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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, 04 Dec 2008 03:30:10 -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/04/t1228386660f3pqdgz1squzvd3.htm/, Retrieved Thu, 04 Dec 2008 10:31:31 +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/04/t1228386660f3pqdgz1squzvd3.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 «

31.75 0 27.85 0 27.33 0 29.11 0 28.17 0 28.93 0 32.33 0 34.74 0 33.70 0 34.35 0 35.35 0 34.44 0 33.70 0 32.39 0 28.30 0 29.11 0 28.67 0 28.18 0 29.28 0 29.73 0 26.26 0 26.82 0 27.72 0 27.10 0 27.03 0 25.98 0 25.72 0 25.93 0 24.94 0 21.70 0 17.90 0 17.06 0 16.41 0 16.68 0 18.24 0 16.41 0 15.71 0 13.95 0 12.22 0 14.91 0 14.61 0 15.01 0 15.57 0 16.07 0 15.39 0 15.16 0 15.44 0 15.70 0 17.57 0 18.42 0 17.93 0 18.42 0 17.61 0 17.98 0 17.78 0 17.74 0 19.04 0 19.85 0 20.23 0 20.23 0 21.07 0 21.28 0 21.83 0 21.83 0 22.22 0 22.68 0 23.58 0 23.73 0 23.68 0 23.92 0 24.85 0 26.28 0 27.75 0 29.59 0 29.26 0 29.25 0 28.68 0 26.05 0 27.11 0 29.53 0 31.01 0 32.95 0 32.09 0 31.74 0 32.50 0 33.60 0 32.47 0 34.38 0 32.31 0 30.71 0 30.26 0 27.20 0 24.85 0 22.27 1 18.11 1 18.30 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	4 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

x[t] = + 24.5380573593074 -4.85069264069265y[t] + 1.47173881673881M1[t] + 0.972141053391049M2[t] -0.0249567099567129M3[t] + 0.962945526695523M4[t] + 0.249597763347756M5[t] -0.493750000000006M6[t] -0.169597763347771M7[t] + 0.0820544733044677M8[t] -0.597543290043295M9[t] + 0.219195526695523M10[t] + 0.225847763347759M11[t] -0.00290223665223661t + 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)	24.5380573593074	2.731479	8.9834	0	0
y	-4.85069264069265	4.327308	-1.1209	0.265582	0.132791
M1	1.47173881673881	3.398493	0.4331	0.666111	0.333055
M2	0.972141053391049	3.397596	0.2861	0.775503	0.387752
M3	-0.0249567099567129	3.396898	-0.0073	0.994156	0.497078
M4	0.962945526695523	3.3964	0.2835	0.777493	0.388747
M5	0.249597763347756	3.396101	0.0735	0.941591	0.470795
M6	-0.493750000000006	3.396001	-0.1454	0.884758	0.442379
M7	-0.169597763347771	3.396101	-0.0499	0.960292	0.480146
M8	0.0820544733044677	3.3964	0.0242	0.980784	0.490392
M9	-0.597543290043295	3.396898	-0.1759	0.8608	0.4304
M10	0.219195526695523	3.356684	0.0653	0.948093	0.474047
M11	0.225847763347759	3.356381	0.0673	0.946515	0.473258
t	-0.00290223665223661	0.026025	-0.1115	0.911478	0.455739

Multiple Linear Regression - Regression Statistics
Multiple R	0.16959143867086
R-squared	0.0287612560704521
Adjusted R-squared	-0.125215617967159
F-TEST (value)	0.186789452963090
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0.99912968162542
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.71256042907549
Sum Squared Residuals	3694.79433614719

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	31.75	26.0068939393940	5.74310606060604
2	27.85	25.5043939393939	2.34560606060607
3	27.33	24.5043939393939	2.82560606060606
4	29.11	25.4893939393939	3.62060606060605
5	28.17	24.7731439393939	3.39685606060606
6	28.93	24.0268939393939	4.90310606060607
7	32.33	24.3481439393939	7.98185606060607
8	34.74	24.5968939393939	10.1431060606061
9	33.7	23.9143939393939	9.78560606060607
10	34.35	24.7282305194805	9.62176948051948
11	35.35	24.7319805194805	10.6180194805195
12	34.44	24.5032305194805	9.93676948051948
13	33.7	25.9720670995671	7.7279329004329
14	32.39	25.4695670995671	6.92043290043289
15	28.3	24.4695670995671	3.8304329004329
16	29.11	25.4545670995671	3.6554329004329
17	28.67	24.7383170995671	3.9316829004329
18	28.18	23.9920670995671	4.1879329004329
19	29.28	24.3133170995671	4.9666829004329
20	29.73	24.5620670995671	5.1679329004329
21	26.26	23.8795670995671	2.38043290043290
22	26.82	24.6934036796537	2.12659632034632
23	27.72	24.6971536796537	3.02284632034632
24	27.1	24.4684036796537	2.63159632034632
25	27.03	25.9372402597403	1.09275974025974
26	25.98	25.4347402597403	0.545259740259741
27	25.72	24.4347402597403	1.28525974025974
28	25.93	25.4197402597403	0.510259740259738
29	24.94	24.7034902597403	0.236509740259741
30	21.7	23.9572402597403	-2.25724025974026
31	17.9	24.2784902597403	-6.37849025974026
32	17.06	24.5272402597403	-7.46724025974026
33	16.41	23.8447402597403	-7.43474025974026
34	16.68	24.6585768398268	-7.97857683982684
35	18.24	24.6623268398268	-6.42232683982684
36	16.41	24.4335768398268	-8.02357683982684
37	15.71	25.9024134199134	-10.1924134199134
38	13.95	25.3999134199134	-11.4499134199134
39	12.22	24.3999134199134	-12.1799134199134
40	14.91	25.3849134199134	-10.4749134199134
41	14.61	24.6686634199134	-10.0586634199134
42	15.01	23.9224134199134	-8.91241341991342
43	15.57	24.2436634199134	-8.67366341991342
44	16.07	24.4924134199134	-8.42241341991342
45	15.39	23.8099134199134	-8.41991341991342
46	15.16	24.62375	-9.46375
47	15.44	24.6275	-9.1875
48	15.7	24.39875	-8.69875
49	17.57	25.8675865800866	-8.29758658008658
50	18.42	25.3650865800866	-6.94508658008658
51	17.93	24.3650865800866	-6.43508658008658
52	18.42	25.3500865800866	-6.93008658008658
53	17.61	24.6338365800866	-7.02383658008658
54	17.98	23.8875865800866	-5.90758658008658
55	17.78	24.2088365800866	-6.42883658008658
56	17.74	24.4575865800866	-6.71758658008658
57	19.04	23.7750865800866	-4.73508658008658
58	19.85	24.5889231601732	-4.73892316017316
59	20.23	24.5926731601732	-4.36267316017316
60	20.23	24.3639231601732	-4.13392316017317
61	21.07	25.8327597402597	-4.76275974025974
62	21.28	25.3302597402597	-4.05025974025974
63	21.83	24.3302597402597	-2.50025974025974
64	21.83	25.3152597402597	-3.48525974025974
65	22.22	24.5990097402597	-2.37900974025974
66	22.68	23.8527597402597	-1.17275974025974
67	23.58	24.1740097402597	-0.594009740259739
68	23.73	24.4227597402597	-0.692759740259737
69	23.68	23.7402597402597	-0.060259740259739
70	23.92	24.5540963203463	-0.63409632034632
71	24.85	24.5578463203463	0.292153679653682
72	26.28	24.3290963203463	1.95090367965368
73	27.75	25.7979329004329	1.9520670995671
74	29.59	25.2954329004329	4.2945670995671
75	29.26	24.2954329004329	4.9645670995671
76	29.25	25.2804329004329	3.9695670995671
77	28.68	24.5641829004329	4.1158170995671
78	26.05	23.8179329004329	2.23206709956710
79	27.11	24.1391829004329	2.9708170995671
80	29.53	24.3879329004329	5.1420670995671
81	31.01	23.7054329004329	7.3045670995671
82	32.95	24.5192694805195	8.43073051948052
83	32.09	24.5230194805195	7.56698051948052
84	31.74	24.2942694805195	7.44573051948051
85	32.5	25.7631060606061	6.73689393939394
86	33.6	25.2606060606061	8.33939393939394
87	32.47	24.2606060606061	8.20939393939394
88	34.38	25.2456060606061	9.13439393939394
89	32.31	24.5293560606061	7.78064393939394
90	30.71	23.7831060606061	6.92689393939394
91	30.26	24.1043560606061	6.15564393939394
92	27.2	24.3531060606061	2.84689393939394
93	24.85	23.6706060606061	1.17939393939394
94	22.27	19.63375	2.63625
95	18.11	19.6375	-1.52750000000000
96	18.3	19.40875	-1.10875000000000

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0233039992779185	0.0466079985558369	0.976696000722082
18	0.00969152938305513	0.0193830587661103	0.990308470616945
19	0.0117164607118378	0.0234329214236756	0.988283539288162
20	0.0232054612625277	0.0464109225250554	0.976794538737472
21	0.0585383380584871	0.117076676116974	0.941461661941513
22	0.0881629839395471	0.176325967879094	0.911837016060453
23	0.124297637563389	0.248595275126777	0.875702362436611
24	0.162149678372764	0.324299356745527	0.837850321627236
25	0.165960492310302	0.331920984620605	0.834039507689698
26	0.165677317546455	0.331354635092909	0.834322682453545
27	0.209114659987271	0.418229319974542	0.79088534001273
28	0.274357481283222	0.548714962566444	0.725642518716778
29	0.416077792344605	0.83215558468921	0.583922207655395
30	0.611137732051483	0.777724535897033	0.388862267948517
31	0.9020110505061	0.195977898987799	0.0979889494938996
32	0.990716789505973	0.0185664209880541	0.00928321049402705
33	0.998421518840766	0.00315696231846803	0.00157848115923401
34	0.999446806392837	0.00110638721432541	0.000553193607162703
35	0.999907051506621	0.000185896986757568	9.29484933787838e-05
36	0.999974310578598	5.13788428036134e-05	2.56894214018067e-05
37	0.999974654282874	5.06914342517159e-05	2.53457171258580e-05
38	0.999962119755361	7.57604892775795e-05	3.78802446387897e-05
39	0.9999513824532	9.7235093601152e-05	4.8617546800576e-05
40	0.999905566918584	0.000188866162832651	9.44330814163256e-05
41	0.999818884611017	0.000362230777965598	0.000181115388982799
42	0.999707609481414	0.00058478103717173	0.000292390518585865
43	0.999572326438675	0.000855347122650901	0.000427673561325450
44	0.999500186681281	0.000999626637438152	0.000499813318719076
45	0.999353679722032	0.00129264055593681	0.000646320277968407
46	0.998979320832477	0.00204135833504527	0.00102067916752263
47	0.998203861365678	0.00359227726864315	0.00179613863432158
48	0.997007831202532	0.00598433759493538	0.00299216879746769
49	0.996423046482246	0.00715390703550754	0.00357695351775377
50	0.99713935647367	0.00572128705266001	0.00286064352633000
51	0.99784251372001	0.00431497255998174	0.00215748627999087
52	0.997785780710986	0.00442843857802720	0.00221421928901360
53	0.997477194616023	0.00504561076795384	0.00252280538397692
54	0.997232821454696	0.00553435709060837	0.00276717854530418
55	0.996466209438201	0.0070675811235976	0.0035337905617988
56	0.995001379978313	0.00999724004337345	0.00499862002168672
57	0.995076806185815	0.00984638762836989	0.00492319381418494
58	0.995500783196898	0.00899843360620325	0.00449921680310163
59	0.994346623502232	0.0113067529955363	0.00565337649776813
60	0.993361654378922	0.0132766912421561	0.00663834562107803
61	0.993367159497039	0.0132656810059224	0.00663284050296119
62	0.99551063307341	0.00897873385318119	0.00448936692659059
63	0.996563207144642	0.00687358571071549	0.00343679285535775
64	0.99769772291176	0.00460455417648079	0.00230227708824040
65	0.997824045300166	0.0043519093996686	0.0021759546998343
66	0.99704900286219	0.00590199427561827	0.00295099713780914
67	0.995830269665004	0.00833946066999114	0.00416973033499557
68	0.993635270730608	0.012729458538785	0.0063647292693925
69	0.990525897615546	0.0189482047689078	0.00947410238445388
70	0.995761618583238	0.00847676283352447	0.00423838141676224
71	0.99523462831841	0.00953074336317927	0.00476537168158964
72	0.99349234980465	0.0130153003906984	0.0065076501953492
73	0.99045199330145	0.0190960133970987	0.00954800669854937
74	0.985686452618924	0.0286270947621519	0.0143135473810759
75	0.976001842722612	0.0479963145547756	0.0239981572773878
76	0.969345366599115	0.0613092668017706	0.0306546334008853
77	0.951615159998275	0.0967696800034505	0.0483848400017253
78	0.953250920427368	0.0934981591452633	0.0467490795726317
79	0.980339695984878	0.0393206080302442	0.0196603040151221

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	34	0.53968253968254	NOK
5% type I error level	49	0.777777777777778	NOK
10% type I error level	52	0.825396825396825	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/10i16c1228386605.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/10i16c1228386605.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/1e3kz1228386604.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/1e3kz1228386604.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/2kox01228386604.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/2kox01228386604.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/3bbu01228386605.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/4853z1228386605.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/5arzq1228386605.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/6qwdq1228386605.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/7rgtl1228386605.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/81s601228386605.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/96a381228386605.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/04/t1228386660f3pqdgz1squzvd3/96a381228386605.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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