Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2009 » Nov » 19 »

*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, 19 Nov 2009 10:01:56 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf.htm/, Retrieved Thu, 19 Nov 2009 18:02:58 +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/2009/Nov/19/t12586501667agdfturgglszuf.htm/},
    year = {2009},
}
@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 = {2009},
    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:

ws7l3

Dataseries X:

» Textbox « » Textfile « » CSV «

98,8 6,3 100,5 6,1 110,4 6,1 96,4 6,3 101,9 6,3 106,2 6 81 6,2 94,7 6,4 101 6,8 109,4 7,5 102,3 7,5 90,7 7,6 96,2 7,6 96,1 7,4 106 7,3 103,1 7,1 102 6,9 104,7 6,8 86 7,5 92,1 7,6 106,9 7,8 112,6 8 101,7 8,1 92 8,2 97,4 8,3 97 8,2 105,4 8 102,7 7,9 98,1 7,6 104,5 7,6 87,4 8,3 89,9 8,4 109,8 8,4 111,7 8,4 98,6 8,4 96,9 8,6 95,1 8,9 97 8,8 112,7 8,3 102,9 7,5 97,4 7,2 111,4 7,4 87,4 8,8 96,8 9,3 114,1 9,3 110,3 8,7 103,9 8,2 101,6 8,3 94,6 8,5 95,9 8,6 104,7 8,5 102,8 8,2 98,1 8,1 113,9 7,9 80,9 8,6 95,7 8,7 113,2 8,7 105,9 8,5 108,8 8,4 102,3 8,5 99 8,7 100,7 8,7 115,5 8,6 100,7 8,5 109,9 8,3 114,6 8 85,4 8,2 100,5 8,1 114,8 8,1 116,5 8 112,9 7,9 102 7,9 106 8 105,3 8 118,8 7,9 106,1 8 109,3 7,7 117,2 7,2 92,5 7,5 104,2 7,3 112,5 7 122,4 7 113,3 7 100 7,2 110,7 7,3 112,8 7,1 109,8 6,8 117,3 6,4 109,1 6,1 115,9 6,5 96 7,7 99,8 7,9 116,8 7,5 115,7 6,9 99,4 6,6 94,3 6,9 91 7,7

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	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 97.741544080691 -0.815049795615903X[t] + 1.87018159774291M1[t] + 4.29996328167822M2[t] + 13.7930268025441M3[t] + 7.1032140785195M4[t] + 6.04071323204974M5[t] + 13.6699054875868M6[t] -9.8692386652739M7[t] -0.254348328168483M8[t] + 14.0461607844849M9[t] + 15.7957292849124M10[t] + 7.6397334180042M11[t] + 0.114302764901383t + 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)	97.741544080691	4.790567	20.4029	0	0
X	-0.815049795615903	0.576386	-1.4141	0.161081	0.080541
M1	1.87018159774291	2.049273	0.9126	0.364092	0.182046
M2	4.29996328167822	2.112714	2.0353	0.045014	0.022507
M3	13.7930268025441	2.114485	6.5231	0	0
M4	7.1032140785195	2.122793	3.3462	0.001233	0.000616
M5	6.04071323204974	2.138728	2.8244	0.005929	0.002965
M6	13.6699054875868	2.148476	6.3626	0	0
M7	-9.8692386652739	2.108615	-4.6804	1.1e-05	6e-06
M8	-0.254348328168483	2.108411	-0.1206	0.904272	0.452136
M9	14.0461607844849	2.107866	6.6637	0	0
M10	15.7957292849124	2.107358	7.4955	0	0
M11	7.6397334180042	2.108595	3.6231	5e-04	0.00025
t	0.114302764901383	0.015436	7.4047	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.897678632759792
R-squared	0.80582692771349
Adjusted R-squared	0.77541427783729
F-TEST (value)	26.4964391788857
F-TEST (DF numerator)	13
F-TEST (DF denominator)	83
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.21420383050917
Sum Squared Residuals	1474.03965578149

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	98.8	94.591214730954	4.20878526904587
2	100.5	97.298309138915	3.20169086108504
3	110.4	106.905675424682	3.49432457531785
4	96.4	100.167155506436	-3.76715550643581
5	101.9	99.2189574248674	2.68104257513259
6	106.2	107.206967383991	-1.00696738399063
7	81	83.6191160369082	-2.61911603690818
8	94.7	93.1852991797918	1.51470082020822
9	101	107.274091139100	-6.27409113910019
10	109.4	108.567427547498	0.83257245250211
11	102.3	100.525734445491	1.77426555450891
12	90.7	92.9187988128267	-2.21879881282667
13	96.2	94.903283175471	1.29671682452899
14	96.1	97.6103775834309	-1.51037758343087
15	106	107.299248848760	-1.29924884875968
16	103.1	100.886748848760	2.21325115124032
17	102	100.101560726314	1.89843927368552
18	104.7	107.926560726314	-3.22656072631448
19	86	83.9311844814241	2.06881551857592
20	92.1	93.5788726038693	-1.47887260386929
21	106.9	107.830674522301	-0.930674522300902
22	112.6	109.531535828507	3.06846417149346
23	101.7	101.408337746938	0.291662253061851
24	92	93.8014021142738	-1.80140211427375
25	97.4	95.7043814973565	1.69561850264354
26	97	98.3299709257547	-1.32997092575474
27	105.4	108.100347170645	-2.70034717064514
28	102.7	101.606342191084	1.09365780891645
29	98.1	100.9026590482	-2.80265904819995
30	104.5	108.646154068638	-4.14615406863835
31	87.4	84.650777823748	2.74922217625205
32	89.9	94.2984659461931	-4.39846594619314
33	109.8	108.713277823748	1.08672217625204
34	111.7	110.577149089077	1.12285091092324
35	98.6	102.53545598707	-3.93545598706998
36	96.9	94.847015374844	2.05298462515603
37	95.1	96.5869847988035	-1.48698479880352
38	97	99.2125742272018	-2.21257422720179
39	112.7	109.227465410777	3.47253458922304
40	102.9	103.303995288147	-0.403995288146497
41	97.4	102.600312145263	-5.20031214526289
42	111.4	110.180797206578	1.21920279342188
43	87.4	85.6148861047566	1.78511389524341
44	96.8	94.9365543089554	1.86344569104457
45	114.1	109.351366186510	4.74863381348976
46	110.3	111.704267329209	-1.40426732920859
47	103.9	104.070099125010	-0.170099125009744
48	101.6	96.4631634923453	5.13683650765465
49	94.6	98.2846378958665	-3.68463789586648
50	95.9	100.747217365142	-4.84721736514156
51	104.7	110.436088630470	-5.73608863047037
52	102.8	104.105093610032	-1.30509361003197
53	98.1	103.238400508025	-5.13840050802518
54	113.9	111.144905487587	2.75509451241324
55	80.9	87.1495292426964	-6.24952924269637
56	95.7	96.7972173651416	-1.09721736514157
57	113.2	111.212029242696	1.98797075730363
58	105.9	113.238910467148	-7.33891046714835
59	108.8	105.278722344703	3.52127765529684
60	102.3	97.6717867120388	4.62821328796124
61	99	99.49326111556	-0.493261115559886
62	100.7	102.037345564397	-1.33734556439657
63	115.5	111.726216829725	3.77378317027462
64	100.7	105.232211850164	-4.53221185016378
65	109.9	104.447023727719	5.45297627228142
66	114.6	112.435033686842	2.16496631315823
67	85.4	88.8471823397593	-3.44718233975931
68	100.5	98.6578804213277	1.84211957867230
69	114.8	113.072692298883	1.72730770111749
70	116.5	115.018068543773	1.48193145622709
71	112.9	107.057880421328	5.8421195786723
72	102	99.5324497682249	2.46755023177511
73	106	101.435429151308	4.56457084869239
74	105.3	103.979513600144	1.32048639985570
75	118.8	113.668384865473	5.13161513452689
76	106.1	107.011369926788	-0.911369926788338
77	109.3	106.307686783905	2.99231321609527
78	117.2	114.458706702151	2.74129329784892
79	92.5	90.789350375507	1.71064962449295
80	104.2	100.681553436637	3.51844656336298
81	112.5	115.340880252877	-2.84088025287660
82	122.4	117.204751518205	5.19524848179461
83	113.3	109.163058416199	4.13694158380138
84	100	101.474617803973	-1.47461780397262
85	110.7	103.377597187055	7.32240281294466
86	112.8	106.084691595015	6.7153084049848
87	109.8	115.936572819467	-6.1365728194672
88	117.3	109.687082778590	7.61291722140962
89	109.1	108.983399635707	0.116600364293227
90	115.9	116.400874737899	-0.500874737898805
91	96	91.9979735952005	4.00202640479954
92	99.8	101.564156738084	-1.76415673808407
93	116.8	116.304988533885	0.495011466114762
94	115.7	118.657889676584	-2.95788967658358
95	99.4	110.860711513262	-11.4607115132616
96	94.3	103.090765921474	-8.79076592147399
97	91	104.423210447626	-13.4232104476256

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.223105237931204	0.446210475862408	0.776894762068796
18	0.13311395570591	0.26622791141182	0.86688604429409
19	0.240801131624693	0.481602263249386	0.759198868375307
20	0.149062227509721	0.298124455019443	0.850937772490279
21	0.154453197672434	0.308906395344869	0.845546802327566
22	0.0944037259190161	0.188807451838032	0.905596274080984
23	0.0642078344450043	0.128415668890009	0.935792165554996
24	0.0359339605552340	0.0718679211104679	0.964066039444766
25	0.0202194504380543	0.0404389008761085	0.979780549561946
26	0.0111505554688626	0.0223011109377251	0.988849444531137
27	0.00830157050777617	0.0166031410155523	0.991698429492224
28	0.00498667148896423	0.00997334297792846	0.995013328511036
29	0.0061768230848428	0.0123536461696856	0.993823176915157
30	0.00358186468775765	0.0071637293755153	0.996418135312242
31	0.00359143928641604	0.00718287857283207	0.996408560713584
32	0.00291338607523676	0.00582677215047352	0.997086613924763
33	0.00360106719579675	0.0072021343915935	0.996398932804203
34	0.0020103221341131	0.0040206442682262	0.997989677865887
35	0.00212876696928205	0.00425753393856409	0.997871233030718
36	0.00210287813096675	0.00420575626193351	0.997897121869033
37	0.00142450998058553	0.00284901996117106	0.998575490019414
38	0.00079359966790419	0.00158719933580838	0.999206400332096
39	0.000797486067621148	0.00159497213524230	0.999202513932379
40	0.000418313473088073	0.000836626946176146	0.999581686526912
41	0.000664628846908569	0.00132925769381714	0.999335371153091
42	0.000717033306184243	0.00143406661236849	0.999282966693816
43	0.000422794851364416	0.000845589702728831	0.999577205148636
44	0.000347623777441791	0.000695247554883583	0.999652376222558
45	0.000731676901023386	0.00146335380204677	0.999268323098977
46	0.000462091014492731	0.000924182028985461	0.999537908985507
47	0.000252305323157075	0.00050461064631415	0.999747694676843
48	0.000358996046849122	0.000717992093698245	0.999641003953151
49	0.000400137209974444	0.000800274419948889	0.999599862790026
50	0.000467204067856567	0.000934408135713134	0.999532795932143
51	0.00078294570679655	0.0015658914135931	0.999217054293204
52	0.000483431941026912	0.000966863882053825	0.999516568058973
53	0.000754160958005082	0.00150832191601016	0.999245839041995
54	0.000811656364315824	0.00162331272863165	0.999188343635684
55	0.00210200050089299	0.00420400100178599	0.997897999499107
56	0.00163273286029716	0.00326546572059432	0.998367267139703
57	0.00116440592690678	0.00232881185381357	0.998835594073093
58	0.00484054835322773	0.00968109670645546	0.995159451646772
59	0.00457895594372798	0.00915791188745596	0.995421044056272
60	0.00430291946687428	0.00860583893374856	0.995697080533126
61	0.00300308086143475	0.0060061617228695	0.996996919138565
62	0.00300035669764804	0.00600071339529607	0.996999643302352
63	0.00260560780577954	0.00521121561155909	0.99739439219422
64	0.00488445790205607	0.00976891580411215	0.995115542097944
65	0.00596535846027948	0.0119307169205590	0.99403464153972
66	0.00412544007389114	0.00825088014778227	0.99587455992611
67	0.0116386887059827	0.0232773774119653	0.988361311294017
68	0.0108488757209146	0.0216977514418293	0.989151124279085
69	0.00728890441581026	0.0145778088316205	0.99271109558419
70	0.00636439535978418	0.0127287907195684	0.993635604640216
71	0.00516724199270997	0.0103344839854199	0.99483275800729
72	0.00283375703817047	0.00566751407634095	0.99716624296183
73	0.00158667438271847	0.00317334876543694	0.998413325617282
74	0.00196005991343260	0.00392011982686521	0.998039940086567
75	0.00256697018317846	0.00513394036635693	0.997433029816821
76	0.00607174664230603	0.0121434932846121	0.993928253357694
77	0.00398111839975299	0.00796223679950598	0.996018881600247
78	0.00383489627703424	0.00766979255406848	0.996165103722966
79	0.008090213912916	0.016180427825832	0.991909786087084
80	0.00315763761347364	0.00631527522694729	0.996842362386526

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	44	0.6875	NOK
5% type I error level	56	0.875	NOK
10% type I error level	57	0.890625	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/10xjyr1258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/10xjyr1258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/1wlhb1258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/1wlhb1258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/2ech51258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/2ech51258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/33gyn1258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/33gyn1258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/4a7be1258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/4a7be1258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/5vzcd1258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/5vzcd1258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/6zwr31258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/6zwr31258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/74cg01258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/74cg01258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/8ka771258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/8ka771258650109.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/93vhk1258650109.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586501667agdfturgglszuf/93vhk1258650109.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

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