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verbetering ws10

*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, 19 Dec 2010 17:13:34 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9.htm/, Retrieved Sun, 19 Dec 2010 18:11:41 +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/2010/Dec/19/t1292778698bi795lbe45a5wa9.htm/},
    year = {2010},
}
@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 = {2010},
    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 «

24 26 38 23 10 11 25 23 36 15 10 11 30 25 23 25 10 11 19 23 30 18 10 11 22 19 26 21 10 11 22 29 26 19 10 11 25 25 30 15 13 12 23 21 27 22 10 11 17 22 34 19 10 11 21 25 28 20 13 9 19 24 36 26 10 11 19 18 42 26 10 11 15 22 31 21 10 11 23 22 26 19 10 11 27 28 16 19 13 12 14 12 23 19 10 11 23 20 45 28 10 11 19 21 30 27 10 11 18 23 45 18 10 11 20 28 30 19 10 11 23 24 24 24 10 11 25 24 29 21 13 12 19 24 30 22 13 9 24 23 31 25 10 11 25 29 34 15 10 11 26 24 41 34 10 11 29 18 37 23 10 11 32 25 33 19 10 11 29 26 48 15 10 11 28 22 44 15 10 11 17 22 29 17 10 11 28 22 44 30 13 9 26 30 43 28 10 11 25 23 31 23 10 11 14 17 28 23 10 11 25 23 26 21 10 11 26 23 30 18 10 11 20 25 27 19 15 11 18 24 34 24 10 11 32 24 47 15 10 11 25 21 37 24 13 16 21 24 27 20 10 11 20 28 30 20 10 11 30 20 36 44 10 11 24 29 39 20 10 11 26 27 32 20 10 11 24 22 25 20 10 11 22 28 19 11 10 11 14 16 29 21 10 11 24 25 26 21 13 9 24 24 31 19 13 12 24 28 31 21 10 11 24 24 31 17 10 11 22 24 39 19 10 11 27 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	7 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

O[t] = + 20.8400279089439 + 0.347329743559927PS[t] + 0.0089567410966607CMD[t] -0.22783385858432PEC[t] -0.0737135911517484happiness[t] -0.0560868512284809depression[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)	20.8400279089439	4.323281	4.8204	4e-06	2e-06
PS	0.347329743559927	0.078795	4.408	2.3e-05	1.1e-05
CMD	0.0089567410966607	0.048935	0.183	0.85508	0.42754
PEC	-0.22783385858432	0.061304	-3.7164	0.000308	0.000154
happiness	-0.0737135911517484	0.251993	-0.2925	0.770392	0.385196
depression	-0.0560868512284809	0.246594	-0.2274	0.820464	0.410232

Multiple Linear Regression - Regression Statistics
Multiple R	0.443449335678153
R-squared	0.196647313313395
Adjusted R-squared	0.163174284701453
F-TEST (value)	5.87479894912285
F-TEST (DF numerator)	5
F-TEST (DF denominator)	120
p-value	6.83532930995101e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.62619197983225
Sum Squared Residuals	1577.91219295197

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	26	22.9220278935852	3.07797210641484
2	23	25.0741150236263	-2.0741150236263
3	25	24.4159875213261	0.584012478673851
4	23	22.2528945399338	0.747105460066184
5	19	22.575555230474	-3.575555230474
6	29	23.0312229476426	5.96877705235736
7	25	24.7431469523626	0.25685304763739
8	21	22.7040078565463	-1.70400785654626
9	22	21.3662281586163	0.633771841383713
10	25	22.3650057566934	2.63499424330657
11	24	20.4839641178392	3.51603588216078
12	18	20.5377045644192	-2.53770456441919
13	22	20.1890307310378	1.81096926896219
14	22	23.3785526912026	-1.37855269120256
15	28	24.4010766297919	3.59892337020807
16	12	20.2257147758732	-8.22571477587324
17	20	21.4982260447802	-1.49822604478024
18	21	20.2023898126749	0.79761018732506
19	23	22.0399159128238	0.9600840871762
20	28	22.3723904249094	5.62760957509057
21	24	22.2214699160876	1.77853008391236
22	24	23.36718705976	0.632812940239969
23	24	21.2325920345983	2.76740796540175
24	23	22.4036629887399	0.596337011260127
25	29	25.056201541433	3.94379845856702
26	24	21.1373851595675	2.86261484043254
27	18	24.6497198702881	-6.64971987028811
28	25	26.5672175709185	-1.56721757091853
29	26	26.5709148910259	-0.570914891025935
30	22	26.1877581830794	-4.18775818307937
31	22	21.7771121703016	0.222887829698377
32	22	22.6612832333163	-0.661283233316288
33	30	22.5223017932667	7.4776982067333
34	23	23.2066604494684	-0.20666044946844
35	17	19.3591630470193	-2.35916304701926
36	23	23.6175444611538	-0.617544461153775
37	23	24.6842027448533	-1.6842027448533
38	25	21.9769522458607	3.0230477541393
39	24	20.5743886092546	3.42561139074538
40	24	27.6039473806091	-3.60394738060905
41	21	22.5309920078664	-1.53099200786643
42	24	22.465016086595	1.53498391340495
43	28	22.1445565663251	5.8554434336749
44	20	20.2035818424807	-0.203581842480664
45	29	23.6144862104348	5.38551378956524
46	27	24.246448509878	2.75355149012201
47	22	23.4890918350815	-1.48909183508151
48	28	24.7911966286406	3.20880337135943
49	16	19.8237875052846	-3.82378750528456
50	25	23.1612476465956	1.83875235340443
51	24	23.4934385155621	0.506561484437935
52	28	23.3149984230772	4.68500157692285
53	24	24.2263338574144	-0.226333857414431
54	24	23.1476605818992	0.852339418100776
55	21	24.3301174304669	-3.33011743046695
56	25	22.6369083283292	2.36309167167083
57	25	24.1179958838057	0.882004116194282
58	22	22.4261308714894	-0.426130871489389
59	23	23.385308262029	-0.385308262028988
60	26	23.7451400067773	2.25485999322274
61	25	23.6981551310237	1.30184486897628
62	21	22.1714267896151	-1.17142678961509
63	25	22.6928499451794	2.30715005482063
64	24	22.182584700982	1.81741529901802
65	29	23.6511702552702	5.34882974472981
66	22	24.189649812579	-2.189649812579
67	27	23.2604008960484	3.7395991039516
68	26	21.2766607476497	4.72333925235032
69	24	22.2190610257418	1.78093897425823
70	27	23.1775333958483	3.82246660415169
71	24	21.4789684727655	2.52103152723453
72	24	24.8715995784349	-0.871599578434878
73	29	23.5838630069775	5.41613699302254
74	22	22.387093596368	-0.387093596368001
75	24	20.7364670294433	3.26353297055671
76	24	23.0298788578212	0.970121142178815
77	23	22.333733192863	0.666266807137023
78	20	22.5203286060557	-2.52032860605568
79	27	22.2461389691074	4.75386103089261
80	26	23.4491974795113	2.55080252048866
81	25	21.9606493327753	3.0393506672247
82	21	20.3210286172018	0.678971382798237
83	19	20.8297420042916	-1.82974200429162
84	21	22.0742467273733	-1.07424672737327
85	16	20.0850950979696	-4.08509509796957
86	29	21.9794198954174	7.02058010458259
87	15	22.0945134398526	-7.09451343985255
88	17	22.1244419138616	-5.12444191386155
89	15	21.1405954703022	-6.1405954703022
90	21	20.7635652357925	0.236434764207512
91	19	20.2078012936799	-1.20780129367992
92	24	19.6809666571107	4.31903334288934
93	17	25.3219114746584	-8.32191147465845
94	23	25.3640070004989	-2.36400700049885
95	14	22.5882852147216	-8.58828521472155
96	19	23.2658123770534	-4.26581237705338
97	24	22.0084912889776	1.99150871102237
98	13	21.4359089001786	-8.43590890017855
99	22	26.3686071659101	-4.36860716591015
100	16	21.2856174887463	-5.28561748874634
101	19	22.7957764377831	-3.7957764377831
102	25	23.904530247323	1.09546975267696
103	25	23.0170067855567	1.98299321444328
104	23	21.3616737580601	1.63832624193991
105	24	24.1435220172743	-0.143522017274251
106	26	23.7706661402458	2.22933385975421
107	26	21.6815041809405	4.31849581905953
108	25	24.4996664139139	0.500333586086067
109	21	20.7104538866021	0.289546113397905
110	26	23.9530871959572	2.04691280404276
111	23	22.4509585018127	0.549041498187338
112	13	22.0362185927164	-9.0362185927164
113	24	21.7176937162198	2.28230628378024
114	14	23.2868662313111	-9.28686623131107
115	10	17.0955276326346	-7.09552763263464
116	24	24.3682976251396	-0.368297625139552
117	22	21.1224742680332	0.877525731966756
118	24	24.8921424338498	-0.89214243384981
119	20	22.938597285957	-2.93859728595701
120	13	18.3986415666582	-5.39864156665822
121	20	21.2332211319878	-1.23322113198783
122	22	24.2016648043947	-2.20166480439468
123	24	22.7479344815808	1.25206551841922
124	20	20.5809921200653	-0.580992120065317
125	22	21.7448304660067	0.255169533993337
126	20	17.8373240555238	2.16267594447624

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.789487876015771	0.421024247968457	0.210512123984229
10	0.659993281638657	0.680013436722687	0.340006718361343
11	0.530216927668033	0.939566144663935	0.469783072331967
12	0.560396332028488	0.879207335943023	0.439603667971512
13	0.443824736552885	0.887649473105769	0.556175263447115
14	0.360537538824683	0.721075077649366	0.639462461175317
15	0.28510840221015	0.5702168044203	0.71489159778985
16	0.607951483930378	0.784097032139245	0.392048516069622
17	0.57678716194685	0.8464256761063	0.42321283805315
18	0.492209716017797	0.984419432035594	0.507790283982203
19	0.419928969345172	0.839857938690345	0.580071030654828
20	0.550970131991321	0.898059736017357	0.449029868008679
21	0.482801427463313	0.965602854926625	0.517198572536687
22	0.411486171897891	0.822972343795783	0.588513828102109
23	0.344810808470437	0.689621616940873	0.655189191529563
24	0.277800199708289	0.555600399416579	0.722199800291711
25	0.258183327656064	0.516366655312128	0.741816672343936
26	0.214540363756841	0.429080727513682	0.785459636243159
27	0.450387868605568	0.900775737211135	0.549612131394432
28	0.396341800504182	0.792683601008364	0.603658199495818
29	0.332836847289079	0.665673694578158	0.667163152710921
30	0.332966132351049	0.665932264702098	0.667033867648951
31	0.275150779830368	0.550301559660736	0.724849220169632
32	0.241541136619828	0.483082273239656	0.758458863380172
33	0.415948228590627	0.831896457181255	0.584051771409373
34	0.356623810431562	0.713247620863125	0.643376189568438
35	0.335391066579616	0.670782133159231	0.664608933420384
36	0.282619210310497	0.565238420620995	0.717380789689503
37	0.240211936255059	0.480423872510117	0.759788063744941
38	0.208405653446288	0.416811306892576	0.791594346553712
39	0.196302399460353	0.392604798920705	0.803697600539647
40	0.18373218360077	0.367464367201541	0.81626781639923
41	0.16270816241737	0.32541632483474	0.83729183758263
42	0.134239750903095	0.268479501806191	0.865760249096905
43	0.190031590380504	0.380063180761008	0.809968409619496
44	0.161119954041182	0.322239908082364	0.838880045958818
45	0.209043469487937	0.418086938975873	0.790956530512063
46	0.191828882973318	0.383657765946636	0.808171117026682
47	0.163250577737481	0.326501155474962	0.836749422262519
48	0.154431408682642	0.308862817365285	0.845568591317358
49	0.171382926740734	0.342765853481469	0.828617073259265
50	0.146108587637366	0.292217175274732	0.853891412362634
51	0.117989952815611	0.235979905631221	0.88201004718439
52	0.135342597009094	0.270685194018188	0.864657402990906
53	0.108031405832875	0.21606281166575	0.891968594167125
54	0.0855706486889305	0.171141297377861	0.91442935131107
55	0.0839997662363628	0.167999532472726	0.916000233763637
56	0.0717065895042771	0.143413179008554	0.928293410495723
57	0.0557580081324345	0.111516016264869	0.944241991867566
58	0.0425147181609193	0.0850294363218387	0.95748528183908
59	0.0319268666323418	0.0638537332646836	0.968073133367658
60	0.0264397483392631	0.0528794966785261	0.973560251660737
61	0.0200006548621681	0.0400013097243363	0.979999345137832
62	0.015114126251213	0.030228252502426	0.984885873748787
63	0.0122698566936963	0.0245397133873926	0.987730143306304
64	0.00936940160530582	0.0187388032106116	0.990630598394694
65	0.0145241293517702	0.0290482587035405	0.98547587064823
66	0.0117102021192613	0.0234204042385226	0.988289797880739
67	0.0122055419134885	0.0244110838269771	0.987794458086511
68	0.0154431101039839	0.0308862202079678	0.984556889896016
69	0.0123720437735352	0.0247440875470703	0.987627956226465
70	0.0124914788935698	0.0249829577871396	0.98750852110643
71	0.0105761059127259	0.0211522118254517	0.989423894087274
72	0.0077908573885663	0.0155817147771326	0.992209142611434
73	0.0135340851874653	0.0270681703749306	0.986465914812535
74	0.00963699965219385	0.0192739993043877	0.990363000347806
75	0.00876073324956693	0.0175214664991339	0.991239266750433
76	0.00683564719205188	0.0136712943841038	0.993164352807948
77	0.0057232851993201	0.0114465703986402	0.99427671480068
78	0.00525446613172948	0.010508932263459	0.99474553386827
79	0.00809900590811866	0.0161980118162373	0.991900994091881
80	0.00738189486329398	0.014763789726588	0.992618105136706
81	0.00667002205772767	0.0133400441154553	0.993329977942272
82	0.00478589030837583	0.00957178061675167	0.995214109691624
83	0.00362540598569518	0.00725081197139037	0.996374594014305
84	0.00255809594562009	0.00511619189124018	0.99744190405438
85	0.00285163459967583	0.00570326919935166	0.997148365400324
86	0.011041208674457	0.022082417348914	0.988958791325543
87	0.0273746743382822	0.0547493486765643	0.972625325661718
88	0.0340673329217458	0.0681346658434916	0.965932667078254
89	0.0529488456232814	0.105897691246563	0.947051154376719
90	0.0431133535403968	0.0862267070807937	0.956886646459603
91	0.0321505676346353	0.0643011352692706	0.967849432365365
92	0.0421282925558704	0.0842565851117408	0.95787170744413
93	0.106494961086686	0.212989922173373	0.893505038913314
94	0.0876127250522878	0.175225450104576	0.912387274947712
95	0.205379393730745	0.410758787461489	0.794620606269255
96	0.200492124520384	0.400984249040767	0.799507875479616
97	0.185707296749847	0.371414593499695	0.814292703250153
98	0.305622432808997	0.611244865617995	0.694377567191003
99	0.313232980809213	0.626465961618425	0.686767019190787
100	0.348110122719759	0.696220245439518	0.651889877280241
101	0.333966180861249	0.667932361722497	0.666033819138751
102	0.274862936846995	0.54972587369399	0.725137063153005
103	0.249576148634699	0.499152297269397	0.750423851365301
104	0.215942228197248	0.431884456394496	0.784057771802752
105	0.164769739961005	0.329539479922011	0.835230260038995
106	0.140124129855075	0.280248259710151	0.859875870144925
107	0.180346350828674	0.360692701657347	0.819653649171326
108	0.149143823342026	0.298287646684052	0.850856176657974
109	0.116876352053506	0.233752704107012	0.883123647946494
110	0.212050978052695	0.42410195610539	0.787949021947305
111	0.157856590051522	0.315713180103045	0.842143409948478
112	0.369944926532761	0.739889853065521	0.630055073467239
113	0.412490928166934	0.824981856333868	0.587509071833066
114	0.873279409712307	0.253441180575386	0.126720590287693
115	0.99924937685948	0.00150124628104132	0.00075062314052066
116	0.998186624687288	0.00362675062542415	0.00181337531271208
117	0.98888629254427	0.02222741491146	0.01111370745573

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	6	0.055045871559633	NOK
5% type I error level	29	0.26605504587156	NOK
10% type I error level	37	0.339449541284404	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9/10w8sg1292778792.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9/10w8sg1292778792.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9/17pc41292778792.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9/17pc41292778792.ps (open in new window)

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http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9/4iycp1292778792.png (open in new window)

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http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9/83zad1292778792.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9/93zad1292778792.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778698bi795lbe45a5wa9/93zad1292778792.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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