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

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 13:01:56 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca.htm/, Retrieved Thu, 27 Nov 2008 20:05:50 +0000

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/2008/Nov/27/t12278163509ba9muabxbjjfca.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
test

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.43 0 1.44 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.48 0 1.57 1 1.58 1 1.58 1 1.58 1 1.58 1 1.59 1 1.6 1 1.6 1 1.61 1 1.61 1 1.61 1 1.62 1 1.63 1 1.63 1 1.64 1 1.64 1 1.64 1 1.64 1 1.64 1 1.65 1 1.65 1 1.65 1 1.65 1 1.65 1 1.66 1 1.66 1 1.67 1 1.68 1 1.68 1 1.68 1 1.68 1 1.69 1 1.7 1 1.7 1 1.71 1 1.72 1 1.73 1 1.74 1 1.74 1 1.75 1 1.75 1 1.75 1 1.76 1 1.79 1 1.83 1 1.84 1 1.85 1

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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 1.38886855205562 + 0.064639945069093X[t] -0.0082963729865816M1[t] -0.00443306010928969M2[t] -0.00498975409836072M3[t] -0.00554644808743174M4[t] -0.00610314207650277M5[t] -0.0091598360655738M6[t] -0.00971653005464484M7[t] -0.00527322404371587M8[t] -0.0020799180327869M9[t] + 0.00236338797814207M10[t] + 0.000556693989071041M11[t] + 0.00305669398907103t + 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)	1.38886855205562	0.013597	102.1457	0	0
X	0.064639945069093	0.01309	4.9382	4e-06	2e-06
M1	-0.0082963729865816	0.015799	-0.5251	0.600921	0.300461
M2	-0.00443306010928969	0.015912	-0.2786	0.781255	0.390628
M3	-0.00498975409836072	0.015878	-0.3142	0.754131	0.377065
M4	-0.00554644808743174	0.015848	-0.35	0.727256	0.363628
M5	-0.00610314207650277	0.015821	-0.3858	0.70068	0.35034
M6	-0.0091598360655738	0.015798	-0.5798	0.56364	0.28182
M7	-0.00971653005464484	0.015779	-0.6158	0.539731	0.269866
M8	-0.00527322404371587	0.015763	-0.3345	0.738826	0.369413
M9	-0.0020799180327869	0.01575	-0.1321	0.895262	0.447631
M10	0.00236338797814207	0.015741	0.1501	0.881022	0.440511
M11	0.000556693989071041	0.015736	0.0354	0.971865	0.485932
t	0.00305669398907103	0.000237	12.8749	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.96904457174087
R-squared	0.939047382020445
Adjusted R-squared	0.929384162096857
F-TEST (value)	97.1774821897844
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0314679069865167
Sum Squared Residuals	0.0811987919491892

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.43	1.38362887305810	0.0463711269418962
2	1.43	1.39054887992447	0.0394511200755299
3	1.43	1.39304887992447	0.0369511200755299
4	1.43	1.39554887992447	0.0344511200755299
5	1.43	1.39804887992447	0.0319511200755299
6	1.43	1.39804887992447	0.0319511200755299
7	1.43	1.40054887992447	0.0294511200755299
8	1.43	1.40804887992447	0.0219511200755299
9	1.43	1.41429887992447	0.0157011200755299
10	1.43	1.42179887992447	0.00820112007552986
11	1.43	1.42304887992447	0.00695112007552987
12	1.43	1.42554887992447	0.00445112007552987
13	1.43	1.42030920092696	0.00969079907304043
14	1.43	1.42722920779332	0.0027707922066774
15	1.43	1.42972920779332	0.000270792206677471
16	1.43	1.43222920779332	-0.00222920779332253
17	1.43	1.43472920779332	-0.00472920779332254
18	1.43	1.43472920779332	-0.00472920779332254
19	1.44	1.43722920779332	0.00277079220667747
20	1.48	1.44472920779332	0.0352707922066775
21	1.48	1.45097920779332	0.0290207922066775
22	1.48	1.45847920779332	0.0215207922066775
23	1.48	1.45972920779332	0.0202707922066775
24	1.48	1.46222920779332	0.0177707922066775
25	1.48	1.45698952879581	0.0230104712041881
26	1.48	1.46390953566217	0.0160904643378251
27	1.48	1.46640953566217	0.0135904643378251
28	1.48	1.46890953566217	0.0110904643378251
29	1.48	1.47140953566217	0.0085904643378251
30	1.48	1.47140953566217	0.0085904643378251
31	1.48	1.47390953566217	0.0060904643378251
32	1.48	1.48140953566217	-0.00140953566217490
33	1.48	1.48765953566217	-0.00765953566217491
34	1.48	1.49515953566217	-0.0151595356621749
35	1.48	1.49640953566217	-0.0164095356621749
36	1.48	1.49890953566217	-0.0189095356621749
37	1.48	1.49366985666466	-0.0136698566646643
38	1.48	1.50058986353103	-0.0205898635310273
39	1.48	1.50308986353103	-0.0230898635310273
40	1.48	1.50558986353103	-0.0255898635310273
41	1.48	1.50808986353103	-0.0280898635310273
42	1.48	1.50808986353103	-0.0280898635310273
43	1.48	1.51058986353103	-0.0305898635310273
44	1.48	1.51808986353103	-0.0380898635310273
45	1.48	1.52433986353103	-0.0443398635310273
46	1.48	1.53183986353103	-0.0518398635310273
47	1.48	1.53308986353103	-0.0530898635310273
48	1.48	1.53558986353103	-0.0555898635310273
49	1.48	1.53035018453352	-0.0503501845335167
50	1.57	1.60191013646897	-0.0319101364689726
51	1.58	1.60441013646897	-0.0244101364689726
52	1.58	1.60691013646897	-0.0269101364689726
53	1.58	1.60941013646897	-0.0294101364689726
54	1.58	1.60941013646897	-0.0294101364689726
55	1.59	1.61191013646897	-0.0219101364689726
56	1.6	1.61941013646897	-0.0194101364689726
57	1.6	1.62566013646897	-0.0256601364689726
58	1.61	1.63316013646897	-0.0231601364689726
59	1.61	1.63441013646897	-0.0244101364689726
60	1.61	1.63691013646897	-0.0269101364689726
61	1.62	1.63167045747146	-0.0116704574714620
62	1.63	1.63859046433783	-0.00859046433782516
63	1.63	1.64109046433783	-0.0110904643378252
64	1.64	1.64359046433783	-0.00359046433782517
65	1.64	1.64609046433783	-0.00609046433782518
66	1.64	1.64609046433783	-0.00609046433782518
67	1.64	1.64859046433783	-0.00859046433782518
68	1.64	1.65609046433783	-0.0160904643378252
69	1.65	1.66234046433783	-0.0123404643378252
70	1.65	1.66984046433783	-0.0198404643378252
71	1.65	1.67109046433783	-0.0210904643378252
72	1.65	1.67359046433782	-0.0235904643378252
73	1.65	1.66835078534031	-0.0183507853403146
74	1.66	1.67527079220668	-0.0152707922066775
75	1.66	1.67777079220668	-0.0177707922066776
76	1.67	1.68027079220668	-0.0102707922066775
77	1.68	1.68277079220668	-0.00277079220667754
78	1.68	1.68277079220668	-0.00277079220667753
79	1.68	1.68527079220668	-0.00527079220667753
80	1.68	1.69277079220668	-0.0127707922066775
81	1.69	1.69902079220668	-0.00902079220667754
82	1.7	1.70652079220668	-0.00652079220667754
83	1.7	1.70777079220668	-0.00777079220667754
84	1.71	1.71027079220668	-0.000270792206677525
85	1.72	1.70503111320917	0.0149688867908331
86	1.73	1.71195112007553	0.0180488799244701
87	1.74	1.71445112007553	0.0255488799244701
88	1.74	1.71695112007553	0.0230488799244701
89	1.75	1.71945112007553	0.0305488799244701
90	1.75	1.71945112007553	0.0305488799244701
91	1.75	1.72195112007553	0.0280488799244701
92	1.76	1.72945112007553	0.0305488799244701
93	1.79	1.73570112007553	0.0542988799244702
94	1.83	1.74320112007553	0.0867988799244702
95	1.84	1.74445112007553	0.0955488799244702
96	1.85	1.74695112007553	0.103048879924470

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	4.9252689411005e-43	9.850537882201e-43	1
18	1.31474232953445e-56	2.62948465906889e-56	1
19	2.42211935908881e-05	4.84423871817762e-05	0.99997577880641
20	0.0242299311272446	0.0484598622544891	0.975770068872755
21	0.0540311108905885	0.108062221781177	0.945968889109411
22	0.0718827736739673	0.143765547347935	0.928117226326033
23	0.081545195269798	0.163090390539596	0.918454804730202
24	0.085838760120067	0.171677520240134	0.914161239879933
25	0.0815584271681764	0.163116854336353	0.918441572831824
26	0.0687331610100807	0.137466322020161	0.93126683898992
27	0.0572358090830736	0.114471618166147	0.942764190916926
28	0.0474361918962436	0.0948723837924872	0.952563808103756
29	0.0392672221082421	0.0785344442164841	0.960732777891758
30	0.0342442295840936	0.0684884591681871	0.965755770415906
31	0.0302153371444415	0.060430674288883	0.969784662855558
32	0.0349620895191092	0.0699241790382184	0.96503791048089
33	0.0364205308826274	0.0728410617652548	0.963579469117373
34	0.0332743219937604	0.0665486439875208	0.96672567800624
35	0.0302183434393319	0.0604366868786639	0.969781656560668
36	0.0266841719452690	0.0533683438905379	0.973315828054731
37	0.0322057443912227	0.0644114887824453	0.967794255608777
38	0.0296964322692769	0.0593928645385539	0.970303567730723
39	0.0259517637185641	0.0519035274371282	0.974048236281436
40	0.0215852755423123	0.0431705510846247	0.978414724457688
41	0.0171205170695296	0.0342410341390591	0.98287948293047
42	0.0138700069498547	0.0277400138997095	0.986129993050145
43	0.0120609457345156	0.0241218914690311	0.987939054265484
44	0.0154776915299179	0.0309553830598358	0.984522308470082
45	0.0164041979235002	0.0328083958470004	0.9835958020765
46	0.0152064996125265	0.0304129992250530	0.984793500387473
47	0.0133727385220922	0.0267454770441844	0.986627261477908
48	0.0115457576188615	0.0230915152377231	0.988454242381138
49	0.00958753692492373	0.0191750738498475	0.990412463075076
50	0.00640484931456588	0.0128096986291318	0.993595150685434
51	0.00490464149435418	0.00980928298870835	0.995095358505646
52	0.00332919187502015	0.0066583837500403	0.99667080812498
53	0.00206083912145367	0.00412167824290734	0.997939160878546
54	0.0012539523520877	0.0025079047041754	0.998746047647912
55	0.000968896876317706	0.00193779375263541	0.999031103123682
56	0.000939266418222957	0.00187853283644591	0.999060733581777
57	0.000626416613762938	0.00125283322752588	0.999373583386237
58	0.000457853040776733	0.000915706081553466	0.999542146959223
59	0.000306320229506953	0.000612640459013905	0.999693679770493
60	0.000185423868545989	0.000370847737091977	0.999814576131454
61	0.000266478451446842	0.000532956902893683	0.999733521548553
62	0.000606215029747008	0.00121243005949402	0.999393784970253
63	0.00102170089071796	0.00204340178143592	0.998978299109282
64	0.00334322788906104	0.00668645577812209	0.996656772110939
65	0.00693352005994011	0.0138670401198802	0.99306647994006
66	0.0148512090309684	0.0297024180619369	0.985148790969032
67	0.0328987077371209	0.0657974154742419	0.96710129226288
68	0.0634383455537336	0.126876691107467	0.936561654446266
69	0.111353577349704	0.222707154699409	0.888646422650295
70	0.0985026442409381	0.197005288481876	0.901497355759062
71	0.0810885764568149	0.162177152913630	0.918911423543185
72	0.0581549858812732	0.116309971762546	0.941845014118727
73	0.0485581409189609	0.0971162818379218	0.95144185908104
74	0.0456615695625621	0.0913231391251242	0.954338430437438
75	0.0336345904812864	0.0672691809625728	0.966365409518714
76	0.0364181924476282	0.0728363848952565	0.963581807552372
77	0.0495632719289897	0.0991265438579794	0.95043672807101
78	0.0781535214625273	0.156307042925055	0.921846478537473
79	0.171367173653072	0.342734347306144	0.828632826346928

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	17	0.26984126984127	NOK
5% type I error level	31	0.492063492063492	NOK
10% type I error level	49	0.777777777777778	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/10mkhu1227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/10mkhu1227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/1ml1b1227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/1ml1b1227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/2j9m71227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/2j9m71227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/3pqi81227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/3pqi81227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/4ssjz1227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/4ssjz1227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/5jjmt1227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/5jjmt1227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/609rr1227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/609rr1227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/7wr6f1227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/7wr6f1227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/8twvc1227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/8twvc1227816104.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/9i7za1227816104.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278163509ba9muabxbjjfca/9i7za1227816104.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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