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workshop 7 month

*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: Tue, 23 Nov 2010 15:17:04 +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/Nov/23/t1290525408zvnwj27u3fa26di.htm/, Retrieved Tue, 23 Nov 2010 16:17:06 +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/Nov/23/t1290525408zvnwj27u3fa26di.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 «

9 3 3 3 3 3 3 9 5 5 5 1 5 5 9 4 4 4 3 3 4 9 4 4 4 3 4 4 9 5 4 4 1 4 4 9 5 3 5 1 5 5 9 2 1 3 5 3 2 9 5 4 4 2 4 4 9 4 4 4 2 4 4 9 4 4 4 2 5 4 9 5 4 5 4 5 4 9 3 3 3 3 3 3 9 5 4 4 2 4 4 9 3 3 3 3 3 3 9 5 4 5 1 5 4 9 3 3 3 3 3 3 9 4 5 4 2 4 4 9 5 4 5 1 5 5 9 4 3 3 3 4 3 9 3 3 3 3 3 3 9 4 4 3 2 4 3 9 4 3 4 2 4 3 9 3 3 3 3 3 3 9 3 3 3 3 4 3 9 4 5 5 1 4 4 9 5 5 5 1 4 4 9 4 4 4 1 4 4 9 4 4 4 1 4 4 9 4 5 4 1 4 4 9 4 4 4 3 4 4 9 4 4 5 1 4 4 9 3 3 3 3 3 3 9 4 4 4 1 4 4 9 5 4 5 2 5 5 9 4 4 4 1 4 4 9 4 4 4 2 4 4 9 3 4 4 2 4 4 9 4 4 4 2 3 4 9 4 3 4 1 5 4 9 4 4 4 3 4 4 9 5 2 4 1 5 3 9 4 4 4 1 4 4 9 3 3 3 3 3 3 9 3 3 3 3 3 3 9 4 4 4 1 3 4 9 4 3 4 2 4 3 9 4 4 4 4 4 4 9 5 4 4 2 4 4 9 4 4 4 1 4 3 9 5 4 4 1 4 4 9 4 4 4 2 4 4 9 4 4 4 2 4 4 9 4 3 3 3 4 4 10 4 4 5 5 4 5 10 4 3 4 2 3 3 10 5 4 4 1 4 4 10 4 4 4 1 4 4 10 4 3 5 1 4 4 10 4 4 4 1 4 4 10 4 4 4 2 4 4 10 3 3 2 2 3 3 10 4 4 4 2 4 4 10 5 4 1 4 4 10 1 3 2 3 3 3 10 3 3 3 3 3 3 10 5 4 5 1 4 4 10 4 4 3 2 4 4 10 4 4 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	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation

Part_of_team[t] = + 1.16128089731477 + 0.211061793737835month[t] + 0.00834396277775193Respect_of_coach[t] -0.0429963883775932Respect_of_team[t] -0.119630578090363Be_on_different_team[t] + 0.183772781787723Be_liked[t] + 0.153489628576142Proudness[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)	1.16128089731477	0.866303	1.3405	0.182057	0.091028
month	0.211061793737835	0.062821	3.3597	0.000984	0.000492
Respect_of_coach	0.00834396277775193	0.100456	0.0831	0.933911	0.466955
Respect_of_team	-0.0429963883775932	0.078237	-0.5496	0.583415	0.291708
Be_on_different_team	-0.119630578090363	0.03822	-3.13	0.002091	0.001046
Be_liked	0.183772781787723	0.048226	3.8106	2e-04	1e-04
Proudness	0.153489628576142	0.052206	2.9401	0.003788	0.001894

Multiple Linear Regression - Regression Statistics
Multiple R	0.755310210130863
R-squared	0.570493513527928
Adjusted R-squared	0.553759494574471
F-TEST (value)	34.0918410045222
F-TEST (DF numerator)	6
F-TEST (DF denominator)	154
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.81022987195237
Sum Squared Residuals	101.096756592209

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	3	3.60977526097623	-0.609775260976235
2	5	4.45425638668506	0.545743613314936
3	4	3.72861246395256	0.271387536047438
4	4	3.91238524574029	0.087614754259709
5	5	4.151646401921	0.848353598079
6	5	4.43756846112953	0.562431538870468
7	2	3.2003365506639	-1.20033655066390
8	5	4.03201582383065	0.967984176169347
9	4	4.03201582383065	-0.0320158238306529
10	4	4.21578860561838	-0.215788605618376
11	5	3.93353106106006	1.06646893893994
12	3	3.60977526097627	-0.609775260976268
13	5	4.03201582383065	0.967984176169347
14	3	3.60977526097627	-0.609775260976268
15	5	4.29242279533114	0.707577204668856
16	3	3.60977526097627	-0.609775260976268
17	4	4.04035978660840	-0.0403597866084040
18	5	4.44591242390729	0.554087576092714
19	4	3.79354804276399	0.206451957236009
20	3	3.60977526097627	-0.609775260976268
21	4	3.92152258363210	0.0784774163678955
22	4	3.87018223247676	0.129817767523240
23	3	3.60977526097627	-0.609775260976268
24	3	3.79354804276399	-0.793548042763991
25	4	4.11699397632117	-0.116993976321172
26	5	4.11699397632117	0.883006023678828
27	4	4.15164640192102	-0.151646401921015
28	4	4.15164640192102	-0.151646401921015
29	4	4.15999036469877	-0.159990364698766
30	4	3.91238524574029	0.0876147542597093
31	4	4.10865001354342	-0.108650013543421
32	3	3.60977526097627	-0.609775260976268
33	4	4.15164640192102	-0.151646401921015
34	5	4.32628184581692	0.673718154183076
35	4	4.15164640192102	-0.151646401921015
36	4	4.03201582383065	-0.0320158238306529
37	3	4.03201582383065	-1.03201582383065
38	4	3.84824304204293	0.15175695795707
39	4	4.32707522093099	-0.327075220930987
40	4	3.91238524574029	0.0876147542597093
41	5	4.16524162957709	0.834758370422906
42	4	4.15164640192102	-0.151646401921015
43	3	3.60977526097627	-0.609775260976268
44	3	3.60977526097627	-0.609775260976268
45	4	3.96787362013329	0.0321263798667078
46	4	3.87018223247676	0.129817767523240
47	4	3.79275466764993	0.207245332350072
48	5	4.03201582383065	0.967984176169347
49	4	3.99815677334487	0.00184322665512705
50	5	4.15164640192102	0.848353598078985
51	4	4.03201582383065	-0.0320158238306529
52	4	4.03201582383065	-0.0320158238306529
53	4	3.94703767134013	0.0529623286598665
54	4	3.99467912349595	0.00532087650404914
55	4	3.89747124442687	0.102528755573127
56	5	4.36270819565885	0.637291804341148
57	4	4.36270819565885	-0.362708195658852
58	4	4.31136784450351	-0.311367844503507
59	4	4.36270819565885	-0.362708195658852
60	4	4.24307761756849	-0.243077617568489
61	3	3.98346402118206	-0.98346402118206
62	4	4.24307761756849	-0.243077617568489
63	5	5.0537433979774	-0.0537433979774002
64	3	2.98736434832882	0.0126356516711787
65	3	3.41783189858225	-0.417831898582245
66	4	4.00677839206597	-0.00677839206596905
67	4	3.73603228439504	0.263967715604963
68	4	3.78737263555038	0.212627364449618
69	4	3.60160468036997	0.398395319630032
70	3	3.74437624717279	-0.744376247172788
71	4	3.74437624717279	0.255623752827212
72	3	3.41783189858225	-0.417831898582245
73	3	3.99843442928822	-0.998434429288218
74	3	3.59525589098491	-0.595255890984908
75	5	4.06257663298558	0.937423367014421
76	4	3.8168624136479	0.183137586352101
77	4	3.74437624717279	0.255623752827212
78	2	3.78737263555038	-1.78737263555038
79	4	3.78737263555038	0.212627364449618
80	3	3.41783189858225	-0.417831898582245
81	4	3.87880385119786	0.121196148802144
82	4	3.7716652591229	0.228334740877099
83	4	3.96280145456035	0.0371985454396466
84	4	3.74437624717279	0.255623752827212
85	4	3.88714781397561	0.112852186024393
86	4	3.78737263555038	0.212627364449618
87	4	3.74437624717279	0.255623752827212
88	3	3.41783189858225	-0.417831898582245
89	4	3.74437624717279	0.255623752827212
90	3	3.62474566908243	-0.624745669082425
91	3	3.41783189858225	-0.417831898582245
92	5	3.95543804091062	1.04456195908938
93	4	3.99843442928822	0.00156557071178222
94	5	4.18220721107594	0.81779278892406
95	4	3.78737263555038	0.212627364449618
96	3	3.74437624717279	-0.744376247172788
97	3	3.78737263555038	-0.787372635550381
98	4	3.97114541733810	0.0288545826618956
99	4	3.97114541733810	0.0288545826618956
100	4	3.74437624717279	0.255623752827212
101	5	3.78737263555038	1.21262736444962
102	5	3.88714781397561	1.11285218602439
103	3	3.70137985879519	-0.701379858795194
104	5	3.66774205746002	1.33225794253998
105	4	3.53331445343495	0.466685546565049
106	4	3.91244165253303	0.08755834746697
107	4	3.71409309396121	0.285906906038793
108	3	3.57132152715839	-0.571321527158388
109	4	3.44523788591107	0.554762114088929
110	5	4.04725811041143	0.952741889588566
111	4	4.15231291171770	-0.152312911717696
112	5	3.59513318746076	1.40486681253924
113	1	4.00700842990805	-3.00700842990806
114	4	4.37171866823328	-0.371718668233283
115	5	3.89376848183529	1.10623151816471
116	3	3.96707638142718	-0.967076381427176
117	4	2.08380443411593	1.91619556588407
118	3	2.28029045106967	0.71970954893033
119	1	1.73917861010234	-0.73917861010234
120	3	1.57013265561407	1.42986734438593
121	1	2.28029045106967	-1.28029045106967
122	1	1.96594778026766	-0.965947780267656
123	1	2.06186524368210	-1.06186524368210
124	3	1.90003165232821	1.09996834767179
125	2	2.24563802546983	-0.245638025469827
126	1	2.07644102046620	-1.07644102046620
127	2	2.08380443411593	-0.0838044341159338
128	4	2.1194374088438	1.8805625911562
129	2	1.90003165232821	0.0999683476717892
130	1	1.90003165232821	-0.900031652328211
131	2	1.90003165232821	0.0999683476717892
132	1	1.40927961338820	-0.409279613388204
133	2	1.90003165232821	0.0999683476717892
134	2	2.19429767431448	-0.194297674314482
135	1	1.73083464732459	-0.730834647324588
136	3	1.57013265561407	1.42986734438593
137	3	1.55442527918659	1.44557472081341
138	4	1.41664302703793	2.58335697296207
139	2	1.76759922169583	0.232400778304167
140	1	1.56276924196435	-0.562769241964346
141	3	2.06088469455408	0.939115305445918
142	1	2.26021380225393	-1.26021380225393
143	1	2.26021380225393	-1.26021380225393
144	1	2.08380443411593	-1.08380443411593
145	1	2.26021380225393	-1.26021380225393
146	3	1.79788237490741	1.20211762509259
147	2	1.87711191276636	0.122888087233641
148	2	2.11109344606605	-0.111093446066047
149	1	2.04080804573834	-1.04080804573834
150	1	2.41370343083007	-1.41370343083007
151	2	1.90003165232821	0.0999683476717892
152	1	1.90003165232821	-0.900031652328211
153	2	2.05352128090435	-0.053521280904353
154	2	2.23729406269208	-0.237294062692076
155	2	1.78217499847993	0.217825001520067
156	3	1.72362228419022	1.27637771580978
157	1	2.08380443411593	-1.08380443411593
158	4	2.30321019063152	1.69678980936848
159	1	2.20264163709223	-1.20264163709223
160	1	2.41370343083007	-1.41370343083007
161	3	1.75903400976748	1.24096599023252

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.261033607353449	0.522067214706898	0.738966392646551
11	0.219841699255064	0.439683398510129	0.780158300744936
12	0.118971473467103	0.237942946934206	0.881028526532897
13	0.121167478961002	0.242334957922004	0.878832521038998
14	0.0668724421933367	0.133744884386673	0.933127557806663
15	0.0585441141157259	0.117088228231452	0.941455885884274
16	0.0321121102924915	0.064224220584983	0.967887889707508
17	0.0273813567673566	0.0547627135347132	0.972618643232643
18	0.0165576441494398	0.0331152882988796	0.98344235585056
19	0.0244936986611909	0.0489873973223818	0.97550630133881
20	0.0143078419915144	0.0286156839830288	0.985692158008486
21	0.00814022070725672	0.0162804414145134	0.991859779292743
22	0.00420090689880804	0.00840181379761608	0.995799093101192
23	0.00229475763157783	0.00458951526315566	0.997705242368422
24	0.00160599222276849	0.00321198444553697	0.998394007777232
25	0.0031283623252405	0.006256724650481	0.99687163767476
26	0.00225706488947145	0.0045141297789429	0.997742935110528
27	0.00144900400926961	0.00289800801853922	0.99855099599073
28	0.000881909008442485	0.00176381801688497	0.999118090991558
29	0.000625673783157953	0.00125134756631591	0.999374326216842
30	0.000326629013746687	0.000653258027493375	0.999673370986253
31	0.000242496652642210	0.000484993305284421	0.999757503347358
32	0.000133414990615566	0.000266829981231132	0.999866585009384
33	7.12534929170764e-05	0.000142506985834153	0.999928746507083
34	3.93683446416408e-05	7.87366892832816e-05	0.999960631655358
35	2.01911196596362e-05	4.03822393192724e-05	0.99997980888034
36	9.93342473136654e-06	1.98668494627331e-05	0.999990066575269
37	7.20251781260196e-05	0.000144050356252039	0.999927974821874
38	4.24024395185278e-05	8.48048790370556e-05	0.999957597560482
39	2.63371718168281e-05	5.26743436336562e-05	0.999973662828183
40	1.33103418131922e-05	2.66206836263845e-05	0.999986689658187
41	3.01642772341303e-05	6.03285544682607e-05	0.999969835722766
42	1.61191558936880e-05	3.22383117873761e-05	0.999983880844106
43	9.43908973299725e-06	1.88781794659945e-05	0.999990560910267
44	5.67224434989345e-06	1.13444886997869e-05	0.99999432775565
45	3.11265158863802e-06	6.22530317727604e-06	0.999996887348411
46	1.54722520467245e-06	3.09445040934491e-06	0.999998452774795
47	7.53645710918633e-07	1.50729142183727e-06	0.99999924635429
48	2.09390697234421e-06	4.18781394468841e-06	0.999997906093028
49	1.03608850485185e-06	2.07217700970370e-06	0.999998963911495
50	1.91977332278856e-06	3.83954664557713e-06	0.999998080226677
51	9.92667779474897e-07	1.98533555894979e-06	0.99999900733222
52	5.06865913611125e-07	1.01373182722225e-06	0.999999493134086
53	3.01323618756655e-07	6.0264723751331e-07	0.999999698676381
54	1.44951343173615e-07	2.8990268634723e-07	0.999999855048657
55	1.14509486078026e-07	2.29018972156051e-07	0.999999885490514
56	9.17410821469271e-08	1.83482164293854e-07	0.999999908258918
57	8.29743450141144e-08	1.65948690028229e-07	0.999999917025655
58	7.07722543810733e-08	1.41544508762147e-07	0.999999929227746
59	4.14216167696000e-08	8.28432335391999e-08	0.999999958578383
60	2.05558989390729e-08	4.11117978781458e-08	0.999999979444101
61	2.01519890700627e-08	4.03039781401254e-08	0.99999997984801
62	1.12703021358113e-08	2.25406042716226e-08	0.999999988729698
63	1.07114703879871e-08	2.14229407759742e-08	0.99999998928853
64	8.04645289043632e-09	1.60929057808726e-08	0.999999991953547
65	5.54837652011804e-09	1.10967530402361e-08	0.999999994451623
66	2.55243886732591e-09	5.10487773465182e-09	0.999999997447561
67	1.2971451853032e-09	2.5942903706064e-09	0.999999998702855
68	6.33205789010354e-10	1.26641157802071e-09	0.999999999366794
69	3.25273368278388e-10	6.50546736556776e-10	0.999999999674727
70	8.19780690591284e-10	1.63956138118257e-09	0.99999999918022
71	4.0612885768736e-10	8.1225771537472e-10	0.99999999959387
72	2.29211317628570e-10	4.58422635257141e-10	0.999999999770789
73	7.47139586646847e-10	1.49427917329369e-09	0.99999999925286
74	7.5128160859582e-10	1.50256321719164e-09	0.999999999248718
75	1.18138915384737e-09	2.36277830769474e-09	0.99999999881861
76	9.74726229154057e-10	1.94945245830811e-09	0.999999999025274
77	5.0810698949105e-10	1.0162139789821e-09	0.999999999491893
78	5.5221251893794e-08	1.10442503787588e-07	0.999999944778748
79	3.85890021705518e-08	7.71780043411036e-08	0.999999961410998
80	2.25218488425795e-08	4.50436976851589e-08	0.999999977478151
81	1.53187164782841e-08	3.06374329565682e-08	0.999999984681283
82	1.29425462473907e-08	2.58850924947815e-08	0.999999987057454
83	7.61052778759766e-09	1.52210555751953e-08	0.999999992389472
84	4.14348082078551e-09	8.28696164157101e-09	0.999999995856519
85	2.13630403661332e-09	4.27260807322664e-09	0.999999997863696
86	1.28159079478874e-09	2.56318158957747e-09	0.99999999871841
87	6.61918852148587e-10	1.32383770429717e-09	0.999999999338081
88	3.8642988367332e-10	7.7285976734664e-10	0.99999999961357
89	1.95404124989329e-10	3.90808249978658e-10	0.999999999804596
90	2.27881896242416e-10	4.55763792484832e-10	0.999999999772118
91	1.36123088436745e-10	2.7224617687349e-10	0.999999999863877
92	3.1993079693106e-10	6.3986159386212e-10	0.99999999968007
93	2.14210455037159e-10	4.28420910074318e-10	0.99999999978579
94	1.65418136891604e-10	3.30836273783208e-10	0.999999999834582
95	8.91681395982074e-11	1.78336279196415e-10	0.999999999910832
96	1.54269151136039e-10	3.08538302272077e-10	0.99999999984573
97	3.38082332038366e-10	6.76164664076732e-10	0.999999999661918
98	1.77631673138627e-10	3.55263346277253e-10	0.999999999822368
99	9.4053294737118e-11	1.88106589474236e-10	0.999999999905947
100	4.65146579061645e-11	9.3029315812329e-11	0.999999999953485
101	2.37530439273108e-10	4.75060878546216e-10	0.99999999976247
102	5.11688420150711e-10	1.02337684030142e-09	0.999999999488312
103	7.7251670808088e-10	1.54503341616176e-09	0.999999999227483
104	3.59111148419096e-09	7.18222296838193e-09	0.999999996408889
105	2.86019775806195e-09	5.7203955161239e-09	0.999999997139802
106	1.41182925387604e-09	2.82365850775207e-09	0.99999999858817
107	8.84797008128238e-10	1.76959401625648e-09	0.999999999115203
108	6.90972720016737e-10	1.38194544003347e-09	0.999999999309027
109	1.78621987278277e-07	3.57243974556554e-07	0.999999821378013
110	1.46934354046284e-07	2.93868708092569e-07	0.999999853065646
111	1.95778297703408e-07	3.91556595406815e-07	0.999999804221702
112	1.37125495969273e-07	2.74250991938546e-07	0.999999862874504
113	9.1330377784455e-05	0.00018266075556891	0.999908669622215
114	7.82837831146346e-05	0.000156567566229269	0.999921716216885
115	0.000107099987400044	0.000214199974800088	0.9998929000126
116	0.000791711528679997	0.00158342305735999	0.99920828847132
117	0.00344776598977629	0.00689553197955258	0.996552234010224
118	0.00266523899897808	0.00533047799795616	0.997334761001022
119	0.00637808770078157	0.0127561754015631	0.993621912299218
120	0.00745539880207312	0.0149107976041462	0.992544601197927
121	0.0746312647873784	0.149262529574757	0.925368735212622
122	0.149602445212192	0.299204890424384	0.850397554787808
123	0.164917553754920	0.329835107509839	0.83508244624508
124	0.222216727131359	0.444433454262717	0.777783272868641
125	0.197055026630939	0.394110053261878	0.802944973369061
126	0.202178957349594	0.404357914699188	0.797821042650406
127	0.166527576727224	0.333055153454448	0.833472423272776
128	0.457295977745859	0.914591955491717	0.542704022254141
129	0.401338716184322	0.802677432368644	0.598661283815678
130	0.427528252385350	0.855056504770699	0.57247174761465
131	0.369819214288335	0.739638428576669	0.630180785711665
132	0.388924575273993	0.777849150547986	0.611075424726007
133	0.329953598683388	0.659907197366777	0.670046401316612
134	0.459048322070148	0.918096644140297	0.540951677929852
135	0.403948780722765	0.80789756144553	0.596051219277235
136	0.404885011975846	0.809770023951692	0.595114988024154
137	0.575109993427171	0.849780013145659	0.424890006572829
138	0.802513006150375	0.394973987699251	0.197486993849625
139	0.825710744245695	0.348578511508610	0.174289255754305
140	0.772261349473871	0.455477301052258	0.227738650526129
141	0.722728184994198	0.554543630011603	0.277271815005802
142	0.67966332380165	0.6406733523967	0.32033667619835
143	0.62597036760525	0.748059264789499	0.374029632394750
144	0.681655672447787	0.636688655104425	0.318344327552213
145	0.624110696466248	0.751778607067504	0.375889303533752
146	0.621406548693098	0.757186902613805	0.378593451306902
147	0.546789082801115	0.90642183439777	0.453210917198885
148	0.429216707561724	0.858433415123448	0.570783292438276
149	0.825765116261047	0.348469767477906	0.174234883738953
150	0.722448465033529	0.555103069932942	0.277551534966471
151	0.856657745534961	0.286684508930078	0.143342254465039

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	97	0.683098591549296	NOK
5% type I error level	103	0.725352112676056	NOK
10% type I error level	105	0.73943661971831	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/107v2p1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/107v2p1290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/10cnw1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/10cnw1290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/20cnw1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/20cnw1290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/3tl4h1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/3tl4h1290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/4tl4h1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/4tl4h1290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/5tl4h1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/5tl4h1290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/63cm21290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/63cm21290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/7w43n1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/7w43n1290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/8w43n1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/8w43n1290525413.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/9w43n1290525413.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290525408zvnwj27u3fa26di/9w43n1290525413.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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