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multiple regression

*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: Sun, 07 Dec 2008 06:50:02 -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/Dec/07/t12286585476wah7u6gu555d1k.htm/, Retrieved Sun, 07 Dec 2008 14:02:37 +0000

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/2008/Dec/07/t12286585476wah7u6gu555d1k.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
test

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

493 1 481 1 462 1 457 1 442 1 439 1 488 1 521 1 501 1 485 1 464 1 460 1 467 1 460 1 448 1 443 1 436 1 431 1 484 1 510 1 513 1 503 1 471 1 471 1 476 1 475 1 470 1 461 1 455 1 456 1 517 1 525 1 523 1 519 1 509 1 512 1 519 0 517 0 510 0 509 0 501 0 507 0 569 0 580 0 578 0 565 0 547 0 555 0 562 0 561 0 555 0 544 0 537 0 543 0 594 0 611 0 613 0 611 0 594 0 595 0 591 0 589 0 584 0 573 0 567 0 569 0 621 0 629 0 628 0 612 0 595 0 597 0 593 0 590 0 580 0 574 0 573 0 573 0 620 0 626 0 620 0 588 0 566 0 557 0 561 0 549 0 532 0 526 0 511 0 499 0 555 0 565 0 542 0 527 0 510 0 514 0 517 0 508 0 493 0 490 0 469 0 478 0

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	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Aantal_werklozen_(*1000)[t] = + 559.666666666667 -81.111111111111dummyvariabele[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)	559.666666666667	4.530295	123.5387	0	0
dummyvariabele	-81.111111111111	7.625622	-10.6367	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.728574877306835
R-squared	0.53082135184267
Adjusted R-squared	0.526129565361097
F-TEST (value)	113.138429024304
F-TEST (DF numerator)	1
F-TEST (DF denominator)	100
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	36.8042871898853
Sum Squared Residuals	135455.555555556

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	493	478.555555555553	14.4444444444468
2	481	478.555555555555	2.44444444444480
3	462	478.555555555556	-16.5555555555556
4	457	478.555555555556	-21.5555555555556
5	442	478.555555555556	-36.5555555555556
6	439	478.555555555556	-39.5555555555556
7	488	478.555555555556	9.44444444444436
8	521	478.555555555556	42.4444444444444
9	501	478.555555555556	22.4444444444444
10	485	478.555555555556	6.44444444444436
11	464	478.555555555556	-14.5555555555556
12	460	478.555555555556	-18.5555555555556
13	467	478.555555555556	-11.5555555555556
14	460	478.555555555556	-18.5555555555556
15	448	478.555555555556	-30.5555555555556
16	443	478.555555555556	-35.5555555555556
17	436	478.555555555556	-42.5555555555556
18	431	478.555555555556	-47.5555555555556
19	484	478.555555555556	5.44444444444436
20	510	478.555555555556	31.4444444444444
21	513	478.555555555556	34.4444444444444
22	503	478.555555555556	24.4444444444444
23	471	478.555555555556	-7.55555555555564
24	471	478.555555555556	-7.55555555555564
25	476	478.555555555556	-2.55555555555564
26	475	478.555555555556	-3.55555555555564
27	470	478.555555555556	-8.55555555555564
28	461	478.555555555556	-17.5555555555556
29	455	478.555555555556	-23.5555555555556
30	456	478.555555555556	-22.5555555555556
31	517	478.555555555556	38.4444444444444
32	525	478.555555555556	46.4444444444444
33	523	478.555555555556	44.4444444444444
34	519	478.555555555556	40.4444444444444
35	509	478.555555555556	30.4444444444444
36	512	478.555555555556	33.4444444444444
37	519	559.666666666667	-40.6666666666667
38	517	559.666666666667	-42.6666666666667
39	510	559.666666666667	-49.6666666666667
40	509	559.666666666667	-50.6666666666667
41	501	559.666666666667	-58.6666666666667
42	507	559.666666666667	-52.6666666666667
43	569	559.666666666667	9.33333333333334
44	580	559.666666666667	20.3333333333333
45	578	559.666666666667	18.3333333333333
46	565	559.666666666667	5.33333333333334
47	547	559.666666666667	-12.6666666666667
48	555	559.666666666667	-4.66666666666666
49	562	559.666666666667	2.33333333333334
50	561	559.666666666667	1.33333333333334
51	555	559.666666666667	-4.66666666666666
52	544	559.666666666667	-15.6666666666667
53	537	559.666666666667	-22.6666666666667
54	543	559.666666666667	-16.6666666666667
55	594	559.666666666667	34.3333333333333
56	611	559.666666666667	51.3333333333333
57	613	559.666666666667	53.3333333333333
58	611	559.666666666667	51.3333333333333
59	594	559.666666666667	34.3333333333333
60	595	559.666666666667	35.3333333333333
61	591	559.666666666667	31.3333333333333
62	589	559.666666666667	29.3333333333333
63	584	559.666666666667	24.3333333333333
64	573	559.666666666667	13.3333333333333
65	567	559.666666666667	7.33333333333334
66	569	559.666666666667	9.33333333333334
67	621	559.666666666667	61.3333333333333
68	629	559.666666666667	69.3333333333333
69	628	559.666666666667	68.3333333333333
70	612	559.666666666667	52.3333333333333
71	595	559.666666666667	35.3333333333333
72	597	559.666666666667	37.3333333333333
73	593	559.666666666667	33.3333333333333
74	590	559.666666666667	30.3333333333333
75	580	559.666666666667	20.3333333333333
76	574	559.666666666667	14.3333333333333
77	573	559.666666666667	13.3333333333333
78	573	559.666666666667	13.3333333333333
79	620	559.666666666667	60.3333333333333
80	626	559.666666666667	66.3333333333333
81	620	559.666666666667	60.3333333333333
82	588	559.666666666667	28.3333333333333
83	566	559.666666666667	6.33333333333334
84	557	559.666666666667	-2.66666666666666
85	561	559.666666666667	1.33333333333334
86	549	559.666666666667	-10.6666666666667
87	532	559.666666666667	-27.6666666666667
88	526	559.666666666667	-33.6666666666667
89	511	559.666666666667	-48.6666666666667
90	499	559.666666666667	-60.6666666666667
91	555	559.666666666667	-4.66666666666666
92	565	559.666666666667	5.33333333333334
93	542	559.666666666667	-17.6666666666667
94	527	559.666666666667	-32.6666666666667
95	510	559.666666666667	-49.6666666666667
96	514	559.666666666667	-45.6666666666667
97	517	559.666666666667	-42.6666666666667
98	508	559.666666666667	-51.6666666666667
99	493	559.666666666667	-66.6666666666667
100	490	559.666666666667	-69.6666666666667
101	469	559.666666666667	-90.6666666666667
102	478	559.666666666667	-81.6666666666667

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.236686449038992	0.473372898077984	0.763313550961008
6	0.197014279677535	0.394028559355071	0.802985720322465
7	0.154018865474479	0.308037730948957	0.845981134525521
8	0.309222341372234	0.618444682744468	0.690777658627766
9	0.264243868694943	0.528487737389886	0.735756131305057
10	0.180838511315619	0.361677022631239	0.819161488684381
11	0.123622642583862	0.247245285167723	0.876377357416138
12	0.0856643049918253	0.171328609983651	0.914335695008175
13	0.0529874422155175	0.105974884431035	0.947012557784483
14	0.0344408979949063	0.0688817959898126	0.965559102005094
15	0.0286414083649694	0.0572828167299387	0.97135859163503
16	0.0267438157101253	0.0534876314202505	0.973256184289875
17	0.0302277221110737	0.0604554442221475	0.969772277888926
18	0.0382548125137271	0.0765096250274541	0.961745187486273
19	0.0276044747908199	0.0552089495816399	0.97239552520918
20	0.0379920562758407	0.0759841125516814	0.96200794372416
21	0.0503223251097966	0.100644650219593	0.949677674890203
22	0.0471703743267319	0.0943407486534638	0.952829625673268
23	0.0316535074210267	0.0633070148420534	0.968346492578973
24	0.0208133810876316	0.0416267621752632	0.979186618912368
25	0.0133672136226722	0.0267344272453444	0.986632786377328
26	0.00841597730889551	0.0168319546177910	0.991584022691104
27	0.00528990969367582	0.0105798193873516	0.994710090306324
28	0.00365462983391423	0.00730925966782846	0.996345370166086
29	0.00291972668503895	0.0058394533700779	0.997080273314961
30	0.00242342047735639	0.00484684095471279	0.997576579522644
31	0.00384250957196734	0.00768501914393469	0.996157490428033
32	0.00712474582886613	0.0142494916577323	0.992875254171134
33	0.0103530081386571	0.0207060162773142	0.989646991861343
34	0.0121233932254271	0.0242467864508542	0.987876606774573
35	0.0107988651277497	0.0215977302554995	0.98920113487225
36	0.00994278917759416	0.0198855783551883	0.990057210822406
37	0.00734956102225045	0.0146991220445009	0.99265043897775
38	0.00550980399301604	0.0110196079860321	0.994490196006984
39	0.00445431521709073	0.00890863043418145	0.99554568478291
40	0.00367303318625149	0.00734606637250298	0.996326966813748
41	0.00349870003437174	0.00699740006874348	0.996501299965628
42	0.00307818841504205	0.00615637683008409	0.996921811584958
43	0.00509001245249142	0.0101800249049828	0.994909987547509
44	0.00858340916640004	0.0171668183328001	0.9914165908336
45	0.0105796531579951	0.0211593063159902	0.989420346842005
46	0.00897773565135468	0.0179554713027094	0.991022264348645
47	0.00633241187250344	0.0126648237450069	0.993667588127497
48	0.00453226831699372	0.00906453663398744	0.995467731683006
49	0.00336406357648160	0.00672812715296321	0.996635936423518
50	0.00239886559029809	0.00479773118059618	0.997601134409702
51	0.00159727856907319	0.00319455713814638	0.998402721430927
52	0.00104160020330096	0.00208320040660191	0.9989583997967
53	0.00070750389211516	0.00141500778423032	0.999292496107885
54	0.000454203735602326	0.000908407471204651	0.999545796264398
55	0.000645064630684018	0.00129012926136804	0.999354935369316
56	0.00156207428579799	0.00312414857159598	0.998437925714202
57	0.00332144062344441	0.00664288124688883	0.996678559376556
58	0.0056516009646678	0.0113032019293356	0.994348399035332
59	0.00561106853150580	0.0112221370630116	0.994388931468494
60	0.00556403998536929	0.0111280799707386	0.99443596001463
61	0.00499220362927387	0.00998440725854775	0.995007796370726
62	0.00426054377745729	0.00852108755491457	0.995739456222543
63	0.00331074852575493	0.00662149705150986	0.996689251474245
64	0.002230952947163	0.004461905894326	0.997769047052837
65	0.00141973340728922	0.00283946681457843	0.998580266592711
66	0.000895882561273278	0.00179176512254656	0.999104117438727
67	0.00195770966994298	0.00391541933988597	0.998042290330057
68	0.0055477829992703	0.0110955659985406	0.99445221700073
69	0.0139879759653918	0.0279759519307835	0.986012024034608
70	0.0205536565747292	0.0411073131494583	0.97944634342527
71	0.0207714012460978	0.0415428024921957	0.979228598753902
72	0.0223696899268566	0.0447393798537131	0.977630310073143
73	0.0228753752542910	0.0457507505085819	0.97712462474571
74	0.0227391679317655	0.0454783358635311	0.977260832068234
75	0.0196468189268519	0.0392936378537038	0.980353181073148
76	0.0158821574707389	0.0317643149414778	0.984117842529261
77	0.0127748203474081	0.0255496406948162	0.987225179652592
78	0.0103725707164974	0.0207451414329947	0.989627429283503
79	0.0329632512508387	0.0659265025016775	0.96703674874916
80	0.137242512609185	0.274485025218371	0.862757487390815
81	0.416201511155331	0.832403022310661	0.58379848884467
82	0.575353931883207	0.849292136233586	0.424646068116793
83	0.624626860218596	0.750746279562808	0.375373139781404
84	0.645246493412308	0.709507013175384	0.354753506587692
85	0.703546271136658	0.592907457726685	0.296453728863342
86	0.717532855640265	0.56493428871947	0.282467144359735
87	0.68200213993495	0.6359957201301	0.31799786006505
88	0.633171970248266	0.733656059503467	0.366828029751734
89	0.578282408018076	0.843435183963848	0.421717591981924
90	0.545608265600143	0.908783468799714	0.454391734399857
91	0.609909967274801	0.780180065450398	0.390090032725199
92	0.830364431177714	0.339271137644572	0.169635568822286
93	0.899403164445078	0.201193671109845	0.100596835554923
94	0.912552027113106	0.174895945773787	0.0874479728868935
95	0.869099499199348	0.261801001601304	0.130900500800652
96	0.834077715262283	0.331844569475434	0.165922284737717
97	0.850841692400775	0.298316615198449	0.149158307599225

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	25	0.268817204301075	NOK
5% type I error level	55	0.591397849462366	NOK
10% type I error level	65	0.698924731182796	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/101nci1228657796.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/101nci1228657796.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/1mkry1228657795.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/1mkry1228657795.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/22enz1228657795.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/22enz1228657795.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/3cnly1228657795.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/3cnly1228657795.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/4cn8e1228657795.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/4cn8e1228657795.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/5vs311228657795.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/5vs311228657795.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/648f51228657795.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/648f51228657795.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/7yoox1228657795.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/7yoox1228657795.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/8oxcx1228657795.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/8oxcx1228657795.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/9gyg61228657796.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286585476wah7u6gu555d1k/9gyg61228657796.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

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