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paper 1

*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 12:26:54 +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/t12909472523hifhnltz1dm4d5.htm/, Retrieved Sun, 28 Nov 2010 13:27:32 +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/t12909472523hifhnltz1dm4d5.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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

geboortes[t] = + 9.37499683097009 + 0.122482864440576huwelijk[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)	9.37499683097009	0.120633	77.7151	0	0
huwelijk	0.122482864440576	0.030606	4.0019	0.000125	6.3e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.381537512253697
R-squared	0.14557087325674
Adjusted R-squared	0.136481201695641
F-TEST (value)	16.0149761493855
F-TEST (DF numerator)	1
F-TEST (DF denominator)	94
p-value	0.000125324935773441
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.476581915316111
Sum Squared Residuals	21.3502502685990

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9.769	9.56839727392173	0.200602726078266
2	9.321	9.63784505805956	-0.316845058059564
3	9.939	9.67654964322279	0.262450356777214
4	9.336	9.82757101507802	-0.491571015078015
5	10.195	9.9667115490825	0.228288450917491
6	9.464	10.003823857008	-0.539823857008004
7	10.01	10.1405147337237	-0.130514733723687
8	10.213	10.0804981301478	0.132501869852195
9	9.563	10.1403922508592	-0.577392250859245
10	9.89	9.73227934654325	0.157720653456753
11	9.305	9.58823949796113	-0.283239497961131
12	9.391	9.6639339081854	-0.272933908185407
13	9.928	9.5600684391398	0.367931560860202
14	8.686	9.62718904885323	-0.941189048853234
15	9.843	9.697739178771	0.145260821228995
16	9.627	9.72615520332122	-0.0991552033212184
17	10.074	9.91428888310194	0.159711116898057
18	9.503	10.0755988155702	-0.572598815570181
19	10.119	9.92641468668156	0.192585313318440
20	10	10.0791508186390	-0.0791508186389581
21	9.313	10.0642079091772	-0.751207909177207
22	9.866	9.72358306316797	0.142416936832033
23	9.172	9.59081163811438	-0.418811638114382
24	9.241	9.67250770869625	-0.431507708696247
25	9.659	9.51817929950112	0.140820700498879
26	8.904	9.63882492097509	-0.734824920975089
27	9.755	9.65046079309694	0.104539206903058
28	9.08	9.70410828772192	-0.624108287721915
29	9.435	9.90889963706656	-0.473899637066557
30	8.971	10.0341996073893	-1.06319960738927
31	10.063	9.92114792351062	0.141852076489385
32	9.793	10.1586421976609	-0.365642197660893
33	9.454	9.93792807593897	-0.483928075938974
34	9.759	9.74538501303839	0.0136149869616109
35	8.82	9.60612199616945	-0.786121996169454
36	9.403	9.62853636036208	-0.225536360362080
37	9.676	9.5404711808293	0.135528819170694
38	8.642	9.64666382429929	-1.00466382429929
39	9.402	9.67642716035835	-0.274427160358346
40	9.61	9.74587494449615	-0.135874944496152
41	9.294	9.9609548544538	-0.666954854453802
42	9.448	9.98434908156195	-0.536349081561952
43	10.319	9.93927538744782	0.37972461255218
44	9.548	10.1403922508592	-0.592392250859246
45	9.801	9.96401692606482	-0.163016926064817
46	9.596	9.76167523400899	-0.165675234008986
47	8.923	9.60893910205159	-0.685939102051588
48	9.746	9.6478886529437	0.0981113470563093
49	9.829	9.57023451688837	0.258765483111634
50	9.125	9.67716205754499	-0.552162057544989
51	9.782	9.64715375575705	0.134846244242952
52	9.441	9.81679252300724	-0.375792523007244
53	9.162	9.94882905087418	-0.786829050874185
54	9.915	9.98275680432423	-0.067756804324226
55	10.444	10.0817229587922	0.362277041207791
56	10.209	10.0462029281044	0.162797071895556
57	9.985	9.98741115317297	-0.00241115317296757
58	9.842	9.77037151738427	0.0716284826157337
59	9.429	9.61910517980016	-0.190105179800156
60	10.132	9.65523762481013	0.476762375189874
61	9.849	9.5685197567862	0.280480243213802
62	9.172	9.63355815780414	-0.461558157804143
63	10.313	9.64347926982383	0.66952073017617
64	9.819	9.8160576258206	0.00294237417939925
65	9.955	9.94637939358537	0.00862060641462593
66	10.048	9.97222327798234	0.0757767220176644
67	10.082	10.0869897219632	-0.0049897219631542
68	10.541	10.0595535603285	0.481446439671534
69	10.208	10.0279529813028	0.180047018697203
70	10.233	9.75163163912486	0.481368360875142
71	9.439	9.62020752558012	-0.181207525580121
72	9.963	9.65744231637006	0.305557683629943
73	10.158	9.55957850768204	0.598421492317963
74	9.225	9.61898269693572	-0.393982696935716
75	10.474	9.6796117148338	0.7943882851662
76	9.757	9.8024620278677	-0.0454620278676980
77	10.49	9.94417470202544	0.545825297974556
78	10.281	10.0601659746507	0.220834025349332
79	10.444	10.0622481833462	0.381751816653842
80	10.64	10.0839276503521	0.55607234964786
81	10.695	10.1348805219594	0.56011947804058
82	10.786	9.74403770152954	1.04196229847046
83	9.832	9.61151124220484	0.220488757795161
84	9.747	9.68720565242912	0.059794347570884
85	10.411	9.55921105908871	0.851788940911285
86	9.511	9.6309860176509	-0.119986017650892
87	10.402	9.70594553068852	0.696054469311475
88	9.701	9.73497396956094	-0.03397396956094
89	10.54	9.92616972095268	0.61383027904732
90	10.112	10.1353704534172	-0.0233704534171824
91	10.915	10.1607244063564	0.754275593643618
92	11.183	10.0678823951104	1.11511760488957
93	10.384	10.1054846344937	0.278515365506318
94	10.834	9.76241013119563	1.07158986880437
95	9.886	9.61959511125792	0.266404888742081
96	10.216	9.67165032864516	0.544349671354836

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.451898136511276	0.903796273022552	0.548101863488724
6	0.385084640685739	0.770169281371478	0.614915359314261
7	0.268701338333755	0.53740267666751	0.731298661666245
8	0.214903419607008	0.429806839214015	0.785096580392992
9	0.196272926202852	0.392545852405705	0.803727073797148
10	0.136644272221847	0.273288544443695	0.863355727778153
11	0.109144520236776	0.218289040473551	0.890855479763224
12	0.076010471241874	0.152020942483748	0.923989528758126
13	0.0706269731358916	0.141253946271783	0.929373026864108
14	0.250422421169435	0.500844842338869	0.749577578830565
15	0.203622830929254	0.407245661858508	0.796377169070746
16	0.146496942203768	0.292993884407535	0.853503057796232
17	0.121999561939501	0.243999123879001	0.8780004380605
18	0.114736083388784	0.229472166777567	0.885263916611216
19	0.100790418931515	0.201580837863031	0.899209581068485
20	0.071557927438672	0.143115854877344	0.928442072561328
21	0.0954489171712359	0.190897834342472	0.904551082828764
22	0.073647161087001	0.147294322174002	0.926352838912999
23	0.0677541171889386	0.135508234377877	0.932245882811061
24	0.0596107762403864	0.119221552480773	0.940389223759614
25	0.0440222543268646	0.0880445086537293	0.955977745673135
26	0.0699821949163957	0.139964389832791	0.930017805083604
27	0.0536095770322721	0.107219154064544	0.946390422967728
28	0.0625335402586693	0.125067080517339	0.93746645974133
29	0.0545140509220751	0.109028101844150	0.945485949077925
30	0.138221508453200	0.276443016906400	0.8617784915468
31	0.125815384559526	0.251630769119052	0.874184615440474
32	0.105462509052964	0.210925018105929	0.894537490947036
33	0.0970307605551455	0.194061521110291	0.902969239444855
34	0.0759557796731506	0.151911559346301	0.92404422032685
35	0.123103032809667	0.246206065619335	0.876896967190333
36	0.0984795154475077	0.196959030895015	0.901520484552492
37	0.0802681683050862	0.160536336610172	0.919731831694914
38	0.201820113042305	0.40364022608461	0.798179886957695
39	0.174641052117328	0.349282104234655	0.825358947882673
40	0.144738502805761	0.289477005611522	0.855261497194239
41	0.178044277647827	0.356088555295654	0.821955722352173
42	0.191128135450994	0.382256270901988	0.808871864549006
43	0.218923188941862	0.437846377883725	0.781076811058138
44	0.258722308023832	0.517444616047664	0.741277691976168
45	0.234169281879903	0.468338563759805	0.765830718120097
46	0.205390464852297	0.410780929704594	0.794609535147703
47	0.289225523380683	0.578451046761365	0.710774476619317
48	0.256484718029581	0.512969436059162	0.743515281970419
49	0.237201740713679	0.474403481427358	0.762798259286321
50	0.292373506194208	0.584747012388416	0.707626493805792
51	0.260515902010827	0.521031804021655	0.739484097989173
52	0.276217411161873	0.552434822323746	0.723782588838127
53	0.486914740527861	0.973829481055723	0.513085259472139
54	0.47481531716555	0.9496306343311	0.52518468283445
55	0.496489917321256	0.992979834642513	0.503510082678744
56	0.478872641041419	0.957745282082837	0.521127358958581
57	0.459922852127491	0.919845704254982	0.540077147872509
58	0.426173520683216	0.852347041366431	0.573826479316785
59	0.420643090173291	0.841286180346582	0.579356909826709
60	0.430162814544886	0.860325629089773	0.569837185455114
61	0.394592785470545	0.78918557094109	0.605407214529455
62	0.500981869962599	0.998036260074801	0.499018130037401
63	0.55642263513291	0.88715472973418	0.44357736486709
64	0.533029572421497	0.933940855157006	0.466970427578503
65	0.517445071023089	0.965109857953823	0.482554928976911
66	0.496980440876922	0.993960881753845	0.503019559123078
67	0.503186966453592	0.993626067092816	0.496813033546408
68	0.496993667536268	0.993987335072537	0.503006332463732
69	0.472667763239423	0.945335526478846	0.527332236760577
70	0.451038684896732	0.902077369793463	0.548961315103268
71	0.472680358451616	0.945360716903233	0.527319641548384
72	0.425236211680509	0.850472423361018	0.574763788319491
73	0.418940383874565	0.83788076774913	0.581059616125435
74	0.568580289144807	0.862839421710387	0.431419710855193
75	0.610295697030627	0.779408605938745	0.389704302969373
76	0.630015753686022	0.739968492627955	0.369984246313978
77	0.594944940411018	0.810110119177964	0.405055059588982
78	0.562422555817974	0.875154888364052	0.437577444182026
79	0.513212144200535	0.97357571159893	0.486787855799465
80	0.464556146423788	0.929112292847576	0.535443853576212
81	0.410989607100149	0.821979214200297	0.589010392899851
82	0.534646076100786	0.930707847798428	0.465353923899214
83	0.460636626273591	0.921273252547181	0.539363373726409
84	0.431670884293313	0.863341768586626	0.568329115706687
85	0.456797904639896	0.913595809279792	0.543202095360104
86	0.497998356445396	0.995996712890793	0.502001643554604
87	0.42983063946423	0.85966127892846	0.57016936053577
88	0.482762483723426	0.965524967446852	0.517237516276574
89	0.372001580136171	0.744003160272342	0.627998419863829
90	0.514393189669483	0.971213620661034	0.485606810330517
91	0.367156646250098	0.734313292500196	0.632843353749902

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	1	0.0114942528735632	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/10y32n1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/10y32n1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/1rknb1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/1rknb1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/2rknb1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/2rknb1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/3ktme1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/3ktme1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/4ktme1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/4ktme1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/5ktme1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/5ktme1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/6vk4z1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/6vk4z1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/75c3k1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/75c3k1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/85c3k1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/85c3k1290947204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/95c3k1290947204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909472523hifhnltz1dm4d5/95c3k1290947204.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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