Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2009 » Dec » 19 »

*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: Sat, 19 Dec 2009 04:07:28 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt.htm/, Retrieved Sat, 19 Dec 2009 12:08:51 +0100

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/2009/Dec/19/t1261220919fy7jhmspm103tqt.htm/},
    year = {2009},
}
@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 = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

97,4 0 97 0 105,4 0 102,7 0 98,1 0 104,5 0 87,4 0 89,9 0 109,8 0 111,7 0 98,6 0 96,9 0 95,1 0 97 0 112,7 0 102,9 0 97,4 0 111,4 0 87,4 0 96,8 0 114,1 0 110,3 0 103,9 0 101,6 0 94,6 0 95,9 0 104,7 0 102,8 0 98,1 0 113,9 0 80,9 0 95,7 0 113,2 0 105,9 0 108,8 0 102,3 0 99 0 100,7 0 115,5 0 100,7 0 109,9 0 114,6 0 85,4 0 100,5 0 114,8 0 116,5 0 112,9 0 102 0 106 0 105,3 0 118,8 0 106,1 0 109,3 0 117,2 0 92,5 0 104,2 0 112,5 0 122,4 0 113,3 0 100 0 110,7 0 112,8 0 109,8 0 117,3 0 109,1 0 115,9 0 96 0 99,8 0 116,8 0 115,7 1 99,4 1 94,3 1 91 1 93,2 1 103,1 1 94,1 1 91,8 1 102,7 1 82,6 1 89,1 1 104,5 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

Multiple Linear Regression - Estimated Regression Equation

Industriële_Productie[t] = + 104.566666666667 -7.775Dummy_Crisis[t] + 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)	104.566666666667	1.070531	97.6774	0	0
Dummy_Crisis	-7.775	2.78132	-2.7954	0.006504	0.003252

Multiple Linear Regression - Regression Statistics
Multiple R	0.300022170207439
R-squared	0.0900133026159816
Adjusted R-squared	0.0784944836617535
F-TEST (value)	7.81445588941576
F-TEST (DF numerator)	1
F-TEST (DF denominator)	79
p-value	0.00650407935519393
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.89249612548536
Sum Squared Residuals	6247.0425

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	97.4	104.566666666667	-7.16666666666667
2	97	104.566666666667	-7.56666666666667
3	105.4	104.566666666667	0.83333333333334
4	102.7	104.566666666667	-1.86666666666666
5	98.1	104.566666666667	-6.46666666666667
6	104.5	104.566666666667	-0.0666666666666667
7	87.4	104.566666666667	-17.1666666666667
8	89.9	104.566666666667	-14.6666666666667
9	109.8	104.566666666667	5.23333333333333
10	111.7	104.566666666667	7.13333333333334
11	98.6	104.566666666667	-5.96666666666667
12	96.9	104.566666666667	-7.66666666666666
13	95.1	104.566666666667	-9.46666666666667
14	97	104.566666666667	-7.56666666666667
15	112.7	104.566666666667	8.13333333333334
16	102.9	104.566666666667	-1.66666666666666
17	97.4	104.566666666667	-7.16666666666666
18	111.4	104.566666666667	6.83333333333334
19	87.4	104.566666666667	-17.1666666666667
20	96.8	104.566666666667	-7.76666666666667
21	114.1	104.566666666667	9.53333333333333
22	110.3	104.566666666667	5.73333333333333
23	103.9	104.566666666667	-0.66666666666666
24	101.6	104.566666666667	-2.96666666666667
25	94.6	104.566666666667	-9.96666666666667
26	95.9	104.566666666667	-8.66666666666666
27	104.7	104.566666666667	0.133333333333336
28	102.8	104.566666666667	-1.76666666666667
29	98.1	104.566666666667	-6.46666666666667
30	113.9	104.566666666667	9.33333333333334
31	80.9	104.566666666667	-23.6666666666667
32	95.7	104.566666666667	-8.86666666666666
33	113.2	104.566666666667	8.63333333333334
34	105.9	104.566666666667	1.33333333333334
35	108.8	104.566666666667	4.23333333333333
36	102.3	104.566666666667	-2.26666666666667
37	99	104.566666666667	-5.56666666666667
38	100.7	104.566666666667	-3.86666666666666
39	115.5	104.566666666667	10.9333333333333
40	100.7	104.566666666667	-3.86666666666666
41	109.9	104.566666666667	5.33333333333334
42	114.6	104.566666666667	10.0333333333333
43	85.4	104.566666666667	-19.1666666666667
44	100.5	104.566666666667	-4.06666666666667
45	114.8	104.566666666667	10.2333333333333
46	116.5	104.566666666667	11.9333333333333
47	112.9	104.566666666667	8.33333333333334
48	102	104.566666666667	-2.56666666666667
49	106	104.566666666667	1.43333333333333
50	105.3	104.566666666667	0.73333333333333
51	118.8	104.566666666667	14.2333333333333
52	106.1	104.566666666667	1.53333333333333
53	109.3	104.566666666667	4.73333333333333
54	117.2	104.566666666667	12.6333333333333
55	92.5	104.566666666667	-12.0666666666667
56	104.2	104.566666666667	-0.366666666666664
57	112.5	104.566666666667	7.93333333333333
58	122.4	104.566666666667	17.8333333333333
59	113.3	104.566666666667	8.73333333333333
60	100	104.566666666667	-4.56666666666667
61	110.7	104.566666666667	6.13333333333334
62	112.8	104.566666666667	8.23333333333333
63	109.8	104.566666666667	5.23333333333333
64	117.3	104.566666666667	12.7333333333333
65	109.1	104.566666666667	4.53333333333333
66	115.9	104.566666666667	11.3333333333333
67	96	104.566666666667	-8.56666666666667
68	99.8	104.566666666667	-4.76666666666667
69	116.8	104.566666666667	12.2333333333333
70	115.7	96.7916666666667	18.9083333333333
71	99.4	96.7916666666667	2.60833333333334
72	94.3	96.7916666666667	-2.49166666666667
73	91	96.7916666666667	-5.79166666666666
74	93.2	96.7916666666667	-3.59166666666666
75	103.1	96.7916666666667	6.30833333333333
76	94.1	96.7916666666667	-2.69166666666667
77	91.8	96.7916666666667	-4.99166666666667
78	102.7	96.7916666666667	5.90833333333334
79	82.6	96.7916666666667	-14.1916666666667
80	89.1	96.7916666666667	-7.69166666666667
81	104.5	96.7916666666667	7.70833333333334

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.121969051692757	0.243938103385513	0.878030948307243
6	0.0707879266370183	0.141575853274037	0.929212073362982
7	0.274093073527589	0.548186147055179	0.72590692647241
8	0.300125223373351	0.600250446746702	0.699874776626649
9	0.387813845248725	0.775627690497449	0.612186154751275
10	0.47458464088682	0.94916928177364	0.52541535911318
11	0.377897394289510	0.755794788579019	0.62210260571049
12	0.303747898388617	0.607495796777235	0.696252101611383
13	0.256613720590192	0.513227441180383	0.743386279409808
14	0.198389516747733	0.396779033495465	0.801610483252267
15	0.287050384827668	0.574100769655337	0.712949615172332
16	0.222067549359588	0.444135098719176	0.777932450640412
17	0.176432578995313	0.352865157990626	0.823567421004687
18	0.207470630134399	0.414941260268798	0.792529369865601
19	0.332062023754963	0.664124047509925	0.667937976245037
20	0.287519898704439	0.575039797408879	0.71248010129556
21	0.376188086335372	0.752376172670744	0.623811913664628
22	0.37384704991261	0.74769409982522	0.62615295008739
23	0.311408792763344	0.622817585526689	0.688591207236656
24	0.253319545999019	0.506639091998038	0.746680454000981
25	0.24856548230252	0.49713096460504	0.75143451769748
26	0.230473717400712	0.460947434801424	0.769526282599288
27	0.188081120477464	0.376162240954927	0.811918879522536
28	0.148129544099883	0.296259088199767	0.851870455900117
29	0.125037932184127	0.250075864368253	0.874962067815873
30	0.160066602608786	0.320133205217572	0.839933397391214
31	0.515001979292128	0.969996041415743	0.484998020707872
32	0.516342286545748	0.967315426908504	0.483657713454252
33	0.547858746286512	0.904282507426977	0.452141253713488
34	0.496631390115872	0.993262780231743	0.503368609884128
35	0.462277807811308	0.924555615622616	0.537722192188692
36	0.410564510986824	0.821129021973648	0.589435489013176
37	0.382580888911982	0.765161777823965	0.617419111088018
38	0.344601566150031	0.689203132300062	0.655398433849969
39	0.397545361399229	0.795090722798458	0.602454638600771
40	0.36071385574261	0.72142771148522	0.63928614425739
41	0.331502404194228	0.663004808388457	0.668497595805772
42	0.354694209510182	0.709388419020365	0.645305790489818
43	0.661732009561154	0.676535980877692	0.338267990438846
44	0.64232493738962	0.715350125220761	0.357675062610381
45	0.657179701242708	0.685640597514584	0.342820298757292
46	0.692736881646478	0.614526236707044	0.307263118353522
47	0.675628640293967	0.648742719412067	0.324371359706033
48	0.641485477929579	0.717029044140842	0.358514522070421
49	0.587942937339426	0.824114125321148	0.412057062660574
50	0.534772934007589	0.930454131984821	0.465227065992411
51	0.600945683168993	0.798108633662015	0.399054316831007
52	0.542991264837728	0.914017470324545	0.457008735162273
53	0.486461230293454	0.972922460586907	0.513538769706546
54	0.514009138986662	0.971981722026677	0.485990861013338
55	0.659130688934118	0.681738622131764	0.340869311065882
56	0.616668127546861	0.766663744906279	0.383331872453139
57	0.571361593716099	0.857276812567803	0.428638406283901
58	0.694062380504192	0.611875238991615	0.305937619495808
59	0.657716428990501	0.684567142018998	0.342283571009499
60	0.652578319536417	0.694843360927167	0.347421680463583
61	0.589252538772325	0.82149492245535	0.410747461227675
62	0.536618801189411	0.926762397621178	0.463381198810589
63	0.463799578128497	0.927599156256995	0.536200421871503
64	0.47966419086713	0.95932838173426	0.52033580913287
65	0.403746047948837	0.807492095897674	0.596253952051163
66	0.426383932962004	0.852767865924008	0.573616067037996
67	0.438529870668010	0.877059741336019	0.56147012933199
68	0.48720574337521	0.97441148675042	0.51279425662479
69	0.415607017781548	0.831214035563096	0.584392982218452
70	0.763609888942452	0.472780222115097	0.236390111057548
71	0.711832543671076	0.576334912657848	0.288167456328924
72	0.617502346277387	0.764995307445225	0.382497653722613
73	0.53011456459377	0.93977087081246	0.46988543540623
74	0.408634183731518	0.817268367463037	0.591365816268482
75	0.372164331102733	0.744328662205466	0.627835668897267
76	0.233744327084903	0.467488654169806	0.766255672915097

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	0	0	OK
10% type I error level	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/10y0c81261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/10y0c81261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/11lqh1261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/11lqh1261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/2pt7z1261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/2pt7z1261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/3ffyu1261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/3ffyu1261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/4augt1261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/4augt1261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/51dq11261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/51dq11261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/6xl1k1261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/6xl1k1261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/7x6mq1261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/7x6mq1261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/8h69x1261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/8h69x1261220843.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/9mmaa1261220843.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261220919fy7jhmspm103tqt/9mmaa1261220843.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')

}

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.

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