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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: Fri, 03 Dec 2010 20:58:22 +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/03/t12914097994ajmfucgi7m87op.htm/, Retrieved Fri, 03 Dec 2010 21:56:49 +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/03/t12914097994ajmfucgi7m87op.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 162556 1081 1081 213118 213118 6282929 1 29790 29790 309 309 81767 81767 4324047 1 87550 87550 458 458 153198 153198 4108272 0 84738 0 588 0 -26007 0 -1212617 1 54660 54660 299 299 126942 126942 1485329 1 42634 42634 156 156 157214 157214 1779876 0 40949 0 481 0 129352 0 1367203 1 42312 42312 323 323 234817 234817 2519076 1 37704 37704 452 452 60448 60448 912684 1 16275 16275 109 109 47818 47818 1443586 0 25830 0 115 0 245546 0 1220017 0 12679 0 110 0 48020 0 984885 1 18014 18014 239 239 -1710 -1710 1457425 0 43556 0 247 0 32648 0 -572920 1 24524 24524 497 497 95350 95350 929144 0 6532 0 103 0 151352 0 1151176 0 7123 0 109 0 288170 0 790090 1 20813 20813 502 502 114337 114337 774497 1 37597 37597 248 248 37884 37884 990576 0 17821 0 373 0 122844 0 454195 1 12988 12988 119 119 82340 82340 876607 1 22330 22330 84 84 79801 79801 711969 0 13326 0 102 0 165548 0 702380 0 16189 0 295 0 116384 0 264449 0 7146 0 105 0 134028 0 450033 0 15824 0 64 0 63838 0 541063 1 26088 26088 267 2 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	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Wealth[t] = + 169088.919201667 + 44386.189096122Group[t] -7.97639691845783Costs[t] + 44.9817209607593GrCosts[t] + 20.8889825495510Trades[t] -124.124022764451GrTrades[t] + 3.24416126930998Dividends[t] -1.98194712090063GrDiv[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)	169088.919201667	131306.033497	1.2877	0.201064	0.100532
Group	44386.189096122	208024.502483	0.2134	0.83151	0.415755
Costs	-7.97639691845783	5.115122	-1.5594	0.122341	0.06117
GrCosts	44.9817209607593	6.916541	6.5035	0	0
Trades	20.8889825495510	371.115535	0.0563	0.955235	0.477618
GrTrades	-124.124022764451	767.620535	-0.1617	0.871897	0.435948
Dividends	3.24416126930998	1.052572	3.0821	0.002712	0.001356
GrDiv	-1.98194712090063	1.759834	-1.1262	0.263005	0.131502

Multiple Linear Regression - Regression Statistics
Multiple R	0.879612234896614
R-squared	0.773717683779815
Adjusted R-squared	0.756500551023932
F-TEST (value)	44.9388231333357
F-TEST (DF numerator)	7
F-TEST (DF denominator)	92
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	431863.421873433
Sum Squared Residuals	17158553394005.2

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6282929	6386316.03972655	-103387.039726548
2	4324047	1387171.54836454	2936875.45163546
3	4108272	3599378.26289088	508893.737109118
4	-1212617	-578903.183266423	-633713.816733577
5	1485329	2365546.83185312	-880217.831853115
6	1779876	1973493.16237177	-193617.162371775
7	1367203	272149.790901857	1095053.20909814
8	2519076	2042288.80087328	476787.199126724
9	912684	1638359.92865464	-725675.928654639
10	1443586	864840.69385146	578745.30614854
11	1220017	762051.642825089	457965.357174911
12	984885	226038.594905255	758846.405094745
13	1457425	853257.454790668	604167.545209332
14	-572920	-67256.0691685112	-505663.930831489
15	929144	1190037.97917522	-260893.979175218
16	1151176	610148.956165508	541027.043834492
17	790090	1049419.89602645	-259329.896026451
18	774497	1076160.70648901	-301663.706489011
19	990576	1626979.70714124	-636403.707141244
20	454195	433258.887175928	20936.1128240724
21	876607	785746.000153654	90860.999846346
22	711969	1131858.20204154	-419889.202041545
23	702380	601990.539898081	100389.460101919
24	264449	423689.744508243	-159240.744508243
25	450033	549091.376593148	-99058.3765931484
26	541063	251308.076357372	289754.923642628
27	588864	1245967.25845008	-657103.25845008
28	-37216	182271.458702259	-219487.458702259
29	783310	205878.222469817	577431.777530183
30	467359	223386.252329554	243972.747670446
31	688779	445271.311259793	243507.688740207
32	608419	776003.384665646	-167584.384665646
33	696348	555604.605625711	140743.394374289
34	597793	504552.319437318	93240.680562682
35	821730	1130507.00577152	-308777.005771524
36	377934	356188.703843345	21745.296156655
37	651939	375975.638388432	275963.361611568
38	697458	544543.539900742	152914.460099258
39	700368	534902.036434504	165465.963565496
40	225986	398558.90854616	-172572.908546160
41	348695	540842.734605645	-192147.734605645
42	373683	373835.800312194	-152.800312193984
43	501709	442054.646037082	59654.3539629178
44	413743	497746.224305792	-84003.224305792
45	379825	357832.220405325	21992.779594675
46	336260	474661.218654044	-138401.218654044
47	636765	523384.640967973	113380.359032027
48	481231	630284.586576875	-149053.586576875
49	469107	527279.41439274	-58172.4143927402
50	211928	399423.818523135	-187495.818523135
51	563925	455700.324086413	108224.675913587
52	511939	521656.663466601	-9717.66346660082
53	521016	709585.037173761	-188569.037173761
54	543856	423270.973170767	120585.026829233
55	329304	567207.604686967	-237903.604686967
56	423262	351122.225804998	72139.7741950017
57	509665	302724.90430399	206940.09569601
58	455881	412204.190744772	43676.8092552283
59	367772	483832.730816631	-116060.730816631
60	406339	658538.525218427	-252199.525218427
61	493408	533653.905676261	-40245.905676261
62	232942	353402.03877954	-120460.03877954
63	416002	444989.552020461	-28987.5520204609
64	337430	579609.322119898	-242179.322119898
65	361517	340862.952697074	20654.0473029265
66	360962	377823.521245926	-16861.5212459258
67	235561	425714.507730913	-190153.507730913
68	408247	410730.912850956	-2483.91285095649
69	450296	585055.543336288	-134759.543336288
70	418799	482885.739045836	-64086.7390458364
71	247405	354665.000350160	-107260.000350160
72	378519	255930.115313601	122588.884686399
73	326638	505137.097888236	-178499.097888236
74	328233	422113.100354128	-93880.100354128
75	386225	539735.785533787	-153510.785533787
76	283662	357285.808174515	-73623.8081745145
77	370225	534324.488256184	-164099.488256184
78	269236	403642.860295325	-134406.860295325
79	365732	386678.804676956	-20946.8046769559
80	420383	369243.381692076	51139.6183079244
81	345811	356460.047525544	-10649.0475255444
82	431809	490224.10695931	-58415.1069593097
83	418876	614178.381228799	-195302.381228799
84	297476	291522.024092342	5953.97590765828
85	416776	498558.852313691	-81782.8523136906
86	357257	497759.864530765	-140502.864530765
87	458343	434659.818614362	23683.1813856377
88	388386	502337.116178859	-113951.116178859
89	358934	499546.634035402	-140612.634035402
90	407560	500063.927333786	-92503.9273337863
91	392558	464197.229504247	-71639.2295042473
92	373177	423012.511795306	-49835.5117953057
93	428370	359492.053816016	68877.9461839836
94	369419	607675.859824658	-238256.859824658
95	358649	276705.149590825	81943.850409175
96	376641	398131.882316998	-21490.8823169976
97	467427	533660.453725924	-66233.453725924
98	364885	333947.336323015	30937.663676985
99	436230	571292.142706832	-135062.142706832
100	329118	355938.468166609	-26820.4681666091

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	1	1.19037910383521e-22	5.95189551917607e-23
12	1	7.09622549790317e-24	3.54811274895158e-24
13	1	1.75240555565901e-26	8.76202777829504e-27
14	1	8.64771326849173e-30	4.32385663424586e-30
15	1	3.48482187955609e-30	1.74241093977805e-30
16	1	1.54903114429186e-33	7.74515572145929e-34
17	1	5.02888781240301e-34	2.51444390620151e-34
18	1	1.79261876268328e-33	8.9630938134164e-34
19	1	5.7979916699563e-34	2.89899583497815e-34
20	1	8.3294400783142e-34	4.1647200391571e-34
21	1	2.12115621522508e-34	1.06057810761254e-34
22	1	5.13289237992928e-34	2.56644618996464e-34
23	1	2.20112279983348e-33	1.10056139991674e-33
24	1	1.92306207037696e-33	9.61531035188479e-34
25	1	1.11583399949318e-32	5.57916999746588e-33
26	1	6.1191085554401e-32	3.05955427772005e-32
27	1	1.28998389172075e-31	6.44991945860376e-32
28	1	2.08403251733892e-33	1.04201625866946e-33
29	1	9.7099972021833e-36	4.85499860109166e-36
30	1	5.2429184785328e-35	2.6214592392664e-35
31	1	1.71404595742551e-35	8.57022978712756e-36
32	1	8.16951026725391e-35	4.08475513362696e-35
33	1	7.53813570213431e-36	3.76906785106715e-36
34	1	1.15399669668171e-35	5.76998348340855e-36
35	1	1.42187301601965e-35	7.10936508009824e-36
36	1	3.08203244106513e-35	1.54101622053256e-35
37	1	1.41690410036761e-35	7.08452050183807e-36
38	1	4.30924441872554e-37	2.15462220936277e-37
39	1	9.50476240058416e-37	4.75238120029208e-37
40	1	8.3100264884597e-37	4.15501324422985e-37
41	1	6.09921396510381e-36	3.04960698255190e-36
42	1	9.23647907619842e-36	4.61823953809921e-36
43	1	2.29862236800554e-35	1.14931118400277e-35
44	1	1.10584542388862e-34	5.52922711944309e-35
45	1	9.33416216275592e-34	4.66708108137796e-34
46	1	4.84714688174923e-33	2.42357344087461e-33
47	1	3.13892531342864e-32	1.56946265671432e-32
48	1	2.87224279829764e-31	1.43612139914882e-31
49	1	2.59374745085057e-30	1.29687372542528e-30
50	1	7.22299487690739e-31	3.61149743845370e-31
51	1	4.0969232599299e-31	2.04846162996495e-31
52	1	2.25622532240611e-30	1.12811266120306e-30
53	1	2.07686513849199e-29	1.03843256924600e-29
54	1	3.24451528472277e-29	1.62225764236138e-29
55	1	2.63571497776064e-29	1.31785748888032e-29
56	1	1.14631382803522e-28	5.73156914017611e-29
57	1	1.20981261721505e-28	6.04906308607526e-29
58	1	1.35705744747111e-27	6.78528723735554e-28
59	1	1.42748543821370e-26	7.13742719106848e-27
60	1	1.40564114929163e-25	7.02820574645815e-26
61	1	1.98227506457302e-25	9.91137532286511e-26
62	1	2.15815226642559e-25	1.07907613321279e-25
63	1	1.75690317262030e-24	8.78451586310152e-25
64	1	1.47244815175061e-23	7.36224075875307e-24
65	1	1.65521730335069e-22	8.27608651675343e-23
66	1	1.45516959849100e-21	7.27584799245499e-22
67	1	5.47007106774782e-22	2.73503553387391e-22
68	1	5.20918864829693e-21	2.60459432414846e-21
69	1	5.41374926448516e-20	2.70687463224258e-20
70	1	4.57565060986389e-19	2.28782530493195e-19
71	1	1.86605530399817e-18	9.33027651999083e-19
72	1	2.13298750365173e-17	1.06649375182586e-17
73	1	7.3154061652939e-17	3.65770308264695e-17
74	1	6.99764274483525e-16	3.49882137241762e-16
75	0.999999999999997	5.49311158792649e-15	2.74655579396325e-15
76	0.999999999999988	2.44969590945067e-14	1.22484795472534e-14
77	0.9999999999999	1.99749067643547e-13	9.98745338217737e-14
78	0.999999999999916	1.67848509114937e-13	8.39242545574683e-14
79	0.999999999999022	1.95548250133224e-12	9.77741250666118e-13
80	0.999999999990403	1.91940409596353e-11	9.59702047981763e-12
81	0.999999999893168	2.13663085139701e-10	1.06831542569851e-10
82	0.999999999560246	8.79507877185377e-10	4.39753938592689e-10
83	0.999999996455011	7.08997742598527e-09	3.54498871299263e-09
84	0.999999978412269	4.31754620803546e-08	2.15877310401773e-08
85	0.9999997281817	5.43636601460318e-07	2.71818300730159e-07
86	0.99999735487824	5.29024351957893e-06	2.64512175978947e-06
87	0.999984940192528	3.01196149437864e-05	1.50598074718932e-05
88	0.999866066268065	0.000267867463870475	0.000133933731935238
89	0.999647652929996	0.00070469414000808	0.00035234707000404

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	79	1	NOK
5% type I error level	79	1	NOK
10% type I error level	79	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/10m6i21291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/10m6i21291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/1fnlq1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/1fnlq1291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/2fnlq1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/2fnlq1291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/3pwkb1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/3pwkb1291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/4pwkb1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/4pwkb1291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/5pwkb1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/5pwkb1291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/6in2e1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/6in2e1291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/7tfjz1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/7tfjz1291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/8tfjz1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/8tfjz1291409893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/9tfjz1291409893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t12914097994ajmfucgi7m87op/9tfjz1291409893.ps (open in new window)

Parameters (Session):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 8 ; 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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