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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: Thu, 19 Nov 2009 10:16:31 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08.htm/, Retrieved Thu, 19 Nov 2009 18:19:53 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08.htm/},
    year = {2009},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

ws7l4

Dataseries X:

» Textbox « » Textfile « » CSV «

6,3 101,9 6,3 6,1 6,1 6,3 6 106,2 6,3 6,3 6,1 6,1 6,2 81 6 6,3 6,3 6,1 6,4 94,7 6,2 6 6,3 6,3 6,8 101 6,4 6,2 6 6,3 7,5 109,4 6,8 6,4 6,2 6 7,5 102,3 7,5 6,8 6,4 6,2 7,6 90,7 7,5 7,5 6,8 6,4 7,6 96,2 7,6 7,5 7,5 6,8 7,4 96,1 7,6 7,6 7,5 7,5 7,3 106 7,4 7,6 7,6 7,5 7,1 103,1 7,3 7,4 7,6 7,6 6,9 102 7,1 7,3 7,4 7,6 6,8 104,7 6,9 7,1 7,3 7,4 7,5 86 6,8 6,9 7,1 7,3 7,6 92,1 7,5 6,8 6,9 7,1 7,8 106,9 7,6 7,5 6,8 6,9 8 112,6 7,8 7,6 7,5 6,8 8,1 101,7 8 7,8 7,6 7,5 8,2 92 8,1 8 7,8 7,6 8,3 97,4 8,2 8,1 8 7,8 8,2 97 8,3 8,2 8,1 8 8 105,4 8,2 8,3 8,2 8,1 7,9 102,7 8 8,2 8,3 8,2 7,6 98,1 7,9 8 8,2 8,3 7,6 104,5 7,6 7,9 8 8,2 8,3 87,4 7,6 7,6 7,9 8 8,4 89,9 8,3 7,6 7,6 7,9 8,4 109,8 8,4 8,3 7,6 7,6 8,4 111,7 8,4 8,4 8,3 7,6 8,4 98,6 8,4 8,4 8,4 8,3 8,6 96,9 8,4 8,4 8,4 8,4 8,9 95,1 8,6 8,4 8,4 8,4 8,8 97 8,9 8,6 8,4 8,4 8,3 112,7 8,8 8,9 8,6 8,4 7,5 102,9 8,3 8,8 8,9 8,6 7,2 97,4 7,5 8,3 8,8 8,9 7,4 111,4 7,2 7,5 8,3 8,8 8,8 87,4 7,4 7,2 7,5 8,3 9,3 96,8 8,8 7,4 7,2 7,5 9,3 114,1 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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 0.933141956395697 -0.00599446355432254X[t] + 1.55409357038416Y1[t] -0.862743246928274Y2[t] -0.133170668301423Y3[t] + 0.394880751487633Y4[t] -0.107835895318313M1[t] + 0.00865868146602026M2[t] + 0.555132299015706M3[t] -0.358561693549813M4[t] + 0.194164167438996M5[t] + 0.377211037290883M6[t] + 0.0719094385043453M7[t] + 0.228312254709038M8[t] + 0.145801390152894M9[t] -0.0769332094556072M10[t] + 0.0653509691361015M11[t] -0.000644466348359865t + 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)	0.933141956395697	0.500375	1.8649	0.066109	0.033055
X	-0.00599446355432254	0.004393	-1.3645	0.176479	0.08824
Y1	1.55409357038416	0.107422	14.4672	0	0
Y2	-0.862743246928274	0.208198	-4.1439	8.9e-05	4.4e-05
Y3	-0.133170668301423	0.20837	-0.6391	0.5247	0.26235
Y4	0.394880751487633	0.10641	3.7109	0.000394	0.000197
M1	-0.107835895318313	0.086434	-1.2476	0.216055	0.108028
M2	0.00865868146602026	0.093806	0.0923	0.926702	0.463351
M3	0.555132299015706	0.117354	4.7304	1e-05	5e-06
M4	-0.358561693549813	0.121642	-2.9477	0.004266	0.002133
M5	0.194164167438996	0.125427	1.548	0.125826	0.062913
M6	0.377211037290883	0.103936	3.6293	0.000516	0.000258
M7	0.0719094385043453	0.084564	0.8504	0.397832	0.198916
M8	0.228312254709038	0.091622	2.4919	0.014915	0.007458
M9	0.145801390152894	0.094745	1.5389	0.12804	0.06402
M10	-0.0769332094556072	0.093945	-0.8189	0.41543	0.207715
M11	0.0653509691361015	0.092364	0.7075	0.481426	0.240713
t	-0.000644466348359865	0.000878	-0.7343	0.465076	0.232538

Multiple Linear Regression - Regression Statistics
Multiple R	0.980250524311518
R-squared	0.960891090413006
Adjusted R-squared	0.952026404239954
F-TEST (value)	108.395387231424
F-TEST (DF numerator)	17
F-TEST (DF denominator)	75
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.162365640018282
Sum Squared Residuals	1.97719507939096

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6.3	6.4172891034347	-0.1172891034347
2	6	6.2558382209039	-0.255838220903900
3	6.2	6.45986564889862	-0.259865648898616
4	6.4	6.11202087774337	0.287979122256635
5	6.8	6.80455841717319	-0.00455841717318884
6	7.5	7.24059774648184	0.259402253518158
7	7.5	7.77232258971748	-0.27232258971748
8	7.6	7.41940432693112	0.180595673068879
9	7.6	7.52342163630032	0.0765783636996842
10	7.4	7.4907842180474	-0.090784218047402
11	7.3	7.24894296019598	0.0510570398040157
12	7.1	7.25695883651506	-0.156958836515059
13	6.9	6.95716212903442	-0.0571621290344202
14	6.8	6.85289803971516	-0.0528980397151625
15	7.5	7.51510901024108	-0.0151090102410793
16	7.6	7.68600213097033	-0.0860021309703315
17	7.8	7.63519546572805	0.164804534271954
18	8	7.87526627339618	0.124733726603819
19	8.1	8.03602938490578	0.0639706150942221
20	8.2	8.24564868038028	-0.0456486803802792
21	8.3	8.25160029526526	0.0483997047347368
22	8.2	8.1654131305431	0.0345868694568924
23	8	8.04118667551752	-0.0411866755175201
24	7.9	7.79300291056435	0.106997089435653
25	7.6	7.7820415155737	-0.182041515573704
26	7.6	7.46671937135111	0.133280628648889
27	8.3	8.30821773994245	-0.00821773994245075
28	8.4	8.46722174675334	-0.0672217467533438
29	8.4	8.3330381754051	0.066961824594893
30	8.4	8.3245573056516	0.0754426943484016
31	8.4	8.36023817228953	0.0397618277104728
32	8.6	8.56567518533697	0.0343248146630277
33	8.9	8.80412860290708	0.095871397092920
34	8.8	8.8630394779266	-0.0630394779266007
35	8.3	8.4696996475899	-0.169699647589903
36	7.5	7.81070244424565	-0.310702444245649
37	7.2	7.05506969156099	0.144930308439011
38	7.4	7.33806109766577	0.0619389023342349
39	8.8	8.50649522122347	0.29350477877653
40	9.3	9.26303775335145	0.0369622466485494
41	9.3	9.13552280888804	0.164477191111958
42	8.7	8.8018697651094	-0.101869765109394
43	8.2	8.08807984242364	0.111920157576364
44	8.3	8.19566499716361	0.10433500283639
45	8.5	8.82115429262278	-0.321154292622775
46	8.6	8.64418369668743	-0.0441836966874283
47	8.5	8.50517539473154	-0.00517539473154078
48	8.2	8.22173969975753	-0.0217396997575299
49	8.1	7.82713865384113	0.272861346158865
50	7.9	8.00449499913778	-0.104494999137783
51	8.6	8.52406018358941	0.0759398164105859
52	8.7	8.67627065410998	0.0237293458900206
53	8.7	8.66208407924993	0.0379159207500705
54	8.5	8.62977612389866	-0.129776123898662
55	8.4	8.2587268595906	0.141273140409403
56	8.5	8.51007659004603	-0.0100765900460287
57	8.7	8.71502080426232	-0.015020804262318
58	8.7	8.64033645617973	0.0596635438202719
59	8.6	8.46790431645454	0.132095683545457
60	8.5	8.34807152602412	0.151928473975882
61	8.3	8.19428321760962	0.105716782390385
62	8	8.07073202678641	-0.0707320267864107
63	8.2	8.47174908372574	-0.271749083725740
64	8.1	8.02368197180843	0.0763180281915747
65	8.1	8.12305958139089	-0.0230595813908927
66	8	8.23644736243832	-0.236447362438324
67	7.9	7.88896522618824	0.0110347738117600
68	7.9	8.00144012129234	-0.101440121292337
69	8	7.99389832769351	0.00610167230648676
70	8	7.90395373494447	0.0960462650555279
71	7.9	7.83890578936288	0.0610942106371242
72	8	7.68031361714975	0.319686382850246
73	7.7	7.83382272898925	-0.133822728989254
74	7.2	7.36313124836815	-0.163131248368146
75	7.5	7.48599469626874	0.0140053037312642
76	7.3	7.47855998398786	-0.178559983987857
77	7	7.35936675167654	-0.359366751676536
78	7	6.95135296802843	0.0486470319715659
79	7	7.10387785442293	-0.103877854422928
80	7.2	7.30033761974465	-0.100337619744651
81	7.3	7.34539601743944	-0.0453960174394394
82	7.1	7.09228928567126	0.00771071432873867
83	6.8	6.82818521614763	-0.0281852161476333
84	6.4	6.48921096574354	-0.0892109657435433
85	6.1	6.13319295995618	-0.0331929599561825
86	6.5	6.04812499607172	0.451875003928279
87	7.7	7.52850841611049	0.171491583889507
88	7.9	7.99320488127525	-0.0932048812752471
89	7.5	7.54717472048826	-0.0471747204882575
90	6.9	6.94013245499557	-0.0401324549955651
91	6.6	6.59176007046181	0.00823992953818632
92	6.9	6.961752479105	-0.061752479105001
93	7.7	7.5453800235093	0.154619976490705

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.535150433981029	0.929699132037942	0.464849566018971
22	0.579716029072934	0.840567941854132	0.420283970927066
23	0.436436994233255	0.87287398846651	0.563563005766745
24	0.389292707465431	0.778585414930862	0.610707292534569
25	0.307332857045753	0.614665714091506	0.692667142954247
26	0.250741525275134	0.501483050550267	0.749258474724867
27	0.197104030574497	0.394208061148993	0.802895969425503
28	0.131890434941066	0.263780869882132	0.868109565058934
29	0.120444856705101	0.240889713410202	0.8795551432949
30	0.162399237715262	0.324798475430524	0.837600762284738
31	0.119598551529498	0.239197103058996	0.880401448470502
32	0.156499820114130	0.312999640228261	0.84350017988587
33	0.110396179466062	0.220792358932123	0.889603820533938
34	0.0793787366164238	0.158757473232848	0.920621263383576
35	0.0956950807791798	0.191390161558360	0.90430491922082
36	0.224401839915192	0.448803679830384	0.775598160084808
37	0.189303979551631	0.378607959103262	0.810696020448369
38	0.141825901226789	0.283651802453579	0.85817409877321
39	0.217147910597866	0.434295821195732	0.782852089402134
40	0.215061548465236	0.430123096930473	0.784938451534763
41	0.211691381494733	0.423382762989467	0.788308618505267
42	0.169399187228392	0.338798374456783	0.830600812771608
43	0.125961001336344	0.251922002672688	0.874038998663656
44	0.118652706438342	0.237305412876684	0.881347293561658
45	0.513646014321337	0.972707971357326	0.486353985678663
46	0.451369822435387	0.902739644870774	0.548630177564613
47	0.4404517061066	0.8809034122132	0.5595482938934
48	0.441607873462105	0.88321574692421	0.558392126537895
49	0.513628599154578	0.972742801690844	0.486371400845422
50	0.562628742552352	0.874742514895297	0.437371257447648
51	0.49445550910897	0.98891101821794	0.50554449089103
52	0.432179319951759	0.864358639903518	0.567820680048241
53	0.413240216259283	0.826480432518565	0.586759783740717
54	0.464625390879838	0.929250781759675	0.535374609120162
55	0.425716032443968	0.851432064887937	0.574283967556032
56	0.362325158686120	0.724650317372241	0.63767484131388
57	0.296571660651003	0.593143321302006	0.703428339348997
58	0.238960614491348	0.477921228982697	0.761039385508652
59	0.214257787498669	0.428515574997337	0.785742212501331
60	0.196119038759810	0.392238077519621	0.80388096124019
61	0.20918933221082	0.41837866442164	0.79081066778918
62	0.185510724182613	0.371021448365226	0.814489275817387
63	0.312832624155782	0.625665248311563	0.687167375844218
64	0.322099131430791	0.644198262861583	0.677900868569209
65	0.282899657438708	0.565799314877416	0.717100342561292
66	0.372083731192912	0.744167462385824	0.627916268807088
67	0.362826465534303	0.725652931068606	0.637173534465697
68	0.270976948578866	0.541953897157731	0.729023051421134
69	0.195486801289779	0.390973602579558	0.804513198710221
70	0.127757251831311	0.255514503662622	0.872242748168689
71	0.300182961202168	0.600365922404336	0.699817038797832
72	0.385912359492083	0.771824718984166	0.614087640507917

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/1036ex1258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/1036ex1258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/1tq5e1258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/1tq5e1258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/2ft1o1258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/2ft1o1258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/3e7a91258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/3e7a91258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/4vepk1258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/4vepk1258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/5qkvf1258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/5qkvf1258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/66c411258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/66c411258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/7d9hf1258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/7d9hf1258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/8h42g1258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/8h42g1258650986.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/9x60q1258650986.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586511819w26nl9wb5okb08/9x60q1258650986.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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