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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: Wed, 22 Dec 2010 13:52:50 +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/22/t1293025909u15leheeu69j29g.htm/, Retrieved Wed, 22 Dec 2010 14:51:49 +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/22/t1293025909u15leheeu69j29g.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 «

17.98 586.9642 830.36 1609.84 404.45 2342.32 17.83 582.1727 750 1477.27 387.97 2258.39 19.45 586.19396 830.36 1653.84 390.27 2293.62 21.04 590.49531 803.57 1512.02 384.72 2418.80 20.03 589.70506 830.36 1352.37 371.35 2480.15 20.01 590.07518 803.57 1436.84 367.73 2440.06 19.64 595.2568 830.36 1503.27 375.21 2660.66 18.52 604.1796 830.36 1649.09 365.55 2737.27 19.59 605.00987 803.57 1545.82 361.80 2692.82 20.09 610.71165 830.36 1622.21 366.80 2645.08 19.82 618.31404 803.57 1650.19 394.36 2706.27 21.09 614.74292 830.36 1696.73 409.66 2753.20 22.64 609.16284 544.75 1502.97 410.12 2590.54 22.11 611.73756 492.03 1469.44 416.54 2627.25 20.42 620.77222 544.75 1565.22 393.66 2707.21 18.58 618.00755 527.18 1485.36 374.93 2656.76 18.24 612.3328 544.75 1268.54 368.85 2876.66 16.87 604.04309 527.18 994.85 352.66 2880.69 18.64 605.08732 544.75 1352.65 361.82 2905.20 27.17 569.6086 544.75 1574.06 394.86 2614.36 33.69 595.0899 527.18 1531.7 389.56 2452.48 35.92 598.49214 544.75 1473.81 381.33 2442.33 32 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	10 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

PRICE[t] = -169.944836849113 + 0.249358371013949PRODUCTION[t] + 0.0233130772579997RENEW_RISID[t] -0.0087232698957167RENEW_IND[t] + 0.0917792017482259GOLD[t] + 0.00318761707774345DOWJONES[t] -0.180920339320372t + 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)	-169.944836849113	26.474671	-6.4191	0	0
PRODUCTION	0.249358371013949	0.047525	5.2469	0	0
RENEW_RISID	0.0233130772579997	0.011237	2.0747	0.039056	0.019528
RENEW_IND	-0.0087232698957167	0.003421	-2.5497	0.011392	0.005696
GOLD	0.0917792017482259	0.006628	13.8482	0	0
DOWJONES	0.00318761707774345	0.00053	6.0166	0	0
t	-0.180920339320372	0.042638	-4.2432	3.1e-05	1.6e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.916967581324854
R-squared	0.840829545200753
Adjusted R-squared	0.836931493246486
F-TEST (value)	215.705063725557
F-TEST (DF numerator)	6
F-TEST (DF denominator)	245
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.6357027549884
Sum Squared Residuals	22747.4580577103

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	17.98	26.1403749707140	-8.16037497071395
2	17.83	22.2676010521575	-4.43760105215754
3	19.45	23.7459785924967	-4.29597859249674
4	21.04	25.1398640152952	-4.09986401529525
5	20.03	25.7475879822198	-5.71758798221981
6	20.01	23.8375159363709	-3.82751593637088
7	19.64	26.3834431964601	-6.74344319646006
8	18.52	26.5130867692676	-7.99308676926763
9	19.59	26.3296343613763	-6.7396343613763
10	20.09	27.8354065165935	-7.7454065165935
11	19.82	31.4060564212301	-11.5860564212301
12	21.09	32.1070404310160	-11.0170404310160
13	22.64	25.0901738510433	-2.45017385104327
14	22.11	25.3209472014492	-3.21094720144916
15	20.42	25.9414193303606	-5.52141933036061
16	18.58	23.4782952195816	-4.89829521958158
17	18.24	24.3262580593287	-6.08625805932872
18	16.87	22.5830309290855	-5.71303092908547
19	18.64	20.8697488628539	-2.22974886285385
20	27.17	12.0157917859333	15.1542082140667
21	33.69	17.1463126294795	16.5436873705205
22	35.92	17.9406730323167	17.9793269676833
23	32.3	20.7407639391904	11.5592360608096
24	27.34	19.5022310751908	7.83776892480922
25	24.96	19.1253859190718	5.83461408092817
26	20.52	19.9258941358627	0.594105864137272
27	19.86	21.5972376963016	-1.73723769630158
28	20.82	18.6639949006327	2.15600509936735
29	21.24	17.7894718807990	3.45052811920103
30	20.2	17.6470121539898	2.55298784601021
31	21.42	20.9140127056702	0.505987294329817
32	21.69	15.0042354590658	6.68576454093419
33	21.86	14.6227686447655	7.2372313552345
34	23.23	17.2999508663366	5.93004913366342
35	22.47	17.5085194934766	4.96148050652344
36	19.52	17.8591514209079	1.66084857909206
37	18.82	17.7566533545553	1.06334664544471
38	19	18.1228497677298	0.877150232270149
39	18.92	17.1725800946948	1.74741990530524
40	20.24	17.8051413100998	2.43485868990019
41	20.94	17.2525802029993	3.68741979700066
42	22.38	13.9389715244417	8.44102847555827
43	21.76	15.4493272150263	6.31067278497375
44	21.35	12.4529438436795	8.89705615632053
45	21.9	13.9056617117884	7.99433828821163
46	21.69	14.5264316125007	7.1635683874993
47	20.34	12.3814635077506	7.9585364922494
48	19.41	14.1885552621271	5.22144473787285
49	19.08	10.4832285421747	8.59677145782526
50	20.05	11.8821413731586	8.16785862684137
51	20.35	10.5361122290798	9.81388777092021
52	20.27	11.3148456354225	8.95515436457755
53	19.94	17.3456865055839	2.59431349441609
54	19.07	16.2788132658452	2.79118673415485
55	17.87	18.8125059894788	-0.942505989478774
56	18.01	13.2447216975199	4.7652783024801
57	17.51	11.3664406392847	6.14355936071525
58	18.15	13.5301582728420	4.61984172715804
59	16.7	13.4331941144298	3.26680588557023
60	14.51	15.7532649919884	-1.24326499198842
61	15	12.6516917047138	2.34830829528615
62	14.78	14.3953772201988	0.38462277980122
63	14.66	14.0716898205595	0.588310179440534
64	16.38	13.4850363544043	2.89496364559567
65	17.88	18.5251756501387	-0.645175650138683
66	19.07	17.5231261426383	1.54687385736171
67	19.65	12.6161107915605	7.03388920843949
68	18.38	13.9902239402939	4.38977605970611
69	17.46	16.6412167576269	0.818783242373126
70	17.71	16.0210035239272	1.68899647607283
71	18.1	15.4987472114167	2.60125278858334
72	17.16	16.6202439194856	0.539756080514365
73	17.99	16.4097836800508	1.58021631994921
74	18.53	18.9581646112530	-0.428164611252952
75	18.55	16.7228916345566	1.82710836544335
76	19.87	17.8856599449437	1.98434005505632
77	19.74	20.3279872837084	-0.587987283708401
78	18.42	18.7257150713457	-0.305715071345743
79	17.3	16.349754898734	0.950245101266018
80	18.03	15.5090116436728	2.52098835632724
81	18.23	18.9636233691282	-0.7336233691282
82	17.44	16.6777463436266	0.762253656373413
83	17.99	19.6121877515831	-1.62218775158310
84	19.04	21.4558549269982	-2.41585492699824
85	18.88	22.2254480387213	-3.3454480387213
86	19.07	26.6353682635856	-7.56536826358557
87	21.36	24.331812242654	-2.97181224265402
88	23.57	26.748410268222	-3.17841026822198
89	21.25	25.1484682328803	-3.89846823288029
90	20.45	23.1703347902896	-2.72033479028955
91	21.32	21.2379105334992	0.0820894665008082
92	21.96	19.3374310487591	2.62256895124088
93	23.99	23.4892428122223	0.500757187777665
94	24.9	21.5340914091630	3.36590859083696
95	23.71	24.1769092988403	-0.466909298840285
96	25.39	27.3535023522573	-1.96350235225733
97	25.17	22.5349259531834	2.63507404681664
98	22.21	25.4380115054774	-3.22801150547742
99	20.99	24.1676224603036	-3.17762246030363
100	19.72	25.7494009955928	-6.02940099559282
101	20.83	24.2426745161768	-3.41267451617679
102	19.17	24.9570531421939	-5.78705314219389
103	19.63	24.5403707571359	-4.91037075713587
104	19.93	24.2285294810267	-4.29852948102672
105	19.79	26.2949689667466	-6.5049689667466
106	21.26	22.5811747081200	-1.32117470811997
107	20.17	24.1285785217275	-3.95857852172754
108	18.32	23.3896594661659	-5.06965946616591
109	16.71	22.8291176451252	-6.1191176451252
110	16.06	30.2218287592601	-14.1618287592601
111	15.02	29.6312639134218	-14.6112639134218
112	15.44	30.8560104806671	-15.4160104806671
113	14.86	27.6101154052505	-12.7501154052505
114	13.66	30.5104735178990	-16.8504735178990
115	14.08	25.8435727721557	-11.7635727721557
116	13.36	17.5788848651249	-4.21888486512492
117	14.95	19.0989432303841	-4.14894323038413
118	14.39	21.438572101824	-7.04857210182399
119	12.85	26.9165208745912	-14.0665208745912
120	11.28	24.1318547335490	-12.8518547335490
121	12.47	24.2516649915428	-11.7816649915428
122	12.01	27.6242751263835	-15.6142751263835
123	14.66	29.938316222668	-15.278316222668
124	17.34	26.0373715031557	-8.69737150315571
125	17.75	21.4778912705363	-3.72789127053625
126	17.89	22.1138145718596	-4.22381457185956
127	20.07	22.2494816125324	-2.17948161253236
128	21.26	22.8614395208625	-1.60143952086253
129	23.88	22.83597560286	1.04402439713998
130	22.64	31.1689702064054	-8.52897020640541
131	24.97	27.2693469397964	-2.29934693979637
132	26.08	27.9853923670214	-1.90539236702141
133	27.18	30.4587405793625	-3.27874057936248
134	29.35	28.1145127713035	1.23548722869651
135	29.89	29.0434404664277	0.846559533572337
136	25.74	29.553850681364	-3.81385068136399
137	28.78	30.1602635988104	-1.38026359881045
138	31.83	31.2297054636767	0.600294536323305
139	29.77	30.9919030010104	-1.22190300101038
140	31.22	34.2427147360737	-3.02271473607373
141	33.88	32.9485175885014	0.931482411498647
142	33.08	32.8946565816376	0.185343418362355
143	34.4	31.9824329681485	2.4175670318515
144	28.46	30.2844897236162	-1.82448972361625
145	29.58	30.9073258357336	-1.32732583573365
146	29.61	28.6005091615005	1.00949083849947
147	27.24	28.1408319096338	-0.90083190963385
148	27.41	28.1144698556916	-0.70446985569159
149	28.64	28.5228064734036	0.117193526596374
150	27.6	22.6141870970021	4.98581290299793
151	26.45	26.8974624611557	-0.447462461155686
152	27.47	25.3777161063633	2.09228389363673
153	25.88	21.8567086148194	4.0232913851806
154	22.21	21.5469539777390	0.663046022260967
155	19.67	24.3084937341711	-4.63849373417106
156	19.33	23.662415143757	-4.332415143757
157	19.67	21.8838706250749	-2.2138706250749
158	20.74	24.5405241864900	-3.80052418648996
159	24.42	24.9069895644898	-0.486989564489803
160	26.27	22.4153463018452	3.8546536981548
161	27.02	24.1905497217427	2.82945027825729
162	25.52	22.6441801850489	2.87581981495106
163	26.94	20.1598421834385	6.78015781656147
164	28.38	19.8309992618180	8.54900073818196
165	29.67	17.3870952852915	12.2829047147085
166	28.85	22.3128128629633	6.53718713703673
167	26.27	24.8941515487683	1.37584845123175
168	29.42	19.7028064321291	9.7171935678709
169	32.94	23.4852275549421	9.45477244505787
170	35.87	27.2479323123995	8.62206768760046
171	33.55	26.9513570723561	6.59864292764386
172	28.25	24.3188487957724	3.93115120422755
173	28.14	27.9907229270987	0.149277072901289
174	30.72	26.2347859010243	4.48521409897575
175	30.76	27.2927115201431	3.46728847985693
176	31.59	29.8359295328060	1.75407046719395
177	28.29	32.8566602888261	-4.56666028882613
178	30.33	36.5155606547614	-6.18556065476142
179	31.09	37.7708755818229	-6.68087558182288
180	32.15	43.6849713776193	-11.5349713776193
181	34.27	43.2931073838521	-9.02310738385206
182	34.74	43.0441195813623	-8.30411958136226
183	36.76	42.2369807084095	-5.47698070840954
184	36.69	40.9311317239534	-4.24113172395337
185	40.28	38.7983011954447	1.48169880455527
186	38.02	43.6182771294125	-5.59827712941253
187	40.69	43.9084425586163	-3.21844255861634
188	44.94	41.5925005090436	3.34749949095636
189	45.95	43.3775913508179	2.57240864918208
190	53.13	45.5111483926925	7.61885160730755
191	48.46	47.6085362800516	0.851463719948414
192	43.33	47.2132798000787	-3.88327980007874
193	46.84	45.7718374256814	1.06816257431855
194	47.97	46.9681668803431	1.00183311965686
195	54.31	48.4372916338351	5.87270836616486
196	53.04	47.7066672410902	5.33333275890984
197	49.83	48.1589786037588	1.67102139624123
198	56.26	47.0721128619274	9.18788713807258
199	58.7	46.8349806007548	11.8650193992452
200	64.97	47.4995699687243	17.4704300312757
201	65.57	48.3369978254087	17.2330021745913
202	62.37	49.1475359458983	13.2224640541017
203	58.3	52.0569615727079	6.24303842729211
204	59.43	54.9801364382031	4.4498635617969
205	65.51	56.133405821953	9.37659417804702
206	61.63	57.7621935875272	3.8678064124728
207	62.9	57.8378060711521	5.06219392884793
208	69.69	63.8656545639138	5.82434543608618
209	70.94	67.8813249436796	3.05867505632039
210	70.96	59.8969370936817	11.0630629063183
211	74.41	65.1319452344888	9.27805476551122
212	73.05	64.6485533545702	8.40144664542977
213	63.87	61.896243254111	1.97375674588902
214	58.88	62.320310458468	-3.44031045846804
215	59.37	65.4277886965468	-6.0577886965468
216	62.03	65.4257924736437	-3.39579247364367
217	54.57	66.2518138567438	-11.6818138567438
218	59.26	69.0555603807603	-9.79556038076033
219	60.56	68.0390927114699	-7.47909271146991
220	63.97	72.8648733097164	-8.89487330971638
221	63.46	72.425290721594	-8.96529072159404
222	67.48	69.4498457391767	-1.96984573917671
223	74.18	70.4892839625114	3.69071603748858
224	72.39	69.2941857008276	3.09581429917240
225	79.93	77.0436204066346	2.88637959336538
226	86.2	82.3113686435765	3.88863135642354
227	94.62	84.2955776632127	10.3244223367873
228	91.73	83.9096721139864	7.82032788601355
229	92.95	89.4945979079794	3.45540209202064
230	95.35	93.095881680044	2.25411831995608
231	105.56	97.7081669605333	7.85183303946673
232	112.57	92.2386065395067	20.3313934604933
233	125.39	90.193179261362	35.1968207386379
234	133.93	86.3892826873017	47.5407173126983
235	133.44	92.5512873488346	40.8887126511653
236	116.61	80.6215565694656	35.9884434305344
237	103.9	74.7776967412685	29.1223032587315
238	76.65	70.7508906020779	5.89910939792209
239	57.44	63.4417624242048	-6.00176242420476
240	41.02	67.6087190351272	-26.5887190351272
241	41.74	65.9978467490202	-24.2578467490202
242	39.16	71.7201275050298	-32.5601275050297
243	47.98	71.4975249125932	-23.5175249125932
244	49.79	70.513034023874	-20.7230340238741
245	59.16	73.381632435124	-14.2216324351240
246	69.68	75.1508885122486	-5.47088851224857
247	64.09	77.3597795545729	-13.2697795545729
248	71.06	78.086989522683	-7.02698952268302
249	69.46	84.5018934014139	-15.0418934014139
250	75.82	89.3520695729228	-13.5320695729228
251	78.08	98.6535302372746	-20.5735302372746
252	74.3	98.8082349314682	-24.5082349314682

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.00264638252591573	0.00529276505183147	0.997353617474084
11	0.000300137441222249	0.000600274882444498	0.999699862558778
12	3.27035327037567e-05	6.54070654075134e-05	0.999967296467296
13	5.75389915805223e-06	1.15077983161045e-05	0.999994246100842
14	5.84780023795297e-07	1.16956004759059e-06	0.999999415219976
15	7.41461948858214e-08	1.48292389771643e-07	0.999999925853805
16	9.51763401072182e-08	1.90352680214436e-07	0.99999990482366
17	1.03170621917915e-07	2.06341243835830e-07	0.999999896829378
18	3.86773838936181e-08	7.73547677872362e-08	0.999999961322616
19	5.53084972807853e-09	1.10616994561571e-08	0.99999999446915
20	1.77311996837446e-09	3.54623993674892e-09	0.99999999822688
21	1.29244336772600e-08	2.58488673545200e-08	0.999999987075566
22	6.32803071329215e-09	1.26560614265843e-08	0.99999999367197
23	1.26015587996196e-09	2.52031175992392e-09	0.999999998739844
24	2.41108807833235e-09	4.8221761566647e-09	0.999999997588912
25	4.80638153429784e-09	9.61276306859568e-09	0.999999995193618
26	1.55017798837680e-08	3.10035597675359e-08	0.99999998449822
27	2.91958649026353e-08	5.83917298052706e-08	0.999999970804135
28	1.44116147079033e-08	2.88232294158067e-08	0.999999985588385
29	4.0065866210854e-09	8.0131732421708e-09	0.999999995993413
30	3.66286887337373e-09	7.32573774674745e-09	0.999999996337131
31	1.0868841746223e-09	2.1737683492446e-09	0.999999998913116
32	3.75469525381586e-10	7.50939050763173e-10	0.99999999962453
33	1.80733944442488e-10	3.61467888884977e-10	0.999999999819266
34	4.95686120061601e-11	9.91372240123202e-11	0.999999999950431
35	8.36191206084315e-11	1.67238241216863e-10	0.99999999991638
36	3.37973188071617e-11	6.75946376143234e-11	0.999999999966203
37	1.18115279818371e-11	2.36230559636743e-11	0.999999999988188
38	3.31514197279011e-12	6.63028394558023e-12	0.999999999996685
39	9.97260512023614e-13	1.99452102404723e-12	0.999999999999003
40	4.45872772273649e-13	8.91745544547298e-13	0.999999999999554
41	2.70044188665422e-13	5.40088377330844e-13	0.99999999999973
42	9.35122238509349e-14	1.87024447701870e-13	0.999999999999907
43	2.99815782353141e-14	5.99631564706282e-14	0.99999999999997
44	1.03994427079589e-14	2.07988854159178e-14	0.99999999999999
45	3.26672060507497e-15	6.53344121014993e-15	0.999999999999997
46	1.60325636949451e-15	3.20651273898902e-15	0.999999999999998
47	5.96040446341623e-16	1.19208089268325e-15	1
48	3.75648707263206e-16	7.51297414526413e-16	1
49	2.02253603523147e-16	4.04507207046295e-16	1
50	7.28387160487434e-17	1.45677432097487e-16	1
51	2.06800054550421e-17	4.13600109100842e-17	1
52	8.25715988614492e-18	1.65143197722898e-17	1
53	3.49469186692752e-18	6.98938373385503e-18	1
54	1.95344751301968e-18	3.90689502603935e-18	1
55	1.32875800653538e-18	2.65751601307077e-18	1
56	3.74792983915822e-19	7.49585967831643e-19	1
57	2.11647050273184e-19	4.23294100546369e-19	1
58	5.66216702166736e-20	1.13243340433347e-19	1
59	1.98886980147635e-20	3.97773960295271e-20	1
60	8.54158727402734e-21	1.70831745480547e-20	1
61	3.18032242865569e-21	6.36064485731138e-21	1
62	1.22237908356576e-21	2.44475816713151e-21	1
63	2.92070725766067e-21	5.84141451532134e-21	1
64	1.53293540675308e-21	3.06587081350615e-21	1
65	4.03281672720446e-22	8.06563345440892e-22	1
66	1.28541120253342e-22	2.57082240506685e-22	1
67	4.58192226401713e-23	9.16384452803426e-23	1
68	1.90773532466584e-23	3.81547064933168e-23	1
69	5.27291542894663e-24	1.05458308578933e-23	1
70	1.52724882547015e-24	3.05449765094030e-24	1
71	5.98374773457423e-25	1.19674954691485e-24	1
72	1.74223627425563e-25	3.48447254851125e-25	1
73	4.6701146376256e-26	9.3402292752512e-26	1
74	2.08370556291583e-26	4.16741112583165e-26	1
75	2.66822307398739e-26	5.33644614797478e-26	1
76	3.55680288809262e-25	7.11360577618524e-25	1
77	4.79779914597766e-24	9.59559829195533e-24	1
78	1.13662237764223e-23	2.27324475528445e-23	1
79	1.39770381021353e-23	2.79540762042705e-23	1
80	9.62794945181877e-24	1.92558989036375e-23	1
81	9.16824944682184e-24	1.83364988936437e-23	1
82	4.43936903492502e-24	8.87873806985004e-24	1
83	3.94375477373549e-24	7.88750954747099e-24	1
84	4.05345903052769e-24	8.10691806105539e-24	1
85	3.37597324726961e-24	6.75194649453921e-24	1
86	2.16008201601579e-24	4.32016403203158e-24	1
87	2.79445589147862e-24	5.58891178295725e-24	1
88	6.37871894488211e-24	1.27574378897642e-23	1
89	3.58474044435679e-24	7.16948088871358e-24	1
90	1.44652905690770e-24	2.89305811381539e-24	1
91	7.66925330716174e-25	1.53385066143235e-24	1
92	4.4267128685666e-25	8.8534257371332e-25	1
93	3.87910629371722e-25	7.75821258743443e-25	1
94	3.62188291823133e-25	7.24376583646266e-25	1
95	1.33940987392332e-25	2.67881974784664e-25	1
96	8.20173987526507e-26	1.64034797505301e-25	1
97	3.71733307578403e-26	7.43466615156806e-26	1
98	1.79763972442662e-26	3.59527944885325e-26	1
99	7.17334553478795e-27	1.43466910695759e-26	1
100	4.08986009182032e-27	8.17972018364064e-27	1
101	1.95402902621023e-27	3.90805805242046e-27	1
102	1.43234195234512e-27	2.86468390469024e-27	1
103	7.94816789240264e-28	1.58963357848053e-27	1
104	2.9505403762895e-28	5.901080752579e-28	1
105	1.10177022358330e-28	2.20354044716660e-28	1
106	4.04410427411352e-29	8.08820854822704e-29	1
107	1.41359631187211e-29	2.82719262374421e-29	1
108	5.28775449984386e-30	1.05755089996877e-29	1
109	3.13740295399926e-30	6.27480590799852e-30	1
110	2.45579626218117e-30	4.91159252436233e-30	1
111	2.29622881914893e-30	4.59245763829785e-30	1
112	1.93444799060705e-30	3.8688959812141e-30	1
113	1.29129720836245e-30	2.5825944167249e-30	1
114	1.02421662690749e-30	2.04843325381498e-30	1
115	5.98532142602126e-31	1.19706428520425e-30	1
116	2.98365064214721e-31	5.96730128429443e-31	1
117	1.14493022179965e-31	2.28986044359930e-31	1
118	4.96704538380616e-32	9.93409076761232e-32	1
119	3.33167724191595e-32	6.6633544838319e-32	1
120	3.44364618405674e-32	6.88729236811348e-32	1
121	1.93053386535824e-32	3.86106773071648e-32	1
122	1.41838899538010e-32	2.83677799076021e-32	1
123	9.15566579054391e-33	1.83113315810878e-32	1
124	3.61173906449883e-33	7.22347812899766e-33	1
125	1.20646825425468e-33	2.41293650850936e-33	1
126	4.57585464868797e-34	9.15170929737593e-34	1
127	2.77626674724562e-34	5.55253349449123e-34	1
128	2.07452796597655e-34	4.1490559319531e-34	1
129	4.95697607091934e-34	9.91395214183867e-34	1
130	4.00920334486385e-34	8.0184066897277e-34	1
131	4.36410968858707e-34	8.72821937717414e-34	1
132	5.12070838583094e-34	1.02414167716619e-33	1
133	2.79091499664744e-33	5.58182999329488e-33	1
134	3.96696092109706e-32	7.93392184219411e-32	1
135	3.0863540514702e-31	6.1727081029404e-31	1
136	4.72791688918511e-31	9.45583377837021e-31	1
137	4.52765515444313e-30	9.05531030888627e-30	1
138	1.20321596033859e-28	2.40643192067719e-28	1
139	6.98278892542095e-28	1.39655778508419e-27	1
140	5.72419392562846e-27	1.14483878512569e-26	1
141	1.39896759533569e-25	2.79793519067138e-25	1
142	1.10484628091020e-24	2.20969256182039e-24	1
143	1.17407098216309e-23	2.34814196432619e-23	1
144	1.83289962112243e-23	3.66579924224486e-23	1
145	2.85915911695966e-23	5.71831823391932e-23	1
146	2.96041441362128e-23	5.92082882724255e-23	1
147	2.68168248803248e-23	5.36336497606495e-23	1
148	2.05261293259489e-23	4.10522586518978e-23	1
149	1.93734576632018e-23	3.87469153264036e-23	1
150	1.19346928155895e-23	2.38693856311791e-23	1
151	7.92276887233705e-24	1.58455377446741e-23	1
152	4.84664831273768e-24	9.69329662547536e-24	1
153	2.19968022710036e-24	4.39936045420072e-24	1
154	8.75894090737005e-25	1.75178818147401e-24	1
155	5.66450104555198e-25	1.13290020911040e-24	1
156	4.00825882328450e-25	8.01651764656899e-25	1
157	2.18265357313701e-25	4.36530714627403e-25	1
158	9.8080304572837e-26	1.96160609145674e-25	1
159	4.97948068093501e-26	9.95896136187002e-26	1
160	2.32553417484144e-26	4.65106834968287e-26	1
161	1.16565395026064e-26	2.33130790052127e-26	1
162	4.75976988405926e-27	9.51953976811853e-27	1
163	2.75995072854859e-27	5.51990145709719e-27	1
164	2.16353562824514e-27	4.32707125649028e-27	1
165	7.57400110891181e-27	1.51480022178236e-26	1
166	5.93980677389425e-27	1.18796135477885e-26	1
167	2.45728417676931e-27	4.91456835353861e-27	1
168	1.30165837604333e-26	2.60331675208665e-26	1
169	3.72540603949106e-26	7.45081207898212e-26	1
170	1.30748748232396e-25	2.61497496464792e-25	1
171	1.07086936134758e-25	2.14173872269516e-25	1
172	1.14413171351584e-25	2.28826342703168e-25	1
173	6.18877054151048e-26	1.23775410830210e-25	1
174	3.44262218802281e-25	6.88524437604563e-25	1
175	2.93394330557702e-24	5.86788661115404e-24	1
176	2.80035114918838e-23	5.60070229837675e-23	1
177	7.30753793436064e-23	1.46150758687213e-22	1
178	4.70500375093398e-23	9.41000750186797e-23	1
179	3.55091637182677e-23	7.10183274365353e-23	1
180	1.71363075330474e-23	3.42726150660948e-23	1
181	7.36807984718165e-24	1.47361596943633e-23	1
182	3.15683870168829e-24	6.31367740337657e-24	1
183	1.57887417166431e-24	3.15774834332862e-24	1
184	1.24921364905940e-24	2.49842729811880e-24	1
185	6.79698465822457e-24	1.35939693164491e-23	1
186	3.49430392347059e-24	6.98860784694117e-24	1
187	2.77372203203408e-24	5.54744406406816e-24	1
188	1.09952351229737e-23	2.19904702459474e-23	1
189	1.61775596607696e-23	3.23551193215393e-23	1
190	1.06553091567262e-22	2.13106183134525e-22	1
191	1.0158824846719e-22	2.0317649693438e-22	1
192	4.98355129537075e-23	9.9671025907415e-23	1
193	3.24078156095890e-23	6.48156312191781e-23	1
194	2.31518106841502e-23	4.63036213683003e-23	1
195	4.90887673938146e-23	9.81775347876291e-23	1
196	1.00523076671878e-22	2.01046153343756e-22	1
197	8.48714097653528e-22	1.69742819530706e-21	1
198	2.09894137370119e-21	4.19788274740239e-21	1
199	7.19366302453059e-21	1.43873260490612e-20	1
200	8.6643755846291e-20	1.73287511692582e-19	1
201	1.01929004368495e-18	2.03858008736990e-18	1
202	1.77967593254256e-18	3.55935186508512e-18	1
203	1.71803612308369e-18	3.43607224616738e-18	1
204	6.08736628482232e-18	1.21747325696446e-17	1
205	6.95985907235383e-18	1.39197181447077e-17	1
206	2.99260743328708e-18	5.98521486657417e-18	1
207	1.26055332429875e-18	2.5211066485975e-18	1
208	5.47308422768558e-19	1.09461684553712e-18	1
209	3.52171638785607e-19	7.04343277571213e-19	1
210	6.92989130715387e-18	1.38597826143077e-17	1
211	5.51063464217407e-18	1.10212692843481e-17	1
212	1.32461463651098e-17	2.64922927302197e-17	1
213	2.73344686492005e-17	5.46689372984009e-17	1
214	1.51551753495053e-17	3.03103506990106e-17	1
215	2.85061804170246e-17	5.70123608340493e-17	1
216	6.09367120549337e-15	1.21873424109867e-14	0.999999999999994
217	7.11273938383368e-15	1.42254787676674e-14	0.999999999999993
218	9.49580678546305e-15	1.89916135709261e-14	0.99999999999999
219	5.40493691416315e-15	1.08098738283263e-14	0.999999999999995
220	5.04315523139289e-15	1.00863104627858e-14	0.999999999999995
221	2.99738022075243e-14	5.99476044150485e-14	0.99999999999997
222	1.23587278520621e-14	2.47174557041243e-14	0.999999999999988
223	6.89665298268905e-15	1.37933059653781e-14	0.999999999999993
224	3.00500867993624e-15	6.01001735987247e-15	0.999999999999997
225	1.62997746963808e-14	3.25995493927616e-14	0.999999999999984
226	3.73315856860750e-11	7.46631713721499e-11	0.999999999962668
227	3.84785365851856e-11	7.69570731703711e-11	0.999999999961521
228	7.53151675648749e-11	1.50630335129750e-10	0.999999999924685
229	3.22894230908519e-11	6.45788461817038e-11	0.99999999996771
230	7.20281164032202e-10	1.44056232806440e-09	0.999999999279719
231	1.93042978941704e-08	3.86085957883408e-08	0.999999980695702
232	9.7232086206192e-06	1.94464172412384e-05	0.99999027679138
233	0.00094037636551961	0.00188075273103922	0.99905962363448
234	0.00394754170706384	0.00789508341412769	0.996052458292936
235	0.0181560377426126	0.0363120754852253	0.981843962257387
236	0.0218476175663669	0.0436952351327339	0.978152382433633
237	0.0393828572391011	0.0787657144782021	0.960617142760899
238	0.524108568914504	0.951782862170992	0.475891431085496
239	0.792574320599755	0.414851358800489	0.207425679400245
240	0.815932143380156	0.368135713239688	0.184067856619844
241	0.759000969793964	0.481998060412073	0.240999030206036
242	0.77652674033663	0.446946519326739	0.223473259663369

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	225	0.965665236051502	NOK
5% type I error level	227	0.974248927038627	NOK
10% type I error level	228	0.978540772532189	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/10yssy1293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/10yssy1293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/1r9d51293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/1r9d51293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/2kjc81293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/2kjc81293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/3kjc81293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/3kjc81293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/4kjc81293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/4kjc81293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/5kjc81293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/5kjc81293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/6datb1293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/6datb1293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/761be1293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/761be1293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/861be1293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/861be1293025956.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/961be1293025956.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025909u15leheeu69j29g/961be1293025956.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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