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R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 22 Dec 2008 05:56:16 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr.htm/, Retrieved Mon, 22 Dec 2008 13:59:10 +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/2008/Dec/22/t1229950740eo31tpzq64x43nr.htm/},
    year = {2008},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

9.2 0 9.1 0 9.1 0 9.1 0 9.1 0 9.2 0 9.3 0 9.3 0 9.3 0 9.3 0 9.3 0 9.4 0 9.4 0 9.4 0 9.5 0 9.5 0 9.4 0 9.4 0 9.3 0 9.4 0 9.4 0 9.2 0 9.1 0 9.1 0 9.1 0 9.1 0 9 0 8.9 0 8.8 0 8.7 0 8.5 0 8.3 0 8.1 0 7.8 0 7.6 0 7.5 0 7.4 0 7.3 0 7.1 0 6.9 0 6.8 0 6.8 0 6.8 0 6.9 0 6.7 0 6.6 0 6.5 0 6.4 0 6.3 0 6.3 0 6.3 0 6.5 0 6.6 0 6.5 0 6.4 0 6.5 0 6.7 0 7.1 0 7.1 0 7.2 1 7.2 1 7.3 1 7.3 1 7.3 1 7.4 1 7.4 1 7.6 1 7.6 1 7.6 1 7.7 1 7.8 1 7.9 1 8.1 1 8.1 1 8.1 1 8.2 1 8.2 1 8.2 1 8.2 1 8.2 1 8.2 1 8.3 1 8.3 1 8.4 1 8.4 1 8.4 1 8.3 1 8 1 8 1 8.2 1 8.6 1 8.7 1 8.7 1 8.5 1 8.4 1 8.4 1 8.4 1 8.5 1 8.5 1 8.5 1 8.5 1 8.5 1 8.4 1 8.4 1 8.4 1 8.5 1 8.5 1 8.6 1 8.6 1 8.6 1 8.5 1 8.4 1 8.4 1 8.3 1 8.2 1 8.1 1 8.2 1 8.1 1 8 1 7.9 1 7.8 1 7.7 1 7.7 1 7.9 1 7.8 1 7.6 1 7.4 1 7.3 1 7.1 1 7.1 1 7 1 7 1

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	5 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Werkloosheid[t] = + 8.68377224021514 + 1.36999725249565SabenaFailliet[t] + 0.0750171372114794M1[t] + 0.0877841516178363M2[t] + 0.0732784387514679M3[t] + 0.0769545440669175M4[t] + 0.0806306493823677M5[t] + 0.0843067546978174M6[t] + 0.097073769104176M7[t] + 0.118931692601443M8[t] + 0.113516888825983M9[t] + 0.117192994141433M10[t] + 0.0845054630932464M11[t] -0.0218579234972678t + 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)	8.68377224021514	0.283866	30.5911	0	0
SabenaFailliet	1.36999725249565	0.282594	4.8479	4e-06	2e-06
M1	0.0750171372114794	0.3473	0.216	0.82936	0.41468
M2	0.0877841516178363	0.347123	0.2529	0.800793	0.400396
M3	0.0732784387514679	0.346985	0.2112	0.833106	0.416553
M4	0.0769545440669175	0.346887	0.2218	0.824819	0.41241
M5	0.0806306493823677	0.346827	0.2325	0.816568	0.408284
M6	0.0843067546978174	0.346808	0.2431	0.808355	0.404177
M7	0.097073769104176	0.346827	0.2799	0.780051	0.390026
M8	0.118931692601443	0.346887	0.3429	0.732318	0.366159
M9	0.113516888825983	0.346985	0.3272	0.744133	0.372066
M10	0.117192994141433	0.347123	0.3376	0.736255	0.368128
M11	0.0845054630932464	0.3473	0.2433	0.808179	0.40409
t	-0.0218579234972678	0.003698	-5.9111	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.483454213925128
R-squared	0.233727976961964
Adjusted R-squared	0.149308177813706
F-TEST (value)	2.76863933958775
F-TEST (DF numerator)	13
F-TEST (DF denominator)	118
p-value	0.00180659680458561
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.812768904179986
Sum Squared Residuals	77.9500084090285

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9.2	8.73693145392934	0.463068546070665
2	9.1	8.72784054483844	0.372159455161559
3	9.1	8.6914769084748	0.408523091525197
4	9.1	8.67329509029298	0.426704909707017
5	9.1	8.65511327211116	0.444886727888835
6	9.2	8.63693145392935	0.563068546070651
7	9.3	8.62784054483844	0.672159455161561
8	9.3	8.62784054483844	0.67215945516156
9	9.3	8.60056781756571	0.699432182434289
10	9.3	8.5823859993839	0.717614000616107
11	9.3	8.52784054483844	0.772159455161562
12	9.4	8.42147715824792	0.978522841752075
13	9.4	8.47463637196214	0.925363628037864
14	9.4	8.46554546287123	0.934454537128773
15	9.5	8.42918182650759	1.07081817349241
16	9.5	8.41100000832577	1.08899999167423
17	9.4	8.39281819014395	1.00718180985605
18	9.4	8.37463637196213	1.02536362803787
19	9.3	8.36554546287123	0.934454537128775
20	9.4	8.36554546287122	1.03445453712877
21	9.4	8.3382727355985	1.06172726440150
22	9.2	8.32009091741668	0.879909082583319
23	9.1	8.26554546287123	0.834454537128774
24	9.1	8.15918207628071	0.940817923719288
25	9.1	8.21234128999492	0.887658710005077
26	9.1	8.20325038090401	0.896749619095987
27	9	8.16688674454038	0.833113255459623
28	8.9	8.14870492635856	0.751295073641442
29	8.8	8.13052310817674	0.669476891823261
30	8.7	8.11234128999492	0.587658710005078
31	8.5	8.10325038090401	0.396749619095987
32	8.3	8.10325038090401	0.196749619095988
33	8.1	8.07597765363128	0.0240223463687147
34	7.8	8.05779583544947	-0.257795835449467
35	7.6	8.00325038090401	-0.403250380904012
36	7.5	7.8968869943135	-0.396886994313498
37	7.4	7.95004620802771	-0.55004620802771
38	7.3	7.9409552989368	-0.640955298936799
39	7.1	7.90459166257316	-0.804591662573163
40	6.9	7.88640984439134	-0.986409844391344
41	6.8	7.86822802620953	-1.06822802620953
42	6.8	7.85004620802771	-1.05004620802771
43	6.8	7.8409552989368	-1.0409552989368
44	6.9	7.8409552989368	-0.940955298936799
45	6.7	7.81368257166407	-1.11368257166407
46	6.6	7.79550075348225	-1.19550075348225
47	6.5	7.7409552989368	-1.2409552989368
48	6.4	7.63459191234628	-1.23459191234628
49	6.3	7.6877511260605	-1.38775112606050
50	6.3	7.67866021696959	-1.37866021696959
51	6.3	7.64229658060595	-1.34229658060595
52	6.5	7.62411476242413	-1.12411476242413
53	6.6	7.60593294424231	-1.00593294424231
54	6.5	7.5877511260605	-1.08775112606049
55	6.4	7.57866021696959	-1.17866021696959
56	6.5	7.57866021696959	-1.07866021696959
57	6.7	7.55138748969686	-0.851387489696859
58	7.1	7.53320567151504	-0.433205671515041
59	7.1	7.47866021696959	-0.378660216969587
60	7.2	8.74229408287472	-1.54229408287472
61	7.2	8.79545329658893	-1.59545329658893
62	7.3	8.78636238749802	-1.48636238749802
63	7.3	8.74999875113439	-1.44999875113439
64	7.3	8.73181693295257	-1.43181693295257
65	7.4	8.71363511477075	-1.31363511477075
66	7.4	8.69545329658893	-1.29545329658893
67	7.6	8.68636238749802	-1.08636238749802
68	7.6	8.68636238749802	-1.08636238749802
69	7.6	8.6590896602253	-1.05908966022530
70	7.7	8.64090784204348	-0.940907842043477
71	7.8	8.58636238749802	-0.786362387498023
72	7.9	8.47999900090751	-0.579999000907508
73	8.1	8.53315821462172	-0.433158214621721
74	8.1	8.52406730553081	-0.42406730553081
75	8.1	8.48770366916717	-0.387703669167174
76	8.2	8.46952185098536	-0.269521850985356
77	8.2	8.45134003280354	-0.251340032803538
78	8.2	8.43315821462172	-0.233158214621719
79	8.2	8.42406730553081	-0.224067305530811
80	8.2	8.42406730553081	-0.224067305530810
81	8.2	8.39679457825808	-0.196794578258083
82	8.3	8.37861276007626	-0.0786127600762636
83	8.3	8.32406730553081	-0.0240673055308089
84	8.4	8.2177039189403	0.182296081059705
85	8.4	8.2708631326545	0.129136867345493
86	8.4	8.2617722235636	0.138227776436404
87	8.3	8.22540858719996	0.0745914128000404
88	8	8.20722676901814	-0.207226769018142
89	8	8.18904495083632	-0.189044950836324
90	8.2	8.1708631326545	0.0291368673454937
91	8.6	8.1617722235636	0.438227776436403
92	8.7	8.1617722235636	0.538227776436403
93	8.7	8.13449949629087	0.56550050370913
94	8.5	8.11631767810905	0.383682321890949
95	8.4	8.0617722235636	0.338227776436404
96	8.4	7.95540883697308	0.444591163026918
97	8.4	8.0085680506873	0.391431949312707
98	8.5	7.99947714159638	0.500522858403617
99	8.5	7.96311350523275	0.536886494767253
100	8.5	7.94493168705093	0.555068312949072
101	8.5	7.92674986886911	0.573250131130889
102	8.5	7.9085680506873	0.591431949312708
103	8.4	7.89947714159638	0.500522858403617
104	8.4	7.89947714159638	0.500522858403617
105	8.4	7.87220441432366	0.527795585676345
106	8.5	7.85402259614184	0.645977403858162
107	8.5	7.79947714159638	0.700522858403617
108	8.6	7.69311375500587	0.906886244994131
109	8.6	7.74627296872008	0.85372703127992
110	8.6	7.73718205962917	0.86281794037083
111	8.5	7.70081842326553	0.799181576734466
112	8.4	7.68263660508372	0.717363394916285
113	8.4	7.6644547869019	0.735545213098103
114	8.3	7.64627296872008	0.653727031279922
115	8.2	7.63718205962917	0.562817940370829
116	8.1	7.63718205962917	0.46281794037083
117	8.2	7.60990933235644	0.590090667643557
118	8.1	7.59172751417462	0.508272485825375
119	8	7.53718205962917	0.46281794037083
120	7.9	7.43081867303866	0.469181326961345
121	7.8	7.48397788675287	0.316022113247133
122	7.7	7.47488697766196	0.225113022338044
123	7.7	7.43852334129832	0.261476658701680
124	7.9	7.4203415231165	0.479658476883498
125	7.8	7.40215970493468	0.397840295065316
126	7.6	7.38397788675287	0.216022113247134
127	7.4	7.37488697766196	0.0251130223380437
128	7.3	7.37488697766196	-0.0748869776619568
129	7.1	7.34761425038923	-0.247614250389229
130	7.1	7.32943243220741	-0.229432432207412
131	7	7.27488697766196	-0.274886977661956
132	7	7.16852359107144	-0.168523591071442

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.000929383222768834	0.00185876644553767	0.999070616777231
18	0.000144396532712487	0.000288793065424973	0.999855603467288
19	0.000176342346951546	0.000352684693903092	0.999823657653048
20	4.15012416785942e-05	8.30024833571885e-05	0.999958498758321
21	9.52719451686855e-06	1.90543890337371e-05	0.999990472805483
22	9.49598524398028e-06	1.89919704879606e-05	0.999990504014756
23	1.29781805034790e-05	2.59563610069580e-05	0.999987021819497
24	2.29021954287029e-05	4.58043908574058e-05	0.99997709780457
25	2.48785405222571e-05	4.97570810445142e-05	0.999975121459478
26	1.63542912418428e-05	3.27085824836857e-05	0.999983645708758
27	1.98481611788618e-05	3.96963223577236e-05	0.999980151838821
28	3.27391120448243e-05	6.54782240896487e-05	0.999967260887955
29	5.49612462392825e-05	0.000109922492478565	0.99994503875376
30	0.000158692138418578	0.000317384276837155	0.999841307861581
31	0.000768544463226849	0.00153708892645370	0.999231455536773
32	0.00545067867969866	0.0109013573593973	0.994549321320301
33	0.0305926749424556	0.0611853498849113	0.969407325057544
34	0.111804555268495	0.223609110536990	0.888195444731505
35	0.267405186427559	0.534810372855118	0.732594813572441
36	0.488831533370237	0.977663066740473	0.511168466629763
37	0.623869194948735	0.75226161010253	0.376130805051265
38	0.7175750230977	0.5648499538046	0.2824249769023
39	0.803551731226301	0.392896537547398	0.196448268773699
40	0.868763473391474	0.262473053217051	0.131236526608526
41	0.90431390697944	0.191372186041119	0.0956860930205597
42	0.923417632728916	0.153164734542169	0.0765823672710844
43	0.9297554338012	0.140489132397602	0.070244566198801
44	0.92725087166216	0.145498256675679	0.0727491283378397
45	0.926298740542954	0.147402518914092	0.0737012594570462
46	0.919185433808327	0.161629132383347	0.0808145661916734
47	0.909930889003724	0.180138221992552	0.090069110996276
48	0.902847311658022	0.194305376683956	0.097152688341978
49	0.888478915116984	0.223042169766032	0.111521084883016
50	0.870795127714113	0.258409744571775	0.129204872285887
51	0.849023578622103	0.301952842755794	0.150976421377897
52	0.814721936568522	0.370556126862956	0.185278063431478
53	0.776506845621916	0.446986308756168	0.223493154378084
54	0.735048117321577	0.529903765356847	0.264951882678423
55	0.698805128102603	0.602389743794795	0.301194871897397
56	0.661157308209867	0.677685383580265	0.338842691790133
57	0.629551874190612	0.740896251618776	0.370448125809388
58	0.646209280019444	0.707581439961112	0.353790719980556
59	0.667756070065743	0.664487859868514	0.332243929934257
60	0.6638041633526	0.672391673294801	0.336195836647400
61	0.699043487770862	0.601913024458275	0.300956512229137
62	0.729953766338761	0.540092467322477	0.270046233661239
63	0.763094692442161	0.473810615115678	0.236905307557839
64	0.801261152071784	0.397477695856432	0.198738847928216
65	0.83210396852889	0.33579206294222	0.16789603147111
66	0.865675452594616	0.268649094810769	0.134324547405384
67	0.887164891573077	0.225670216853846	0.112835108426923
68	0.907603329778643	0.184793340442714	0.092396670221357
69	0.928868150488649	0.142263699022702	0.0711318495113508
70	0.944649685311484	0.110700629377031	0.0553503146885157
71	0.9549115499075	0.0901769001850015	0.0450884500925008
72	0.970563327014259	0.0588733459714824	0.0294366729857412
73	0.985502204626972	0.0289955907460564	0.0144977953730282
74	0.992776690102858	0.0144466197942847	0.00722330989714236
75	0.99620625342812	0.00758749314375865	0.00379374657187933
76	0.997735710481287	0.00452857903742575	0.00226428951871287
77	0.998553356702455	0.00289328659508908	0.00144664329754454
78	0.99902250703517	0.00195498592966121	0.000977492964830606
79	0.999338054433866	0.00132389113226747	0.000661945566133733
80	0.999528235975396	0.000943528049207116	0.000471764024603558
81	0.999664648927787	0.00067070214442512	0.00033535107221256
82	0.999720055488227	0.000559889023546208	0.000279944511773104
83	0.999744855539646	0.000510288920708729	0.000255144460354365
84	0.999809348988975	0.000381302022050721	0.000190651011025361
85	0.99988115687362	0.000237686252760513	0.000118843126380256
86	0.999922872758144	0.000154254483712736	7.71272418563682e-05
87	0.99995402266762	9.19546647588039e-05	4.59773323794019e-05
88	0.999991982979663	1.60340406732937e-05	8.01702033664686e-06
89	0.999999354636362	1.29072727553609e-06	6.45363637768043e-07
90	0.999999887686738	2.24626523706998e-07	1.12313261853499e-07
91	0.999999876830256	2.46339487195875e-07	1.23169743597938e-07
92	0.999999818114773	3.63770453373547e-07	1.81885226686773e-07
93	0.999999706932454	5.86135091981574e-07	2.93067545990787e-07
94	0.999999612514934	7.74970132135664e-07	3.87485066067832e-07
95	0.999999551199782	8.97600435201076e-07	4.48800217600538e-07
96	0.999999598537386	8.02925228624052e-07	4.01462614312026e-07
97	0.999999797582336	4.04835327098842e-07	2.02417663549421e-07
98	0.999999837076507	3.25846986760418e-07	1.62923493380209e-07
99	0.999999859689363	2.80621273975789e-07	1.40310636987894e-07
100	0.99999993185452	1.36290961043365e-07	6.81454805216823e-08
101	0.999999972967188	5.40656230735254e-08	2.70328115367627e-08
102	0.999999980650884	3.86982321398433e-08	1.93491160699217e-08
103	0.999999987472948	2.50541036606712e-08	1.25270518303356e-08
104	0.999999989300756	2.13984877601580e-08	1.06992438800790e-08
105	0.999999992924954	1.41500928948329e-08	7.07504644741646e-09
106	0.999999988635597	2.27288065475384e-08	1.13644032737692e-08
107	0.999999969217984	6.15640325956914e-08	3.07820162978457e-08
108	0.999999833253273	3.33493453482908e-07	1.66746726741454e-07
109	0.999998982702116	2.03459576873470e-06	1.01729788436735e-06
110	0.99999428073136	1.14385372797371e-05	5.71926863986855e-06
111	0.999966150300701	6.76993985975945e-05	3.38496992987972e-05
112	0.999950063526215	9.9872947570992e-05	4.9936473785496e-05
113	0.99990205734374	0.000195885312520157	9.79426562600786e-05
114	0.99969813713521	0.000603725729579155	0.000301862864789578
115	0.998272029249063	0.00345594150187465	0.00172797075093732

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	56	0.565656565656566	NOK
5% type I error level	59	0.595959595959596	NOK
10% type I error level	62	0.626262626262626	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/10r5hy1229950569.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/10r5hy1229950569.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/155pm1229950569.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/2x7o71229950569.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/3unkq1229950569.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/43gqw1229950569.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/5cm7n1229950569.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/6odtf1229950569.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/7tah11229950569.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/8gvh91229950569.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/9gs5o1229950569.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/22/t1229950740eo31tpzq64x43nr/9gs5o1229950569.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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