Home » date » 2008 » Nov » 27 »

seabelt law Q3.1

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 05:49:31 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb.htm/, Retrieved Thu, 27 Nov 2008 12:53:05 +0000

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/2008/Nov/27/t12277903850ob73t8my68w6mb.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Severijns Britt

Dataseries X:

» Textbox « » Textfile « » CSV «

492865 0 480961 0 461935 0 456608 0 441977 0 439148 0 488180 0 520564 0 501492 0 485025 0 464196 0 460170 0 467037 0 460070 0 447988 0 442867 0 436087 0 431328 0 484015 0 509673 0 512927 0 502831 0 470984 0 471067 0 476049 0 474605 0 470439 0 461251 0 454724 0 455626 0 516847 0 525192 0 522975 0 518585 0 509239 0 512238 0 519164 0 517009 0 509933 0 509127 0 500857 0 506971 0 569323 0 579714 0 577992 0 565464 0 547344 0 554788 0 562325 0 560854 0 555332 0 543599 0 536662 0 542722 1 593530 1 610763 1 612613 1 611324 1 594167 1 595454 1 590865 1 589379 1 584428 1 573100 1 567456 1 569028 1 620735 1 628884 1 628232 1 612117 1 595404 1 597141 1 593408 1 590072 1 579799 1 574205 1 572775 1 572942 1 619567 1 625809 1 619916 1 587625 1 565742 1 557274 1 560576 1 548854 0 531673 0 525919 0 511038 0 498662 0 555362 0 564591 0 541657 0 527070 0 509846 0 514258 0 516922 0 507561 0 492622 0 490243 0 469357 0 477580 0 528379 0 533590 0

Output produced by software:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

W[t] = + 504019.541666667 + 87763.3333333333D[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)	504019.541666667	4010.038343	125.6895	0	0
D	87763.3333333333	7229.199431	12.1401	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.768758533580744
R-squared	0.590989682953217
Adjusted R-squared	0.58697977788413
F-TEST (value)	147.382462370339
F-TEST (DF numerator)	1
F-TEST (DF denominator)	102
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	34026.3036590776
Sum Squared Residuals	118094512751.375

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	492865	504019.541666666	-11154.5416666661
2	480961	504019.541666667	-23058.5416666666
3	461935	504019.541666667	-42084.5416666667
4	456608	504019.541666667	-47411.5416666667
5	441977	504019.541666667	-62042.5416666667
6	439148	504019.541666667	-64871.5416666667
7	488180	504019.541666667	-15839.5416666667
8	520564	504019.541666667	16544.4583333333
9	501492	504019.541666667	-2527.54166666668
10	485025	504019.541666667	-18994.5416666667
11	464196	504019.541666667	-39823.5416666667
12	460170	504019.541666667	-43849.5416666667
13	467037	504019.541666667	-36982.5416666667
14	460070	504019.541666667	-43949.5416666667
15	447988	504019.541666667	-56031.5416666667
16	442867	504019.541666667	-61152.5416666667
17	436087	504019.541666667	-67932.5416666667
18	431328	504019.541666667	-72691.5416666667
19	484015	504019.541666667	-20004.5416666667
20	509673	504019.541666667	5653.45833333332
21	512927	504019.541666667	8907.45833333332
22	502831	504019.541666667	-1188.54166666668
23	470984	504019.541666667	-33035.5416666667
24	471067	504019.541666667	-32952.5416666667
25	476049	504019.541666667	-27970.5416666667
26	474605	504019.541666667	-29414.5416666667
27	470439	504019.541666667	-33580.5416666667
28	461251	504019.541666667	-42768.5416666667
29	454724	504019.541666667	-49295.5416666667
30	455626	504019.541666667	-48393.5416666667
31	516847	504019.541666667	12827.4583333333
32	525192	504019.541666667	21172.4583333333
33	522975	504019.541666667	18955.4583333333
34	518585	504019.541666667	14565.4583333333
35	509239	504019.541666667	5219.45833333332
36	512238	504019.541666667	8218.45833333332
37	519164	504019.541666667	15144.4583333333
38	517009	504019.541666667	12989.4583333333
39	509933	504019.541666667	5913.45833333332
40	509127	504019.541666667	5107.45833333332
41	500857	504019.541666667	-3162.54166666668
42	506971	504019.541666667	2951.45833333332
43	569323	504019.541666667	65303.4583333333
44	579714	504019.541666667	75694.4583333333
45	577992	504019.541666667	73972.4583333333
46	565464	504019.541666667	61444.4583333333
47	547344	504019.541666667	43324.4583333333
48	554788	504019.541666667	50768.4583333333
49	562325	504019.541666667	58305.4583333333
50	560854	504019.541666667	56834.4583333333
51	555332	504019.541666667	51312.4583333333
52	543599	504019.541666667	39579.4583333333
53	536662	504019.541666667	32642.4583333333
54	542722	591782.875	-49060.875
55	593530	591782.875	1747.12500000000
56	610763	591782.875	18980.125
57	612613	591782.875	20830.125
58	611324	591782.875	19541.125
59	594167	591782.875	2384.125
60	595454	591782.875	3671.125
61	590865	591782.875	-917.875000000006
62	589379	591782.875	-2403.87500000001
63	584428	591782.875	-7354.875
64	573100	591782.875	-18682.875
65	567456	591782.875	-24326.875
66	569028	591782.875	-22754.875
67	620735	591782.875	28952.125
68	628884	591782.875	37101.125
69	628232	591782.875	36449.125
70	612117	591782.875	20334.125
71	595404	591782.875	3621.125
72	597141	591782.875	5358.125
73	593408	591782.875	1625.12500000000
74	590072	591782.875	-1710.87500000001
75	579799	591782.875	-11983.875
76	574205	591782.875	-17577.875
77	572775	591782.875	-19007.875
78	572942	591782.875	-18840.875
79	619567	591782.875	27784.125
80	625809	591782.875	34026.125
81	619916	591782.875	28133.125
82	587625	591782.875	-4157.87500000001
83	565742	591782.875	-26040.875
84	557274	591782.875	-34508.875
85	560576	591782.875	-31206.875
86	548854	504019.541666667	44834.4583333333
87	531673	504019.541666667	27653.4583333333
88	525919	504019.541666667	21899.4583333333
89	511038	504019.541666667	7018.45833333332
90	498662	504019.541666667	-5357.54166666668
91	555362	504019.541666667	51342.4583333333
92	564591	504019.541666667	60571.4583333333
93	541657	504019.541666667	37637.4583333333
94	527070	504019.541666667	23050.4583333333
95	509846	504019.541666667	5826.45833333332
96	514258	504019.541666667	10238.4583333333
97	516922	504019.541666667	12902.4583333333
98	507561	504019.541666667	3541.45833333332
99	492622	504019.541666667	-11397.5416666667
100	490243	504019.541666667	-13776.5416666667
101	469357	504019.541666667	-34662.5416666667
102	477580	504019.541666667	-26439.5416666667
103	528379	504019.541666667	24359.4583333333
104	533590	504019.541666667	29570.4583333333

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.304011295768259	0.608022591536518	0.695988704231741
6	0.279081017352114	0.558162034704227	0.720918982647886
7	0.238542034126782	0.477084068253565	0.761457965873218
8	0.444402105685419	0.888804211370838	0.555597894314581
9	0.403529999674403	0.807059999348807	0.596470000325597
10	0.306374239191227	0.612748478382454	0.693625760808773
11	0.239137530199750	0.478275060399499	0.76086246980025
12	0.192568589947368	0.385137179894737	0.807431410052632
13	0.141559124385060	0.283118248770119	0.85844087561494
14	0.111810717878271	0.223621435756541	0.88818928212173
15	0.114335966228458	0.228671932456915	0.885664033771542
16	0.133310013297407	0.266620026594813	0.866689986702593
17	0.184959938882165	0.36991987776433	0.815040061117835
18	0.278830670099341	0.557661340198683	0.721169329900659
19	0.250159948129755	0.50031989625951	0.749840051870245
20	0.314826875585989	0.629653751171978	0.685173124414011
21	0.380054071056724	0.760108142113448	0.619945928943276
22	0.381347050839439	0.762694101678878	0.618652949160561
23	0.347350042066347	0.694700084132695	0.652649957933653
24	0.318344191972288	0.636688383944576	0.681655808027712
25	0.288196129089333	0.576392258178667	0.711803870910667
26	0.264234511220958	0.528469022441916	0.735765488779042
27	0.251523926347133	0.503047852694267	0.748476073652867
28	0.269831751523467	0.539663503046933	0.730168248476533
29	0.330326456604255	0.660652913208511	0.669673543395745
30	0.410626374674824	0.821252749349647	0.589373625325176
31	0.501097883777916	0.997804232444169	0.498902116222084
32	0.616542079369154	0.766915841261692	0.383457920630846
33	0.688864191795504	0.622271616408991	0.311135808204496
34	0.724891236334048	0.550217527331905	0.275108763665952
35	0.72928858446664	0.54142283106672	0.27071141553336
36	0.735993433206031	0.528013133587938	0.264006566793969
37	0.753548685054478	0.492902629891045	0.246451314945522
38	0.760757136240634	0.478485727518733	0.239242863759366
39	0.75458334948667	0.49083330102666	0.24541665051333
40	0.746601487069491	0.506797025861018	0.253398512930509
41	0.737026790910153	0.525946418179694	0.262973209089847
42	0.728228406803024	0.543543186393951	0.271771593196976
43	0.899701333682752	0.200597332634495	0.100298666317248
44	0.979533561815305	0.0409328763693907	0.0204664381846954
45	0.996114096439225	0.00777180712155046	0.00388590356077523
46	0.99861084931149	0.00277830137701870	0.00138915068850935
47	0.998889937776093	0.00222012444781471	0.00111006222390736
48	0.999308502545008	0.00138299490998383	0.000691497454991916
49	0.99969586983082	0.000608260338360412	0.000304130169180206
50	0.999859498282356	0.000281003435288454	0.000140501717644227
51	0.999916186013423	0.000167627973153281	8.38139865766405e-05
52	0.99991764651508	0.000164706969840513	8.23534849202567e-05
53	0.999898749736008	0.000202500527983834	0.000101250263991917
54	0.99994645675491	0.000107086490181024	5.35432450905122e-05
55	0.99991984984373	0.000160300312541331	8.01501562706656e-05
56	0.999896690060325	0.000206619879350798	0.000103309939675399
57	0.999863319189194	0.000273361621611655	0.000136680810805828
58	0.999809610887201	0.000380778225597016	0.000190389112798508
59	0.999668048375304	0.000663903249391632	0.000331951624695816
60	0.999434844371258	0.00113031125748446	0.00056515562874223
61	0.999048821455357	0.00190235708928612	0.000951178544643061
62	0.998432852878307	0.00313429424338526	0.00156714712169263
63	0.997525737564313	0.00494852487137392	0.00247426243568696
64	0.996683516096506	0.00663296780698752	0.00331648390349376
65	0.996126913661052	0.0077461726778965	0.00387308633894825
66	0.995402654144282	0.00919469171143532	0.00459734585571766
67	0.995050505766152	0.00989898846769684	0.00494949423384842
68	0.996046558795151	0.00790688240969744	0.00395344120484872
69	0.99701114103278	0.00597771793443872	0.00298885896721936
70	0.996357293233692	0.00728541353261633	0.00364270676630817
71	0.994300319401703	0.0113993611965935	0.00569968059829676
72	0.991407436877517	0.0171851262449663	0.00859256312248317
73	0.986969112863619	0.0260617742727620	0.0130308871363810
74	0.980380906851204	0.0392381862975922	0.0196190931487961
75	0.971528019676495	0.0569439606470103	0.0284719803235051
76	0.961348142002285	0.077303715995429	0.0386518579977145
77	0.949522802008322	0.100954395983357	0.0504771979916783
78	0.935707938424404	0.128584123151193	0.0642920615755964
79	0.934964142688764	0.130071714622472	0.0650358573112358
80	0.953317339992014	0.093365320015973	0.0466826600079865
81	0.97211232605143	0.0557753478971381	0.0278876739485691
82	0.96619340395506	0.067613192089881	0.0338065960449405
83	0.951414520318648	0.097170959362704	0.048585479681352
84	0.932185944674067	0.135628110651866	0.067814055325933
85	0.905487964701606	0.189024070596788	0.0945120352983942
86	0.91328345531665	0.173433089366698	0.0867165446833492
87	0.888800472890918	0.222399054218164	0.111199527109082
88	0.850552858339077	0.298894283321845	0.149447141660923
89	0.79409562469681	0.411808750606379	0.205904375303189
90	0.742582628599561	0.514834742800878	0.257417371400439
91	0.794669280481415	0.41066143903717	0.205330719518585
92	0.9152945127953	0.169410974409399	0.0847054872046993
93	0.932244514600537	0.135510970798926	0.067755485399463
94	0.915779980280279	0.168440039439442	0.084220019719721
95	0.859611080562453	0.280777838875094	0.140388919437547
96	0.787234799071958	0.425530401856083	0.212765200928042
97	0.703792351969982	0.592415296060035	0.296207648030018
98	0.568862276642859	0.862275446714281	0.431137723357141
99	0.405652800510438	0.811305601020875	0.594347199489562

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	26	0.273684210526316	NOK
5% type I error level	31	0.326315789473684	NOK
10% type I error level	37	0.389473684210526	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/10gpmf1227790160.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/10gpmf1227790160.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/1sizo1227790159.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/1sizo1227790159.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/2d83y1227790159.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/2d83y1227790159.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/3pj4s1227790159.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/3pj4s1227790159.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/4elnv1227790159.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/4elnv1227790159.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/5bphk1227790159.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/6hj1z1227790159.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/6hj1z1227790159.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/7f3c61227790159.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/8jr6z1227790160.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/8jr6z1227790160.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/916ez1227790160.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12277903850ob73t8my68w6mb/916ez1227790160.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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