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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: Tue, 14 Dec 2010 00:04:26 +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/Dec/14/t1292285151xq2v5d21cswuwcy.htm/, Retrieved Tue, 14 Dec 2010 01:06:01 +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/Dec/14/t1292285151xq2v5d21cswuwcy.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 «

1579 0 4,0 45,7 17.0 2146 0 5,9 81,9 21.0 2462 0 7,1 56,8 21.0 3695 0 10,5 65,1 18.0 4831 0 15,1 86,2 20.0 5134 0 16,8 35,1 11.0 6250 0 15,3 133,8 20.0 5760 0 18,4 34,5 13.0 6249 0 16,1 69,9 14.0 2917 0 11,3 98,3 23.0 1741 0 7,9 86,7 24.0 2359 0 5,6 58,2 22.0 1511 1 3,4 83,6 17.0 2059 0 4,8 83,5 18.0 2635 0 6,5 112,3 24.0 2867 0 8,5 134,3 23.0 4403 0 15,1 30,0 8.0 5720 0 15,7 44,5 10.0 4502 0 18,7 120,1 18.0 5749 0 19,2 43,4 13.0 5627 0 12,9 199,4 23.0 2846 0 14,4 68,1 14.0 1762 0 6,2 99,8 15.0 2429 0 3,3 69,5 18.0 1169 0 4,6 71,3 18.0 2154 1 7,2 167,8 20.0 2249 0 7,8 66,3 14.0 2687 0 9,9 41,9 12.0 4359 0 13,6 57,2 20.0 5382 0 17,1 72,3 14.0 4459 0 17,8 96,5 16.0 6398 0 18,6 172,1 19.0 4596 0 14,7 25,8 12.0 3024 0 10,5 105,1 17.0 1887 0 8,6 92,2 16.0 2070 0 4,4 109,3 18.0 1351 0 2,3 101,7 19.0 2218 0 2,8 29,1 8.0 2461 1 8,8 34,6 10.0 3028 0 10,7 46,7 10.0 4784 0 13,9 82,0 19.0 4975 0 19,3 34,4 8.0 4607 0 19,5 72,7 13.0 6249 0 20,4 44,4 8.0 4809 0 15,3 31,0 12.0 315 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	14 seconds
R Server	'George Udny Yule' @ 72.249.76.132
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation

Huwelijken[t] = + 783.757819492143 + 510.48054551803Specialedag[t] + 253.376841202445Temperatuur[t] + 4.64850458396683Neerslag[t] -20.7393173509528`Neerslagdagen `[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)	783.757819492143	329.571224	2.3781	0.01905	0.009525
Specialedag	510.48054551803	245.913126	2.0759	0.040136	0.020068
Temperatuur	253.376841202445	12.923472	19.6059	0	0
Neerslag	4.64850458396683	2.463393	1.887	0.061677	0.030839
`Neerslagdagen `	-20.7393173509528	19.71155	-1.0521	0.294941	0.14747

Multiple Linear Regression - Regression Statistics
Multiple R	0.903839417044672
R-squared	0.816925691803653
Adjusted R-squared	0.810557889779433
F-TEST (value)	128.290058123097
F-TEST (DF numerator)	4
F-TEST (DF denominator)	115
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	706.429999739536
Sum Squared Residuals	57389984.6211801

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1579	1657.13344882299	-78.1334488229914
2	2146	2223.86804364344	-77.8680436434429
3	2462	2411.2427880288	50.7572119712003
4	3695	3373.52458821691	321.475411783093
5	4831	4595.66286976795	235.337130232048
6	5134	4975.51877172998	158.481228270020
7	6250	4867.60705620526	1382.39294379474
8	5760	5336.65398020161	423.346019798393
9	6249	4897.70499035746	1351.29500964254
10	2917	3626.8598266118	-709.859826611799
11	1741	2690.71659599852	-949.716595998516
12	2359	2016.94611529174	342.053884708258
13	1511	2191.76621335191	-680.766213351912
14	2059	2014.80907770796	44.1909222920424
15	2635	2454.99073566464	180.009264335357
16	2867	3084.75083626776	-217.750836267757
17	4403	4583.28872036045	-180.28872036045
18	5720	4761.23950684753	958.76049315247
19	4502	5706.88243819514	-1204.88243819514
20	5749	5580.72714396087	168.272856039132
21	5627	4502.22658597476	1124.77341402524
22	2846	4458.59705206216	-1612.59705206216
23	1762	2507.5252321629	-745.525232162899
24	2429	1569.66475172875	859.335248271247
25	1169	1907.42195354307	-738.421953543073
26	2154	3483.78434383835	-1329.78434383835
27	2249	2777.94259187488	-528.942591874876
28	2687	3238.08908125313	-551.089081253127
29	4359	4080.79097502925	278.209024970755
30	5382	5162.23824256142	219.761757438578
31	4459	5410.61720763323	-951.617207633225
32	6398	5902.52767509021	495.472324909785
33	4596	4379.456995223	216.543004777000
34	3024	3580.20408892653	-556.204088926534
35	1887	3059.56169885967	-1172.56169885967
36	2070	2033.38975949332	36.6102405066764
37	1351	1445.23044077909	-94.2304407790868
38	2218	1462.5699194448	755.430080555201
39	2461	3477.39965268741	-1016.39965268741
40	3028	3504.58201092003	-476.582010920029
41	4784	4292.82625842331	491.17374157669
42	4975	5667.92487358018	-692.924873580175
43	4607	5792.94138063183	-1185.94138063183
44	6249	5993.12444474253	255.875555257467
45	4809	4555.65532378109	253.344676218905
46	3157	2771.84939810104	385.150601898956
47	1910	2817.49008894672	-907.490088946718
48	2228	1871.21375560281	356.786244397193
49	1594	1811.76009993120	-217.760099931196
50	2467	1987.13860628861	479.861393711391
51	2222	2261.16782576316	-39.1678257631586
52	3607	3968.55591261659	-361.555912616588
53	4685	3988.50788304881	696.492116951191
54	4962	4904.93537815785	57.0646218421487
55	5770	5341.69905933541	428.300940664587
56	5480	5707.42596630108	-227.425966301081
57	5000	4902.86181493924	97.1381850607566
58	3228	3751.41601117262	-523.416011172618
59	1993	2417.24396760459	-424.243967604592
60	2288	1471.38901517965	816.610984820346
61	1580	1727.08184724278	-147.081847242782
62	2111	1373.87922955282	737.120770447178
63	2192	2435.86465904146	-243.864659041463
64	3601	3356.61768671298	244.382313287020
65	4665	4574.35128249285	90.6487175071544
66	4876	5493.65128795271	-617.651287952714
67	5813	5662.81525200155	150.184747998448
68	5589	5052.47006001463	536.529939985372
69	5331	5106.5999327226	224.400067277406
70	3075	4305.23987033068	-1230.23987033068
71	2002	2239.14276767349	-237.142767673486
72	2306	1453.24491759563	852.755082404368
73	1507	1114.7716290979	392.228370902099
74	1992	1382.01077843714	609.989221562862
75	2487	1895.67187662093	591.328123379067
76	3490	3022.16457592211	467.835424077888
77	4647	4504.28974745438	142.710252545625
78	5594	5652.41391462215	-58.4139146221518
79	5611	6669.10369882957	-1058.10369882957
80	5788	5418.66714405848	369.332855941517
81	6204	5322.74340098201	881.25659901799
82	3013	4290.851377678	-1277.851377678
83	1931	3007.53365562736	-1076.53365562736
84	2549	2337.68439657833	211.315603421668
85	1504	2451.42075228544	-947.420752285444
86	2090	2576.87996466205	-486.879964662054
87	2702	2683.72863584019	18.2713641598059
88	2939	4407.04664868711	-1468.04664868711
89	4500	4507.45009330905	-7.45009330905295
90	6208	5284.93716559634	923.062834403657
91	6415	5728.52271464467	686.477285355326
92	5657	5136.72827343953	520.271726560471
93	5964	4292.29606686787	1671.70393313213
94	3163	3519.16466013197	-356.164660131974
95	1997	2383.75313661823	-386.753136618229
96	2422	1843.20244764390	578.797552356096
97	1376	2282.35226232258	-906.352262322576
98	2202	2265.781122239	-63.781122239002
99	2683	2535.40319669202	147.596803307982
100	3303	3060.85410787364	242.145892126362
101	5202	4961.55992142758	240.440078572422
102	5231	4835.4870383046	395.512961695403
103	4880	5403.43723122332	-523.437231223324
104	7998	5774.73519985335	2223.26480014665
105	4977	4410.8110622014	566.188937798602
106	3531	3386.71935432891	144.280645671087
107	2025	2307.07468254040	-282.074682540398
108	2205	1424.88209778236	780.117902217636
109	1442	1004.64073845393	437.35926154607
110	2238	1546.55771254639	691.442287453607
111	2179	2487.32090791077	-308.32090791077
112	3218	3858.82314016325	-640.823140163255
113	5139	4280.16648541013	858.833514589872
114	4990	5036.17175413787	-46.171754137871
115	4914	5446.92408804679	-532.92408804679
116	6084	5687.86330547656	396.136694523443
117	5672	5225.47076589272	446.529234107285
118	3548	3782.44071143009	-234.440711430094
119	1793	3178.58369461079	-1385.58369461079
120	2086	1500.10279969565	585.897200304353

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0981585154722223	0.196317030944445	0.901841484527778
9	0.136331501529570	0.272663003059141	0.86366849847043
10	0.251573552450415	0.503147104900831	0.748426447549585
11	0.201974471959175	0.40394894391835	0.798025528040825
12	0.330247968758375	0.66049593751675	0.669752031241625
13	0.237407113916348	0.474814227832696	0.762592886083652
14	0.202551177845609	0.405102355691218	0.797448822154391
15	0.138470336758890	0.276940673517781	0.86152966324111
16	0.135467671171825	0.270935342343649	0.864532328828175
17	0.192805293339014	0.385610586678029	0.807194706660985
18	0.162294102392674	0.324588204785348	0.837705897607326
19	0.521571052906894	0.956857894186213	0.478428947093106
20	0.442855212397931	0.885710424795863	0.557144787602069
21	0.449550533562057	0.899101067124114	0.550449466437943
22	0.818407551418289	0.363184897163423	0.181592448581711
23	0.842198431519605	0.315603136960790	0.157801568480395
24	0.859991407411641	0.280017185176717	0.140008592588359
25	0.856972866017269	0.286054267965462	0.143027133982731
26	0.879959555783768	0.240080888432464	0.120040444216232
27	0.864382293048862	0.271235413902276	0.135617706951138
28	0.845510132741524	0.308979734516952	0.154489867258476
29	0.810122844529424	0.379754310941153	0.189877155470576
30	0.766159441370222	0.467681117259557	0.233840558629778
31	0.814346888339356	0.371306223321288	0.185653111660644
32	0.780365991382519	0.439268017234962	0.219634008617481
33	0.738423936706327	0.523152126587347	0.261576063293673
34	0.719507019040426	0.560985961919148	0.280492980959574
35	0.788410729333393	0.423178541333214	0.211589270666607
36	0.748422934389998	0.503154131220004	0.251577065610002
37	0.703810460371547	0.592379079256907	0.296189539628453
38	0.726082274776606	0.547835450446787	0.273917725223394
39	0.753908490208657	0.492183019582686	0.246091509791343
40	0.730561512047349	0.538876975905302	0.269438487952651
41	0.708261268142401	0.583477463715198	0.291738731857599
42	0.704071383677469	0.591857232645062	0.295928616322531
43	0.780485583917935	0.439028832164131	0.219514416082065
44	0.7473211599449	0.5053576801102	0.2526788400551
45	0.711621533547827	0.576756932904347	0.288378466452173
46	0.678293127115094	0.643413745769813	0.321706872884906
47	0.709125014753202	0.581749970493596	0.290874985246798
48	0.673404586446651	0.653190827106698	0.326595413553349
49	0.639626492860613	0.720747014278773	0.360373507139387
50	0.611020373459211	0.777959253081578	0.388979626540789
51	0.557331091022982	0.885337817954036	0.442668908977018
52	0.567618180361139	0.864763639277722	0.432381819638861
53	0.573377687230909	0.853244625538183	0.426622312769091
54	0.520970622504197	0.958058754991605	0.479029377495802
55	0.489172106754355	0.97834421350871	0.510827893245645
56	0.439299715379685	0.87859943075937	0.560700284620315
57	0.387383245451186	0.774766490902373	0.612616754548814
58	0.365719469859515	0.731438939719029	0.634280530140485
59	0.340856486430637	0.681712972861274	0.659143513569363
60	0.351489641399542	0.702979282799085	0.648510358600457
61	0.303991080742398	0.607982161484797	0.696008919257602
62	0.29918893548984	0.59837787097968	0.70081106451016
63	0.258747440756263	0.517494881512525	0.741252559243737
64	0.224012967005324	0.448025934010647	0.775987032994677
65	0.229971359165417	0.459942718330835	0.770028640834583
66	0.215042075163463	0.430084150326926	0.784957924836537
67	0.179469527464471	0.358939054928942	0.820530472535529
68	0.169668262076744	0.339336524153488	0.830331737923256
69	0.141457218640132	0.282914437280264	0.858542781359868
70	0.208905070052676	0.417810140105351	0.791094929947324
71	0.177284709301934	0.354569418603869	0.822715290698066
72	0.192168246782776	0.384336493565551	0.807831753217224
73	0.164745844266824	0.329491688533647	0.835254155733176
74	0.149443765686731	0.298887531373462	0.850556234313269
75	0.136870096517349	0.273740193034697	0.863129903482652
76	0.122635586057828	0.245271172115657	0.877364413942171
77	0.0971345141660604	0.194269028332121	0.90286548583394
78	0.111192523215783	0.222385046431565	0.888807476784218
79	0.149574629929650	0.299149259859299	0.85042537007035
80	0.124052372338479	0.248104744676959	0.87594762766152
81	0.150155727951437	0.300311455902873	0.849844272048563
82	0.228100398715007	0.456200797430014	0.771899601284993
83	0.287188408029612	0.574376816059223	0.712811591970388
84	0.240622099037847	0.481244198075694	0.759377900962153
85	0.272682684580339	0.545365369160678	0.727317315419661
86	0.257423970802246	0.514847941604492	0.742576029197754
87	0.210710023989220	0.421420047978439	0.78928997601078
88	0.482215336147253	0.964430672294506	0.517784663852747
89	0.419406961403033	0.838813922806066	0.580593038596967
90	0.476574192007864	0.953148384015729	0.523425807992136
91	0.470512631303302	0.941025262606604	0.529487368696698
92	0.430355835347437	0.860711670694874	0.569644164652563
93	0.760125319017977	0.479749361964047	0.239874680982023
94	0.752439479342767	0.495121041314467	0.247560520657233
95	0.704139642230729	0.591720715538543	0.295860357769271
96	0.670653900265121	0.658692199469758	0.329346099734879
97	0.70096656540105	0.598066869197898	0.299033434598949
98	0.659372380615785	0.68125523876843	0.340627619384215
99	0.596154106302148	0.807691787395703	0.403845893697852
100	0.51921049241397	0.96157901517206	0.48078950758603
101	0.441163270440454	0.882326540880909	0.558836729559546
102	0.401642565619681	0.803285131239361	0.598357434380320
103	0.331556746785130	0.663113493570259	0.66844325321487
104	0.875051481630045	0.249897036739910	0.124948518369955
105	0.864891921013786	0.270216157972428	0.135108078986214
106	0.817014114262598	0.365971771474804	0.182985885737402
107	0.736781102768648	0.526437794462704	0.263218897231352
108	0.642501569162587	0.714996861674826	0.357498430837413
109	0.542348511098835	0.91530297780233	0.457651488901165
110	0.456106587438410	0.912213174876819	0.54389341256159
111	0.40144513885777	0.80289027771554	0.59855486114223
112	0.667321699816415	0.66535660036717	0.332678300183585

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/Dec/14/t1292285151xq2v5d21cswuwcy/10ss1l1292285051.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285151xq2v5d21cswuwcy/10ss1l1292285051.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285151xq2v5d21cswuwcy/1l94s1292285051.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285151xq2v5d21cswuwcy/1l94s1292285051.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285151xq2v5d21cswuwcy/2eild1292285051.png (open in new window)

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http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285151xq2v5d21cswuwcy/9ss1l1292285051.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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