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Paper Multiple Linear Regression - interactie

*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: Fri, 19 Nov 2010 13:18:31 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/19/t1290173213l4djsnx1wp8stf2.htm/, Retrieved Fri, 19 Nov 2010 14:27:05 +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/2010/Nov/19/t1290173213l4djsnx1wp8stf2.htm/},
    year = {2010},
}
@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 = {2010},
    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:

Dataseries X:

» Textbox « » Textfile « » CSV «

13 13 0 14 0 13 0 3 0 0 12 12 12 8 8 13 13 5 5 1 10 10 0 10 0 11 0 5 0 0 15 13 13 16 16 18 18 8 8 1 9 12 12 11 11 11 11 4 4 1 12 12 12 14 14 14 14 4 4 1 11 5 0 16 0 14 0 6 0 0 11 12 12 11 11 12 12 6 6 1 15 11 11 16 16 11 11 5 5 1 7 14 0 12 0 12 0 4 0 0 11 14 0 7 0 13 0 6 0 0 11 12 12 13 13 11 11 4 4 1 10 12 12 11 11 12 12 6 6 1 14 11 0 15 0 16 0 6 0 0 10 11 11 7 7 9 9 4 4 1 6 7 0 9 0 11 0 4 0 0 11 9 9 7 7 13 13 2 2 1 15 11 0 14 0 15 0 7 0 0 11 11 11 15 15 10 10 5 5 1 12 12 0 7 0 11 0 4 0 0 14 12 12 15 15 13 13 6 6 1 15 11 0 17 0 16 0 6 0 0 9 11 0 15 0 15 0 7 0 0 13 8 8 14 14 14 14 5 5 1 13 9 0 14 0 14 0 6 0 0 13 10 10 8 8 8 8 4 4 1 12 10 0 14 0 13 0 7 0 0 14 12 12 14 14 15 15 7 7 1 11 8 0 8 0 13 0 4 0 0 9 12 12 11 11 11 11 4 4 1 16 11 0 16 0 15 0 6 0 0 13 11 11 14 14 13 13 6 6 1 16 11 11 16 16 16 16 7 7 1 15 9 9 5 5 11 11 3 3 1 5 15 15 8 8 12 12 3 3 1 11 11 11 8 8 12 12 6 6 1 16 11 0 13 0 14 0 7 0 0 17 11 11 15 15 14 14 5 5 1 9 15 0 6 0 8 0 4 0 0 9 11 11 12 12 13 13 5 5 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	8 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

populair[t] = -1.89771513378114 + 0.259413164999801vrienden[t] -0.299812901556042vrienden_G[t] + 0.280958680758531kennen[t] + 0.0293102671918129kennen_G[t] + 0.309425519926918geliefd[t] -0.0744689878374152geliefd_G[t] + 0.600578736213396celebrity[t] -0.0325734070475991celebrity_G[t] + 4.98632723777635`geslacht(dummy)`[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)	-1.89771513378114	2.809863	-0.6754	0.50098	0.25049
vrienden	0.259413164999801	0.167458	1.5491	0.12448	0.06224
vrienden_G	-0.299812901556042	0.241835	-1.2397	0.217943	0.108972
kennen	0.280958680758531	0.144733	1.9412	0.055018	0.027509
kennen_G	0.0293102671918129	0.176441	0.1661	0.868395	0.434197
geliefd	0.309425519926918	0.181617	1.7037	0.091507	0.045753
geliefd_G	-0.0744689878374152	0.250898	-0.2968	0.767221	0.38361
celebrity	0.600578736213396	0.351503	1.7086	0.090596	0.045298
celebrity_G	-0.0325734070475991	0.437678	-0.0744	0.940821	0.47041
`geslacht(dummy)`	4.98632723777635	3.854619	1.2936	0.198755	0.099377

Multiple Linear Regression - Regression Statistics
Multiple R	0.702750472577834
R-squared	0.493858226708369
Adjusted R-squared	0.448756484533867
F-TEST (value)	10.9498702909878
F-TEST (DF numerator)	9
F-TEST (DF denominator)	101
p-value	9.68036761861413e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.24839448407162
Sum Squared Residuals	510.58305335637

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	13	11.2323455095257	1.76765449047427
2	12	10.9804284119156	1.01957158808442
3	10	9.91257772406525	0.0874222759347496
4	15	16.300978906907	-1.30097890690700
5	9	10.8733168624218	-1.87331686242182
6	12	12.5089933025414	-0.50899330254136
7	11	11.8301192796116	-0.830119279611582
8	11	12.2442840528429	-1.24428405284292
9	15	13.0330666678956	1.96693333210443
10	7	11.2209945292950	-4.22099452929504
11	11	11.3267841178561	-0.326784117856096
12	11	11.4938547583225	-0.493854758322506
13	10	12.2442840528429	-2.24428405284292
14	14	13.7244906287057	0.275509371294307
15	10	9.20272774299768	0.797272257002321
16	6	8.25280081209392	-2.25280081209392
17	11	9.08734268613658	1.91265731386342
18	15	13.7346851642336	1.26531483576636
19	11	12.4878411878557	-1.48784118785573
20	12	8.98794927557586	3.01205072442413
21	14	13.7203163767338	0.279683623266207
22	15	14.2864079902228	0.713592009777246
23	9	14.0156438449922	-5.01564384499217
24	13	13.2385975779321	-0.238597577932119
25	13	12.3058545780937	0.694145421906276
26	13	9.31843989541476	3.68156010458524
27	12	12.85642095938	-0.856420959380003
28	14	14.4479658221283	-0.447965822128252
29	11	8.85010633618903	2.14989366381097
30	9	10.8733168624218	-1.87331686242182
31	16	13.6960237895373	2.30397621046269
32	13	13.4504471653397	-0.450447165339691
33	16	15.3438599866747	0.656140013325316
34	15	8.56489705522268	6.43510294477732
35	5	9.48826201182577	-4.48826201182577
36	11	11.3538769455481	-0.353876945548125
37	16	13.1443009635482	2.85569903645181
38	17	13.4276673162137	3.57233268378626
39	9	8.55695353003598	0.443046469964017
40	9	12.2619039402732	-3.26190394027321
41	13	14.7354549209526	-1.73545492095265
42	6	11.6585366990715	-5.6585366990715
43	12	11.9215979753627	0.0784020246373308
44	8	9.88775899618823	-1.88775899618823
45	14	12.1525443011941	1.84745569880592
46	12	12.8824418361739	-0.882441836173893
47	16	14.8566541306214	1.14334586937863
48	8	9.60795676013254	-1.60795676013254
49	15	15.3666398358006	-0.366639835800635
50	7	8.97775474004792	-1.97775474004792
51	16	13.6460114346102	2.35398856538981
52	14	14.8476348461144	-0.847634846114387
53	16	13.9203602295187	2.07963977048130
54	9	10.2461331935941	-1.24613319359412
55	11	12.7462017139978	-1.74620171399782
56	5	5.60618897131157	-0.606188971311574
57	15	12.8824418361739	2.11755816382611
58	13	12.5317731516673	0.468226848332692
59	11	11.3812261296043	-0.381226129604338
60	11	12.4558916428751	-1.45589164287508
61	12	12.5666858309719	-0.566685830971908
62	14	10.6473796148393	3.35262038516070
63	6	8.1859614643385	-2.18596146433850
64	7	9.29737479550278	-2.29737479550278
65	14	13.1072203680810	0.892779631918951
66	14	13.8450478136579	0.154952186342145
67	10	11.7570781967896	-1.7570781967896
68	13	8.13487869777233	4.86512130222767
69	12	12.1627388367220	-0.162738836722030
70	9	8.70006927140768	0.299930728592323
71	12	12.5831470027268	-0.583147002726832
72	10	9.59987899202004	0.400121007979956
73	10	13.6100912815684	-3.61009128156835
74	16	15.3034602501184	0.696539749881558
75	15	14.1113847977966	0.88861520220338
76	8	10.5093566320612	-2.50935663206122
77	8	8.33735818443535	-0.337358184435352
78	13	11.9317925108906	1.06820748910938
79	16	15.6541289346250	0.345871065374972
80	16	14.7582347700786	1.24176522992140
81	14	15.683498525076	-1.68349852507599
82	11	8.99441038284545	2.00558961715455
83	14	13.9694521550259	0.0305478449740627
84	9	10.8560240705475	-1.85602407054751
85	8	10.7807116546867	-2.78071165468667
86	8	11.3946037776603	-3.39460377766035
87	11	12.5372602089189	-1.53726020891895
88	12	14.4499206521246	-2.44992065212455
89	14	13.5696915450121	0.430308454987889
90	16	12.8246809080933	3.17531909190667
91	16	13.6450039608730	2.35499603912705
92	14	13.3650527538517	0.634947246148343
93	14	10.4427550342956	3.55724496570436
94	14	12.1671684935432	1.83283150645685
95	8	9.59987899202004	-1.59987899202004
96	16	13.3650527538517	2.63494724614834
97	12	10.5948470481044	1.40515295189559
98	12	13.7203163767338	-1.72031637673379
99	16	14.9041025853738	1.09589741462625
100	15	12.829909269439	2.17009073056100
101	10	11.1172926790183	-1.11729267901830
102	12	10.7226920307001	1.27730796929986
103	14	13.1056395888519	0.894360411148143
104	19	14.1902294409128	4.8097705590872
105	15	12.9789063667204	2.02109363327964
106	8	7.76487138821726	0.235128611782735
107	8	10.7977632523285	-2.79776325232855
108	10	14.4527177687780	-4.45271776877798
109	15	13.3865982696104	1.61340173038961
110	16	14.3954332553934	1.60456674460664
111	13	13.3979145986048	-0.397914598604801

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.165597288719971	0.331194577439942	0.83440271128003
14	0.210351109742853	0.420702219485706	0.789648890257147
15	0.114348674210447	0.228697348420893	0.885651325789553
16	0.358234798904938	0.716469597809877	0.641765201095062
17	0.362806288000357	0.725612576000714	0.637193711999643
18	0.295013990481797	0.590027980963594	0.704986009518203
19	0.260026380917058	0.520052761834116	0.739973619082942
20	0.395668264194918	0.791336528389835	0.604331735805082
21	0.320683809149586	0.641367618299173	0.679316190850414
22	0.243530948253018	0.487061896506037	0.756469051746982
23	0.410653358439479	0.821306716878958	0.589346641560521
24	0.367118520299906	0.734237040599811	0.632881479700094
25	0.325553435431326	0.651106870862652	0.674446564568674
26	0.385384587269069	0.770769174538139	0.614615412730931
27	0.369597137685147	0.739194275370294	0.630402862314853
28	0.303682861929228	0.607365723858455	0.696317138070772
29	0.248968607083246	0.497937214166493	0.751031392916754
30	0.223822421656931	0.447644843313862	0.776177578343069
31	0.249821738711901	0.499643477423802	0.750178261288099
32	0.196710141730122	0.393420283460245	0.803289858269878
33	0.161140454437526	0.322280908875051	0.838859545562474
34	0.374561785075089	0.749123570150177	0.625438214924911
35	0.41081771914884	0.82163543829768	0.589182280851160
36	0.377217908303043	0.754435816606085	0.622782091696958
37	0.463194110666349	0.926388221332697	0.536805889333651
38	0.57397265956301	0.852054680873981	0.426027340436991
39	0.533326137776845	0.93334772444631	0.466673862223155
40	0.629103208889356	0.741793582221287	0.370896791110644
41	0.603083226150708	0.793833547698583	0.396916773849292
42	0.804884069482805	0.390231861034391	0.195115930517195
43	0.765011397580755	0.46997720483849	0.234988602419245
44	0.80369745484881	0.392605090302382	0.196302545151191
45	0.801593751590749	0.396812496818503	0.198406248409252
46	0.765925340414129	0.468149319171742	0.234074659585871
47	0.727521546622847	0.544956906754306	0.272478453377153
48	0.742409009051074	0.515181981897851	0.257590990948926
49	0.697812013528398	0.604375972943205	0.302187986471602
50	0.689064665279011	0.621870669441978	0.310935334720989
51	0.703795694011564	0.592408611976873	0.296204305988436
52	0.671877621425377	0.656244757149246	0.328122378574623
53	0.661184671457661	0.677630657084677	0.338815328542339
54	0.687656934275827	0.624686131448346	0.312343065724173
55	0.66974554188583	0.660508916228339	0.330254458114170
56	0.630310262955382	0.739379474089237	0.369689737044618
57	0.626557706538067	0.746884586923866	0.373442293461933
58	0.576209769544114	0.847580460911772	0.423790230455886
59	0.520602352864816	0.958795294270367	0.479397647135183
60	0.64398255377078	0.71203489245844	0.35601744622922
61	0.607604642010938	0.784790715978123	0.392395357989062
62	0.684358461815664	0.631283076368672	0.315641538184336
63	0.695846186662776	0.608307626674448	0.304153813337224
64	0.713307171739462	0.573385656521076	0.286692828260538
65	0.761620131664892	0.476759736670216	0.238379868335108
66	0.711261235003801	0.577477529992398	0.288738764996199
67	0.697248918103363	0.605502163793274	0.302751081896637
68	0.905351657980154	0.189296684039692	0.094648342019846
69	0.881374997013562	0.237250005972876	0.118625002986438
70	0.848240116220561	0.303519767558878	0.151759883779439
71	0.870787835828388	0.258424328343223	0.129212164171612
72	0.833749985673657	0.332500028652685	0.166250014326343
73	0.910982116091957	0.178035767816086	0.0890178839080432
74	0.885916774298048	0.228166451403904	0.114083225701952
75	0.861934293405213	0.276131413189573	0.138065706594787
76	0.849687714442442	0.300624571115116	0.150312285557558
77	0.882277728476775	0.235444543046450	0.117722271523225
78	0.877557128779461	0.244885742441077	0.122442871220539
79	0.83856160593666	0.322876788126679	0.161438394063340
80	0.799827285651723	0.400345428696554	0.200172714348277
81	0.762976690346653	0.474046619306694	0.237023309653347
82	0.843239307744664	0.313521384510673	0.156760692255336
83	0.858042397656176	0.283915204687648	0.141957602343824
84	0.820623808656903	0.358752382686193	0.179376191343097
85	0.805536390543286	0.388927218913428	0.194463609456714
86	0.77676450700221	0.446470985995581	0.223235492997791
87	0.720173910446566	0.559652179106868	0.279826089553434
88	0.676590785667262	0.646818428665477	0.323409214332738
89	0.619416081165973	0.761167837668053	0.380583918834026
90	0.582616095541613	0.834767808916774	0.417383904458387
91	0.516372623966596	0.967254752066807	0.483627376033404
92	0.41878860885355	0.8375772177071	0.58121139114645
93	0.545870602893893	0.908258794212215	0.454129397106107
94	0.444233276159902	0.888466552319804	0.555766723840098
95	0.335050066531944	0.670100133063887	0.664949933468056
96	0.350797727987731	0.701595455975461	0.64920227201227
97	0.282848264971987	0.565696529943974	0.717151735028013
98	0.821677693134675	0.356644613730649	0.178322306865325

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/2010/Nov/19/t1290173213l4djsnx1wp8stf2/103qfr1290172702.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290173213l4djsnx1wp8stf2/103qfr1290172702.ps (open in new window)

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http://www.freestatistics.org/blog/date/2010/Nov/19/t1290173213l4djsnx1wp8stf2/8shy71290172702.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290173213l4djsnx1wp8stf2/8shy71290172702.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290173213l4djsnx1wp8stf2/93qfr1290172702.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290173213l4djsnx1wp8stf2/93qfr1290172702.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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