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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 00:16:35 +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/t1292285767eg4qpeo2i79c434.htm/, Retrieved Tue, 14 Dec 2010 01:16:18 +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/t1292285767eg4qpeo2i79c434.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 2146 0 5,9 81,9 2462 0 7,1 56,8 3695 0 10,5 65,1 4831 0 15,1 86,2 5134 0 16,8 35,1 6250 0 15,3 133,8 5760 0 18,4 34,5 6249 0 16,1 69,9 2917 0 11,3 98,3 1741 0 7,9 86,7 2359 0 5,6 58,2 1511 1 3,4 83,6 2059 0 4,8 83,5 2635 0 6,5 112,3 2867 0 8,5 134,3 4403 0 15,1 30,0 5720 0 15,7 44,5 4502 0 18,7 120,1 5749 0 19,2 43,4 5627 0 12,9 199,4 2846 0 14,4 68,1 1762 0 6,2 99,8 2429 0 3,3 69,5 1169 0 4,6 71,3 2154 1 7,2 167,8 2249 0 7,8 66,3 2687 0 9,9 41,9 4359 0 13,6 57,2 5382 0 17,1 72,3 4459 0 17,8 96,5 6398 0 18,6 172,1 4596 0 14,7 25,8 3024 0 10,5 105,1 1887 0 8,6 92,2 2070 0 4,4 109,3 1351 0 2,3 101,7 2218 0 2,8 29,1 2461 1 8,8 34,6 3028 0 10,7 46,7 4784 0 13,9 82,0 4975 0 19,3 34,4 4607 0 19,5 72,7 6249 0 20,4 44,4 4809 0 15,3 31,0 3157 0 7,9 64,0 1910 0 8,3 65,4 2228 0 4,5 64,5 1594 0 3,2 153,8 2467 0 5,0 48,8 2222 0 6,6 25,0 3607 1 11,1 37,2 4685 0 12,8 40,8 4962 0 16,3 78,4 5770 0 17,4 112,4 5480 0 18,9 122,7 5000 0 15,8 82,9 3228 0 11,7 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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Huwelijken[t] = + 505.099332666385 + 525.423297499213Specialedag[t] + 259.288481142119Temperatuur[t] + 2.90633574238225Neerslag[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)	505.099332666385	196.233544	2.574	0.011313	0.005657
Specialedag	525.423297499213	245.615872	2.1392	0.034515	0.017258
Temperatuur	259.288481142119	11.643517	22.2689	0	0
Neerslag	2.90633574238225	1.824716	1.5928	0.113935	0.056968

Multiple Linear Regression - Regression Statistics
Multiple R	0.902864000622884
R-squared	0.815163403620759
Adjusted R-squared	0.810383146817848
F-TEST (value)	170.527115431181
F-TEST (DF numerator)	3
F-TEST (DF denominator)	116
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	706.75573306493
Sum Squared Residuals	57942425.2815371

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1579	1675.07280066171	-96.0728006617079
2	2146	2272.93026870599	-126.930268705990
3	2462	2511.12741894273	-49.1274189427278
4	3695	3416.83084148771	278.169158512286
5	4831	4670.88153890573	160.118461094273
6	5134	4963.1582004116	170.841799588404
7	6250	4861.08081647155	1388.91918352845
8	5760	5376.27596879356	383.724031206443
9	6249	4882.79674744702	1366.20325255298
10	2917	3720.7519730485	-803.7519730485
11	1741	2805.45764255366	-1064.45764255366
12	2359	2126.26356726889	232.736432731106
13	1511	2155.07313411195	-644.073134111954
14	2059	1992.36307663747	66.6369233625295
15	2635	2516.85596395968	118.144036040319
16	2867	3099.37231257633	-232.372312576329
17	4403	4507.54547018384	-104.545470183844
18	5720	4705.26042713366	1014.73957286634
19	4502	5702.84485268411	-1200.84485268411
20	5749	5609.57314181445	139.426858185547
21	5627	4429.44408643074	1197.55591356926
22	2846	4436.77492516912	-1590.77492516913
23	1762	2402.74022283727	-640.740222837268
24	2429	1562.74165453094	866.25834546906
25	1169	1905.04808435198	-736.048084351983
26	2154	3385.08283196059	-1231.08283196059
27	2249	2720.23954529485	-471.239545294852
28	2687	3193.83076357917	-506.830763579175
29	4359	4197.66508066346	161.334919336537
30	5382	5149.06043437085	232.939565629148
31	4459	5400.89569613599	-941.895696135985
32	6398	5828.04546317378	569.954536826221
33	4596	4391.62346760899	204.376532391009
34	3024	3533.08427118300	-509.084271183005
35	1887	3002.94442593625	-1115.94442593625
36	2070	1963.63114633409	106.368853665915
37	1351	1397.03718429353	-46.0371842935301
38	2218	1315.68144996764	902.318550032362
39	2461	3412.82048090267	-951.820480902666
40	3028	3415.21196005630	-387.211960056305
41	4784	4347.52875141718	436.471248582821
42	4975	5609.34496824723	-634.344968247225
43	4607	5772.51532340889	-1165.51532340889
44	6249	5923.62565492738	325.374345072622
45	4809	4562.30950215465	246.690497845349
46	3157	2739.48382120159	417.516178798415
47	1910	2847.26808369777	-937.268083697768
48	2228	1859.35615318957	368.643846810428
49	1594	1781.81690949955	-187.816909499553
50	2467	1943.37092260523	523.62907739477
51	2222	2289.06170176392	-67.0617017639225
52	3607	4016.74046045973	-409.740460459733
53	4685	3942.5703895747	742.429610425301
54	4962	4959.35829748569	2.64170251431157
55	5770	5343.39104198302	426.608958016985
56	5480	5762.25902184273	-282.259021842731
57	5000	4842.79256775535	157.207432244651
58	3228	3735.24285821421	-507.242858214213
59	1993	2392.40233417871	-399.402334178711
60	2288	1447.98218625304	840.01781374696
61	1580	1854.24966886730	-274.249668867304
62	2111	1362.51424896619	748.485751033809
63	2192	2484.74222369407	-292.742223694069
64	3601	3413.46815861088	187.531841389125
65	4665	4679.36842201292	-14.3684220129220
66	4876	5458.58773030998	-582.587730309984
67	5813	5609.01100175137	203.988998248634
68	5589	5063.13614994955	525.863850050454
69	5331	5024.93823171032	306.061768289681
70	3075	4297.66469666222	-1222.66469666222
71	2002	2263.75491434439	-261.754914344386
72	2306	1561.99467382225	744.005326177755
73	1507	1000.23822899053	506.761771009469
74	1992	1357.51101819713	634.488981802869
75	2487	1861.68122178348	625.318778216523
76	3490	3050.17365143767	439.826348562329
77	4647	4522.96817670386	124.031823296142
78	5594	5591.19681607771	2.80318392228733
79	5611	6608.5291481437	-997.529148143702
80	5788	5319.45329596685	468.54670403315
81	6204	5302.74567451128	901.254325488715
82	3013	4350.62246718059	-1337.62246718059
83	1931	3072.71815021423	-1141.71815021423
84	2549	2305.19059544643	243.809404553567
85	1504	2611.1678284877	-1107.16782848770
86	2090	2545.52543425606	-455.525434256057
87	2702	2759.30936425679	-57.309364256794
88	2939	4212.92461299868	-1273.92461299868
89	4500	4591.22627310364	-91.2262731036425
90	6208	5330.95625829778	877.043741702218
91	6415	5771.3271720984	643.672827901597
92	5657	5130.23171205238	526.768287947623
93	5964	4328.47185553148	1635.52814446852
94	3163	3417.12147506195	-254.121475061953
95	1997	2476.6452771616	-479.645277161598
96	2422	1827.42725356957	594.572746430434
97	1376	2395.95239707658	-1019.95239707658
98	2202	2189.64335291364	12.3566470863615
99	2683	2546.95693566644	136.043064333563
100	3303	3048.42984999224	254.570150007758
101	5202	4914.08191991153	287.918080088465
102	5231	4882.79674744702	348.203252552984
103	4880	5468.44760537327	-588.447605373274
104	7998	5853.53568006162	2144.46431993838
105	4977	4340.61600564247	636.383994357529
106	3531	3438.04709240711	92.952907592895
107	2025	2490.65814873204	-465.658148732042
108	2205	1356.95141750947	848.048582490534
109	1442	869.409787661708	572.590212338292
110	2238	1604.48963566804	633.510364331964
111	2179	2440.54425394905	-261.544253949048
112	3218	3883.09376040907	-665.093760409072
113	5139	4364.11653160957	774.883468390431
114	4990	4970.817926895	19.1820731050013
115	4914	5566.24707279215	-652.247072792147
116	6084	5644.8647243113	439.135275688701
117	5672	5211.8550023144	460.144997685604
118	3548	3740.22442252246	-192.224422522462
119	1793	3305.01850249840	-1512.01850249840
120	2086	1491.86785596301	594.132144036988

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.148222429568752	0.296444859137504	0.851777570431248
8	0.0676539919194865	0.135307983838973	0.932346008080513
9	0.16525153859388	0.33050307718776	0.83474846140612
10	0.507596842554986	0.984806314890028	0.492403157445014
11	0.627850219294707	0.744299561410586	0.372149780705293
12	0.58389968951034	0.83220062097932	0.41610031048966
13	0.488528011465952	0.977056022931903	0.511471988534048
14	0.406927455325146	0.813854910650293	0.593072544674853
15	0.318549897148497	0.637099794296993	0.681450102851503
16	0.26314083676478	0.52628167352956	0.73685916323522
17	0.217530991362509	0.435061982725018	0.782469008637491
18	0.221413927163821	0.442827854327642	0.778586072836179
19	0.554079654851474	0.891840690297051	0.445920345148526
20	0.486368039965243	0.972736079930487	0.513631960034757
21	0.584138355230532	0.831723289538935	0.415861644769468
22	0.842243272209105	0.31551345558179	0.157756727790895
23	0.824786957314128	0.350426085371744	0.175213042685872
24	0.856468250655436	0.287063498689127	0.143531749344564
25	0.847943638429916	0.304112723140169	0.152056361570084
26	0.864051375155735	0.271897249688530	0.135948624844265
27	0.839227858376145	0.321544283247711	0.160772141623855
28	0.813276337411391	0.373447325177218	0.186723662588609
29	0.76957085748322	0.46085828503356	0.23042914251678
30	0.721865080196941	0.556269839606118	0.278134919803059
31	0.774657127298557	0.450685745402886	0.225342872701443
32	0.742559273302118	0.514881453395763	0.257440726697882
33	0.697382352124954	0.605235295750092	0.302617647875046
34	0.67201023543497	0.65597952913006	0.32798976456503
35	0.737764763945518	0.524470472108965	0.262235236054482
36	0.69297283426726	0.61405433146548	0.30702716573274
37	0.64303213457522	0.71393573084956	0.35696786542478
38	0.698472850266936	0.603054299466128	0.301527149733064
39	0.711831995693159	0.576336008613682	0.288168004306841
40	0.674600034369018	0.650799931261964	0.325399965630982
41	0.642433502715997	0.715132994568005	0.357566497284003
42	0.628826786365426	0.742346427269147	0.371173213634574
43	0.708481049852671	0.583037900294658	0.291518950147329
44	0.676393303564863	0.647213392870275	0.323606696435137
45	0.635745650853958	0.728508698292084	0.364254349146042
46	0.603203858199159	0.793592283601683	0.396796141800841
47	0.642935470838553	0.714129058322894	0.357064529161447
48	0.606110579777599	0.787778840444802	0.393889420222401
49	0.563415252406514	0.873169495186971	0.436584747593486
50	0.540568016883539	0.918863966232922	0.459431983116461
51	0.486373182676405	0.97274636535281	0.513626817323595
52	0.493385088951102	0.986770177902205	0.506614911048898
53	0.508482531957605	0.98303493608479	0.491517468042395
54	0.454678101538284	0.909356203076567	0.545321898461717
55	0.423536381918268	0.847072763836536	0.576463618081732
56	0.378933778102611	0.757867556205221	0.621066221897389
57	0.332299369481983	0.664598738963967	0.667700630518017
58	0.309256476461317	0.618512952922635	0.690743523538683
59	0.281517056733529	0.563034113467057	0.718482943266471
60	0.296864084643359	0.593728169286717	0.703135915356641
61	0.260606312909936	0.521212625819871	0.739393687090064
62	0.25913441764626	0.51826883529252	0.74086558235374
63	0.225294962097919	0.450589924195838	0.774705037902081
64	0.190022909344026	0.380045818688052	0.809977090655974
65	0.196759332773716	0.393518665547431	0.803240667226284
66	0.180604045807647	0.361208091615294	0.819395954192353
67	0.150797469692944	0.301594939385887	0.849202530307056
68	0.141310320660169	0.282620641320338	0.858689679339831
69	0.120319213810005	0.240638427620009	0.879680786189995
70	0.179741997354227	0.359483994708454	0.820258002645773
71	0.152445654183607	0.304891308367214	0.847554345816393
72	0.151562728832606	0.303125457665212	0.848437271167394
73	0.134040032757734	0.268080065515468	0.865959967242266
74	0.123613286521128	0.247226573042256	0.876386713478872
75	0.115902820582353	0.231805641164705	0.884097179417647
76	0.101404019721639	0.202808039443278	0.898595980278361
77	0.0795202321720993	0.159040464344199	0.9204797678279
78	0.0852080137513282	0.170416027502656	0.914791986248672
79	0.105377143985960	0.210754287971921	0.89462285601404
80	0.0937819033150267	0.187563806630053	0.906218096684973
81	0.111657515095232	0.223315030190465	0.888342484904768
82	0.194732688350597	0.389465376701193	0.805267311649403
83	0.268093613175287	0.536187226350574	0.731906386824713
84	0.227051253936317	0.454102507872634	0.772948746063683
85	0.303708201638187	0.607416403276375	0.696291798361813
86	0.279185584582388	0.558371169164776	0.720814415417612
87	0.231825255695822	0.463650511391644	0.768174744304178
88	0.393704348439318	0.787408696878636	0.606295651560682
89	0.335741748608648	0.671483497217296	0.664258251391352
90	0.385558940633763	0.771117881267526	0.614441059366237
91	0.374687460804674	0.749374921609349	0.625312539195326
92	0.342732945289847	0.685465890579693	0.657267054710153
93	0.631145426163501	0.737709147672998	0.368854573836499
94	0.575511093972105	0.84897781205579	0.424488906027895
95	0.550769182098204	0.898461635803592	0.449230817901796
96	0.524852958725069	0.950294082549862	0.475147041274931
97	0.64024745762209	0.719505084755821	0.359752542377911
98	0.58309069319579	0.83381861360842	0.41690930680421
99	0.532464081181419	0.935071837637162	0.467535918818581
100	0.457736262719814	0.915472525439628	0.542263737280186
101	0.392481014538126	0.784962029076252	0.607518985461874
102	0.349290550218698	0.698581100437395	0.650709449781302
103	0.286613249299429	0.573226498598858	0.713386750700571
104	0.773068469093978	0.453863061812045	0.226931530906022
105	0.819346120483335	0.36130775903333	0.180653879516665
106	0.757832774465767	0.484334451068466	0.242167225534233
107	0.738235091181976	0.523529817636048	0.261764908818024
108	0.652578602006906	0.694842795986188	0.347421397993094
109	0.547698846613279	0.904602306773443	0.452301153386721
110	0.441960040534157	0.883920081068315	0.558039959465843
111	0.347926614633423	0.695853229266846	0.652073385366577
112	0.576928891204829	0.846142217590341	0.423071108795171
113	0.415060294216856	0.830120588433712	0.584939705783144

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/t1292285767eg4qpeo2i79c434/10l1ip1292285786.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285767eg4qpeo2i79c434/10l1ip1292285786.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285767eg4qpeo2i79c434/1eild1292285786.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285767eg4qpeo2i79c434/1eild1292285786.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285767eg4qpeo2i79c434/26r2g1292285786.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285767eg4qpeo2i79c434/26r2g1292285786.ps (open in new window)

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http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285767eg4qpeo2i79c434/9l1ip1292285786.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292285767eg4qpeo2i79c434/9l1ip1292285786.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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