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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: Thu, 02 Dec 2010 10:31:36 +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/02/t1291286019k5nn7n58al9apf3.htm/, Retrieved Thu, 02 Dec 2010 11:33:57 +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/02/t1291286019k5nn7n58al9apf3.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 «

3 4 4 4 4 3 4 3 4 4 4 2 4 4 4 2 4 4 4 2 3 4 5 2 5 4 4 3 4 4 3 2 3 2 4 1 3 4 4 2 4 3 4 2 4 2 4 4 4 2 4 2 4 3 2 2 4 4 3 2 4 5 4 1 4 4 3 2 5 2 2 2 4 4 4 3 4 3 3 4 4 4 3 4 4 2 4 4 5 2 3 4 4 4 3 4 4 4 4 2 4 4 4 2 4 4 4 4 4 3 4 2 4 4 4 3 4 5 4 2 4 4 5 4 3 4 4 3 4 4 4 4 4 4 4 2 3 4 3 2 3 4 4 4 2 4 3 5 3 4 4 2 4 4 4 4 4 5 3 4 4 4 5 5 4 4 4 3 5 2 3 2 4 4 3 3 4 4 3 2 4 4 4 1 3 4 4 2 4 4 4 4 4 4 4 4 3 4 4 4 4 3 4 4 3 4 4 2 3 4 5 2 5 4 3 1 2 3 3 4 4 3 5 2 4 4 5 4 4 4 4 2 3 5 4 2 4 3 3 1 3 4 4 2 4 4 4 2 4 4 5 2 4 4 4 2 4 4 4 1 4 4 5 5 5 4 4 3 5 4 3 2 3 4 4 1 4 4 4 2 3 4 4 4 4 4 4 2 5 4 4 5 5 3 4 4 4 4 4 3 4 4 3 2 4 3 4 2 4 4 3 2 4 4 5 2 4 4 4 4 4 4 5 2 5 4 5 2 4 2 4 4 4 4 4 4 4 3 4 2 4 3 4 2 4 4 4 3 4 4 5 2 4 4 4 4 4 3 3 2 3 2 4 4 4 5 3 1 4 2 4 2 4 4 4 3 4 4 4 2 4 4 4 4 4 3 4 2 4 4 3 3 4 3 3 2 4 4 3 2 4 4 3 2 5 3 3 2 5 2 2 2 4 4 3 2 4 3 5 3 3 2 2 2 4 3 2 2 4 4 3 4 4 4 3 3

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	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Competence[t] = + 4.59399534068267 -0.0960101540567729Focus[t] -0.0680960967195073Neatness[t] -0.0295001752219747Upset[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)	4.59399534068267	0.387591	11.8527	0	0
Focus	-0.0960101540567729	0.078476	-1.2234	0.223907	0.111954
Neatness	-0.0680960967195073	0.081349	-0.8371	0.404448	0.202224
Upset	-0.0295001752219747	0.054097	-0.5453	0.58669	0.293345

Multiple Linear Regression - Regression Statistics
Multiple R	0.173299448047482
R-squared	0.030032698693562
Adjusted R-squared	0.00231934722766380
F-TEST (value)	1.08369060777502
F-TEST (DF numerator)	3
F-TEST (DF denominator)	105
p-value	0.359376524702930
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.585286899054971
Sum Squared Residuals	35.9688791915653

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	3	3.81956963668958	-0.819569636689579
2	4	3.94507996596839	0.0549200340316064
3	4	3.87856998713359	0.121430012866407
4	4	3.87856998713359	0.121430012866407
5	4	3.87856998713359	0.121430012866407
6	3	3.81047389041409	-0.810473890414085
7	5	3.84906981191162	1.15093018808838
8	4	3.9466660838531	0.0533339161468998
9	3	4.10009047046911	-1.10009047046911
10	3	3.87856998713359	-0.878569987133593
11	4	3.97458014119037	0.025419858809634
12	4	4.01158994480319	-0.0115899448031898
13	4	4.07059029524714	-0.0705902952471392
14	4	4.11077233462938	-0.110772334629381
15	4	3.9466660838531	0.0533339161468998
16	4	3.81206000829879	0.187939991701206
17	4	3.9466660838531	0.0533339161468998
18	5	4.20678248868615	0.793217511313846
19	4	3.84906981191162	0.150930188088382
20	4	3.98367588746592	0.016324112534076
21	4	3.88766573340915	0.112334266590849
22	4	4.01158994480319	-0.0115899448031898
23	5	4.0796860415227	0.920313958477303
24	4	3.88766573340915	0.112334266590849
25	4	3.87856998713359	0.121430012866407
26	4	3.87856998713359	0.121430012866407
27	4	3.81956963668964	0.180430363310357
28	4	3.97458014119037	0.025419858809634
29	4	3.84906981191162	0.150930188088382
30	4	3.78255983307682	0.217440166923180
31	4	3.75147353997014	0.248526460029864
32	3	3.84906981191162	-0.849069811911618
33	4	3.81956963668964	0.180430363310357
34	4	3.87856998713359	0.121430012866407
35	3	3.9466660838531	-0.9466660838531
36	3	3.81956963668964	-0.819569636689643
37	2	3.85816555818718	-1.85816555818718
38	3	3.87856998713359	-0.878569987133593
39	4	3.81956963668964	0.180430363310357
40	4	3.79165557935238	0.208344420647622
41	4	3.72197336474816	0.278026635251839
42	4	3.84906981191162	0.150930188088382
43	5	4.13868639196665	0.861313608033353
44	4	3.91716590863113	0.0828340913688745
45	4	3.9466660838531	0.0533339161468998
46	4	3.90807016235557	0.0919298376444325
47	3	3.87856998713359	-0.878569987133593
48	4	3.81956963668964	0.180430363310357
49	4	3.81956963668964	0.180430363310357
50	3	3.81956963668964	-0.819569636689643
51	4	3.91557979074642	0.0844202092535834
52	3	3.87856998713359	-0.878569987133593
53	3	3.81047389041409	-0.810473890414085
54	5	3.97616625907507	1.02383374092493
55	2	3.98367588746592	-1.98367588746592
56	4	3.90648404447086	0.0935159555291413
57	4	3.75147353997014	0.248526460029864
58	4	3.87856998713359	0.121430012866407
59	3	3.78255983307682	-0.78255983307682
60	4	4.07217641313185	-0.072176413131848
61	3	3.87856998713359	-0.878569987133593
62	4	3.87856998713359	0.121430012866407
63	4	3.81047389041409	0.189526109585915
64	4	3.87856998713359	0.121430012866407
65	4	3.90807016235557	0.0919298376444325
66	4	3.72197336474816	0.278026635251839
67	5	3.84906981191162	1.15093018808838
68	5	3.9466660838531	1.0533339161469
69	3	3.90807016235557	-0.908070162355567
70	4	3.87856998713359	0.121430012866407
71	3	3.81956963668964	-0.819569636689643
72	4	3.87856998713359	0.121430012866407
73	5	3.79006946146767	1.20993053853233
74	5	3.91557979074642	1.08442020925358
75	4	3.84906981191162	0.150930188088382
76	4	3.9466660838531	0.0533339161468998
77	4	3.97458014119037	0.025419858809634
78	4	3.9466660838531	0.0533339161468998
79	4	3.81047389041409	0.189526109585915
80	4	3.81956963668964	0.180430363310357
81	4	3.81047389041409	0.189526109585915
82	5	3.81047389041409	1.18952610958591
83	4	4.01158994480319	-0.0115899448031898
84	4	3.81956963668964	0.180430363310357
85	4	3.97458014119037	0.025419858809634
86	4	3.97458014119037	0.025419858809634
87	4	3.84906981191162	0.150930188088382
88	4	3.81047389041409	0.189526109585915
89	4	3.81956963668964	0.180430363310357
90	4	4.04267623790987	-0.0426762379098734
91	3	4.01158994480319	-1.01158994480319
92	4	3.8801561050183	0.119843894981698
93	4	4.07059029524714	-0.0705902952471392
94	4	3.84906981191162	0.150930188088382
95	4	3.87856998713359	0.121430012866407
96	4	3.81956963668964	0.180430363310357
97	4	3.97458014119037	0.025419858809634
98	4	3.91716590863113	0.0828340913688745
99	4	4.04267623790987	-0.0426762379098734
100	4	3.9466660838531	0.0533339161468998
101	4	3.9466660838531	0.0533339161468998
102	5	4.04267623790987	0.957323762090127
103	5	4.20678248868615	0.793217511313846
104	4	3.9466660838531	0.0533339161468998
105	4	3.87698386924888	0.123016130751116
106	3	4.20678248868615	-1.20678248868615
107	4	4.11077233462938	-0.110772334629381
108	4	3.88766573340915	0.112334266590849
109	4	3.91716590863113	0.0828340913688745

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.818847944712356	0.362304110575288	0.181152055287644
8	0.83325098144124	0.333498037117521	0.166749018558760
9	0.824542533248707	0.350914933502586	0.175457466751293
10	0.862861097871087	0.274277804257827	0.137138902128913
11	0.822882890596403	0.354234218807194	0.177117109403597
12	0.756613525870179	0.486772948259642	0.243386474129821
13	0.698440722302175	0.60311855539565	0.301559277697825
14	0.644773495945132	0.710453008109736	0.355226504054868
15	0.554333304145811	0.891333391708378	0.445666695854189
16	0.487595777940736	0.975191555881471	0.512404222059264
17	0.400573573439339	0.801147146878677	0.599426426560661
18	0.42893623573634	0.85787247147268	0.57106376426366
19	0.354779081640489	0.709558163280978	0.64522091835951
20	0.295705581941601	0.591411163883202	0.704294418058399
21	0.238541184631894	0.477082369263789	0.761458815368105
22	0.184981355444063	0.369962710888126	0.815018644555937
23	0.228467486318936	0.456934972637871	0.771532513681064
24	0.185115638316743	0.370231276633486	0.814884361683257
25	0.148197650709730	0.296395301419459	0.85180234929027
26	0.116106124249636	0.232212248499272	0.883893875750364
27	0.086251158589991	0.172502317179982	0.91374884141001
28	0.063497666818047	0.126995333636094	0.936502333181953
29	0.0458363329190485	0.091672665838097	0.954163667080951
30	0.0341280121227328	0.0682560242454656	0.965871987877267
31	0.0268266803763613	0.0536533607527226	0.973173319623639
32	0.048978957288047	0.097957914576094	0.951021042711953
33	0.0349215177333739	0.0698430354667478	0.965078482266626
34	0.025239430078	0.050478860156	0.974760569922
35	0.0599372227240794	0.119874445448159	0.94006277727592
36	0.0916778607372034	0.183355721474407	0.908322139262797
37	0.554245374502466	0.891509250995067	0.445754625497533
38	0.609253486604223	0.781493026791554	0.390746513395777
39	0.565232420625727	0.869535158748546	0.434767579374273
40	0.518976189150381	0.96204762169924	0.48102381084962
41	0.485077491476927	0.970154982953853	0.514922508523073
42	0.433785926428223	0.867571852856447	0.566214073571777
43	0.493590236654629	0.987180473309258	0.506409763345371
44	0.436998042071688	0.873996084143376	0.563001957928312
45	0.381170663013497	0.762341326026995	0.618829336986503
46	0.330397422250201	0.660794844500403	0.669602577749799
47	0.388445479598808	0.776890959197616	0.611554520401192
48	0.342284096880638	0.684568193761277	0.657715903119362
49	0.29782959753229	0.59565919506458	0.70217040246771
50	0.344638981179195	0.68927796235839	0.655361018820805
51	0.294979520308707	0.589959040617414	0.705020479691293
52	0.353403274570852	0.706806549141704	0.646596725429148
53	0.396515834320215	0.79303166864043	0.603484165679785
54	0.509177879108704	0.981644241782593	0.490822120891296
55	0.94801819718706	0.103963605625878	0.0519818028129391
56	0.932402595218174	0.135194809563652	0.0675974047818258
57	0.918536032604665	0.162927934790669	0.0814639673953347
58	0.896126942802547	0.207746114394905	0.103873057197453
59	0.928929188403855	0.142141623192291	0.0710708115961453
60	0.907715915535759	0.184568168928482	0.0922840844642409
61	0.945243436439503	0.109513127120994	0.0547565635604969
62	0.928696790759549	0.142606418480902	0.0713032092404511
63	0.910118026926717	0.179763946146567	0.0898819730732833
64	0.8858847738253	0.228230452349401	0.114115226174700
65	0.856039469588632	0.287921060822735	0.143960530411368
66	0.831526342097762	0.336947315804476	0.168473657902238
67	0.904693218914549	0.190613562170902	0.095306781085451
68	0.948693907045988	0.102612185908024	0.0513060929540122
69	0.975826620618669	0.0483467587626622	0.0241733793813311
70	0.966264910242338	0.0674701795153248	0.0337350897576624
71	0.987857934619893	0.0242841307602146	0.0121420653801073
72	0.982329599632934	0.0353408007341331	0.0176704003670665
73	0.994279228737036	0.0114415425259276	0.00572077126296379
74	0.9990937397548	0.00181252049040168	0.00090626024520084
75	0.99844211330055	0.00311577339890225	0.00155788669945112
76	0.997401691724402	0.00519661655119586	0.00259830827559793
77	0.995689011814536	0.00862197637092817	0.00431098818546409
78	0.993115056176063	0.0137698876478739	0.00688494382393694
79	0.989745707441608	0.0205085851167841	0.0102542925583921
80	0.984179016249116	0.0316419675017678	0.0158209837508839
81	0.97750229134775	0.0449954173045020	0.0224977086522510
82	0.991998171655602	0.0160036566887966	0.00800182834439832
83	0.98826372651996	0.0234725469600807	0.0117362734800404
84	0.98177636812894	0.0364472637421223	0.0182236318710612
85	0.971237619887101	0.0575247602257976	0.0287623801128988
86	0.955792783412647	0.0884144331747058	0.0442072165873529
87	0.934276400005866	0.131447199988268	0.065723599994134
88	0.905047757183366	0.189904485633268	0.0949522428166338
89	0.870227173957855	0.25954565208429	0.129772826042145
90	0.821031832237242	0.357936335525516	0.178968167762758
91	0.893458126066792	0.213083747866416	0.106541873933208
92	0.847246351225899	0.305507297548202	0.152753648774101
93	0.789255745622583	0.421488508754834	0.210744254377417
94	0.71491609987826	0.570167800243481	0.285083900121741
95	0.62910148711871	0.74179702576258	0.37089851288129
96	0.53114148777096	0.93771702445808	0.46885851222904
97	0.433535674516938	0.867071349033877	0.566464325483062
98	0.329114999084381	0.658229998168761	0.67088500091562
99	0.23667279689751	0.47334559379502	0.76332720310249
100	0.155964278371576	0.311928556743151	0.844035721628424
101	0.0945647947033798	0.189129589406760	0.90543520529662
102	0.129303024658811	0.258606049317621	0.87069697534119

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.0416666666666667	NOK
5% type I error level	15	0.15625	NOK
10% type I error level	24	0.25	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/10wo7q1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/10wo7q1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/1p5sw1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/1p5sw1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/2p5sw1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/2p5sw1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/3ixsh1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/3ixsh1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/4ixsh1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/4ixsh1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/5ixsh1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/5ixsh1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/6bork1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/6bork1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/7mfqn1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/7mfqn1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/8mfqn1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/8mfqn1291285886.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/9mfqn1291285886.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291286019k5nn7n58al9apf3/9mfqn1291285886.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)

library(lmtest)

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

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

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

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

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

x <- x1

if (par3 == 'First Differences'){

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

for (i in 1:n-1) {

for (j in 1:k) {

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

}

}

x <- x2

}

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

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

for (i in 1:11){

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

}

x <- cbind(x, x2)

}

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

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

for (i in 1:3){

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

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

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

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

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

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

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

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

j <- j + 1

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

}

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

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

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

}

gqarr

}

bitmap(file='test0.png')

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

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

grid()

dev.off()

bitmap(file='test1.png')

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

grid()

dev.off()

bitmap(file='test2.png')

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

grid()

dev.off()

bitmap(file='test3.png')

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

dev.off()

bitmap(file='test4.png')

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

qqline(mysum$resid)

grid()

dev.off()

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

bitmap(file='test5.png')

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

dum

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

dum1

z <- as.data.frame(dum1)

z

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

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

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

grid()

dev.off()

bitmap(file='test7.png')

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

grid()

dev.off()

bitmap(file='test8.png')

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

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

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

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

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

myeq <- colnames(x)[1]

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

for (i in 1:k){

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

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

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

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

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

}

}

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

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant1)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant5)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant10)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

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

}

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

Software written by Ed van Stee & Patrick Wessa

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