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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 09:42:15 -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/t12586491378iybqaft9mcpgav.htm/, Retrieved Thu, 19 Nov 2009 17:45:49 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav.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:

ws7l2

Dataseries X:

» Textbox « » Textfile « » CSV «

98,8 6,3 100,5 6,1 110,4 6,1 96,4 6,3 101,9 6,3 106,2 6 81 6,2 94,7 6,4 101 6,8 109,4 7,5 102,3 7,5 90,7 7,6 96,2 7,6 96,1 7,4 106 7,3 103,1 7,1 102 6,9 104,7 6,8 86 7,5 92,1 7,6 106,9 7,8 112,6 8 101,7 8,1 92 8,2 97,4 8,3 97 8,2 105,4 8 102,7 7,9 98,1 7,6 104,5 7,6 87,4 8,3 89,9 8,4 109,8 8,4 111,7 8,4 98,6 8,4 96,9 8,6 95,1 8,9 97 8,8 112,7 8,3 102,9 7,5 97,4 7,2 111,4 7,4 87,4 8,8 96,8 9,3 114,1 9,3 110,3 8,7 103,9 8,2 101,6 8,3 94,6 8,5 95,9 8,6 104,7 8,5 102,8 8,2 98,1 8,1 113,9 7,9 80,9 8,6 95,7 8,7 113,2 8,7 105,9 8,5 108,8 8,4 102,3 8,5 99 8,7 100,7 8,7 115,5 8,6 100,7 8,5 109,9 8,3 114,6 8 85,4 8,2 100,5 8,1 114,8 8,1 116,5 8 112,9 7,9 102 7,9 106 8 105,3 8 118,8 7,9 106,1 8 109,3 7,7 117,2 7,2 92,5 7,5 104,2 7,3 112,5 7 122,4 7 113,3 7 100 7,2 110,7 7,3 112,8 7,1 109,8 6,8 117,3 6,4 109,1 6,1 115,9 6,5 96 7,7 99,8 7,9 116,8 7,5 115,7 6,9 99,4 6,6 94,3 6,9 91 7,7

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] = + 100.808693644493 -0.421986537277549X[t] + 1.28993303416181M1[t] + 3.17167550485208M2[t] + 12.8478278608285M3[t] + 6.35093055337301M4[t] + 5.48625841420152M5[t] + 13.2690597604738M6[t] -10.4210993268639M7[t] -0.736125841420165M8[t] + 13.6835993268639M9[t] + 15.5769503365680M10[t] + 7.57947685112434M11[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)	100.808693644493	6.11349	16.4895	0	0
X	-0.421986537277549	0.735183	-0.574	0.567509	0.283755
M1	1.28993303416181	2.623095	0.4918	0.624172	0.312086
M2	3.17167550485208	2.699231	1.175	0.243303	0.121652
M3	12.8478278608285	2.703607	4.7521	8e-06	4e-06
M4	6.35093055337301	2.716073	2.3383	0.02175	0.010875
M5	5.48625841420152	2.737922	2.0038	0.048314	0.024157
M6	13.2690597604738	2.751215	4.823	6e-06	3e-06
M7	-10.4210993268639	2.69934	-3.8606	0.000221	0.000111
M8	-0.736125841420165	2.699481	-0.2727	0.78576	0.39288
M9	13.6835993268639	2.69934	5.0692	2e-06	1e-06
M10	15.5769503365680	2.699153	5.7711	0	0
M11	7.57947685112434	2.700982	2.8062	0.006229	0.003115

Multiple Linear Regression - Regression Statistics
Multiple R	0.823138113126992
R-squared	0.677556353282265
Adjusted R-squared	0.631492975179731
F-TEST (value)	14.7092198009029
F-TEST (DF numerator)	12
F-TEST (DF denominator)	84
p-value	4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.3981798736166
Sum Squared Residuals	2447.78905962523

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	98.8	99.440111493805	-0.64011149380493
2	100.5	101.406251271952	-0.906251271951684
3	110.4	111.082403627928	-0.682403627928092
4	96.4	104.501109013017	-8.1011090130171
5	101.9	103.636436873846	-1.73643687384559
6	106.2	111.545834181301	-5.34583418130114
7	81	87.771277786508	-6.77127778650797
8	94.7	97.3718539644962	-2.67185396449618
9	101	111.622784517869	-10.6227845178692
10	109.4	113.220744951479	-3.82074495147908
11	102.3	105.223271466035	-2.92327146603534
12	90.7	97.6015959611832	-6.90159596118324
13	96.2	98.891528995345	-2.6915289953451
14	96.1	100.857668773491	-4.75766877349087
15	106	110.576019783195	-4.57601978319505
16	103.1	104.163519783195	-1.06351978319505
17	102	103.383244951479	-1.38324495147908
18	104.7	111.208244951479	-6.50824495147908
19	86	87.2226952880471	-1.22269528804714
20	92.1	96.8654701197631	-4.76547011976311
21	106.9	111.200797980592	-4.30079798059163
22	112.6	113.009751682840	-0.409751682840315
23	101.7	104.970079543669	-3.27007954366883
24	92	97.3484040388167	-5.34840403881674
25	97.4	98.5961384192508	-1.19613841925080
26	97	100.520079543669	-3.52007954366882
27	105.4	110.280629207101	-4.88062920710076
28	102.7	103.825930553373	-1.12593055337301
29	98.1	103.087854375385	-4.9878543753848
30	104.5	110.870655721657	-6.37065572165704
31	87.4	86.8851060582251	0.514893941774906
32	89.9	96.527880889941	-6.62788088994106
33	109.8	110.947606058225	-1.14760605822511
34	111.7	112.840957067929	-1.14095706792928
35	98.6	104.843483582486	-6.24348358248557
36	96.9	97.1796094239057	-0.279609423905713
37	95.1	98.3429464968843	-3.24294649688429
38	97	100.266887621302	-3.26688762130230
39	112.7	110.154033245918	2.5459667540825
40	102.9	103.994725168284	-1.09472516828402
41	97.4	103.256648990296	-5.85664899029581
42	111.4	110.955053029113	0.444946970887453
43	87.4	86.6741127895863	0.725887210413681
44	96.8	96.1480930063913	0.651906993608724
45	114.1	110.567818174675	3.53218182532468
46	110.3	112.714361106746	-2.41436110674603
47	103.9	104.927880889941	-1.02788088994107
48	101.6	97.306205385089	4.29379461491101
49	94.6	98.5117411117953	-3.9117411117953
50	95.9	100.351284928758	-4.45128492875780
51	104.7	110.069635938462	-5.36963593846199
52	102.8	103.699334592190	-0.899334592189746
53	98.1	102.876861106746	-4.77686110674603
54	113.9	110.744059760474	3.15594023952623
55	80.9	86.7585100970418	-5.85851009704183
56	95.7	96.4012849287578	-0.701284928757804
57	113.2	110.821010097042	2.37898990295816
58	105.9	112.798758414202	-6.89875841420153
59	108.8	104.843483582486	3.95651641751443
60	102.3	97.2218080776335	5.07819192236652
61	99	98.4273438043398	0.572656195660208
62	100.7	100.30908627503	0.390913724969953
63	115.5	110.027437284734	5.47256271526576
64	100.7	103.572738631006	-2.87273863100648
65	109.9	102.792463799291	7.10753620070949
66	114.6	110.701861106746	3.89813889325398
67	85.4	86.9273047119529	-1.52730471195285
68	100.5	96.6544768511243	3.84552314887567
69	114.8	111.074202019408	3.72579798059163
70	116.5	113.009751682840	3.49024831715969
71	112.9	105.054476851124	7.84552314887566
72	102	97.475	4.52499999999999
73	106	98.722734380434	7.27726561956593
74	105.3	100.604476851124	4.69552314887566
75	118.8	110.322827860829	8.47717213917147
76	106.1	103.783731899645	2.31626810035474
77	109.3	103.045655721657	6.25434427834296
78	117.2	111.039450336568	6.16054966343194
79	92.5	87.2226952880471	5.27730471195286
80	104.2	96.9920660809464	7.20793391905363
81	112.5	111.538387210414	0.961612789586324
82	122.4	113.431738220118	8.96826177988215
83	113.3	105.434264734674	7.86573526532586
84	100	97.7703905760943	2.22960942390571
85	110.7	99.0181249565284	11.6818750434716
86	112.8	100.984264734674	11.8157352653259
87	109.8	110.787013051834	-0.987013051833833
88	117.3	104.458910359289	12.8410896407107
89	109.1	103.720834181301	5.37916581869887
90	115.9	111.334840912662	4.56515908733766
91	96	87.1382979805916	8.86170201940837
92	99.8	96.7388741585798	3.06112584142015
93	116.8	111.327393941775	5.4726060582251
94	115.7	113.473936873846	2.22606312615439
95	99.4	105.603059349585	-6.20305934958515
96	94.3	97.8969865372776	-3.59698653727756
97	91	98.8493303416173	-7.84933034161734

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.293730444507971	0.587460889015942	0.706269555492029
17	0.158249967339331	0.316499934678662	0.841750032660669
18	0.0839358006837008	0.167871601367402	0.916064199316299
19	0.101745477994550	0.203490955989100	0.89825452200545
20	0.0615388342185457	0.123077668437091	0.938461165781454
21	0.0769674978779492	0.153934995755898	0.92303250212205
22	0.0510950549135213	0.102190109827043	0.948904945086479
23	0.0279842888753446	0.0559685777506892	0.972015711124655
24	0.0162221938317037	0.0324443876634073	0.983777806168296
25	0.0080679249614446	0.0161358499228892	0.991932075038555
26	0.00439178142147315	0.0087835628429463	0.995608218578527
27	0.00299515663886491	0.00599031327772983	0.997004843361135
28	0.00211388486199971	0.00422776972399941	0.997886115138
29	0.00186274383235201	0.00372548766470402	0.998137256167648
30	0.00122103273722899	0.00244206547445798	0.998778967262771
31	0.00111364143568897	0.00222728287137793	0.99888635856431
32	0.00111255422252478	0.00222510844504956	0.998887445777475
33	0.00188579054956729	0.00377158109913458	0.998114209450433
34	0.000965181632262513	0.00193036326452503	0.999034818367738
35	0.000865796511221119	0.00173159302244224	0.99913420348878
36	0.00109446410095447	0.00218892820190895	0.998905535899046
37	0.000703053261031495	0.00140610652206299	0.999296946738969
38	0.000404431114824305	0.00080886222964861	0.999595568885176
39	0.000528702032005832	0.00105740406401166	0.999471297967994
40	0.000344619050986396	0.000689238101972791	0.999655380949014
41	0.000456954741542652	0.000913909483085304	0.999543045258457
42	0.000776226134315593	0.00155245226863119	0.999223773865684
43	0.000464976044477627	0.000929952088955254	0.999535023955522
44	0.000350455147675001	0.000700910295350001	0.999649544852325
45	0.00071084351105	0.0014216870221	0.99928915648895
46	0.000408746931639050	0.000817493863278101	0.999591253068361
47	0.000273276837968217	0.000546553675936434	0.999726723162032
48	0.00074292430634312	0.00148584861268624	0.999257075693657
49	0.000596688630745976	0.00119337726149195	0.999403311369254
50	0.000617542804768237	0.00123508560953647	0.999382457195232
51	0.000868382569683474	0.00173676513936695	0.999131617430317
52	0.000542391856141581	0.00108478371228316	0.999457608143858
53	0.0007145580035043	0.0014291160070086	0.999285441996496
54	0.000913366723759769	0.00182673344751954	0.99908663327624
55	0.00159257323656935	0.00318514647313869	0.99840742676343
56	0.00111099697977535	0.0022219939595507	0.998889003020225
57	0.000929338477844018	0.00185867695568804	0.999070661522156
58	0.00234015785324282	0.00468031570648564	0.997659842146757
59	0.00280509302944386	0.00561018605888773	0.997194906970556
60	0.00386202267103595	0.00772404534207189	0.996137977328964
61	0.00247489566949674	0.00494979133899349	0.997525104330503
62	0.00237994471572415	0.0047598894314483	0.997620055284276
63	0.00260590131182205	0.00521180262364411	0.997394098688178
64	0.00392825125522085	0.0078565025104417	0.99607174874478
65	0.00588959122609823	0.0117791824521965	0.994110408773902
66	0.00470473488681766	0.00940946977363532	0.995295265113182
67	0.00568855134245127	0.0113771026849025	0.994311448657549
68	0.00511168619090299	0.0102233723818060	0.994888313809097
69	0.00401205661588896	0.00802411323177793	0.995987943384111
70	0.00364850683525539	0.00729701367051078	0.996351493164745
71	0.00686481005235964	0.0137296201047193	0.99313518994764
72	0.00650494174212236	0.0130098834842447	0.993495058257878
73	0.00876539451257772	0.0175307890251554	0.991234605487422
74	0.00907420549480598	0.0181484109896120	0.990925794505194
75	0.0189444008239603	0.0378888016479207	0.98105559917604
76	0.0219144817452674	0.0438289634905348	0.978085518254733
77	0.0151777285515092	0.0303554571030185	0.98482227144849
78	0.0101026945427356	0.0202053890854711	0.989897305457264
79	0.00692290957370739	0.0138458191474148	0.993077090426293
80	0.00545554672740167	0.0109110934548033	0.994544453272598
81	0.00236411698880378	0.00472823397760755	0.997635883011196

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	43	0.651515151515151	NOK
5% type I error level	58	0.878787878787879	NOK
10% type I error level	59	0.893939393939394	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/10b7sv1258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/10b7sv1258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/1jmyl1258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/1jmyl1258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/222y81258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/222y81258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/37q9q1258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/37q9q1258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/4ttok1258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/4ttok1258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/5ecyj1258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/5ecyj1258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/6988r1258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/6988r1258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/7phvj1258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/7phvj1258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/8tcjt1258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/8tcjt1258648930.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/9g2o81258648930.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586491378iybqaft9mcpgav/9g2o81258648930.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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