893	3.838011	-0.1055	0.916324	0.458162
t	0.725210859185112	0.042433	17.0909	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.937658309087253
R-squared	0.879203104600367
Adjusted R-squared	0.855409776718622
F-TEST (value)	36.9516659867866
F-TEST (DF numerator)	13
F-TEST (DF denominator)	66
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.6472229330464
Sum Squared Residuals	2916.24779962678

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	25.62	13.5319590542349	12.0880409457652
2	27.5	14.2862447685205	13.2137552314795
3	24.5	16.1310001294387	8.36899987056134
4	25.66	17.8348161970919	7.82518380290808
5	28.31	18.3833876256634	9.92661237433662
6	27.85	19.2833876256634	8.56661237433662
7	24.61	21.8843465504595	2.72565344954051
8	25.68	22.7248161970920	2.95518380290805
9	25.62	20.8070304612331	4.81296953876688
10	20.54	19.9653637945664	0.574636205433555
11	18.8	17.4803637945665	1.31963620543354
12	18.71	18.6103637945665	0.099636205433546
13	19.46	22.2344893644561	-2.77448936445613
14	20.12	22.9887750787419	-2.86877507874185
15	23.54	24.83353043966	-1.29353043966001
16	25.6	26.5373465073133	-0.93734650731328
17	25.39	27.0859179358847	-1.69591793588471
18	24.09	27.9859179358847	-3.89591793588472
19	25.69	30.5868768606809	-4.89687686068085
20	26.56	31.4273465073133	-4.86734650731329
21	28.33	29.5095607714545	-1.17956077145446
22	27.5	28.6678941047878	-1.16789410478779
23	24.23	26.1828941047878	-1.95289410478779
24	28.23	27.3128941047878	0.917105895212207
25	31.29	30.9370196746775	0.352980325322531
26	32.72	31.6913053889632	1.02869461103680
27	30.46	33.5360607498814	-3.07606074988135
28	24.89	35.2398768175346	-10.3498768175346
29	25.68	35.7884482461061	-10.1084482461061
30	27.52	34.5417357725331	-7.02173577253311
31	28.4	37.1426946973292	-8.74269469732925
32	29.71	37.9831643439617	-8.27316434396167
33	26.85	36.0653786081028	-9.21537860810285
34	29.62	35.2237119414362	-5.60371194143619
35	28.69	32.7387119414362	-4.04871194143619
36	29.76	36.0154244150091	-6.25542441500914
37	31.3	37.4928375113259	-6.19283751132587
38	30.86	38.2471232256116	-7.38712322561159
39	33.46	42.2385910601027	-8.77859106010269
40	33.15	41.795694654183	-8.64569465418303
41	37.99	42.3442660827545	-4.35426608275445
42	35.24	43.2442660827545	-8.00426608275445
43	38.24	45.8452250075506	-7.60522500755059
44	43.16	46.685694654183	-3.52569465418303
45	43.33	44.7679089183242	-1.4379089183242
46	49.67	43.9262422516575	5.74375774834246
47	43.17	41.4412422516575	1.72875774834247
48	39.56	42.5712422516575	-3.01124225165753
49	44.36	46.1953678215472	-1.83536782154721
50	45.22	46.9496535358329	-1.72965353583294
51	53.1	48.7944088967511	4.30559110324891
52	52.1	50.4982249644044	1.60177503559564
53	48.52	51.0467963929758	-2.52679639297579
54	54.84	51.9467963929758	2.89320360702421
55	57.57	54.5477553177719	3.02224468222807
56	64.14	55.3882249644044	8.75177503559563
57	62.85	53.4704392285455	9.37956077145446
58	58.75	52.6287725618789	6.12122743812112
59	55.33	50.1437725618789	5.18622743812112
60	57.03	51.2737725618789	5.75622743812112
61	63.18	54.8978981317686	8.28210186823145
62	60.19	55.6521838460543	4.53781615394572
63	62.12	57.4969392069724	4.62306079302756
64	70.12	59.2007552746257	10.9192447253743
65	69.75	59.7493267031971	10.0006732968029
66	68.56	62.7960391767701	5.76396082322992
67	73.77	63.2502856279933	10.5197143720067
68	73.23	64.0907552746257	9.1392447253743
69	61.96	64.3196820123398	-2.35968201233983
70	57.81	63.4780153456732	-5.66801534567316
71	58.76	60.9930153456732	-2.23301534567315
72	62.47	59.9763028721002	2.49369712789978
73	53.68	63.6004284419899	-9.9204284419899
74	57.56	64.3547141562756	-6.79471415627562
75	62.05	66.1994695171938	-4.14946951719378
76	67.49	67.903285584847	-0.413285584847054
77	67.21	68.4518570134185	-1.24185701341848
78	71.05	69.3518570134185	1.69814298658152
79	76.93	71.9528159382146	4.97718406178539
80	70.76	74.93999805842	-4.17999805841997

Charts produced by software:

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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)

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')

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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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.

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