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multiple linear regression met seiz en met trend

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Sun, 28 Nov 2010 10:07:54 +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/28/t1290938833xj43j5i5khbxbm7.htm/, Retrieved Sun, 28 Nov 2010 11:07:23 +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/28/t1290938833xj43j5i5khbxbm7.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 «

1.579 9.769 2.146 9.321 2.462 9.939 3.695 9.336 4.831 10.195 5.134 9.464 6.250 10.010 5.760 10.213 6.249 9.563 2.917 9.890 1.741 9.305 2.359 9.391 1.511 9.928 2.059 8.686 2.635 9.843 2.867 9.627 4.403 10.074 5.720 9.503 4.502 10.119 5.749 10.000 5.627 9.313 2.846 9.866 1.762 9.172 2.429 9.241 1.169 9.659 2.154 8.904 2.249 9.755 2.687 9.080 4.359 9.435 5.382 8.971 4.459 10.063 6.398 9.793 4.596 9.454 3.024 9.759 1.887 8.820 2.070 9.403 1.351 9.676 2.218 8.642 2.461 9.402 3.028 9.610 4.784 9.294 4.975 9.448 4.607 10.319 6.249 9.548 4.809 9.801 3.157 9.596 1.910 8.923 2.228 9.746 1.594 9.829 2.467 9.125 2.222 9.782 3.607 9.441 4.685 9.162 4.962 9.915 5.770 10.444 5.480 10.209 5.000 9.985 3.228 9.842 1.993 9.429 2.288 10.132 1.580 9.849 2.111 9.172 2.192 10.313 3.601 9.819 4.665 9.955 4.876 10.048 5.813 10.082 5.589 10.541 5.331 10.208 3.075 10.233 2.002 9.439 2.306 9.963 1.507 10.158 1.992 9.225 2.487 10.474 3.490 9.757 4.647 10.490 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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

huwelijken[t] = + 2.02339519389154 + 0.0206906598277340geboortes[t] -0.838996818852548M1[t] -0.143411722839301M2[t] + 0.107295750823278M3[t] + 0.927473554199439M4[t] + 2.28832384063904M5[t] + 3.0371022378412M6[t] + 3.09509021922129M7[t] + 3.49918571539286M8[t] + 3.14300565779907M9[t] + 0.71777680606665M10[t] -0.418680426327679M11[t] + 0.00197522726513127t + 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)	2.02339519389154	1.284063	1.5758	0.118928	0.059464
geboortes	0.0206906598277340	0.138189	0.1497	0.881347	0.440674
M1	-0.838996818852548	0.190709	-4.3994	3.2e-05	1.6e-05
M2	-0.143411722839301	0.20217	-0.7094	0.480113	0.240056
M3	0.107295750823278	0.192387	0.5577	0.578564	0.289282
M4	0.927473554199439	0.186992	4.96	4e-06	2e-06
M5	2.28832384063904	0.188961	12.11	0	0
M6	3.0371022378412	0.186338	16.2989	0	0
M7	3.09509021922129	0.204729	15.118	0	0
M8	3.49918571539286	0.202291	17.2978	0	0
M9	3.14300565779907	0.188645	16.6609	0	0
M10	0.71777680606665	0.193704	3.7055	0.000382	0.000191
M11	-0.418680426327679	0.192952	-2.1699	0.03291	0.016455
t	0.00197522726513127	0.001887	1.0467	0.298308	0.149154

Multiple Linear Regression - Regression Statistics
Multiple R	0.976305094874342
R-squared	0.953171638277599
Adjusted R-squared	0.945747629711852
F-TEST (value)	128.390428140858
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.372111359493414
Sum Squared Residuals	11.354282836851

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.579	1.38850065816125	0.190499341838751
2	2.146	2.07679156583681	0.0692084341631907
3	2.462	2.34226109453805	0.119738905461946
4	3.695	3.15193765730323	0.543062342696772
5	4.831	4.53253644779999	0.298463552200014
6	5.134	5.2681651999332	-0.134165199933197
7	6.25	5.33942550884436	0.910574491155643
8	5.76	5.7496964362261	0.010303563773903
9	6.249	5.38204267700941	0.866957322990587
10	2.917	2.9655548983058	-0.0485548983057997
11	1.741	1.81896885717737	-0.0779688571773661
12	2.359	2.24140390751536	0.117596092484637
13	1.511	1.41549320025544	0.0955067997445597
14	2.059	2.08735572402777	-0.0283557240277711
15	2.635	2.36397751837617	0.271022481623830
16	2.867	3.18166136649467	-0.314661366494673
17	4.403	4.5537356051424	-0.150735605142403
18	5.72	5.29267486284806	0.427325137151944
19	4.502	5.36538351794716	-0.863383517947156
20	5.749	5.76899205286436	-0.0199920528643632
21	5.627	5.40057273923405	0.226427260765947
22	2.846	2.9887610496515	-0.1427610496515
23	1.762	1.83991972660185	-0.077919726601854
24	2.429	2.26200303572278	0.166996964277222
25	1.169	1.43363013994336	-0.264630139943355
26	2.154	2.11556901505179	0.0384309849482069
27	2.249	2.38585946749290	-0.136859467492904
28	2.687	3.19404630275048	-0.507046302750478
29	4.359	4.56421700069406	-0.205217000694056
30	5.382	5.30537015900128	0.0766298409987232
31	4.459	5.38792756817838	-0.928927568178378
32	6.398	5.7884118134616	0.609588186538403
33	4.596	5.42719284945134	-0.831192849451339
34	3.024	3.01024987623151	0.013750123768492
35	1.887	1.85633934152407	0.0306606584759322
36	2.07	2.28905764979645	-0.219057649796447
37	1.351	1.45768460834200	-0.106684608342002
38	2.218	2.1338507893585	0.0841492106414983
39	2.461	2.40225839175529	0.0587416082447105
40	3.028	3.22871507964075	-0.200715079640752
41	4.784	4.58500234483992	0.198997655160079
42	4.975	5.33894233092068	-0.363942330920681
43	4.607	5.41692710427585	-0.809927104275853
44	6.249	5.80704532898538	0.441954671014623
45	4.809	5.45807523559314	-0.649075235593138
46	3.157	3.03058002586116	0.126419974138838
47	1.91	1.8821732066679	0.0278267933321007
48	2.228	2.31985727329893	-0.0918572732989346
49	1.594	1.48455300647722	0.109446993522780
50	2.467	2.16754710523687	0.299452894763127
51	2.222	2.43382356967140	-0.211823569671404
52	3.607	3.24892108531144	0.35807891468856
53	4.685	4.60597390492423	0.0790260950757647
54	4.962	5.37230759624181	-0.410307596241809
55	5.77	5.44321616393589	0.326783836064105
56	5.48	5.84442458231308	-0.364424582313085
57	5	5.48558504418302	-0.485585044183017
58	3.228	3.05937265536036	0.168627344639640
59	1.993	1.91634540772231	0.0766545922776923
60	2.288	2.35154659517401	-0.0635465951740152
61	1.58	1.50866954685535	0.0713304531446496
62	2.111	2.19222229343035	-0.0812222934303513
63	2.192	2.46851303722151	-0.276513037221506
64	3.601	3.2804448819079	0.320555118092101
65	4.665	4.64608432534920	0.0189156746507965
66	4.876	5.39876218118047	-0.522762181180472
67	5.813	5.45942887225983	0.353571127740169
68	5.589	5.87499660855747	-0.285996608557468
69	5.331	5.51390178850618	-0.182901788506176
70	3.075	3.09116543053458	-0.0161654305345795
71	2.002	1.94025504150216	0.0617449584978393
72	2.306	2.37175260084470	-0.0657526008447032
73	1.507	1.53876568792370	-0.0317656879236954
74	1.992	2.21702162558280	-0.225021625582797
75	2.487	2.49554696063535	-0.00854696063534637
76	3.49	3.30286478818015	0.187135211819845
77	4.647	4.68085655553862	-0.0338565555386166
78	5.594	5.42728583210191	0.166714167898090
79	5.611	5.49062161829905	0.120378381700954
80	5.788	5.90074771106199	-0.112747711061989
81	6.204	5.54768086702386	0.656319132976143
82	3.013	3.12631009260089	-0.113310092600892
83	1.931	1.97208919799604	-0.0410891979960358
84	2.549	2.39098614550349	0.158013854496512
85	1.504	1.56770315204169	-0.0637031520416876
86	2.09	2.24664188147510	-0.156641881475104
87	2.702	2.51775996030932	0.184240039690675
88	2.939	3.32540883841138	-0.386408838411377
89	4.5	4.70559381571158	-0.205593815711579
90	6.208	5.4474918377726	0.760508162227402
91	6.415	5.52406964625948	0.890930353740516
92	5.657	5.93568546653002	-0.278685466530024
93	5.964	5.56494879899901	0.399051201000993
94	3.163	3.1510059714542	0.0119940285458016
95	1.997	1.99690922080831	9.07791916912176e-05
96	2.422	2.42439279214427	-0.00239279214426983

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.409981086220062	0.819962172440123	0.590018913779938
18	0.684268998505433	0.631462002989135	0.315731001494567
19	0.975585277154663	0.0488294456906742	0.0244147228453371
20	0.956708988313468	0.0865820233730642	0.0432910116865321
21	0.948638065646876	0.102723868706247	0.0513619343531237
22	0.92329960351834	0.153400792963318	0.0767003964816592
23	0.892848564540205	0.214302870919590	0.107151435459795
24	0.8650597591696	0.269880481660801	0.134940240830400
25	0.80828234523746	0.383435309525081	0.191717654762541
26	0.796552222336987	0.406895555326027	0.203447777663013
27	0.733436811015118	0.533126377969764	0.266563188984882
28	0.722867326733364	0.554265346533272	0.277132673266636
29	0.653458924731271	0.693082150537458	0.346541075268729
30	0.58591861578827	0.82816276842346	0.41408138421173
31	0.671658766228124	0.656682467543752	0.328341233771876
32	0.874097151203562	0.251805697592875	0.125902848796438
33	0.945520696103542	0.108958607792916	0.054479303896458
34	0.94568998213504	0.108620035729921	0.0543100178649606
35	0.929229992224631	0.141540015550737	0.0707700077753687
36	0.90539396547781	0.18921206904438	0.09460603452219
37	0.892052621562077	0.215894756875847	0.107947378437924
38	0.877667916204206	0.244664167591589	0.122332083795794
39	0.844687332137643	0.310625335724715	0.155312667862357
40	0.84217237966835	0.315655240663299	0.157827620331649
41	0.820467943922994	0.359064112154013	0.179532056077006
42	0.780899900085713	0.438200199828574	0.219100099914287
43	0.880937091056567	0.238125817886865	0.119062908943433
44	0.92159341000847	0.156813179983061	0.0784065899915305
45	0.93946426775949	0.121071464481019	0.0605357322405097
46	0.93228415089198	0.135431698216042	0.067715849108021
47	0.916195299785097	0.167609400429806	0.0838047002149031
48	0.906371103609992	0.187257792780016	0.0936288963900079
49	0.907271248912406	0.185457502175189	0.0927287510875945
50	0.947481912860108	0.105036174279785	0.0525180871398923
51	0.926384129194112	0.147231741611775	0.0736158708058877
52	0.95311121914084	0.0937775617183212	0.0468887808591606
53	0.93954725237711	0.120905495245778	0.0604527476228892
54	0.933153148871945	0.133693702256111	0.0668468511280553
55	0.95511074027453	0.0897785194509405	0.0448892597254702
56	0.940366410787981	0.119267178424038	0.0596335892120189
57	0.96181035088946	0.0763792982210787	0.0381896491105394
58	0.95771760113364	0.0845647977327191	0.0422823988663596
59	0.946486664466992	0.107026671066016	0.0535133355330079
60	0.924623310521104	0.150753378957793	0.0753766894788963
61	0.905578541810422	0.188842916379157	0.0944214581895784
62	0.878684210080633	0.242631579838734	0.121315789919367
63	0.846796195640545	0.30640760871891	0.153203804359455
64	0.888556472527084	0.222887054945832	0.111443527472916
65	0.866532952194093	0.266934095611815	0.133467047805907
66	0.957805254670387	0.0843894906592258	0.0421947453296129
67	0.94736722774159	0.105265544516819	0.0526327722584094
68	0.919313119038387	0.161373761923226	0.080686880961613
69	0.96004598393378	0.079908032132439	0.0399540160662195
70	0.936104377300296	0.127791245399408	0.0638956226997042
71	0.907127701550331	0.185744596899337	0.0928722984496685
72	0.859919128188775	0.280161743622450	0.140080871811225
73	0.793907743200731	0.412184513598538	0.206092256799269
74	0.705600301450938	0.588799397098123	0.294399698549062
75	0.618397963054621	0.763204073890759	0.381602036945379
76	0.676371989596477	0.647256020807046	0.323628010403523
77	0.588937918703887	0.822124162592226	0.411062081296113
78	0.652976022053418	0.694047955893164	0.347023977946582
79	0.940417805328805	0.119164389342391	0.0595821946711955

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	1	0.0158730158730159	OK
10% type I error level	8	0.126984126984127	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/10x9k61290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/10x9k61290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/1jhmx1290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/1jhmx1290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/2jhmx1290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/2jhmx1290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/3jhmx1290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/3jhmx1290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/4brmi1290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/4brmi1290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/5brmi1290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/5brmi1290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/6brmi1290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/6brmi1290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/740ll1290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/740ll1290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/8x9k61290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/8x9k61290938866.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/9x9k61290938866.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290938833xj43j5i5khbxbm7/9x9k61290938866.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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