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Paper: Multiple Linear Regression + happiness

*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, 05 Dec 2010 13:52:33 +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/05/t1291557092egywkg02ait4cut.htm/, Retrieved Sun, 05 Dec 2010 14:51:33 +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/05/t1291557092egywkg02ait4cut.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

13 13 14 13 3 13 0 12 8 13 5 18 0 15 12 16 6 13 12 12 7 12 6 17 10 10 10 11 5 13 12 12 7 12 3 17 0 9 11 11 4 13 12 12 14 14 4 13 0 11 6 9 4 18 0 11 16 14 6 13 0 11 11 12 6 13 15 15 16 11 5 13 7 7 12 12 4 13 11 11 7 13 6 14 0 11 13 11 4 13 10 10 11 12 6 17 0 14 15 16 6 14 10 10 7 9 4 12 6 6 9 11 4 13 11 11 7 13 2 17 15 15 14 15 7 13 14 14 15 13 6 13 0 9 15 15 7 13 13 13 14 14 5 14 16 16 8 14 4 13 13 13 8 8 4 12 0 12 14 13 7 16 0 14 14 15 7 14 11 11 8 13 4 17 9 9 11 11 4 13 16 16 16 15 6 14 12 12 10 15 6 16 0 10 8 9 5 14 13 13 14 13 6 13 16 16 16 16 7 11 14 14 13 13 6 12 0 5 8 12 3 13 8 8 10 12 4 15 11 11 8 12 6 13 16 16 13 14 7 13 17 17 15 14 5 13 9 9 6 8 4 14 9 9 12 13 5 11 13 13 16 16 6 14 0 6 15 11 6 14 12 12 12 14 5 13 8 8 8 13 4 13 0 14 13 13 5 13 12 12 14 13 5 13 11 11 12 12 4 13 16 16 16 16 6 13 8 8 10 15 2 13 15 15 15 15 8 14 7 7 8 12 3 13 0 16 16 14 6 10 14 14 19 12 6 15 9 9 6 12 5 13 14 14 13 13 5 13 11 11 15 12 6 16 0 15 13 13 6 13 15 15 14 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Popularity[t] = + 1.14960641266419 + 0.145704706690706`Pop*geslacht`[t] + 0.192348397617101KnowingPeople[t] + 0.307797941811503Liked[t] + 0.626424739881038Celebrity[t] -0.0149836217025702Happiness[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	1.14960641266419	2.010445	0.5718	0.568507	0.284253
`Pop*geslacht`	0.145704706690706	0.03354	4.3442	2.9e-05	1.5e-05
KnowingPeople	0.192348397617101	0.06818	2.8212	0.005594	0.002797
Liked	0.307797941811503	0.101932	3.0196	0.003088	0.001544
Celebrity	0.626424739881038	0.15674	3.9966	0.000111	5.5e-05
Happiness	-0.0149836217025702	0.114996	-0.1303	0.896548	0.448274

Multiple Linear Regression - Regression Statistics
Multiple R	0.745527854553803
R-squared	0.555811781915597
Adjusted R-squared	0.537456896870787
F-TEST (value)	30.2814090395383
F-TEST (DF numerator)	5
F-TEST (DF denominator)	121
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.91674864098073
Sum Squared Residuals	444.54496767688

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	13	11.4225055473420	1.57749445265797
2	12	9.55218534590946	2.44781465409054
3	15	11.9463156102063	3.05368438979374
4	12	11.4419038483529	0.558096151647064
5	10	10.8532514329406	-0.85325143294057
6	12	9.56262962870982	2.43737037129018
7	9	8.96212802376957	0.0378719762304292
8	12	12.2110235223439	-0.211023522343855
9	11	7.30987204354821	3.69012795645179
10	11	12.1001133170517	-1.10011331705166
11	11	10.5227754453431	0.477224554656852
12	15	12.7358653520967	2.26413464790329
13	7	10.4822073100331	-3.48220731003312
14	11	11.6489479485814	-0.648947948581443
15	11	9.34682481900377	1.65317518099623
16	10	11.9198880254399	-1.91988802543993
17	14	12.508377181355	1.49162281864501
18	10	9.0491692382878	0.950830761712207
19	6	9.4516594686796	-3.45165946867961
20	11	9.09829812394958	1.90170187605042
21	15	14.8352098038706	0.164790196129411
22	14	13.6398328712929	0.360167128707059
23	9	12.8419876011271	-3.8419876011271
24	13	12.9681693472130	0.0318306527869711
25	16	11.6397519634041	4.36024803659593
26	13	9.37083381416551	3.62916618583449
27	12	11.9890924547793	0.0109075452207191
28	14	12.6346555818074	1.36534441819257
29	11	10.5434960013288	0.456503998671241
30	9	10.2734703839859	-1.27347038398593
31	16	14.7242029442119	1.27579705578811
32	12	12.9573264883413	-0.957326488341318
33	10	8.38092806547373	1.61907193452627
34	13	13.3017797669851	-0.301779766985134
35	16	15.7033764910121	0.296623508987860
36	14	13.2701196977613	0.72988030223869
37	5	8.06645603284873	-3.06645603284873
38	8	10.2132479780845	-2.21324797808448
39	11	11.5484820260896	-0.548482026089612
40	16	14.4807681711327	1.51923182886731
41	17	13.7583201932955	3.24167980670447
42	9	8.37335094876334	0.626649051236658
43	9	11.7378066485122	-2.73780664851221
44	13	14.5948867659513	-1.59488676595127
45	6	10.9693874722975	-4.96938747229748
46	12	12.4527514669907	-0.452751466990691
47	8	10.1663163680669	-2.16631636806692
48	14	10.5888454425078	3.41115455749218
49	12	12.5296503204134	-0.52965032041339
50	11	11.0650261367959	-0.0650261367959418
51	16	15.0469845077260	0.953015492274038
52	8	9.91375956716205	-1.91375956716205
53	15	15.6389993196662	-0.638999319666158
54	7	9.08638897968368	-2.08638897968368
55	16	12.1450641821594	3.85493581784063
56	14	14.0714612765447	-0.0714612765447031
57	9	10.2459510775930	-1.24595107759296
58	14	12.6287113361777	1.3712886638223
59	11	12.8499699443016	-1.84996994430161
60	15	11.2152701823889	3.78472981761115
61	15	12.9667644404855	2.03323555951449
62	13	12.483006629487	0.516993370513005
63	11	12.1146983626829	-1.11469836268288
64	11	12.6388103472520	-1.63881034725196
65	12	12.7713782650602	-0.771378265060225
66	12	13.3484234579115	-1.34842345791153
67	12	12.2218523786019	-0.221852378601888
68	12	12.3373019227963	-0.337301922796289
69	14	11.3483425500227	2.65165744997734
70	6	7.62176479606623	-1.62176479606623
71	7	9.52046532194761	-2.52046532194761
72	14	14.1785299014932	-0.178529901493247
73	10	11.3533977723692	-1.35339777236917
74	13	7.45944359659229	5.54055640340771
75	12	12.4635803232487	-0.463580323248723
76	9	9.32869276251284	-0.328692762512839
77	12	10.7747936657889	1.22520633421109
78	16	15.5923556287398	0.407644371260238
79	10	10.3422762372539	-0.342276237253932
80	16	13.3421339405557	2.6578660594443
81	15	13.9778859756007	1.02211402439925
82	10	8.38508283091827	1.61491716908173
83	8	8.37919854002656	-0.379198540026565
84	11	12.9995414973457	-1.99954149734569
85	13	12.7247345741338	0.275265425866169
86	16	15.8357904018190	0.164209598181039
87	14	15.5335521321795	-1.53355213217952
88	9	10.2626415277279	-1.26264152772789
89	8	10.1574682567187	-2.15746825671874
90	8	11.0042278927122	-3.00422789271217
91	11	12.2321286517871	-1.23212865178713
92	12	13.9748481977926	-1.97484819779257
93	14	13.8407647162766	0.159235283723396
94	15	14.7583109504479	0.241689049552111
95	16	14.0466918288688	1.95330817113125
96	16	14.0466918288688	1.95330817113125
97	11	12.7347229282279	-1.73472292822791
98	14	13.9476308131044	0.0523691868955562
99	14	11.5021402568681	2.49785974313194
100	12	11.5185287852981	0.48147121470185
101	13	13.7719588630086	-0.771958863008597
102	12	9.55020460099961	2.44979539900039
103	16	15.9151371922538	0.0848628077461647
104	12	13.3484234579115	-1.34842345791153
105	11	11.8923827216606	-0.892382721660642
106	4	6.42861926976637	-2.42861926976637
107	16	15.9151371922538	0.0848628077461647
108	10	11.3361687417751	-1.33616874177513
109	13	13.3403304577568	-0.340330457756836
110	14	13.9476308131044	0.0523691868955562
111	7	10.0031801026615	-3.00318010266147
112	12	12.8374482622249	-0.837448262224893
113	12	10.5394658954781	1.46053410452192
114	13	13.6095777087966	-0.609577708796637
115	15	13.2187826609313	1.78121733906873
116	12	11.1338319900639	0.866168009936052
117	10	10.941181670742	-0.941181670742004
118	8	11.1005390497595	-3.10053904975946
119	10	13.6726099281531	-3.67260992815312
120	15	14.2407330523045	0.75926694769546
121	16	15.2226424552081	0.77735754479187
122	13	13.5435077116320	-0.54350771163197
123	16	15.4810608499899	0.518939150010101
124	9	10.1516206654555	-1.15162066545551
125	14	13.6783835620646	0.321616437935357
126	14	13.1396865318643	0.860313468135663
127	12	10.3811288497305	1.61887115026952

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.371210845437871	0.742421690875742	0.628789154562129
10	0.23809177946175	0.4761835589235	0.76190822053825
11	0.137710156486570	0.275420312973140	0.86228984351343
12	0.451411921298573	0.902823842597147	0.548588078701426
13	0.75896902059116	0.482061958817679	0.241030979408840
14	0.670034125683065	0.65993174863387	0.329965874316935
15	0.610990218849433	0.778019562301134	0.389009781150567
16	0.731455101039702	0.537089797920596	0.268544898960298
17	0.662009596209172	0.675980807581656	0.337990403790828
18	0.613298842395407	0.773402315209187	0.386701157604593
19	0.798327715930552	0.403344568138897	0.201672284069448
20	0.752531377706426	0.494937244587148	0.247468622293574
21	0.699086475412243	0.601827049175513	0.300913524587757
22	0.636518634010163	0.726962731979674	0.363481365989837
23	0.794690402827003	0.410619194345994	0.205309597172997
24	0.740359387608204	0.519281224783593	0.259640612391797
25	0.866335377104698	0.267329245790603	0.133664622895302
26	0.925912204406751	0.148175591186497	0.0740877955932486
27	0.901682589548316	0.196634820903369	0.0983174104516844
28	0.893084114461584	0.213831771076831	0.106915885538416
29	0.87520866458139	0.249582670837221	0.124791335418610
30	0.867204445020864	0.265591109958271	0.132795554979136
31	0.845428494827383	0.309143010345233	0.154571505172617
32	0.830710738669701	0.338578522660597	0.169289261330299
33	0.812547007242765	0.37490598551447	0.187452992757235
34	0.769157473411544	0.461685053176911	0.230842526588456
35	0.727808657967352	0.544382684065297	0.272191342032648
36	0.68226148903326	0.635477021933481	0.317738510966740
37	0.793529316822643	0.412941366354713	0.206470683177357
38	0.824102373416532	0.351795253166936	0.175897626583468
39	0.790412544684459	0.419174910631082	0.209587455315541
40	0.76895817178643	0.46208365642714	0.23104182821357
41	0.818992388078625	0.36201522384275	0.181007611921375
42	0.78863613832077	0.422727723358461	0.211363861679230
43	0.826088296300098	0.347823407399805	0.173911703699902
44	0.824033004354149	0.351933991291703	0.175966995645851
45	0.95530337956034	0.0893932408793211	0.0446966204396606
46	0.942558098377341	0.114883803245318	0.0574419016226591
47	0.948198615915776	0.103602768168449	0.0518013840842243
48	0.972825369329558	0.054349261340883	0.0271746306704415
49	0.964311440144964	0.071377119710073	0.0356885598550365
50	0.95259928962439	0.0948014207512193	0.0474007103756096
51	0.940655315694983	0.118689368610035	0.0593446843050174
52	0.94186646256309	0.116267074873818	0.0581335374369091
53	0.927750181358013	0.144499637283973	0.0722498186419865
54	0.93012706537	0.139745869260001	0.0698729346300005
55	0.968849929635307	0.0623001407293855	0.0311500703646928
56	0.958716816616615	0.0825663667667707	0.0412831833833854
57	0.950754659071177	0.0984906818576455	0.0492453409288228
58	0.943560894026763	0.112878211946475	0.0564391059732374
59	0.942659085654353	0.114681828691295	0.0573409143456475
60	0.972983951317317	0.054032097365367	0.0270160486826835
61	0.974108160291989	0.0517836794160219	0.0258918397080110
62	0.9659903121587	0.0680193756825982	0.0340096878412991
63	0.958810692537398	0.0823786149252043	0.0411893074626021
64	0.959017830298732	0.0819643394025357	0.0409821697012678
65	0.948035662411159	0.103928675177682	0.051964337588841
66	0.940402425447651	0.119195149104697	0.0595975745523486
67	0.923166965228349	0.153666069543302	0.076833034771651
68	0.90261060117203	0.194778797655940	0.0973893988279698
69	0.928463181961221	0.143073636077558	0.0715368180387788
70	0.920292919382043	0.159414161235914	0.079707080617957
71	0.932559648489005	0.13488070302199	0.067440351510995
72	0.91326151521953	0.173476969560940	0.0867384847804701
73	0.901474834395182	0.197050331209636	0.098525165604818
74	0.992430000789858	0.0151399984202831	0.00756999921014154
75	0.989147070243647	0.0217058595127059	0.0108529297563529
76	0.985614010092917	0.0287719798141666	0.0143859899070833
77	0.984009455285303	0.0319810894293947	0.0159905447146974
78	0.977966214127457	0.0440675717450856	0.0220337858725428
79	0.969578408203367	0.0608431835932653	0.0304215917966327
80	0.982063657941782	0.0358726841164363	0.0179363420582182
81	0.976616134772123	0.046767730455754	0.023383865227877
82	0.985282223224546	0.0294355535509079	0.0147177767754539
83	0.980052938841317	0.0398941223173661	0.0199470611586830
84	0.98141227305712	0.03717545388576	0.01858772694288
85	0.974022954667682	0.0519540906646358	0.0259770453323179
86	0.963780105951582	0.072439788096835	0.0362198940484175
87	0.961283555042116	0.0774328899157678	0.0387164449578839
88	0.954217751382825	0.0915644972343493	0.0457822486171746
89	0.948004824034839	0.103990351930323	0.0519951759651614
90	0.981975445744954	0.0360491085100926	0.0180245542550463
91	0.975448074631278	0.0491038507374437	0.0245519253687218
92	0.973973691574183	0.0520526168516347	0.0260263084258173
93	0.963824391845402	0.0723512163091952	0.0361756081545976
94	0.948697975103711	0.102604049792577	0.0513020248962887
95	0.94543415343657	0.109131693126860	0.0545658465634298
96	0.943892488476407	0.112215023047185	0.0561075115235925
97	0.92806991490276	0.14386017019448	0.07193008509724
98	0.901054698479418	0.197890603041163	0.0989453015205817
99	0.923722217979177	0.152555564041647	0.0762777820208234
100	0.896519592452332	0.206960815095336	0.103480407547668
101	0.860902809161314	0.278194381677371	0.139097190838686
102	0.964461046088696	0.0710779078226081	0.0355389539113041
103	0.94633178010295	0.107336439794099	0.0536682198970494
104	0.936255798868542	0.127488402262915	0.0637442011314577
105	0.907244939757646	0.185510120484708	0.0927550602423538
106	0.877967846111107	0.244064307777786	0.122032153888893
107	0.829683141537433	0.340633716925135	0.170316858462567
108	0.771652202442691	0.456695595114618	0.228347797557309
109	0.698149793019342	0.603700413961317	0.301850206980659
110	0.616350444183599	0.767299111632801	0.383649555816401
111	0.732515750717656	0.534968498564687	0.267484249282344
112	0.688087031102424	0.623825937795152	0.311912968897576
113	1	1.75138639950101e-124	8.75693199750507e-125
114	1	1.72852499221003e-107	8.64262496105017e-108
115	1	2.70034242475026e-93	1.35017121237513e-93
116	1	2.29470327577666e-81	1.14735163788833e-81
117	1	7.15218084853774e-64	3.57609042426887e-64
118	1	3.30868351578948e-49	1.65434175789474e-49

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	6	0.0545454545454545	NOK
5% type I error level	18	0.163636363636364	NOK
10% type I error level	38	0.345454545454545	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291557092egywkg02ait4cut/10pbal1291557143.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291557092egywkg02ait4cut/10pbal1291557143.ps (open in new window)

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http://www.freestatistics.org/blog/date/2010/Dec/05/t1291557092egywkg02ait4cut/9pbal1291557143.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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