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4 parameters

*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, 02 Dec 2010 19:17:20 +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/02/t1291317370xgtideskykzrfp5.htm/, Retrieved Thu, 02 Dec 2010 20:16:20 +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/02/t1291317370xgtideskykzrfp5.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 1081 213118 6282929 1 29790 309 81767 4324047 1 87550 458 153198 4108272 0 84738 588 -26007 -1212617 1 54660 299 126942 1485329 1 42634 156 157214 1779876 0 40949 481 129352 1367203 1 42312 323 234817 2519076 1 37704 452 60448 912684 1 16275 109 47818 1443586 0 25830 115 245546 1220017 0 12679 110 48020 984885 1 18014 239 -1710 1457425 0 43556 247 32648 -572920 1 24524 497 95350 929144 0 6532 103 151352 1151176 0 7123 109 288170 790090 1 20813 502 114337 774497 1 37597 248 37884 990576 0 17821 373 122844 454195 1 12988 119 82340 876607 1 22330 84 79801 711969 0 13326 102 165548 702380 0 16189 295 116384 264449 0 7146 105 134028 450033 0 15824 64 63838 541063 1 26088 267 74996 588864 0 11326 129 31080 -37216 0 8568 37 32168 783310 0 14416 361 49857 467359 1 3369 28 87161 688779 1 11819 85 106113 608419 1 6620 44 80570 696348 1 4519 49 102129 597793 0 2220 22 301670 821730 0 18562 155 102313 377934 0 10327 91 88577 651939 1 5336 81 112477 697458 1 2365 79 191778 70 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	9 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Wealth[t] = -191286.673295896 + 285064.424055682Group[t] + 26.8409962495057Costs[t] -265.782661298832Trades[t] + 4.28215324408698Dividends[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)	-191286.673295896	136841.296711	-1.3979	0.165407	0.082703
Group	285064.424055682	119021.423951	2.3951	0.018579	0.009289
Costs	26.8409962495057	3.833843	7.0011	0	0
Trades	-265.782661298832	433.703931	-0.6128	0.541459	0.27073
Dividends	4.28215324408698	1.099394	3.895	0.000183	9.1e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.761489545827322
R-squared	0.579866328404301
Adjusted R-squared	0.562176489600272
F-TEST (value)	32.7796276058897
F-TEST (DF numerator)	4
F-TEST (DF denominator)	95
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	579091.205622821
Sum Squared Residuals	31857929320820.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6282929	5082235.61530373	1200693.38469627
2	4324047	1161383.01100049	3162663.98899951
3	4108272	2977995.82621678	1130276.17378322
4	-1212617	1815519.50263203	-3028136.50263203
5	1485329	2025022.68714031	-539693.687140308
6	1779876	1869869.12981448	-89993.1298144844
7	1367203	1333888.90846951	33314.0915304871
8	2519076	2149148.56278612	369927.437213880
9	912684	1244504.50974265	-331820.509742647
10	1443586	706408.65846467	737177.34153533
11	1220017	1522916.85425205	-302899.854252051
12	984885	325423.224189772	659461.775810228
13	1457425	506446.919100573	950978.080899427
14	-572920	1051955.18111971	-1624875.18111971
15	929144	1028235.67194084	-99091.6719408383
16	1151176	604775.557891148	546400.442108852
17	790090	1204919.53325630	-414829.533256305
18	774497	1008605.06519791	-234108.065197908
19	990576	1199229.68024933	-208653.680249333
20	454195	713946.621318701	-259751.621318701
21	876607	763352.971471928	113254.028528072
22	711969	1012531.56449353	-300562.564493532
23	702380	848188.516524647	-145808.516524647
24	264449	663210.453064015	-398761.453064015
25	450033	546540.341465184	-96507.3414651838
26	541063	489799.259829181	51263.7401708187
27	588864	1044186.05504365	-455322.05504365
28	-37216	211517.809744680	-248733.809744680
29	783310	166601.329657602	616708.670342398
30	467359	313200.902198544	154158.097801456
31	688779	549999.911515869	138779.088484131
32	608419	842812.086412095	-234393.086412095
33	696348	604783.795710454	91564.2042895462
34	597793	639380.89107302	-41587.8910730192
35	821730	1154250.28897315	-332520.288973151
36	377934	703859.531448381	-325925.531448381
37	651939	441014.360696048	210924.639303952
38	697458	697116.661617115	341.338382885078
39	700368	957482.661491773	-257114.661491773
40	225986	221123.812046129	4862.1879538707
41	348695	348153.324464841	541.675535159316
42	373683	573710.487001295	-200027.487001295
43	501709	271815.031260625	229893.968739375
44	413743	468415.599750683	-54672.599750683
45	379825	212604.3685541	167220.6314459
46	336260	621338.069210161	-285078.069210161
47	636765	1022505.70612255	-385740.706122549
48	481231	795845.820561051	-314614.820561051
49	469107	440015.429292914	29091.5707070861
50	211928	188416.688623091	23511.3113769090
51	563925	612765.844254289	-48840.8442542888
52	511939	742359.175020328	-230420.175020328
53	521016	1067532.53205851	-546516.53205851
54	543856	707277.968113737	-163421.968113737
55	329304	847428.906054742	-518124.906054742
56	423262	109622.880835187	313639.119164813
57	509665	122527.752535341	387137.247464659
58	455881	310965.144485786	144915.855514214
59	367772	240792.406226713	126979.593773287
60	406339	691621.228432584	-285282.228432584
61	493408	634698.679153068	-141290.679153068
62	232942	97951.8044467842	134990.195553216
63	416002	573930.178184297	-157928.178184297
64	337430	620804.057912187	-283374.057912187
65	361517	112235.999435205	249281.000564795
66	360962	301684.566755986	59277.433244014
67	235561	158624.929210587	76936.0707894134
68	408247	564822.51024213	-156575.510242130
69	450296	383410.139953018	66885.8600469817
70	418799	614730.927592323	-195931.927592323
71	247405	277124.438145097	-29719.4381450967
72	378519	100260.016597592	278258.983402408
73	326638	295784.131347223	30853.8686527769
74	328233	160170.149309479	168062.850690521
75	386225	361488.560961723	24736.4390382773
76	283662	456170.547960335	-172508.547960335
77	370225	314838.793344933	55386.206655067
78	269236	135969.451591452	133266.548408548
79	365732	109433.00563201	256298.99436799
80	420383	570938.139907145	-150555.139907145
81	345811	99977.8214655644	245833.178534436
82	431809	584013.681245498	-152204.681245498
83	418876	411639.980886051	7236.01911394867
84	297476	-4541.86795523082	302017.867955231
85	416776	258407.050855702	158368.949144298
86	357257	502501.592045361	-145244.592045361
87	458343	270126.642290883	188216.357709117
88	388386	264659.158675936	123726.841324064
89	358934	304567.127014048	54366.8729859519
90	407560	263547.689313455	144012.310686545
91	392558	257765.486437581	134792.513562419
92	373177	517038.10998087	-143861.109980870
93	428370	115188.579036879	313181.420963121
94	369419	792644.347867473	-423225.347867473
95	358649	8152.11365182494	350496.886348175
96	376641	422335.226362685	-45694.2263626851
97	467427	297749.722209187	169677.277790813
98	364885	379644.59580242	-14759.5958024197
99	436230	428965.845724742	7264.15427525829
100	329118	441518.781983859	-112400.781983859

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.999999999998418	3.1645702196305e-12	1.58228510981525e-12
9	1	2.36435079898823e-19	1.18217539949411e-19
10	1	1.05113266549439e-20	5.25566332747197e-21
11	1	1.04818315386112e-20	5.24091576930562e-21
12	1	3.32324264361246e-24	1.66162132180623e-24
13	1	1.31715937173074e-27	6.58579685865371e-28
14	1	9.08570979688364e-36	4.54285489844182e-36
15	1	2.11898839197200e-37	1.05949419598600e-37
16	1	5.21421389494501e-41	2.60710694747250e-41
17	1	1.41045377311976e-40	7.05226886559882e-41
18	1	8.7186085688568e-41	4.3593042844284e-41
19	1	7.89986124836895e-41	3.94993062418448e-41
20	1	5.79159243840955e-40	2.89579621920477e-40
21	1	6.97662388197713e-41	3.48831194098856e-41
22	1	1.61949913869317e-40	8.09749569346587e-41
23	1	8.91735065134776e-40	4.45867532567388e-40
24	1	1.34505148480314e-39	6.7252574240157e-40
25	1	1.01293590469622e-38	5.06467952348111e-39
26	1	3.39284412051721e-38	1.69642206025860e-38
27	1	1.04364273708496e-37	5.21821368542481e-38
28	1	1.58269665158338e-39	7.91348325791692e-40
29	1	1.11211486706492e-42	5.56057433532462e-43
30	1	4.24902649237626e-42	2.12451324618813e-42
31	1	1.07868208352872e-42	5.39341041764361e-43
32	1	3.42402082412782e-42	1.71201041206391e-42
33	1	3.59803350497438e-43	1.79901675248719e-43
34	1	5.61612281627169e-43	2.80806140813584e-43
35	1	2.79448873341134e-42	1.39724436670567e-42
36	1	1.25018769993058e-41	6.25093849965291e-42
37	1	2.38035206535222e-42	1.19017603267611e-42
38	1	1.07853972957077e-43	5.39269864785383e-44
39	1	6.48048062700721e-44	3.24024031350360e-44
40	1	1.04125383161291e-43	5.20626915806456e-44
41	1	7.91340835382049e-43	3.95670417691024e-43
42	1	6.64003025379098e-42	3.32001512689549e-42
43	1	1.8860886243338e-41	9.430443121669e-42
44	1	2.10387129360014e-40	1.05193564680007e-40
45	1	2.08755675271351e-39	1.04377837635676e-39
46	1	1.01186543311014e-38	5.05932716555072e-39
47	1	2.68308654922045e-38	1.34154327461023e-38
48	1	2.18542102607907e-37	1.09271051303953e-37
49	1	2.14528088423108e-36	1.07264044211554e-36
50	1	1.73432395562127e-36	8.67161977810637e-37
51	1	9.74703370610281e-37	4.87351685305140e-37
52	1	4.35336278505796e-36	2.17668139252898e-36
53	1	3.50711547270463e-35	1.75355773635231e-35
54	1	2.19231275847791e-35	1.09615637923896e-35
55	1	4.31422468234738e-35	2.15711234117369e-35
56	1	2.23282309408219e-34	1.11641154704110e-34
57	1	4.95950960621056e-35	2.47975480310528e-35
58	1	3.6504039905664e-34	1.8252019952832e-34
59	1	5.05414092112506e-33	2.52707046056253e-33
60	1	6.56416632167164e-32	3.28208316083582e-32
61	1	1.80763792116053e-31	9.03818960580265e-32
62	1	2.77939645489286e-31	1.38969822744643e-31
63	1	3.06657190517476e-30	1.53328595258738e-30
64	1	2.68778857857251e-29	1.34389428928625e-29
65	1	3.49727890136217e-28	1.74863945068109e-28
66	1	1.55361707575962e-27	7.76808537879808e-28
67	1	3.41308770417749e-28	1.70654385208875e-28
68	1	3.55956774590701e-27	1.77978387295351e-27
69	1	5.4767360026223e-26	2.73836800131115e-26
70	1	6.11732437960281e-25	3.05866218980141e-25
71	1	3.87907224319380e-24	1.93953612159690e-24
72	1	5.62334656819256e-23	2.81167328409628e-23
73	1	2.80573683584832e-22	1.40286841792416e-22
74	1	3.3915842844131e-21	1.69579214220655e-21
75	1	4.43824921330119e-20	2.21912460665059e-20
76	1	2.82492196198215e-19	1.41246098099107e-19
77	1	2.95943265775402e-18	1.47971632887701e-18
78	1	1.43630831613879e-18	7.18154158069395e-19
79	1	2.01252677927244e-17	1.00626338963622e-17
80	1	3.50172108077631e-16	1.75086054038816e-16
81	0.999999999999998	4.67594783510287e-15	2.33797391755143e-15
82	0.999999999999986	2.84176358588887e-14	1.42088179294443e-14
83	0.999999999999788	4.23528052907799e-13	2.11764026453900e-13
84	0.99999999999945	1.09910415396630e-12	5.49552076983148e-13
85	0.999999999989664	2.06729176226892e-11	1.03364588113446e-11
86	0.999999999811667	3.76665188122917e-10	1.88332594061458e-10
87	0.999999998697094	2.60581208301199e-09	1.30290604150599e-09
88	0.999999981361623	3.72767532024662e-08	1.86383766012331e-08
89	0.999999928468822	1.43062356055788e-07	7.15311780278942e-08
90	0.99999876883545	2.46232910008815e-06	1.23116455004408e-06
91	0.999984569458153	3.08610836936538e-05	1.54305418468269e-05
92	0.999711194978223	0.000577610043554435	0.000288805021777217

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	85	1	NOK
5% type I error level	85	1	NOK
10% type I error level	85	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/10nibq1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/10nibq1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/1ghef1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/1ghef1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/29qvi1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/29qvi1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/39qvi1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/39qvi1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/49qvi1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/49qvi1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/5jzcl1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/5jzcl1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/6jzcl1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/6jzcl1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/7cquo1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/7cquo1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/8cquo1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/8cquo1291317430.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/9nibq1291317430.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291317370xgtideskykzrfp5/9nibq1291317430.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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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