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ws3

*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: Tue, 17 Nov 2009 08:57:36 -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/17/t1258473497cgrbse54l6svaa5.htm/, Retrieved Tue, 17 Nov 2009 16:58:29 +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/17/t1258473497cgrbse54l6svaa5.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

403,5 0 395,1 395,3 403,3 0 403,5 395,1 405,7 0 403,3 403,5 406,7 0 405,7 403,3 407,2 0 406,7 405,7 412,4 0 407,2 406,7 415,9 0 412,4 407,2 414,0 0 415,9 412,4 411,8 0 414,0 415,9 409,9 0 411,8 414,0 412,4 0 409,9 411,8 415,9 0 412,4 409,9 416,3 0 415,9 412,4 417,2 0 416,3 415,9 421,8 0 417,2 416,3 421,4 0 421,8 417,2 415,1 0 421,4 421,8 412,4 0 415,1 421,4 411,8 0 412,4 415,1 408,8 0 411,8 412,4 404,5 0 408,8 411,8 402,5 0 404,5 408,8 409,4 0 402,5 404,5 410,7 0 409,4 402,5 413,4 0 410,7 409,4 415,2 0 413,4 410,7 417,7 0 415,2 413,4 417,8 0 417,7 415,2 417,9 0 417,8 417,7 418,4 0 417,9 417,8 418,2 0 418,4 417,9 416,6 0 418,2 418,4 418,9 0 416,6 418,2 421,0 0 418,9 416,6 423,5 0 421,0 418,9 432,3 0 423,5 421,0 432,3 0 432,3 423,5 428,6 0 432,3 432,3 426,7 0 428,6 432,3 427,3 0 426,7 428,6 428,5 0 427,3 426,7 437,0 0 428,5 427,3 442,0 0 437,0 428,5 444,9 0 442,0 437,0 441,4 0 444,9 442,0 440,3 0 441,4 444,9 447,1 0 440,3 441,4 455,3 0 447,1 440,3 478,6 0 455,3 44 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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 14.3225012216094 -3.18098980898201X[t] + 1.47915198643717Y1[t] -0.518658312352378Y2[t] + 0.133388825673378t + 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)	14.3225012216094	6.27913	2.281	0.024476	0.012238
X	-3.18098980898201	3.248082	-0.9793	0.329559	0.164779
Y1	1.47915198643717	0.081567	18.1343	0	0
Y2	-0.518658312352378	0.08114	-6.3921	0	0
t	0.133388825673378	0.065337	2.0415	0.04359	0.021795

Multiple Linear Regression - Regression Statistics
Multiple R	0.997939861436151
R-squared	0.995883967043205
Adjusted R-squared	0.995734293117503
F-TEST (value)	6653.69042987728
F-TEST (DF numerator)	4
F-TEST (DF denominator)	110
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.27669185542326
Sum Squared Residuals	5824.52687946615

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	403.5	393.843209015711	9.65679098428868
2	403.3	406.505206189928	-3.2052061899278
3	405.7	401.986034794554	3.71396520544628
4	406.7	405.773120050147	0.926879949853032
5	407.2	406.140880912612	1.05911908738821
6	412.4	406.495187419151	5.9048125808486
7	415.9	414.060837418122	1.83916258187818
8	414	416.674234972093	-2.67423497209290
9	411.8	412.181930930302	-0.381930930302359
10	409.9	410.046636179283	-0.146636179283528
11	412.4	408.510684517901	3.88931548209854
12	415.9	413.327404103137	2.5725958968627
13	416.3	417.34117910046	-1.04117910045979
14	417.2	416.250924627475	0.949075372525225
15	421.8	417.508086916001	4.29191308399942
16	421.4	423.978782398168	-2.57878239816786
17	415.1	421.134682192445	-6.03468219244532
18	412.4	412.156876828506	0.243123171494364
19	411.8	411.564102658619	0.235897341381475
20	408.8	412.210377735781	-3.41037773578110
21	404.5	408.217505589554	-3.71750558955441
22	402.5	403.546515810605	-1.04651581060508
23	409.4	402.951831406519	6.44816859348061
24	410.7	414.328685563314	-3.62868556331393
25	413.4	412.806229616124	0.59377038387574
26	415.2	416.25907299912	-1.05907299911985
27	417.7	417.654557957029	0.0454420429712915
28	417.8	420.552241786561	-2.75224178656070
29	417.9	419.536900029997	-1.63690002999694
30	418.4	419.766338223079	-1.36633822307872
31	418.2	420.587437210735	-2.38743721073546
32	416.6	420.165666482945	-3.5656664829452
33	418.9	418.036143792790	0.863856207210331
34	421	422.401435487032	-1.40143548703223
35	423.5	424.448129365813	-0.948129365813238
36	432.3	427.19021570164	5.10978429836047
37	432.3	439.043496227079	-6.74349622707904
38	428.6	434.612691904051	-6.01269190405147
39	426.7	429.273218379907	-2.57321837990738
40	427.3	428.515254187054	-1.21525418705385
41	428.5	430.521584998059	-2.02158499805912
42	437	432.118761220046	4.88123877995437
43	442	444.202551955612	-2.20255195561208
44	444.9	447.323105058476	-2.4231050584761
45	441.4	449.152743083055	-7.75274308305534
46	440.3	442.604990850377	-2.30499085037671
47	447.1	442.926616584203	4.17338341579742
48	455.3	453.688763061236	1.61123693876370
49	478.6	462.424321651698	16.1756783483017
50	486.5	492.768953600068	-6.26895360006813
51	487.8	492.502904440785	-4.70290444078466
52	485.9	490.461790181243	-4.56179018124263
53	483.8	487.110534426627	-3.31053442662721
54	488.4	485.123154874252	3.27684512574784
55	494	493.149825293476	0.850174706523593
56	493.6	499.180637006377	-5.580637006377
57	487.3	495.817878488302	-8.51787848830223
58	482.1	486.840073124362	-4.74007312436237
59	484.2	482.549418988383	1.65058101161747
60	496.8	488.486050209806	8.31394979019375
61	501.1	506.167571608648	-5.06757160864796
62	499.8	506.126219240361	-6.3262192403612
63	495.5	502.106479740551	-6.60647974055103
64	498.1	496.553770830603	1.54622916939735
65	503.8	502.763185564128	1.03681443587206
66	516.2	509.979229100377	6.22077089962308
67	526.1	525.497750177463	0.602249822537321
68	527.1	533.843380595694	-6.74338059569445
69	525.1	530.321204115516	-5.22120411551647
70	528.9	526.977630655963	1.92236934403682
71	540.1	533.769113654802	6.33088634519757
72	549	548.498103141633	0.501896858366855
73	556	555.986971548251	0.0130284517494041
74	568.9	561.858365299048	7.04163470095198
75	589.1	577.442206563294	11.6577934367059
76	590.3	600.763773285653	-10.4637732856527
77	603.3	592.195246585532	11.1047534144675
78	638.8	610.935221260066	27.8647787399338
79	643	656.835947543678	-13.8359475436781
80	656.7	644.769404623878	11.9305953761219
81	656.1	662.988810751861	-6.8888107518608
82	654.1	655.129089506444	-1.02908950644422
83	659.9	652.615369346655	7.28463065334526
84	662.1	662.365156318368	-0.265156318368306
85	669.2	662.74446130256	6.45553869744028
86	673.1	672.238780944762	0.8612190552382
87	678.3	674.458388499838	3.84161150016176
88	677.4	680.26060023681	-2.86060023681051
89	678.5	676.365729050458	2.1342709495419
90	672.4	678.59297754233	-6.19297754232958
91	665.3	669.133015107149	-3.83301510714862
92	667.9	661.928240534468	5.97175946553243
93	672.1	669.58989854258	2.51010145742053
94	662.5	674.587214099173	-12.0872140991728
95	682.3	658.34237894317	23.9576210568306
96	692.1	692.742096898881	-0.642096898881373
97	702.7	697.101740607062	5.59825939293798
98	721.4	707.831289027916	13.5687109720839
99	733.2	730.127041889029	3.07295811097089
100	747.7	738.015513713672	9.6844862863283
101	737.6	753.476438256926	-15.8764382569259
102	729.3	731.149846490474	-1.84984649047443
103	706.1	724.244722783478	-18.1447227834782
104	674.3	694.366649516334	-20.0666495163343
105	659	659.49587801988	-0.495878019880785
106	645.7	653.491575785871	-7.79157578587121
107	646.1	641.887715370922	4.21228462907827
108	633	646.329930736475	-13.3299307364746
109	622.3	626.87896521488	-4.57896521488015
110	628.2	617.979851677492	10.2201483225081
111	637.3	632.389881165315	4.91011883468478
112	639.6	642.923469024688	-3.32346902468752
113	638.5	641.73911677676	-3.23911677675991
114	650.5	639.052524298942	11.4474757010581
115	655.4	657.506261105449	-2.10626110544889

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0229769007663864	0.0459538015327727	0.977023099233614
9	0.0243311496071924	0.0486622992143847	0.975668850392808
10	0.0290990540562845	0.058198108112569	0.970900945943715
11	0.0106003475379032	0.0212006950758065	0.989399652462097
12	0.00382129239784047	0.00764258479568095	0.99617870760216
13	0.00125633349049151	0.00251266698098303	0.998743666509508
14	0.00038120317690639	0.00076240635381278	0.999618796823094
15	0.000253667164978831	0.000507334329957663	0.99974633283502
16	7.64227095512754e-05	0.000152845419102551	0.999923577290449
17	0.000200252356164835	0.00040050471232967	0.999799747643835
18	0.000162448531741624	0.000324897063483249	0.999837551468258
19	9.84752373237073e-05	0.000196950474647415	0.999901524762676
20	8.99652214839157e-05	0.000179930442967831	0.999910034778516
21	6.61173570651626e-05	0.000132234714130325	0.999933882642935
22	2.51752830949185e-05	5.0350566189837e-05	0.999974824716905
23	5.44706700090183e-05	0.000108941340018037	0.999945529329991
24	2.16351251372732e-05	4.32702502745463e-05	0.999978364874863
25	1.03419218973295e-05	2.06838437946589e-05	0.999989658078103
26	4.24381524605465e-06	8.4876304921093e-06	0.999995756184754
27	2.10813094042634e-06	4.21626188085268e-06	0.99999789186906
28	7.65125253933757e-07	1.53025050786751e-06	0.999999234874746
29	2.82221435008822e-07	5.64442870017644e-07	0.999999717778565
30	1.04818567692531e-07	2.09637135385062e-07	0.999999895181432
31	3.54645895210230e-08	7.09291790420461e-08	0.99999996453541
32	1.21691066237309e-08	2.43382132474618e-08	0.999999987830893
33	6.2125915281806e-09	1.24251830563612e-08	0.999999993787408
34	2.41285499992283e-09	4.82570999984566e-09	0.999999997587145
35	1.11039557073494e-09	2.22079114146989e-09	0.999999998889604
36	1.61868498973029e-08	3.23736997946058e-08	0.99999998381315
37	6.22498818298106e-09	1.24499763659621e-08	0.999999993775012
38	2.44082067697163e-09	4.88164135394326e-09	0.99999999755918
39	8.52322197621955e-10	1.70464439524391e-09	0.999999999147678
40	3.32368755954365e-10	6.6473751190873e-10	0.999999999667631
41	1.21441164808283e-10	2.42882329616565e-10	0.99999999987856
42	9.44681034775554e-10	1.88936206955111e-09	0.999999999055319
43	5.2037380313025e-10	1.0407476062605e-09	0.999999999479626
44	2.51296371783233e-10	5.02592743566465e-10	0.999999999748704
45	1.30371956793445e-10	2.60743913586890e-10	0.999999999869628
46	5.17031525165696e-11	1.03406305033139e-10	0.999999999948297
47	1.89534546738177e-10	3.79069093476353e-10	0.999999999810465
48	2.89059783341128e-10	5.78119566682257e-10	0.99999999971094
49	1.34192142431997e-06	2.68384284863993e-06	0.999998658078576
50	1.01417662179946e-06	2.02835324359892e-06	0.999998985823378
51	5.54431051498493e-07	1.10886210299699e-06	0.999999445568948
52	2.83409223541876e-07	5.66818447083752e-07	0.999999716590776
53	1.33436588711018e-07	2.66873177422035e-07	0.999999866563411
54	1.11393329866957e-07	2.22786659733914e-07	0.99999988860667
55	5.93153965102735e-08	1.18630793020547e-07	0.999999940684604
56	3.59740280617694e-08	7.19480561235387e-08	0.999999964025972
57	4.18304785857955e-08	8.3660957171591e-08	0.999999958169521
58	2.33944848220043e-08	4.67889696440087e-08	0.999999976605515
59	1.41177359104766e-08	2.82354718209531e-08	0.999999985882264
60	5.32745674252418e-08	1.06549134850484e-07	0.999999946725433
61	3.11310750371321e-08	6.22621500742642e-08	0.999999968868925
62	2.33839260360350e-08	4.67678520720700e-08	0.999999976616074
63	2.06878358325708e-08	4.13756716651416e-08	0.999999979312164
64	1.23615452939668e-08	2.47230905879335e-08	0.999999987638455
65	7.41913125898957e-09	1.48382625179791e-08	0.999999992580869
66	1.21255118451460e-08	2.42510236902919e-08	0.999999987874488
67	7.0187221479334e-09	1.40374442958668e-08	0.999999992981278
68	7.47232670782399e-09	1.49446534156480e-08	0.999999992527673
69	7.31919566791137e-09	1.46383913358227e-08	0.999999992680804
70	5.27021890098909e-09	1.05404378019782e-08	0.999999994729781
71	7.30962728519218e-09	1.46192545703844e-08	0.999999992690373
72	5.3258558927286e-09	1.06517117854572e-08	0.999999994674144
73	4.37293981294657e-09	8.74587962589314e-09	0.99999999562706
74	5.61944206535925e-09	1.12388841307185e-08	0.999999994380558
75	1.49598990829468e-08	2.99197981658937e-08	0.9999999850401
76	2.83961592242696e-07	5.67923184485392e-07	0.999999716038408
77	3.42152411152521e-07	6.84304822305042e-07	0.99999965784759
78	0.000102414271451823	0.000204828542903647	0.999897585728548
79	0.00167925412221261	0.00335850824442522	0.998320745877787
80	0.00139862309942174	0.00279724619884349	0.998601376900578
81	0.00315057770405978	0.00630115540811955	0.99684942229594
82	0.00279296122199579	0.00558592244399158	0.997207038778004
83	0.00187343746617866	0.00374687493235732	0.998126562533821
84	0.00139142829056882	0.00278285658113763	0.998608571709431
85	0.000882291251991807	0.00176458250398361	0.999117708748008
86	0.000558937848897	0.001117875697794	0.999441062151103
87	0.000325138959997548	0.000650277919995097	0.999674861040002
88	0.000257319462324491	0.000514638924648983	0.999742680537675
89	0.0001460640130663	0.0002921280261326	0.999853935986934
90	0.000188991260006213	0.000377982520012426	0.999811008739994
91	0.000186211581099981	0.000372423162199961	0.9998137884189
92	0.000100849220094899	0.000201698440189799	0.999899150779905
93	5.91216598580282e-05	0.000118243319716056	0.999940878340142
94	0.00165084461631283	0.00330168923262566	0.998349155383687
95	0.00994221485261884	0.0198844297052377	0.990057785147381
96	0.0141575250202065	0.0283150500404131	0.985842474979793
97	0.00964827372307008	0.0192965474461402	0.99035172627693
98	0.0122667837149448	0.0245335674298896	0.987733216285055
99	0.0077831975348561	0.0155663950697122	0.992216802465144
100	0.0786366072303045	0.157273214460609	0.921363392769695
101	0.0800686578244058	0.160137315648812	0.919931342175594
102	0.504131167198471	0.991737665603059	0.495868832801529
103	0.847223452286071	0.305553095427857	0.152776547713929
104	0.822147333159903	0.355705333680195	0.177852666840097
105	0.899138519420207	0.201722961159586	0.100861480579793
106	0.807366122911388	0.385267754177224	0.192633877088612
107	0.657345988886192	0.685308022227616	0.342654011113808

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	83	0.83	NOK
5% type I error level	91	0.91	NOK
10% type I error level	92	0.92	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258473497cgrbse54l6svaa5/10vhw51258473450.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258473497cgrbse54l6svaa5/10vhw51258473450.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258473497cgrbse54l6svaa5/1t2f41258473450.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258473497cgrbse54l6svaa5/1t2f41258473450.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258473497cgrbse54l6svaa5/23wx21258473450.png (open in new window)

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http://www.freestatistics.org/blog/date/2009/Nov/17/t1258473497cgrbse54l6svaa5/9p7u81258473450.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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