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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: Wed, 22 Dec 2010 10:14:12 +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/22/t12930127587sk8tg3dzes9fx5.htm/, Retrieved Wed, 22 Dec 2010 11:12:38 +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/22/t12930127587sk8tg3dzes9fx5.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 «

377 370 358 357 349 348 369 381 368 361 351 351 358 354 347 345 343 340 362 370 373 371 354 357 363 364 363 358 357 357 380 378 376 380 379 384 392 394 392 396 392 396 419 421 420 418 410 418 426 428 430 424 423 427 441 449 452 462 455 461 461 463 462 456 455 456 472 472 471 465 459 465 468 467 463 460 462 461 476 476 471 453 443 442 444 438 427 424 416 406 431 434 418 412 404 409 412 406 398 397 385 390 413 413 401 397 397 409 419 424 428 430 424 433 456 459 446 441 439 454 460 457 451 444 437 443 471 469 454 444 436

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Werkloosheid[t] = + 368.565957446808 + 4.88136686009029M1[t] + 2.81418439716312M2[t] -2.07117988394584M3[t] -5.32018052869116M4[t] -10.3873629916183M5[t] -9.81818181818181M6[t] + 10.6600902643456M7[t] + 12.8656350741457M8[t] + 5.61663442940039M9[t] + 0.731270148291424M10[t] -6.97227595099936M11[t] + 0.70354609929078t + 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)	368.565957446808	10.364987	35.5587	0	0
M1	4.88136686009029	12.896437	0.3785	0.705736	0.352868
M2	2.81418439716312	12.894804	0.2182	0.827618	0.413809
M3	-2.07117988394584	12.893535	-0.1606	0.872654	0.436327
M4	-5.32018052869116	12.892628	-0.4127	0.68061	0.340305
M5	-10.3873629916183	12.892083	-0.8057	0.422027	0.211014
M6	-9.81818181818181	12.891902	-0.7616	0.447832	0.223916
M7	10.6600902643456	12.892083	0.8269	0.409979	0.20499
M8	12.8656350741457	12.892628	0.9979	0.320367	0.160183
M9	5.61663442940039	12.893535	0.4356	0.663911	0.331955
M10	0.731270148291424	12.894804	0.0567	0.954872	0.477436
M11	-6.97227595099936	12.896437	-0.5406	0.589778	0.294889
t	0.70354609929078	0.068392	10.2869	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.702027458729559
R-squared	0.492842552810283
Adjusted R-squared	0.441267219197769
F-TEST (value)	9.55578022069654
F-TEST (DF numerator)	12
F-TEST (DF denominator)	118
p-value	9.71223101942087e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	29.5055485017248
Sum Squared Residuals	102728.132301741

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	377	374.150870406189	2.84912959381061
2	370	372.787234042553	-2.78723404255319
3	358	368.605415860735	-10.605415860735
4	357	366.059961315280	-9.05996131528048
5	349	361.696324951644	-12.6963249516441
6	348	362.969052224371	-14.9690522243714
7	369	384.150870406190	-15.1508704061896
8	381	387.059961315280	-6.05996131528047
9	368	380.514506769826	-12.5145067698259
10	361	376.332688588008	-15.3326885880077
11	351	369.332688588008	-18.3326885880077
12	351	377.008510638298	-26.0085106382979
13	358	382.593423597679	-24.5934235976789
14	354	381.229787234043	-27.2297872340426
15	347	377.047969052224	-30.0479690522244
16	345	374.50251450677	-29.5025145067698
17	343	370.138878143133	-27.1388781431335
18	340	371.411605415861	-31.4116054158607
19	362	392.593423597679	-30.5934235976789
20	370	395.50251450677	-25.5025145067698
21	373	388.957059961315	-15.9570599613153
22	371	384.775241779497	-13.7752417794971
23	354	377.775241779497	-23.7752417794971
24	357	385.451063829787	-28.4510638297872
25	363	391.035976789168	-28.0359767891683
26	364	389.672340425532	-25.6723404255319
27	363	385.490522243714	-22.4905222437137
28	358	382.945067698259	-24.9450676982592
29	357	378.581431334623	-21.5814313346228
30	357	379.85415860735	-22.8541586073501
31	380	401.035976789168	-21.0359767891683
32	378	403.945067698259	-25.9450676982592
33	376	397.399613152805	-21.3996131528046
34	380	393.217794970986	-13.2177949709865
35	379	386.217794970986	-7.21779497098646
36	384	393.893617021277	-9.8936170212766
37	392	399.478529980658	-7.47852998065766
38	394	398.114893617021	-4.11489361702128
39	392	393.933075435203	-1.93307543520310
40	396	391.387620889749	4.61237911025145
41	392	387.023984526112	4.97601547388781
42	396	388.296711798839	7.70328820116054
43	419	409.478529980658	9.52147001934236
44	421	412.387620889749	8.61237911025145
45	420	405.842166344294	14.157833655706
46	418	401.660348162476	16.3396518375242
47	410	394.660348162476	15.3396518375242
48	418	402.336170212766	15.6638297872340
49	426	407.921083172147	18.078916827853
50	428	406.557446808511	21.4425531914894
51	430	402.375628626692	27.6243713733075
52	424	399.830174081238	24.1698259187621
53	423	395.466537717602	27.5334622823984
54	427	396.739264990329	30.2607350096712
55	441	417.921083172147	23.078916827853
56	449	420.830174081238	28.1698259187621
57	452	414.284719535783	37.7152804642166
58	462	410.102901353965	51.8970986460348
59	455	403.102901353965	51.8970986460348
60	461	410.778723404255	50.2212765957447
61	461	416.363636363636	44.6363636363636
62	463	415	48
63	462	410.818181818182	51.1818181818182
64	456	408.272727272727	47.7272727272727
65	455	403.909090909091	51.0909090909091
66	456	405.181818181818	50.8181818181818
67	472	426.363636363636	45.6363636363636
68	472	429.272727272727	42.7272727272727
69	471	422.727272727273	48.2727272727273
70	465	418.545454545455	46.4545454545455
71	459	411.545454545455	47.4545454545455
72	465	419.221276595745	45.7787234042553
73	468	424.806189555126	43.1938104448743
74	467	423.442553191489	43.5574468085106
75	463	419.260735009671	43.7392649903288
76	460	416.715280464217	43.2847195357834
77	462	412.35164410058	49.6483558994197
78	461	413.624371373308	47.3756286266925
79	476	434.806189555126	41.1938104448743
80	476	437.715280464217	38.2847195357834
81	471	431.169825918762	39.8301740812379
82	453	426.988007736944	26.0119922630561
83	443	419.988007736944	23.0119922630561
84	442	427.663829787234	14.3361702127660
85	444	433.248742746615	10.7512572533849
86	438	431.885106382979	6.11489361702128
87	427	427.703288201161	-0.703288201160537
88	424	425.157833655706	-1.15783365570599
89	416	420.79419729207	-4.79419729206962
90	406	422.066924564797	-16.0669245647969
91	431	443.248742746615	-12.2487427466151
92	434	446.157833655706	-12.157833655706
93	418	439.612379110251	-21.6123791102515
94	412	435.430560928433	-23.4305609284333
95	404	428.430560928433	-24.4305609284333
96	409	436.106382978723	-27.1063829787234
97	412	441.691295938104	-29.6912959381045
98	406	440.327659574468	-34.3276595744681
99	398	436.14584139265	-38.1458413926499
100	397	433.600386847195	-36.6003868471953
101	385	429.236750483559	-44.236750483559
102	390	430.509477756286	-40.5094777562863
103	413	451.691295938104	-38.6912959381044
104	413	454.600386847195	-41.6003868471954
105	401	448.054932301741	-47.0549323017408
106	397	443.873114119923	-46.8731141199226
107	397	436.873114119923	-39.8731141199226
108	409	444.548936170213	-35.5489361702128
109	419	450.133849129594	-31.1338491295938
110	424	448.770212765957	-24.7702127659574
111	428	444.588394584139	-16.5883945841393
112	430	442.042940038685	-12.0429400386847
113	424	437.679303675048	-13.6793036750484
114	433	438.952030947776	-5.95203094777563
115	456	460.133849129594	-4.1338491295938
116	459	463.042940038685	-4.04294003868471
117	446	456.49748549323	-10.4974854932302
118	441	452.315667311412	-11.315667311412
119	439	445.315667311412	-6.31566731141199
120	454	452.991489361702	1.00851063829786
121	460	458.576402321083	1.42359767891682
122	457	457.212765957447	-0.212765957446806
123	451	453.030947775629	-2.03094777562862
124	444	450.485493230174	-6.48549323017408
125	437	446.121856866538	-9.12185686653772
126	443	447.394584139265	-4.394584139265
127	471	468.576402321083	2.42359767891683
128	469	471.485493230174	-2.48549323017408
129	454	464.940038684720	-10.9400386847195
130	444	460.758220502901	-16.7582205029014
131	436	453.758220502901	-17.7582205029014

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.000874151442854066	0.00174830288570813	0.999125848557146
17	0.000358584527576834	0.000717169055153668	0.999641415472423
18	5.71634975513657e-05	0.000114326995102731	0.999942836502449
19	9.86485729001657e-06	1.97297145800331e-05	0.99999013514271
20	1.05222545934078e-06	2.10445091868156e-06	0.99999894777454
21	5.08461663637548e-06	1.01692332727510e-05	0.999994915383364
22	1.31362237338477e-05	2.62724474676955e-05	0.999986863776266
23	5.22077085888763e-06	1.04415417177753e-05	0.999994779229141
24	2.79227820754819e-06	5.58455641509638e-06	0.999997207721792
25	7.3875334628359e-07	1.47750669256718e-06	0.999999261246654
26	3.26407327942931e-07	6.52814655885861e-07	0.999999673592672
27	4.32055334365418e-07	8.64110668730835e-07	0.999999567944666
28	2.35727186369017e-07	4.71454372738033e-07	0.999999764272814
29	1.83494230176992e-07	3.66988460353985e-07	0.99999981650577
30	1.61535072744545e-07	3.23070145489090e-07	0.999999838464927
31	1.46207726936248e-07	2.92415453872496e-07	0.999999853792273
32	6.0136032742238e-08	1.20272065484476e-07	0.999999939863967
33	2.5895879468284e-08	5.1791758936568e-08	0.99999997410412
34	1.99336010797388e-08	3.98672021594776e-08	0.999999980066399
35	9.13488761982276e-08	1.82697752396455e-07	0.999999908651124
36	4.26799227240409e-07	8.53598454480818e-07	0.999999573200773
37	5.99861196048363e-07	1.19972239209673e-06	0.999999400138804
38	1.20026871796821e-06	2.40053743593643e-06	0.999998799731282
39	3.03767812178962e-06	6.07535624357924e-06	0.999996962321878
40	1.0512770544955e-05	2.102554108991e-05	0.999989487229455
41	2.30563622993122e-05	4.61127245986245e-05	0.9999769436377
42	6.23247023586954e-05	0.000124649404717391	0.999937675297641
43	0.000133479728960672	0.000266959457921344	0.99986652027104
44	0.000183090018521183	0.000366180037042367	0.999816909981479
45	0.000250889100991333	0.000501778201982666	0.999749110899009
46	0.000298078740947974	0.000596157481895947	0.999701921259052
47	0.000371887268207633	0.000743774536415267	0.999628112731792
48	0.000560354408921433	0.00112070881784287	0.999439645591079
49	0.000532561088545182	0.00106512217709036	0.999467438911455
50	0.000548028019319247	0.00109605603863849	0.99945197198068
51	0.000703533719309032	0.00140706743861806	0.999296466280691
52	0.00068743692403647	0.00137487384807294	0.999312563075964
53	0.000685854499910686	0.00137170899982137	0.99931414550009
54	0.000752450401432113	0.00150490080286423	0.999247549598568
55	0.000657984749456846	0.00131596949891369	0.999342015250543
56	0.00056611685310169	0.00113223370620338	0.999433883146898
57	0.00052789390142759	0.00105578780285518	0.999472106098572
58	0.000802472970954459	0.00160494594190892	0.999197527029046
59	0.00117273256729030	0.00234546513458060	0.99882726743271
60	0.00168557690221786	0.00337115380443572	0.998314423097782
61	0.00128019062689852	0.00256038125379705	0.998719809373101
62	0.00104367631806492	0.00208735263612985	0.998956323681935
63	0.000924974340544518	0.00184994868108904	0.999075025659455
64	0.000701270973420012	0.00140254194684002	0.99929872902658
65	0.000576865099695175	0.00115373019939035	0.999423134900305
66	0.000466958221769267	0.000933916443538535	0.99953304177823
67	0.000319808046093577	0.000639616092187155	0.999680191953906
68	0.000201690989041695	0.000403381978083389	0.999798309010958
69	0.000147011881853382	0.000294023763706764	0.999852988118147
70	0.000112833154985362	0.000225666309970724	0.999887166845015
71	8.9378427583952e-05	0.000178756855167904	0.999910621572416
72	7.69586584866899e-05	0.000153917316973380	0.999923041341513
73	6.85470132695938e-05	0.000137094026539188	0.99993145298673
74	6.72015008095592e-05	0.000134403001619118	0.99993279849919
75	7.39842771423583e-05	0.000147968554284717	0.999926015722858
76	8.74207943900195e-05	0.000174841588780039	0.99991257920561
77	0.000174664224903207	0.000349328449806415	0.999825335775097
78	0.000373423783415054	0.000746847566830107	0.999626576216585
79	0.000652458587868082	0.00130491717573616	0.999347541412132
80	0.00141165653713905	0.00282331307427811	0.99858834346286
81	0.00681195615779106	0.0136239123155821	0.993188043842209
82	0.0318687561869613	0.0637375123739227	0.968131243813039
83	0.115013494033935	0.230026988067870	0.884986505966065
84	0.253338537903599	0.506677075807197	0.746661462096401
85	0.478202777237329	0.956405554474658	0.521797222762671
86	0.705845476800723	0.588309046398554	0.294154523199277
87	0.856852495643326	0.286295008713349	0.143147504356674
88	0.941221817381237	0.117556365237525	0.0587781826187627
89	0.98461684109357	0.0307663178128618	0.0153831589064309
90	0.993623249242785	0.0127535015144295	0.00637675075721474
91	0.996988207496555	0.00602358500689051	0.00301179250344526
92	0.99896711043989	0.00206577912022094	0.00103288956011047
93	0.999750588520295	0.000498822959410849	0.000249411479705425
94	0.999965237869944	6.95242601113807e-05	3.47621300556903e-05
95	0.999995618696052	8.76260789689672e-06	4.38130394844836e-06
96	0.999996927710341	6.14457931752989e-06	3.07228965876495e-06
97	0.999996964082128	6.07183574442758e-06	3.03591787221379e-06
98	0.999995723515114	8.55296977135885e-06	4.27648488567943e-06
99	0.999993214369472	1.35712610567045e-05	6.78563052835226e-06
100	0.999987828820046	2.43423599072663e-05	1.21711799536332e-05
101	0.999980920282848	3.81594343037285e-05	1.90797171518642e-05
102	0.999969344016535	6.13119669300316e-05	3.06559834650158e-05
103	0.999955995394406	8.80092111886052e-05	4.40046055943026e-05
104	0.999945126602527	0.000109746794946214	5.48733974731068e-05
105	0.999933800428884	0.000132399142232926	6.6199571116463e-05
106	0.99990953635731	0.000180927285378081	9.04636426890405e-05
107	0.99983892991273	0.000322140174539195	0.000161070087269598
108	0.999927377741634	0.000145244516731076	7.26222583655379e-05
109	0.99997994876379	4.01024724185187e-05	2.00512362092593e-05
110	0.999994369400858	1.12611982841705e-05	5.63059914208525e-06
111	0.999994266796076	1.14664078473724e-05	5.73320392368622e-06
112	0.99997003064664	5.99387067186541e-05	2.99693533593271e-05
113	0.999837651106186	0.00032469778762781	0.000162348893813905
114	0.998894646054235	0.00221070789153045	0.00110535394576522
115	0.996962145016434	0.00607570996713198	0.00303785498356599

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	90	0.9	NOK
5% type I error level	93	0.93	NOK
10% type I error level	94	0.94	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930127587sk8tg3dzes9fx5/10sd1e1293012842.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930127587sk8tg3dzes9fx5/10sd1e1293012842.ps (open in new window)

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Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)

library(lmtest)

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

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

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

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

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

x <- x1

if (par3 == 'First Differences'){

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

for (i in 1:n-1) {

for (j in 1:k) {

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

}

}

x <- x2

}

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

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

for (i in 1:11){

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

}

x <- cbind(x, x2)

}

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

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

for (i in 1:3){

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

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

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

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

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

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

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

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

j <- j + 1

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

}

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

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

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

}

gqarr

}

bitmap(file='test0.png')

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

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

grid()

dev.off()

bitmap(file='test1.png')

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

grid()

dev.off()

bitmap(file='test2.png')

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

grid()

dev.off()

bitmap(file='test3.png')

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

dev.off()

bitmap(file='test4.png')

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

qqline(mysum$resid)

grid()

dev.off()

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

bitmap(file='test5.png')

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

dum

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

dum1

z <- as.data.frame(dum1)

z

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

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

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

grid()

dev.off()

bitmap(file='test7.png')

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

grid()

dev.off()

bitmap(file='test8.png')

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

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

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

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

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

myeq <- colnames(x)[1]

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

for (i in 1:k){

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

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

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

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

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

}

}

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

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant1)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant5)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant10)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

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

}

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

Software written by Ed van Stee & Patrick Wessa

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