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Paper - Multiple lineair regression

*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, 01 Dec 2010 13:16:45 +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/01/t1291209394xljxqcxa4vsen2w.htm/, Retrieved Wed, 01 Dec 2010 14:16:34 +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/01/t1291209394xljxqcxa4vsen2w.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 «

1 162556 807 213118 1 29790 444 81767 1 87550 412 153198 0 84738 428 -26007 1 54660 315 126942 1 42634 168 157214 0 40949 263 129352 1 45187 267 234817 1 37704 228 60448 1 16275 129 47818 0 25830 104 245546 0 12679 122 48020 1 18014 393 -1710 0 43556 190 32648 1 24811 280 95350 0 6575 63 151352 0 7123 102 288170 1 21950 265 114337 1 37597 234 37884 0 17821 277 122844 1 12988 73 82340 1 22330 67 79801 0 13326 103 165548 0 16189 290 116384 0 7146 83 134028 0 15824 56 63838 1 27664 236 74996 0 11920 73 31080 0 8568 34 32168 0 14416 139 49857 1 3369 26 87161 1 11819 70 106113 1 6984 40 80570 1 4519 42 102129 0 2220 12 301670 0 18562 211 102313 0 10327 74 88577 1 5336 80 112477 1 2365 83 191778 0 4069 131 79804 0 8636 203 128294 0 13718 56 96448 0 4525 89 93811 0 6869 88 117520 0 4628 39 69159 1 3689 25 101792 1 4891 49 210568 1 7489 149 136996 0 4901 58 121920 0 2284 41 76403 1 3160 90 108094 1 4150 136 134759 1 7285 97 188873 1 1134 63 146216 1 4658 114 15660 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Orders[t] = + 45.7945811606209 + 10.0989931803165Group[t] + 0.00487769208390905Costs[t] -4.40797450353478e-05Dividends[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)	45.7945811606209	13.852164	3.306	0.001332	0.000666
Group	10.0989931803165	12.421404	0.813	0.418212	0.209106
Costs	0.00487769208390905	0.000286	17.0807	0	0
Dividends	-4.40797450353478e-05	0.000115	-0.3837	0.702069	0.351035

Multiple Linear Regression - Regression Statistics
Multiple R	0.874706146469247
R-squared	0.76511084267108
Adjusted R-squared	0.75777055650455
F-TEST (value)	104.234470606873
F-TEST (DF numerator)	3
F-TEST (DF denominator)	96
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	60.4832287994374
Sum Squared Residuals	351189.212736488

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	807	839.397501630414	-32.3975016304135
2	444	197.595753008283	246.404246991717
3	412	476.182587507249	-64.1825875072493
4	428	460.26683489604	-32.2668348960402
5	315	316.912652653129	-1.91265265312888
6	168	256.919145610329	-88.9191456103286
7	263	239.829391124800	23.1706088751998
8	267	265.951173046570	1.04882695342970
9	228	237.137544244747	-9.13754424474746
10	129	133.170207758457	-4.17020775845691
11	104	160.961762613542	-56.9617626135422
12	122	105.522129735906	16.4778702640936
13	393	143.835695904485	249.164304095515
14	190	256.808222051449	-66.8082220514494
15	280	172.710988945684	107.289011054316
16	63	71.193849041733	-8.19384904173297
17	102	67.8359217474689	34.1640782525311
18	265	157.918969774634	107.081030225366
19	234	237.610246558747	-3.61024655874678
20	277	127.304999588842	149.695000411158
21	73	115.615512920538	-42.6155129205376
22	67	161.294830841061	-94.2948308410607
23	103	103.497392239681	-0.497392239681171
24	290	119.629361260831	170.370638739169
25	83	74.7426487246374	8.25735127536259
26	56	120.165217932831	-64.1652179328312
27	236	187.524243591526	48.4757564084736
28	73	102.566672325118	-29.5666723251182
29	34	86.1686896972566	-52.1686896972566
30	139	113.913706394026	25.0862936059736
31	26	68.484484314601	-42.484484314601
32	70	108.865583095723	-38.8655830957226
33	40	86.4078707974602	-46.4078707974602
34	42	73.4340445874074	-31.4340445874074
35	12	43.3255209020857	-31.3255209020857
36	211	131.824370668339	79.1756293316608
37	74	92.2620557351537	-18.2620557351537
38	80	76.9629818183353	3.03701818166473
39	83	58.9757907759934	24.0242092240066
40	131	62.124170277246	68.875829722754
41	203	82.2631631876945	120.736836812305
42	56	108.455357918516	-52.455357918516
43	89	63.7309728787984	25.2690271212016
44	88	74.1191964484381	13.8808035515619
45	39	65.3200290380524	-26.3200290380524
46	25	69.4004150318398	-44.4004150318398
47	49	70.4685825707334	-21.4685825707334
48	149	86.3838616064697	62.6161383935303
49	58	64.3259475491496	-6.32594754914958
50	41	53.5674051203335	-12.5674051203335
51	90	66.5423253662391	23.4576746337609
52	136	70.1958541279415	65.8041458720585
53	97	83.1020874881536	13.8979125118464
54	63	54.9797131640019	8.02028683599815
55	114	71.71062335729	42.28937664271
56	77	54.7187948902316	22.2812051097684
57	6	61.8567559285823	-55.8567559285823
58	47	68.1259901087957	-21.1259901087957
59	51	47.6769657346223	3.32303426537766
60	85	97.534401176312	-12.5344011763120
61	43	78.878786445081	-35.878786445081
62	32	50.5885399658316	-18.5885399658316
63	25	66.9486995644051	-41.9486995644051
64	77	89.1405312028352	-12.1405312028352
65	54	56.2409531723265	-2.24095317232652
66	251	106.434915186852	144.565084813148
67	15	46.5257035133101	-31.5257035133101
68	44	61.480855953518	-17.4808559535180
69	73	46.9608548380528	26.0391451619472
70	85	71.160107636645	13.8398923633550
71	49	69.5414473046244	-20.5414473046244
72	38	69.0426577717164	-31.0426577717164
73	35	48.0630949845917	-13.0630949845917
74	9	45.001959989979	-36.001959989979
75	34	50.1877788567941	-16.1877788567941
76	20	61.0810988201581	-41.0810988201581
77	29	45.6718613713201	-16.6718613713201
78	11	46.482949419796	-35.482949419796
79	52	48.9760275391441	3.02397246085592
80	13	53.9464818012494	-40.9464818012494
81	29	49.8377252246699	-20.8377252246699
82	66	74.7335887175991	-8.73358871759909
83	33	43.9416331788179	-10.9416331788179
84	15	48.0183626198973	-33.0183626198973
85	15	43.7736230829941	-28.7736230829941
86	68	80.825758941222	-12.825758941222
87	100	59.5804097838371	40.4195902161629
88	13	43.8460045920571	-30.8460045920571
89	45	51.3334711467826	-6.33347114678256
90	14	44.0048113215194	-30.0048113215194
91	36	51.3429793026504	-15.3429793026504
92	40	66.6235451937197	-26.6235451937198
93	68	53.9808581996351	14.0191418003649
94	29	82.1653762826792	-53.1653762826792
95	43	53.3543609151836	-10.3543609151836
96	30	68.139251258135	-38.139251258135
97	9	42.1405226171751	-33.1405226171751
98	22	59.696938122024	-37.6969381220240
99	19	52.5644456448636	-33.5644456448636
100	9	59.4118594817504	-50.4118594817504

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.99938030138636	0.00123939722727854	0.00061969861363927
8	0.998358007979299	0.00328398404140280	0.00164199202070140
9	0.998105072720163	0.00378985455967432	0.00189492727983716
10	0.997201512902782	0.00559697419443593	0.00279848709721797
11	0.99625149826027	0.00749700347946175	0.00374850173973088
12	0.992573997962	0.0148520040759988	0.00742600203799938
13	0.999954934810484	9.01303790316722e-05	4.50651895158361e-05
14	0.99998773740764	2.45251847220188e-05	1.22625923610094e-05
15	0.99998414293163	3.17141367395205e-05	1.58570683697603e-05
16	0.999966792965499	6.64140690025053e-05	3.32070345012527e-05
17	0.999972045436986	5.59091260279875e-05	2.79545630139938e-05
18	0.999969334501872	6.13309962563112e-05	3.06654981281556e-05
19	0.999975339848616	4.93203027675686e-05	2.46601513837843e-05
20	0.999998375841786	3.24831642883184e-06	1.62415821441592e-06
21	0.999999447531877	1.10493624703781e-06	5.52468123518904e-07
22	0.999999988813796	2.23724077721399e-08	1.11862038860699e-08
23	0.999999980884161	3.82316774225091e-08	1.91158387112546e-08
24	0.999999999796325	4.07350790931446e-10	2.03675395465723e-10
25	0.999999999492118	1.01576474914219e-09	5.07882374571095e-10
26	0.999999999925463	1.49074695388788e-10	7.45373476943942e-11
27	0.999999999802597	3.94805219135855e-10	1.97402609567927e-10
28	0.999999999777774	4.4445124927184e-10	2.2222562463592e-10
29	0.999999999861598	2.76803983186828e-10	1.38401991593414e-10
30	0.999999999642615	7.1477065875434e-10	3.5738532937717e-10
31	0.999999999654708	6.90584992181556e-10	3.45292496090778e-10
32	0.999999999798551	4.02897361442436e-10	2.01448680721218e-10
33	0.9999999998307	3.38598788635981e-10	1.69299394317991e-10
34	0.99999999971465	5.70700414088547e-10	2.85350207044273e-10
35	0.999999999790805	4.183891170377e-10	2.0919455851885e-10
36	0.999999999633776	7.32447181492801e-10	3.66223590746400e-10
37	0.999999999583987	8.3202681113691e-10	4.16013405568455e-10
38	0.99999999897133	2.05734125917419e-09	1.02867062958710e-09
39	0.99999999773051	4.53898133844747e-09	2.26949066922374e-09
40	0.999999998892031	2.21593774935317e-09	1.10796887467658e-09
41	0.999999999916185	1.67630566929695e-10	8.38152834648473e-11
42	0.999999999998954	2.09214154651619e-12	1.04607077325810e-12
43	0.999999999997307	5.38511449329614e-12	2.69255724664807e-12
44	0.999999999993013	1.39747943055888e-11	6.98739715279438e-12
45	0.999999999988967	2.20649366923947e-11	1.10324683461973e-11
46	0.999999999984555	3.08900884735146e-11	1.54450442367573e-11
47	0.999999999983991	3.20175321391646e-11	1.60087660695823e-11
48	0.999999999980236	3.95288782497152e-11	1.97644391248576e-11
49	0.999999999961213	7.75744100376064e-11	3.87872050188032e-11
50	0.999999999896497	2.07005778482932e-10	1.03502889241466e-10
51	0.999999999877197	2.45605720504706e-10	1.22802860252353e-10
52	0.999999999983466	3.30671960278908e-11	1.65335980139454e-11
53	0.999999999957028	8.59448466081113e-11	4.29724233040556e-11
54	0.999999999951158	9.76845988736136e-11	4.88422994368068e-11
55	0.99999999997319	5.36182649433174e-11	2.68091324716587e-11
56	0.999999999962772	7.44567407789866e-11	3.72283703894933e-11
57	0.99999999999151	1.69806471456701e-11	8.49032357283505e-12
58	0.999999999993435	1.31307367250917e-11	6.56536836254585e-12
59	0.999999999983723	3.25538325723286e-11	1.62769162861643e-11
60	0.999999999985358	2.92839998359911e-11	1.46419999179955e-11
61	0.999999999981193	3.76137130680994e-11	1.88068565340497e-11
62	0.999999999940565	1.18870866808293e-10	5.94354334041463e-11
63	0.999999999857877	2.84245243842806e-10	1.42122621921403e-10
64	0.99999999979682	4.06359281193314e-10	2.03179640596657e-10
65	0.999999999347286	1.30542819704512e-09	6.52714098522558e-10
66	0.999999999966766	6.64688880811658e-11	3.32344440405829e-11
67	0.99999999990625	1.87497987565740e-10	9.37489937828701e-11
68	0.999999999749257	5.0148527920526e-10	2.5074263960263e-10
69	0.999999999892139	2.15723027667232e-10	1.07861513833616e-10
70	0.999999999961355	7.72892082457807e-11	3.86446041228903e-11
71	0.999999999874632	2.50736198417672e-10	1.25368099208836e-10
72	0.99999999995215	9.56984960346385e-11	4.78492480173192e-11
73	0.999999999808043	3.83913225876775e-10	1.91956612938388e-10
74	0.999999999506761	9.86477609720943e-10	4.93238804860472e-10
75	0.99999999798508	4.02983902960578e-09	2.01491951480289e-09
76	0.999999992504774	1.49904521366898e-08	7.4952260683449e-09
77	0.99999997207674	5.58465195542801e-08	2.79232597771400e-08
78	0.999999938973512	1.22052976665153e-07	6.10264883325763e-08
79	0.999999834099648	3.31800703639066e-07	1.65900351819533e-07
80	0.999999615710224	7.6857955262764e-07	3.8428977631382e-07
81	0.999998803075651	2.39384869735073e-06	1.19692434867536e-06
82	0.99999740336606	5.19326787856315e-06	2.59663393928158e-06
83	0.999994978725768	1.00425484634813e-05	5.02127423174065e-06
84	0.999996720597317	6.55880536538087e-06	3.27940268269044e-06
85	0.999986772297818	2.64554043632050e-05	1.32277021816025e-05
86	0.999947448231101	0.00010510353779793	5.2551768898965e-05
87	0.999995721837073	8.55632585383927e-06	4.27816292691964e-06
88	0.999978734301212	4.25313975753259e-05	2.12656987876630e-05
89	0.999944192961312	0.000111614077376185	5.58070386880927e-05
90	0.999707670735233	0.000584658529533923	0.000292329264766961
91	0.998513870485837	0.00297225902832671	0.00148612951416336
92	0.996405965567034	0.0071880688659312	0.0035940344329656
93	0.999308214599662	0.00138357080067675	0.000691785400338373

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	86	0.988505747126437	NOK
5% type I error level	87	1	NOK
10% type I error level	87	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/10ebif1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/10ebif1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/10k2o1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/10k2o1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/20k2o1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/20k2o1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/30k2o1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/30k2o1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/4ab1r1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/4ab1r1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/5ab1r1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/5ab1r1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/6ab1r1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/6ab1r1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/7lk1u1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/7lk1u1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/8ebif1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/8ebif1291209396.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/9ebif1291209396.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291209394xljxqcxa4vsen2w/9ebif1291209396.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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