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WS 10 verbetering

*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 17:10:32 +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/t1292778536dts2e9wieisp70r.htm/, Retrieved Sun, 19 Dec 2010 18:09:07 +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/t1292778536dts2e9wieisp70r.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	9 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

PS[t] = + 2.56178589149283 + 0.401224374199192O[t] + 0.104589813674580CMD[t] + 0.186385617399127PEC[t] + 0.183599932659208happiness[t] + 0.113176959210136depression[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)	2.56178589149283	5.071203	0.5052	0.614371	0.307185
O	0.401224374199192	0.091021	4.408	2.3e-05	1.1e-05
CMD	0.104589813674580	0.051728	2.0219	0.045409	0.022704
PEC	0.186385617399127	0.067465	2.7627	0.006636	0.003318
happiness	0.183599932659208	0.270416	0.679	0.498475	0.249237
depression	0.113176959210136	0.264892	0.4273	0.669958	0.334979

Multiple Linear Regression - Regression Statistics
Multiple R	0.457337136720517
R-squared	0.209157256623721
Adjusted R-squared	0.176205475649709
F-TEST (value)	6.34737335710863
F-TEST (DF numerator)	5
F-TEST (DF denominator)	120
p-value	2.90293032559896e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.89738634624616
Sum Squared Residuals	1822.75443982872

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	24	24.3358476183895	-0.33584761838947
2	25	21.4319099292496	3.56809007075041
3	30	22.7385472738697	7.26145272613029
4	19	21.3635278993995	-2.36352789939949
5	22	19.8994280001018	2.1005719998982
6	22	23.5389005072955	-1.53890050729546
7	25	22.2707965527883	2.72920344721174
8	23	20.9928521795739	2.00714782042611
9	17	21.5670483972978	-4.56704839729775
10	21	22.6540141348043	-1.65401413480433
11	19	23.8833760948392	-4.88337609483919
12	19	22.1035687316915	-3.10356873169152
13	15	21.6260501910723	-6.62605019107227
14	23	20.7303298879011	2.26967011209888
15	27	22.7557547535382	4.24424524646177
16	14	16.4043167048855	-2.40431670488546
17	23	23.5925581559119	-0.592558155911898
18	19	22.2385497075933	-3.23854970759327
19	18	22.9323751045182	-4.9323751045182
20	20	23.5560353877946	-3.55603538779458
21	23	22.255527095946	0.744472904054021
22	25	22.8832960693093	2.11670393069075
23	19	22.8347406227526	-3.83474062275255
24	24	22.7728170348680	1.22718296513203
25	25	23.6300765470956	1.36992345290442
26	26	25.8974101024051	0.102589897594889
27	29	21.0214628111212	7.97853718887876
28	32	22.6661317062207	9.33386829377925
29	29	23.8906608159421	5.10933918405788
30	28	21.8674040644470	6.13259593555296
31	17	20.6713280941266	-3.6713280941266
32	28	24.9876342049913	3.01236579500869
33	26	27.3956222705547	-1.39562227055465
34	25	22.4000458000697	2.59995419993028
35	14	19.6789301138508	-5.67893011385083
36	25	21.5043254968986	3.49567450310144
37	26	21.3635278993995	4.6364721006005
38	20	22.9565924874693	-2.95659248746931
39	18	23.3014252326918	-5.30142523269177
40	32	22.9836222538692	9.01637774613084
41	25	23.5282061451462	1.47179385485376
42	21	21.8237540673732	-0.823754067373208
43	20	23.7424210051937	-3.74242100519371
44	30	25.6334197112267	4.36658028877328
45	24	25.0849537024641	-1.08495370246412
46	26	23.5503762583437	2.44962374165632
47	24	20.8121256916257	3.18787430837433
48	22	20.9144624981812	1.08553750181881
49	14	19.0095243185280	-5.00952431852796
50	24	22.6312201248543	1.3687798751457
51	24	22.7197044618602	1.28029553813984
52	24	24.0333964362674	-0.0333964362674200
53	24	21.6829564698741	2.31704353012586
54	22	22.8924462140690	-0.892446214069036
55	27	20.9110563758493	6.08894362415066
56	19	20.956486903603	-1.95648690360299
57	25	22.4569520788716	2.54304792112841
58	20	21.7762280246469	-1.77622802464691
59	21	20.7815770372521	0.218422962747915
60	27	24.6268230494631	2.37317695053694
61	25	23.2480825683682	1.75191743163184
62	20	21.2476198268231	-1.24761982682311
63	21	22.0385928241733	-1.03859282417327
64	22	23.0104498016181	-1.01044980161807
65	23	23.8754639582692	-0.875463958269228
66	25	22.0899974656707	2.91000253432935
67	25	24.6324821789140	0.367517821086038
68	17	22.1260477573487	-5.12604775734872
69	25	23.7106343426303	1.28936565736969
70	19	23.4326027621104	-4.43260276211044
71	20	22.9058599879435	-2.90585998794349
72	26	22.1605476096628	3.83945239033723
73	23	24.1438754707087	-1.1438754707087
74	27	24.2448290707356	2.75517092926436
75	17	23.556192879941	-6.556192879941
76	19	19.4503625988835	-0.450362598883485
77	17	20.6286618683366	-3.6286618683366
78	22	21.2526371359058	0.747362864094181
79	21	23.3184026210445	-2.31840262104449
80	32	26.5134732334044	5.48652676659564
81	21	24.8759315672614	-3.87593156726138
82	21	24.0527843238856	-3.05278432388561
83	18	21.3896285700693	-3.3896285700693
84	23	22.1795479138187	0.82045208618125
85	20	20.5006092577210	-0.500609257720984
86	20	25.0621596925141	-5.06215969251409
87	17	18.7222699940789	-1.72226999407893
88	18	18.6652062231306	-0.665206223130644
89	19	19.3994304338253	-0.399430433825346
90	15	20.9901148442330	-5.99011484423305
91	14	19.0037076969306	-5.00370769693064
92	18	24.2561473296374	-6.25614732963745
93	35	21.2156030729439	13.7843969270561
94	29	19.9810663116799	9.01893368832007
95	25	18.8587046645851	6.14129533541488
96	20	18.9902939230562	1.00970607694381
97	22	23.8243743010647	-1.82437430106468
98	13	16.6620061462447	-3.66200614624475
99	26	20.7035023401522	5.29649765984779
100	17	18.2183938290314	-1.21839382903139
101	25	21.6908686064441	3.30913139355589
102	20	20.3155342476811	-0.315534247681059
103	19	22.5481281186717	-3.54812811867172
104	21	22.7728170348680	-1.77281703486797
105	22	20.2870811082801	1.71291889171988
106	24	22.8581764530708	1.14182354692922
107	21	23.6143953612009	-2.61439536120090
108	26	24.4548349772222	1.54516502277778
109	16	20.4109013174265	-4.41090131742647
110	23	22.1005164120578	0.899483587942148
111	18	20.9634995101457	-2.96349951014575
112	21	18.0968266270041	2.90317337299590
113	21	22.9176446514515	-1.91764465145148
114	23	20.3611195047672	2.63888049523280
115	21	21.8303555965840	-0.830355596583968
116	21	21.2739774512514	-0.273977451251404
117	23	23.9084957110777	-0.908495711077662
118	27	24.2638984553466	2.73610154465344
119	21	20.0552649631273	0.944735036872654
120	10	17.3874919412321	-7.38749194123207
121	20	21.2781684811967	-1.27816848119669
122	26	21.0213053189748	4.97869468102517
123	24	24.3473233694376	-0.347323369437573
124	24	23.0602288514634	0.93977114853658
125	22	22.7899519153671	-0.7899519153671
126	17	25.1064371050550	-8.10643710505495

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.810200344079332	0.379599311841336	0.189799655920668
10	0.686128757729698	0.627742484540604	0.313871242270302
11	0.685574994356296	0.628850011287409	0.314425005643704
12	0.569494112604042	0.861011774791917	0.430505887395958
13	0.725089447774103	0.549821104451794	0.274910552225897
14	0.635987669993903	0.728024660012194	0.364012330006097
15	0.569105337442318	0.861789325115364	0.430894662557682
16	0.532961697576568	0.934076604846863	0.467038302423432
17	0.48944928850164	0.97889857700328	0.51055071149836
18	0.448770053784658	0.897540107569317	0.551229946215342
19	0.395492393246608	0.790984786493216	0.604507606753392
20	0.375587446110625	0.751174892221249	0.624412553889375
21	0.300320542010091	0.600641084020182	0.699679457989909
22	0.237173478934672	0.474346957869344	0.762826521065328
23	0.191700746579684	0.383401493159369	0.808299253420316
24	0.154077286466847	0.308154572933694	0.845922713533153
25	0.129713184710910	0.259426369421820	0.87028681528909
26	0.1002723563717	0.2005447127434	0.8997276436283
27	0.373241159329312	0.746482318658623	0.626758840670688
28	0.697119552700294	0.605760894599412	0.302880447299706
29	0.744286648567422	0.511426702865157	0.255713351432579
30	0.786834916636654	0.426330166726693	0.213165083363346
31	0.791485491655368	0.417029016689263	0.208514508344632
32	0.803196080402422	0.393607839195157	0.196803919597579
33	0.76590727048112	0.46818545903776	0.23409272951888
34	0.736985922338808	0.526028155322385	0.263014077661193
35	0.77247980740293	0.455040385194139	0.227520192597070
36	0.762381259090202	0.475237481819596	0.237618740909798
37	0.769497691897441	0.461004616205118	0.230502308102559
38	0.769125892862051	0.461748214275897	0.230874107137949
39	0.801820031094049	0.396359937811902	0.198179968905951
40	0.8999848977771	0.200030204445800	0.100015102222900
41	0.877601638337408	0.244796723325183	0.122398361662592
42	0.84881842085036	0.302363158299279	0.151181579149639
43	0.851423644140993	0.297152711718013	0.148576355859007
44	0.87958134385829	0.240837312283419	0.120418656141710
45	0.855342405344264	0.289315189311471	0.144657594655736
46	0.833458465051666	0.333083069896667	0.166541534948334
47	0.820125797970548	0.359748404058903	0.179874202029452
48	0.784369811275781	0.431260377448438	0.215630188724219
49	0.804417846192366	0.391164307615267	0.195582153807634
50	0.77274598947685	0.454508021046299	0.227254010523150
51	0.733130788256584	0.533738423486833	0.266869211743416
52	0.688428165933219	0.623143668133561	0.311571834066781
53	0.655609401552553	0.688781196894893	0.344390598447447
54	0.611157074498873	0.777685851002254	0.388842925501127
55	0.680480030897095	0.63903993820581	0.319519969102905
56	0.644276740852568	0.711446518294864	0.355723259147432
57	0.615525208341637	0.768949583316726	0.384474791658363
58	0.576138036989654	0.847723926020691	0.423861963010346
59	0.524441361322293	0.951117277355415	0.475558638677707
60	0.493366932419026	0.986733864838052	0.506633067580974
61	0.454003976049744	0.908007952099488	0.545996023950256
62	0.406943447594011	0.813886895188021	0.593056552405989
63	0.360017370997845	0.72003474199569	0.639982629002155
64	0.315019665897997	0.630039331795993	0.684980334102003
65	0.274387612686281	0.548775225372562	0.725612387313719
66	0.258905167541622	0.517810335083244	0.741094832458378
67	0.221775225045502	0.443550450091003	0.778224774954498
68	0.243598932646602	0.487197865293205	0.756401067353398
69	0.206206827483325	0.412413654966651	0.793793172516675
70	0.223510100930921	0.447020201861841	0.77648989906908
71	0.203269112031963	0.406538224063926	0.796730887968037
72	0.200081410632635	0.40016282126527	0.799918589367365
73	0.168349426685137	0.336698853370275	0.831650573314862
74	0.154068392113270	0.308136784226540	0.84593160788673
75	0.207130206272853	0.414260412545705	0.792869793727147
76	0.170868440780831	0.341736881561662	0.829131559219169
77	0.160579794228314	0.321159588456627	0.839420205771686
78	0.131685079614631	0.263370159229263	0.868314920385369
79	0.111803068804735	0.223606137609471	0.888196931195265
80	0.148261401588488	0.296522803176975	0.851738598411512
81	0.140175247960607	0.280350495921215	0.859824752039393
82	0.1239317664035	0.247863532807	0.8760682335965
83	0.118650190919902	0.237300381839804	0.881349809080098
84	0.0948332263418622	0.189666452683724	0.905166773658138
85	0.0733843390642159	0.146768678128432	0.926615660935784
86	0.0785670940896702	0.157134188179340	0.92143290591033
87	0.0624912769739471	0.124982553947894	0.937508723026053
88	0.0472875819684033	0.0945751639368066	0.952712418031597
89	0.034835637944189	0.069671275888378	0.965164362055811
90	0.0502670220420310	0.100534044084062	0.949732977957969
91	0.0619836979377086	0.123967395875417	0.938016302062291
92	0.0854835122983494	0.170967024596699	0.91451648770165
93	0.616217118300944	0.767565763398111	0.383782881699056
94	0.842569024902682	0.314861950194636	0.157430975097318
95	0.913564954345945	0.172870091308111	0.0864350456540555
96	0.886677954873779	0.226644090252443	0.113322045126221
97	0.85799209346619	0.284015813067619	0.142007906533810
98	0.87956477372636	0.240870452547278	0.120435226273639
99	0.89227868702727	0.215442625945461	0.107721312972731
100	0.85715061848665	0.285698763026699	0.142849381513349
101	0.868093895919323	0.263812208161355	0.131906104080677
102	0.822800633403738	0.354398733192525	0.177199366596262
103	0.822672546823004	0.354654906353992	0.177327453176996
104	0.769141404900029	0.461717190199942	0.230858595099971
105	0.721993071575649	0.556013856848702	0.278006928424351
106	0.668103618866922	0.663792762266155	0.331896381133077
107	0.626550248608943	0.746899502782114	0.373449751391057
108	0.545210604964165	0.90957879007167	0.454789395035835
109	0.535653682054754	0.928692635890493	0.464346317945246
110	0.464109102839964	0.928218205679929	0.535890897160036
111	0.393828894590045	0.78765778918009	0.606171105409955
112	0.39039063127268	0.78078126254536	0.60960936872732
113	0.408909922025395	0.817819844050789	0.591090077974605
114	0.541782343836233	0.916435312327535	0.458217656163767
115	0.987629718833147	0.0247405623337063	0.0123702811668532
116	0.982298281837527	0.0354034363249469	0.0177017181624735
117	0.939471483149105	0.121057033701789	0.0605285168508945

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	2	0.018348623853211	OK
10% type I error level	4	0.036697247706422	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778536dts2e9wieisp70r/101wje1292778568.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778536dts2e9wieisp70r/101wje1292778568.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778536dts2e9wieisp70r/1nm3n1292778568.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778536dts2e9wieisp70r/1nm3n1292778568.ps (open in new window)

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http://www.freestatistics.org/blog/date/2010/Dec/19/t1292778536dts2e9wieisp70r/91wje1292778568.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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