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*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, 05 Dec 2009 11:46:25 -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/05/t1260038852e2eky82zlzaa6q1.htm/, Retrieved Sat, 05 Dec 2009 19:47:44 +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/05/t1260038852e2eky82zlzaa6q1.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 «

7969 0 8255 8776 8823 9051 8758 0 7969 8255 8776 8823 8693 0 8758 7969 8255 8776 8271 0 8693 8758 7969 8255 7790 0 8271 8693 8758 7969 7769 0 7790 8271 8693 8758 8170 0 7769 7790 8271 8693 8209 0 8170 7769 7790 8271 9395 0 8209 8170 7769 7790 9260 0 9395 8209 8170 7769 9018 0 9260 9395 8209 8170 8501 0 9018 9260 9395 8209 8500 0 8501 9018 9260 9395 9649 0 8500 8501 9018 9260 9319 0 9649 8500 8501 9018 8830 0 9319 9649 8500 8501 8436 0 8830 9319 9649 8500 8169 0 8436 8830 9319 9649 8269 0 8169 8436 8830 9319 7945 0 8269 8169 8436 8830 9144 0 7945 8269 8169 8436 8770 0 9144 7945 8269 8169 8834 0 8770 9144 7945 8269 7837 0 8834 8770 9144 7945 7792 0 7837 8834 8770 9144 8616 0 7792 7837 8834 8770 8518 0 8616 7792 7837 8834 7940 0 8518 8616 7792 7837 7545 0 7940 8518 8616 7792 7531 0 7545 7940 8518 8616 7665 0 7531 7545 7940 8518 7599 0 7665 7531 7545 7940 8444 0 7599 7665 7531 7545 8549 0 8444 7599 7665 7531 7986 0 8549 8444 7599 7665 7335 0 7986 8549 8444 7599 7287 0 7335 7986 etc...

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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = -53.6518988484805 + 389.313447739161X[t] + 1.06481823529856Y1[t] -0.253504368502961Y2[t] -0.202623347892135Y3[t] + 0.390229059988102Y4[t] + 0.938161684005079t + 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)	-53.6518988484805	278.585629	-0.1926	0.847747	0.423874
X	389.313447739161	203.330778	1.9147	0.058937	0.029469
Y1	1.06481823529856	0.100807	10.563	0	0
Y2	-0.253504368502961	0.151694	-1.6712	0.098412	0.049206
Y3	-0.202623347892135	0.15203	-1.3328	0.186207	0.093103
Y4	0.390229059988102	0.104855	3.7216	0.000357	0.000178
t	0.938161684005079	3.285311	0.2856	0.775916	0.387958

Multiple Linear Regression - Regression Statistics
Multiple R	0.97728926035259
R-squared	0.955094298400514
Adjusted R-squared	0.951886748286265
F-TEST (value)	297.764419691423
F-TEST (DF numerator)	6
F-TEST (DF denominator)	84
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	493.393695460646
Sum Squared Residuals	20448736.4525063

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	7969	8256.82388074314	-287.823880743136
2	8758	8005.85087479543	752.149125204573
3	8693	9006.6588719542	-313.658871954206
4	8271	8593.00983883832	-322.009838838315
5	7790	7889.59715653553	-99.5971565355306
6	7769	7806.39783649278	-37.3978364927831
7	8170	7967.0525803967	202.947419603304
8	8209	8333.09161319512	-124.091613195121
9	9395	8090.45734673754	1304.54265326246
10	9260	9254.93649234952	5.06350765048006
11	9018	8960.04755371115	57.9524462888555
12	8501	8512.43043494026	-11.4304349402629
13	8500	8514.37144326396	-14.3714432639594
14	9649	8641.6604723202	1007.3395276798
15	9319	9876.64912907386	-557.64912907386
16	8830	9033.37495303348	-203.374953033483
17	8436	8364.06898347442	71.931016525579
18	8169	8584.67129137948	-415.671291379477
19	8269	8371.49093275211	-102.490932752115
20	7945	8435.60817309158	-490.608173091585
21	9144	7966.54497394045	1177.45502605955
22	8770	9201.88212133634	-431.882121336345
23	8834	8605.2993958995	228.700604100497
24	7837	8399.8169489039	-562.816948903907
25	7792	7866.57282544846	-74.572825448457
26	8616	7913.42445924083	702.575540759168
27	8518	9030.17068108118	-512.170681081177
28	7940	8337.9287339065	-397.928733906492
29	7545	7563.72343733864	-18.7234373386381
30	7531	7631.98975459805	-100.989754598051
31	7665	7797.02853374937	-132.028533749366
32	7599	7798.68522586669	-199.685225866689
33	8444	7544.07204681678	899.927953183218
34	8549	8428.89817019188	120.101829808118
35	7986	8393.09489019652	-407.09489019652
36	7335	7570.95057978656	-235.950579786559
37	7287	7329.88313391964	-42.8831339196438
38	7870	7599.79236036677	270.207639633231
39	7839	8145.89660162245	-306.896601622452
40	7327	7721.71915382154	-394.719153821543
41	7259	7048.46860775574	210.531392244264
42	6964	7340.57823187068	-376.578231870675
43	7271	7136.27936446095	134.720635539050
44	6956	7352.88162203274	-396.881622032741
45	7608	6973.81451001628	634.185489983719
46	7692	7571.545096694	120.454903305999
47	7255	7680.26981788152	-425.269817881525
48	6804	6939.55546706389	-135.555467063888
49	6655	6808.45099955334	-153.450999553342
50	7341	6886.38735844056	454.612641559439
51	7602	7576.41601113087	25.5839888691315
52	7086	7535.56530821606	-449.565308216059
53	6625	6723.7488737145	-98.7488737145055
54	6272	6579.42652442540	-307.426524425395
55	6576	6527.75279509811	48.2472049018884
56	6491	6833.93391081884	-342.933910818836
57	7649	6558.92763962897	1090.07236037103
58	7400	7615.12483317645	-215.124833176446
59	6913	7193.2178143519	-280.217814351898
60	6532	6470.90477624466	61.0952237553373
61	6486	6691.94228283222	-205.942282832224
62	7295	6741.99450457856	553.005495421444
63	7556	7503.18976290293	52.810237097074
64	7088	7826.9172997677	-738.917299767692
65	6952	7081.48306194851	-129.48306194851
66	6773	7319.05660382183	-546.056603821826
67	6917	7360.5464069742	-443.546406974206
68	7371	7405.12525174213	-34.1252517421308
69	8221	7836.18469030156	384.815309698436
70	7953	8528.09860485466	-575.098604854659
71	8027	7992.3887509464	34.6112490536075
72	7287	8144.99678032757	-857.996780327568
73	8076	7725.2078828464	350.792117153598
74	8933	8634.30534905233	298.694650947671
75	9433	9526.59601951766	-93.5960195176592
76	9479	9394.05072916581	84.949270834186
77	9199	9451.46086460913	-252.460864609126
78	9469	9375.70334992214	93.2966500778647
79	10015	9920.9175143086	94.0824856914075
80	10999	10509.4853271391	489.51467286094
81	13009	11255.8188064267	1753.18119357332
82	13699	13142.3228207016	556.677179298449
83	13895	13382.1254764783	512.874523521749
84	13248	13393.5624637788	-145.562463778832
85	13973	13300.4266715286	672.573328471395
86	15095	14466.9192554304	628.08074456959
87	15201	15686.3750117986	-485.375011798626
88	14823	15116.3718759299	-293.371875929857
89	14538	14743.5099537661	-205.509953766094
90	14547	14953.1585001142	-406.158500114213
91	14407	15153.8846768012	-746.884676801216

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.89397314128492	0.21205371743016	0.10602685871508
11	0.849365868015715	0.301268263968569	0.150634131984284
12	0.755306292890817	0.489387414218366	0.244693707109183
13	0.652137005127738	0.695725989744524	0.347862994872262
14	0.77444569325223	0.451108613495541	0.225554306747771
15	0.738235159624512	0.523529680750975	0.261764840375488
16	0.66478216632633	0.670435667347339	0.335217833673669
17	0.596921190440057	0.806157619119887	0.403078809559943
18	0.618192849933302	0.763614300133397	0.381807150066698
19	0.570772653239892	0.858454693520216	0.429227346760108
20	0.627715914800387	0.744568170399225	0.372284085199613
21	0.751877938258775	0.496244123482449	0.248122061741225
22	0.736210621559769	0.527578756880463	0.263789378440231
23	0.680341472728478	0.639317054543045	0.319658527271522
24	0.721687433539776	0.556625132920447	0.278312566460224
25	0.688404155625863	0.623191688748274	0.311595844374137
26	0.705944359045618	0.588111281908764	0.294055640954382
27	0.690154432924912	0.619691134150176	0.309845567075088
28	0.69432536453812	0.611349270923761	0.305674635461880
29	0.65374174914022	0.69251650171956	0.34625825085978
30	0.607993994548525	0.78401201090295	0.392006005451475
31	0.55408374042737	0.891832519145259	0.445916259572629
32	0.502469663942555	0.99506067211489	0.497530336057445
33	0.622241180836077	0.755517638327846	0.377758819163923
34	0.568242019339213	0.863515961321574	0.431757980660787
35	0.52534079173257	0.94931841653486	0.47465920826743
36	0.481292951467691	0.962585902935383	0.518707048532309
37	0.427603131636863	0.855206263273726	0.572396868363137
38	0.399694667231359	0.799389334462718	0.600305332768641
39	0.348676660524961	0.697353321049922	0.651323339475039
40	0.320800189095517	0.641600378191034	0.679199810904483
41	0.287476969021716	0.574953938043432	0.712523030978284
42	0.262492012866358	0.524984025732716	0.737507987133642
43	0.226024042340609	0.452048084681218	0.77397595765939
44	0.202874303369910	0.405748606739821	0.79712569663009
45	0.259693546785521	0.519387093571043	0.740306453214479
46	0.228155587264774	0.456311174529548	0.771844412735226
47	0.193889963555326	0.387779927110651	0.806110036444674
48	0.161075876588869	0.322151753177738	0.838924123411131
49	0.133868031404317	0.267736062808634	0.866131968595683
50	0.169892077993099	0.339784155986199	0.8301079220069
51	0.151092715951951	0.302185431903902	0.848907284048049
52	0.123223595353424	0.246447190706848	0.876776404646576
53	0.0997625863640436	0.199525172728087	0.900237413635956
54	0.0829224912407482	0.165844982481496	0.917077508759252
55	0.0689352049322392	0.137870409864478	0.931064795067761
56	0.0552422614705541	0.110484522941108	0.944757738529446
57	0.208817550911718	0.417635101823436	0.791182449088282
58	0.165652001065968	0.331304002131936	0.834347998934032
59	0.129563105612798	0.259126211225595	0.870436894387202
60	0.097646599199982	0.195293198399964	0.902353400800018
61	0.0739698861465624	0.147939772293125	0.926030113853438
62	0.0803235098137166	0.160647019627433	0.919676490186283
63	0.0602377053478535	0.120475410695707	0.939762294652146
64	0.0439108511537503	0.0878217023075006	0.95608914884625
65	0.0371971982980791	0.0743943965961582	0.962802801701921
66	0.0247403661661561	0.0494807323323122	0.975259633833844
67	0.0158300688018093	0.0316601376036185	0.98416993119819
68	0.0117552434032038	0.0235104868064075	0.988244756596796
69	0.0199218704623624	0.0398437409247249	0.980078129537638
70	0.0151910671832110	0.0303821343664219	0.984808932816789
71	0.0119808024015595	0.0239616048031189	0.98801919759844
72	0.0244853440990360	0.0489706881980719	0.975514655900964
73	0.0288842706774096	0.0577685413548192	0.97111572932259
74	0.0256630102017726	0.0513260204035453	0.974336989798227
75	0.0187829291714798	0.0375658583429596	0.98121707082852
76	0.0174106903985939	0.0348213807971879	0.982589309601406
77	0.0275149278157864	0.0550298556315729	0.972485072184214
78	0.0286631397289115	0.0573262794578231	0.971336860271088
79	0.0956850900263294	0.191370180052659	0.90431490997367
80	0.572714803821567	0.854570392356867	0.427285196178433
81	0.520678982828234	0.958642034343532	0.479321017171766

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	9	0.125	NOK
10% type I error level	15	0.208333333333333	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/10sedt1260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/10sedt1260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/1e8i61260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/1e8i61260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/2mnz91260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/2mnz91260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/3aa1l1260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/3aa1l1260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/4tz4y1260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/4tz4y1260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/5hgzj1260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/5hgzj1260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/6373u1260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/6373u1260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/7wumi1260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/7wumi1260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/8pmyj1260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/8pmyj1260038780.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/9xgdl1260038780.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260038852e2eky82zlzaa6q1/9xgdl1260038780.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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')

}

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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