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multiple regression

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Sun, 07 Dec 2008 07:08:38 -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/Dec/07/t1228663385xlqlmuobc29bqbz.htm/, Retrieved Sun, 07 Dec 2008 15:23:05 +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/Dec/07/t1228663385xlqlmuobc29bqbz.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:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
test

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

493 0 481 0 462 0 457 0 442 0 439 0 488 0 521 0 501 0 485 0 464 0 460 0 467 0 460 0 448 0 443 0 436 0 431 0 484 0 510 0 513 0 503 0 471 0 471 0 476 0 475 0 470 0 461 0 455 0 456 0 517 0 525 0 523 0 519 0 509 0 512 0 519 1 517 1 510 1 509 1 501 1 507 1 569 1 580 1 578 1 565 1 547 1 555 1 562 1 561 1 555 1 544 1 537 1 543 1 594 1 611 1 613 1 611 1 594 1 595 1 591 1 589 1 584 1 573 1 567 1 569 1 621 1 629 1 628 1 612 1 595 1 597 1 593 1 590 1 580 1 574 1 573 1 573 1 620 1 626 1 620 1 588 1 566 1 557 1 561 1 549 1 532 1 526 1 511 1 499 1 555 1 565 1 542 1 527 1 510 1 514 1 517 1 508 1 493 1 490 1 469 1 478 1

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

Aantal_werklozen_(*1000)[t] = + 487.074864498645 + 97.6558265582656dummyvariabele[t] -7.12776648599808M1[t] -12.2854561878952M2[t] -22.6653681120144M3[t] -28.7119467028004M4[t] -37.9807475158085M5[t] -37.2495483288166M6[t] + 21.9412262872629M7[t] + 37.1029810298103M8[t] + 31.2647357723577M9[t] + 18.0514905149052M10[t] -0.911754742547421M11[t] -0.286754742547426t + 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)	487.074864498645	11.330988	42.9861	0	0
dummyvariabele	97.6558265582656	10.545991	9.26	0	0
M1	-7.12776648599808	13.840109	-0.515	0.607839	0.303919
M2	-12.2854561878952	13.826048	-0.8886	0.376655	0.188327
M3	-22.6653681120144	13.814097	-1.6407	0.104421	0.05221
M4	-28.7119467028004	13.804261	-2.0799	0.04044	0.02022
M5	-37.9807475158085	13.796545	-2.7529	0.007174	0.003587
M6	-37.2495483288166	13.790953	-2.701	0.008292	0.004146
M7	21.9412262872629	14.209403	1.5441	0.126144	0.063072
M8	37.1029810298103	14.200108	2.6129	0.010558	0.005279
M9	31.2647357723577	14.192874	2.2028	0.030218	0.015109
M10	18.0514905149052	14.187705	1.2723	0.206606	0.103303
M11	-0.911754742547421	14.184602	-0.0643	0.948895	0.474447
t	-0.286754742547426	0.171294	-1.6741	0.097671	0.048836

Multiple Linear Regression - Regression Statistics
Multiple R	0.868748517815421
R-squared	0.754723987206491
Adjusted R-squared	0.718490030771087
F-TEST (value)	20.8291906668255
F-TEST (DF numerator)	13
F-TEST (DF denominator)	88
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	28.367136200452
Sum Squared Residuals	70813.1086269196

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	493	479.660343270099	13.3396567299015
2	481	474.215898825655	6.7841011743451
3	462	463.549232158988	-1.54923215898829
4	457	457.215898825655	-0.215898825654941
5	442	447.660343270099	-5.66034327009938
6	439	448.104787714544	-9.10478771454385
7	488	507.008807588076	-19.0088075880759
8	521	521.883807588076	-0.883807588075885
9	501	515.758807588076	-14.7588075880759
10	485	502.258807588076	-17.2588075880759
11	464	483.008807588076	-19.0088075880759
12	460	483.633807588076	-23.6338075880759
13	467	476.21928635953	-9.21928635953038
14	460	470.774841915086	-10.7748419150858
15	448	460.108175248419	-12.1081752484192
16	443	453.774841915086	-10.7748419150858
17	436	444.21928635953	-8.21928635953029
18	431	444.663730803975	-13.6637308039747
19	484	503.567750677507	-19.5677506775068
20	510	518.442750677507	-8.4427506775068
21	513	512.317750677507	0.682249322493207
22	503	498.817750677507	4.18224932249321
23	471	479.567750677507	-8.56775067750679
24	471	480.192750677507	-9.19275067750679
25	476	472.778229448961	3.22177055103873
26	475	467.333785004517	7.66621499548327
27	470	456.66711833785	13.3328816621499
28	461	450.333785004517	10.6662149954833
29	455	440.778229448961	14.2217705510388
30	456	441.222673893406	14.7773261065944
31	517	500.126693766938	16.8733062330623
32	525	515.001693766938	9.99830623306232
33	523	508.876693766938	14.1233062330623
34	519	495.376693766938	23.6233062330623
35	509	476.126693766938	32.8733062330623
36	512	476.751693766938	35.2483062330623
37	519	566.992999096658	-47.9929990966577
38	517	561.548554652213	-44.5485546522132
39	510	550.881887985547	-40.8818879855465
40	509	544.548554652213	-35.5485546522132
41	501	534.992999096658	-33.9929990966576
42	507	535.437443541102	-28.4374435411021
43	569	594.341463414634	-25.3414634146341
44	580	609.216463414634	-29.2164634146342
45	578	603.091463414634	-25.0914634146341
46	565	589.591463414634	-24.5914634146341
47	547	570.341463414634	-23.3414634146341
48	555	570.966463414634	-15.9664634146341
49	562	563.551942186089	-1.55194218608864
50	561	558.107497741644	2.89250225835590
51	555	547.440831074977	7.55916892502258
52	544	541.107497741644	2.89250225835592
53	537	531.551942186089	5.44805781391147
54	543	531.996386630533	11.0036133694670
55	594	590.900406504065	3.09959349593496
56	611	605.775406504065	5.22459349593496
57	613	599.650406504065	13.3495934959350
58	611	586.150406504065	24.8495934959350
59	594	566.900406504065	27.0995934959350
60	595	567.525406504065	27.4745934959350
61	591	560.11088527552	30.8891147244805
62	589	554.666440831075	34.333559168925
63	584	543.999774164408	40.0002258355917
64	573	537.666440831075	35.333559168925
65	567	528.110885275519	38.8891147244806
66	569	528.555329719964	40.4446702800361
67	621	587.459349593496	33.5406504065041
68	629	602.334349593496	26.6656504065041
69	628	596.209349593496	31.7906504065041
70	612	582.709349593496	29.2906504065041
71	595	563.459349593496	31.5406504065041
72	597	564.084349593496	32.9156504065041
73	593	556.66982836495	36.3301716350496
74	590	551.225383920506	38.7746160794941
75	580	540.558717253839	39.4412827461608
76	574	534.225383920506	39.7746160794941
77	573	524.66982836495	48.3301716350497
78	573	525.114272809395	47.8857271906053
79	620	584.018292682927	35.9817073170732
80	626	598.893292682927	27.1067073170732
81	620	592.768292682927	27.2317073170732
82	588	579.268292682927	8.73170731707318
83	566	560.018292682927	5.98170731707319
84	557	560.643292682927	-3.64329268292681
85	561	553.228771454381	7.7712285456187
86	549	547.784327009937	1.21567299006324
87	532	537.11766034327	-5.11766034327008
88	526	530.784327009937	-4.78432700993675
89	511	521.228771454381	-10.2287714543812
90	499	521.673215898826	-22.6732158988256
91	555	580.577235772358	-25.5772357723577
92	565	595.452235772358	-30.4522357723577
93	542	589.327235772358	-47.3272357723577
94	527	575.827235772358	-48.8272357723577
95	510	556.577235772358	-46.5772357723577
96	514	557.202235772358	-43.2022357723577
97	517	549.787714543812	-32.7877145438122
98	508	544.343270099368	-36.3432700993677
99	493	533.676603432701	-40.676603432701
100	490	527.343270099368	-37.3432700993676
101	469	517.787714543812	-48.7877145438121
102	478	518.232158988257	-40.2321589882565

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0112515401976309	0.0225030803952618	0.98874845980237
18	0.00283650786192230	0.00567301572384461	0.997163492138078
19	0.000998760494918218	0.00199752098983644	0.999001239505082
20	0.000183776991449051	0.000367553982898101	0.99981622300855
21	0.0005816958158392	0.0011633916316784	0.99941830418416
22	0.00111409007164974	0.00222818014329949	0.99888590992835
23	0.000524622385526123	0.00104924477105225	0.999475377614474
24	0.000297916031361651	0.000595832062723302	0.999702083968638
25	9.21139301842904e-05	0.000184227860368581	0.999907886069816
26	3.68236383187506e-05	7.36472766375013e-05	0.999963176361681
27	2.94610851196538e-05	5.89221702393075e-05	0.99997053891488
28	1.34234684136919e-05	2.68469368273839e-05	0.999986576531586
29	7.8288687439271e-06	1.56577374878542e-05	0.999992171131256
30	5.96999563137674e-06	1.19399912627535e-05	0.999994030004369
31	9.70159411478527e-06	1.94031882295705e-05	0.999990298405885
32	3.34009164599193e-06	6.68018329198386e-06	0.999996659908354
33	1.33695210814161e-06	2.67390421628322e-06	0.999998663047892
34	8.52035301072317e-07	1.70407060214463e-06	0.999999147964699
35	2.2828231270289e-06	4.5656462540578e-06	0.999997717176873
36	6.01005977361201e-06	1.20201195472240e-05	0.999993989940226
37	3.69030817677607e-06	7.38061635355213e-06	0.999996309691823
38	2.40391335316654e-06	4.80782670633308e-06	0.999997596086647
39	1.71523590707801e-06	3.43047181415603e-06	0.999998284764093
40	1.32047518932838e-06	2.64095037865675e-06	0.99999867952481
41	1.08486714875193e-06	2.16973429750386e-06	0.999998915132851
42	1.20102809433826e-06	2.40205618867651e-06	0.999998798971906
43	1.88963173084851e-06	3.77926346169702e-06	0.99999811036827
44	1.8524131653121e-06	3.7048263306242e-06	0.999998147586835
45	2.05105471301393e-06	4.10210942602785e-06	0.999997948945287
46	2.21980083009361e-06	4.43960166018721e-06	0.99999778019917
47	3.13076684941095e-06	6.2615336988219e-06	0.99999686923315
48	5.55184369493626e-06	1.11036873898725e-05	0.999994448156305
49	1.22629582635826e-05	2.45259165271652e-05	0.999987737041736
50	3.15369546828818e-05	6.30739093657636e-05	0.999968463045317
51	9.13461618354811e-05	0.000182692323670962	0.999908653838165
52	0.000233429098244817	0.000466858196489633	0.999766570901755
53	0.000682108899644625	0.00136421779928925	0.999317891100355
54	0.00242875532435302	0.00485751064870603	0.997571244675647
55	0.00974166559454207	0.0194833311890841	0.990258334405458
56	0.0286569788145944	0.0573139576291888	0.971343021185406
57	0.066364414945564	0.132728829891128	0.933635585054436
58	0.104143080854105	0.208286161708211	0.895856919145895
59	0.161033926467128	0.322067852934256	0.838966073532872
60	0.222928034129289	0.445856068258577	0.777071965870711
61	0.30771033398828	0.61542066797656	0.69228966601172
62	0.390998842047791	0.781997684095582	0.609001157952209
63	0.446259132504994	0.892518265009989	0.553740867495006
64	0.562640140393814	0.874719719212372	0.437359859606186
65	0.661390633366494	0.677218733267013	0.338609366633506
66	0.764419321313052	0.471161357373897	0.235580678686948
67	0.846808235756817	0.306383528486366	0.153191764243183
68	0.925755224724632	0.148489550550736	0.074244775275368
69	0.937528925380513	0.124942149238974	0.0624710746194868
70	0.930182258582334	0.139635482835332	0.0698177414176661
71	0.917996594212233	0.164006811575535	0.0820034057877673
72	0.893309521802976	0.213380956394047	0.106690478197024
73	0.904098373795543	0.191803252408913	0.0959016262044567
74	0.888079574748065	0.22384085050387	0.111920425251935
75	0.85099432659729	0.29801134680542	0.14900567340271
76	0.815981414158454	0.368037171683091	0.184018585841546
77	0.757493995128895	0.48501200974221	0.242506004871105
78	0.704882586218914	0.590234827562173	0.295117413781086
79	0.687566328801818	0.624867342396365	0.312433671198182
80	0.657842908756106	0.684314182487788	0.342157091243894
81	0.89849356004635	0.203012879907300	0.101506439953650
82	0.948341600815973	0.103316798368053	0.0516583991840267
83	0.972198060197025	0.0556038796059502	0.0278019398029751
84	0.949578758690852	0.100842482618297	0.0504212413091485
85	0.907465503576784	0.185068992846432	0.0925344964232161

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	37	0.536231884057971	NOK
5% type I error level	39	0.565217391304348	NOK
10% type I error level	41	0.594202898550725	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/10wf8m1228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/10wf8m1228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/13q8q1228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/13q8q1228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/2s2lm1228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/2s2lm1228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/3xb3n1228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/3xb3n1228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/4j98n1228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/4j98n1228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/54o001228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/54o001228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/6pwc21228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/6pwc21228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/76vmx1228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/76vmx1228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/87dio1228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/87dio1228658906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/9ndfa1228658906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663385xlqlmuobc29bqbz/9ndfa1228658906.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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