Home » date » 2008 » Nov » 26 »

Gilliam Schoorel

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Wed, 26 Nov 2008 03:26:38 -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/Nov/26/t1227695463j3os8qyvbc8v731.htm/, Retrieved Wed, 26 Nov 2008 10:31:13 +0000

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/Nov/26/t1227695463j3os8qyvbc8v731.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},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

-3,3 0 -3,5 0 -3,5 0 -8,4 0 -15,7 0 -18,7 0 -22,8 0 -20,7 0 -14 0 -6,3 0 0,7 1 0,2 1 0,8 1 1,2 1 4,5 1 0,4 1 5,9 1 6,5 1 12,8 1 4,2 1 -3,3 0 -12,5 0 -16,3 0 -10,5 0 -11,8 0 -11,4 0 -17,7 0 -17,3 0 -18,6 0 -17,9 0 -21,4 0 -19,4 0 -15,5 0 -7,7 0 -0,7 0 -1,6 0 1,4 1 0,7 1 9,5 1 1,4 1 4,1 1 6,6 1 18,4 1 16,9 1 9,2 1 -4,3 0 -5,9 0 -7,7 0 -5,4 0 -2,3 0 -4,8 0 2,3 0 -5,2 0 -10 0 -17,1 0 -14,4 0 -3,9 0 3,7 1 6,5 1 0,9 1 -4,1 0 -7 0 -12,2 0 -2,5 0 4,4 1 13,7 1 12,3 1 13,4 1 2,2 1 1,7 1 -7,2 0 -4,8 0 -2,9 0 -2,4 0 -2,5 0 -5,3 0 -7,1 0 -8 0 -8,9 0 -7,7 0 -1,1 0 4 1 9,6 1 10,9 1 13 1 14,9 1 20,1 1 10,8 1 11 1 3,8 1 10,8 1 7,6 1 10,2 1 2,2 1 -0,1 0 -1,7 0 -4,8 0

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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Registraties[t] = -8.74107142857142 + 15.8898519163763D[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)	-8.74107142857142	0.809611	-10.7966	0	0
D	15.8898519163763	1.245289	12.76	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.794683173034995
R-squared	0.631521345504968
Adjusted R-squared	0.627642622826073
F-TEST (value)	162.816833732714
F-TEST (DF numerator)	1
F-TEST (DF denominator)	95
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.05857198281356
Sum Squared Residuals	3487.09797473868

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	-3.3	-8.74107142857148	5.44107142857148
2	-3.5	-8.74107142857143	5.24107142857143
3	-3.5	-8.74107142857143	5.24107142857143
4	-8.4	-8.74107142857143	0.341071428571426
5	-15.7	-8.74107142857143	-6.95892857142857
6	-18.7	-8.74107142857143	-9.95892857142857
7	-22.8	-8.74107142857143	-14.0589285714286
8	-20.7	-8.74107142857143	-11.9589285714286
9	-14	-8.74107142857143	-5.25892857142857
10	-6.3	-8.74107142857143	2.44107142857143
11	0.7	7.14878048780488	-6.44878048780488
12	0.2	7.14878048780488	-6.94878048780488
13	0.8	7.14878048780488	-6.34878048780488
14	1.2	7.14878048780488	-5.94878048780488
15	4.5	7.14878048780488	-2.64878048780488
16	0.4	7.14878048780488	-6.74878048780488
17	5.9	7.14878048780488	-1.24878048780488
18	6.5	7.14878048780488	-0.648780487804878
19	12.8	7.14878048780488	5.65121951219512
20	4.2	7.14878048780488	-2.94878048780488
21	-3.3	-8.74107142857143	5.44107142857143
22	-12.5	-8.74107142857143	-3.75892857142857
23	-16.3	-8.74107142857143	-7.55892857142858
24	-10.5	-8.74107142857143	-1.75892857142857
25	-11.8	-8.74107142857143	-3.05892857142857
26	-11.4	-8.74107142857143	-2.65892857142857
27	-17.7	-8.74107142857143	-8.95892857142857
28	-17.3	-8.74107142857143	-8.55892857142858
29	-18.6	-8.74107142857143	-9.85892857142857
30	-17.9	-8.74107142857143	-9.15892857142857
31	-21.4	-8.74107142857143	-12.6589285714286
32	-19.4	-8.74107142857143	-10.6589285714286
33	-15.5	-8.74107142857143	-6.75892857142857
34	-7.7	-8.74107142857143	1.04107142857143
35	-0.7	-8.74107142857143	8.04107142857143
36	-1.6	-8.74107142857143	7.14107142857143
37	1.4	7.14878048780488	-5.74878048780488
38	0.7	7.14878048780488	-6.44878048780488
39	9.5	7.14878048780488	2.35121951219512
40	1.4	7.14878048780488	-5.74878048780488
41	4.1	7.14878048780488	-3.04878048780488
42	6.6	7.14878048780488	-0.548780487804878
43	18.4	7.14878048780488	11.2512195121951
44	16.9	7.14878048780488	9.75121951219512
45	9.2	7.14878048780488	2.05121951219512
46	-4.3	-8.74107142857143	4.44107142857143
47	-5.9	-8.74107142857143	2.84107142857143
48	-7.7	-8.74107142857143	1.04107142857143
49	-5.4	-8.74107142857143	3.34107142857143
50	-2.3	-8.74107142857143	6.44107142857143
51	-4.8	-8.74107142857143	3.94107142857143
52	2.3	-8.74107142857143	11.0410714285714
53	-5.2	-8.74107142857143	3.54107142857143
54	-10	-8.74107142857143	-1.25892857142857
55	-17.1	-8.74107142857143	-8.35892857142858
56	-14.4	-8.74107142857143	-5.65892857142857
57	-3.9	-8.74107142857143	4.84107142857143
58	3.7	7.14878048780488	-3.44878048780488
59	6.5	7.14878048780488	-0.648780487804878
60	0.9	7.14878048780488	-6.24878048780488
61	-4.1	-8.74107142857143	4.64107142857143
62	-7	-8.74107142857143	1.74107142857143
63	-12.2	-8.74107142857143	-3.45892857142857
64	-2.5	-8.74107142857143	6.24107142857143
65	4.4	7.14878048780488	-2.74878048780488
66	13.7	7.14878048780488	6.55121951219512
67	12.3	7.14878048780488	5.15121951219512
68	13.4	7.14878048780488	6.25121951219512
69	2.2	7.14878048780488	-4.94878048780488
70	1.7	7.14878048780488	-5.44878048780488
71	-7.2	-8.74107142857143	1.54107142857143
72	-4.8	-8.74107142857143	3.94107142857143
73	-2.9	-8.74107142857143	5.84107142857143
74	-2.4	-8.74107142857143	6.34107142857143
75	-2.5	-8.74107142857143	6.24107142857143
76	-5.3	-8.74107142857143	3.44107142857143
77	-7.1	-8.74107142857143	1.64107142857143
78	-8	-8.74107142857143	0.741071428571426
79	-8.9	-8.74107142857143	-0.158928571428574
80	-7.7	-8.74107142857143	1.04107142857143
81	-1.1	-8.74107142857143	7.64107142857143
82	4	7.14878048780488	-3.14878048780488
83	9.6	7.14878048780488	2.45121951219512
84	10.9	7.14878048780488	3.75121951219512
85	13	7.14878048780488	5.85121951219512
86	14.9	7.14878048780488	7.75121951219512
87	20.1	7.14878048780488	12.9512195121951
88	10.8	7.14878048780488	3.65121951219512
89	11	7.14878048780488	3.85121951219512
90	3.8	7.14878048780488	-3.34878048780488
91	10.8	7.14878048780488	3.65121951219512
92	7.6	7.14878048780488	0.451219512195121
93	10.2	7.14878048780488	3.05121951219512
94	2.2	7.14878048780488	-4.94878048780488
95	-0.1	-8.74107142857143	8.64107142857143
96	-1.7	-8.74107142857143	7.04107142857143
97	-4.8	-8.74107142857143	3.94107142857143

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.619407178148704	0.761185643702593	0.380592821851296
6	0.815370463173028	0.369259073653945	0.184629536826972
7	0.948438309558682	0.103123380882635	0.0515616904413175
8	0.967246450120836	0.0655070997583283	0.0327535498791641
9	0.94690155480113	0.106196890397740	0.0530984451988699
10	0.934123973031322	0.131752053937355	0.0658760269686775
11	0.903056365965525	0.193887268068950	0.0969436340344749
12	0.866333683360675	0.267332633278650	0.133666316639325
13	0.822014617415582	0.355970765168836	0.177985382584418
14	0.771289347707604	0.457421304584791	0.228710652292396
15	0.723351596454205	0.55329680709159	0.276648403545795
16	0.671464732479209	0.657070535041582	0.328535267520791
17	0.629321133743638	0.741357732512724	0.370678866256362
18	0.585869943843298	0.828260112313404	0.414130056156702
19	0.672992231991175	0.65401553601765	0.327007768008825
20	0.609233635372831	0.781532729254337	0.390766364627169
21	0.64488718142889	0.710225637142219	0.355112818571109
22	0.586947242215596	0.826105515568808	0.413052757784404
23	0.585678751589905	0.82864249682019	0.414321248410095
24	0.520631197155736	0.958737605688527	0.479368802844264
25	0.459074771710308	0.918149543420615	0.540925228289692
26	0.397983578006578	0.795967156013157	0.602016421993422
27	0.436995210291025	0.87399042058205	0.563004789708975
28	0.465678746879866	0.931357493759731	0.534321253120134
29	0.534453417775381	0.931093164449239	0.465546582224619
30	0.586637686103996	0.826724627792007	0.413362313896004
31	0.757833264126023	0.484333471747953	0.242166735873977
32	0.848054262456401	0.303891475087197	0.151945737543599
33	0.865038524276753	0.269922951446494	0.134961475723247
34	0.856741980748174	0.286516038503651	0.143258019251826
35	0.922075015477528	0.155849969044944	0.077924984522472
36	0.948155978566449	0.103688042867103	0.0518440214335514
37	0.946057540679219	0.107884918641563	0.0539424593207814
38	0.949104033185653	0.101791933628694	0.0508959668143472
39	0.942772186489094	0.114455627021811	0.0572278135109056
40	0.944228791112063	0.111542417775873	0.0557712088879366
41	0.934734582166082	0.130530835667836	0.0652654178339178
42	0.919771950905123	0.160456098189753	0.0802280490948765
43	0.97559321658964	0.0488135668207204	0.0244067834103602
44	0.989815007116656	0.0203699857666882	0.0101849928833441
45	0.986090931158565	0.0278181376828702	0.0139090688414351
46	0.985200823463574	0.0295983530728514	0.0147991765364257
47	0.981866760741674	0.0362664785166516	0.0181332392583258
48	0.97661749198156	0.0467650160368807	0.0233825080184403
49	0.971773162962593	0.0564536740748137	0.0282268370374069
50	0.973486022120663	0.0530279557586731	0.0265139778793366
51	0.96808411246303	0.063831775073939	0.0319158875369695
52	0.985891814491164	0.0282163710176714	0.0141081855088357
53	0.981580645546712	0.0368387089065765	0.0184193544532882
54	0.97660569623075	0.0467886075384989	0.0233943037692494
55	0.991072294473073	0.0178554110538549	0.00892770552692747
56	0.995012267467388	0.00997546506522418	0.00498773253261209
57	0.993559119692222	0.0128817606155560	0.00644088030777801
58	0.992647640784038	0.0147047184319235	0.00735235921596176
59	0.989472326469041	0.0210553470619183	0.0105276735309591
60	0.993388011595537	0.0132239768089266	0.00661198840446331
61	0.991164928747581	0.0176701425048376	0.00883507125241882
62	0.987564027834262	0.0248719443314753	0.0124359721657376
63	0.990815220340263	0.0183695593194737	0.00918477965973685
64	0.988855826405349	0.0222883471893022	0.0111441735946511
65	0.987763931442514	0.0244721371149731	0.0122360685574865
66	0.987902857098942	0.0241942858021163	0.0120971429010582
67	0.985405547374998	0.0291889052500038	0.0145944526250019
68	0.985027222038844	0.0299455559223124	0.0149727779611562
69	0.988687063569364	0.0226258728612728	0.0113129364306364
70	0.993928308828736	0.0121433823425277	0.00607169117126383
71	0.991372677219043	0.0172546455619150	0.00862732278095752
72	0.986884807124112	0.026230385751777	0.0131151928758885
73	0.981924920347968	0.0361501593040641	0.0180750796520320
74	0.976375219556635	0.0472495608867298	0.0236247804433649
75	0.96916841240584	0.0616631751883188	0.0308315875941594
76	0.954193236857074	0.0916135262858525	0.0458067631429263
77	0.936495520281505	0.127008959436989	0.0635044797184947
78	0.920846527760585	0.158306944478829	0.0791534722394146
79	0.918225292491317	0.163549415017366	0.081774707508683
80	0.914826865560594	0.170346268878811	0.0851731344394056
81	0.885869320128053	0.228261359743895	0.114130679871947
82	0.897344693940639	0.205310612118723	0.102655306059361
83	0.85207112692289	0.295857746154221	0.147928873077110
84	0.793792254359319	0.412415491281362	0.206207745640681
85	0.743517525940345	0.51296494811931	0.256482474059655
86	0.737506771012239	0.524986457975522	0.262493228987761
87	0.963421633653644	0.0731567326927123	0.0365783663463562
88	0.945676478725682	0.108647042548637	0.0543235212743185
89	0.931220925335725	0.137558149328549	0.0687790746642745
90	0.907187220368562	0.185625559262876	0.0928127796314378
91	0.876556325056788	0.246887349886423	0.123443674943212
92	0.757620805565348	0.484758388869304	0.242379194434652

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.0113636363636364	NOK
5% type I error level	29	0.329545454545455	NOK
10% type I error level	36	0.409090909090909	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/102m5g1227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/102m5g1227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/1zt241227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/1zt241227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/2c3gv1227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/2c3gv1227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/3wjor1227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/3wjor1227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/4efxx1227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/4efxx1227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/5c2ar1227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/5c2ar1227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/60vz61227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/60vz61227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/7dmrd1227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/7dmrd1227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/89pzt1227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/89pzt1227695192.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/9phj41227695192.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227695463j3os8qyvbc8v731/9phj41227695192.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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

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.

FreeStatistics.org is powered by