Home » date » 2008 » Nov » 27 »

Gilliam Schoorel

*Unverified author*

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 02:14:06 -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/t1227777306e9keunckb5zzgnr.htm/, Retrieved Thu, 27 Nov 2008 09:15:20 +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/t1227777306e9keunckb5zzgnr.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)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

-3,3 0 -3,5 0 -3,5 0 -8,4 0 -15,7 0 -18,7 0 -22,8 0 -20,7 0 -14 0 -6,3 0 0,7 0 0,2 0 0,8 0 1,2 0 4,5 0 0,4 0 5,9 0 6,5 0 12,8 0 4,2 0 -3,3 0 -12,5 0 -16,3 0 -10,5 0 -11,8 0 -11,4 0 -17,7 0 -17,3 0 -18,6 0 -17,9 0 -21,4 0 -19,4 0 -15,5 0 -7,7 0 -0,7 0 -1,6 0 1,4 0 0,7 0 9,5 0 1,4 0 4,1 0 6,6 0 18,4 0 16,9 0 9,2 0 -4,3 0 -5,9 0 -7,7 0 -5,4 0 -2,3 0 -4,8 0 2,3 0 -5,2 0 -10 0 -17,1 0 -14,4 0 -3,9 0 3,7 0 6,5 0 0,9 0 -4,1 0 -7 0 -12,2 0 -2,5 0 4,4 0 13,7 0 12,3 0 13,4 0 2,2 0 1,7 0 -7,2 0 -4,8 0 -2,9 0 -2,4 0 -2,5 0 -5,3 0 -7,1 0 -8 0 -8,9 1 -7,7 1 -1,1 1 4 1 9,6 1 10,9 1 13 1 14,9 1 20,1 1 10,8 1 11 1 3,8 1 10,8 1 7,6 1 10,2 1 2,2 1 -0,1 1 -1,7 1 -4,8 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	4 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Registraties[t] = -3.85897435897436 + 9.3642375168691Dummies[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)	-3.85897435897436	1.047055	-3.6856	0.00038	0.00019
Dummies	9.3642375168691	2.365802	3.9582	0.000146	7.3e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.376257138236009
R-squared	0.141569434073551
Adjusted R-squared	0.132533322853273
F-TEST (value)	15.667075207734
F-TEST (DF numerator)	1
F-TEST (DF denominator)	95
p-value	0.000145733276324722
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.24733713684387
Sum Squared Residuals	8123.75819163293

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	-3.3	-3.85897435897437	0.558974358974365
2	-3.5	-3.85897435897434	0.35897435897434
3	-3.5	-3.85897435897436	0.358974358974359
4	-8.4	-3.85897435897436	-4.54102564102564
5	-15.7	-3.85897435897436	-11.8410256410256
6	-18.7	-3.85897435897436	-14.8410256410256
7	-22.8	-3.85897435897436	-18.9410256410256
8	-20.7	-3.85897435897436	-16.8410256410256
9	-14	-3.85897435897436	-10.1410256410256
10	-6.3	-3.85897435897436	-2.44102564102564
11	0.7	-3.85897435897436	4.55897435897436
12	0.2	-3.85897435897436	4.05897435897436
13	0.8	-3.85897435897436	4.65897435897436
14	1.2	-3.85897435897436	5.05897435897436
15	4.5	-3.85897435897436	8.35897435897436
16	0.4	-3.85897435897436	4.25897435897436
17	5.9	-3.85897435897436	9.75897435897436
18	6.5	-3.85897435897436	10.3589743589744
19	12.8	-3.85897435897436	16.6589743589744
20	4.2	-3.85897435897436	8.05897435897436
21	-3.3	-3.85897435897436	0.558974358974359
22	-12.5	-3.85897435897436	-8.64102564102564
23	-16.3	-3.85897435897436	-12.4410256410256
24	-10.5	-3.85897435897436	-6.64102564102564
25	-11.8	-3.85897435897436	-7.94102564102564
26	-11.4	-3.85897435897436	-7.54102564102564
27	-17.7	-3.85897435897436	-13.8410256410256
28	-17.3	-3.85897435897436	-13.4410256410256
29	-18.6	-3.85897435897436	-14.7410256410256
30	-17.9	-3.85897435897436	-14.0410256410256
31	-21.4	-3.85897435897436	-17.5410256410256
32	-19.4	-3.85897435897436	-15.5410256410256
33	-15.5	-3.85897435897436	-11.6410256410256
34	-7.7	-3.85897435897436	-3.84102564102564
35	-0.7	-3.85897435897436	3.15897435897436
36	-1.6	-3.85897435897436	2.25897435897436
37	1.4	-3.85897435897436	5.25897435897436
38	0.7	-3.85897435897436	4.55897435897436
39	9.5	-3.85897435897436	13.3589743589744
40	1.4	-3.85897435897436	5.25897435897436
41	4.1	-3.85897435897436	7.95897435897436
42	6.6	-3.85897435897436	10.4589743589744
43	18.4	-3.85897435897436	22.2589743589744
44	16.9	-3.85897435897436	20.7589743589744
45	9.2	-3.85897435897436	13.0589743589744
46	-4.3	-3.85897435897436	-0.441025641025641
47	-5.9	-3.85897435897436	-2.04102564102564
48	-7.7	-3.85897435897436	-3.84102564102564
49	-5.4	-3.85897435897436	-1.54102564102564
50	-2.3	-3.85897435897436	1.55897435897436
51	-4.8	-3.85897435897436	-0.94102564102564
52	2.3	-3.85897435897436	6.15897435897436
53	-5.2	-3.85897435897436	-1.34102564102564
54	-10	-3.85897435897436	-6.14102564102564
55	-17.1	-3.85897435897436	-13.2410256410256
56	-14.4	-3.85897435897436	-10.5410256410256
57	-3.9	-3.85897435897436	-0.0410256410256412
58	3.7	-3.85897435897436	7.55897435897436
59	6.5	-3.85897435897436	10.3589743589744
60	0.9	-3.85897435897436	4.75897435897436
61	-4.1	-3.85897435897436	-0.241025641025641
62	-7	-3.85897435897436	-3.14102564102564
63	-12.2	-3.85897435897436	-8.34102564102564
64	-2.5	-3.85897435897436	1.35897435897436
65	4.4	-3.85897435897436	8.25897435897436
66	13.7	-3.85897435897436	17.5589743589744
67	12.3	-3.85897435897436	16.1589743589744
68	13.4	-3.85897435897436	17.2589743589744
69	2.2	-3.85897435897436	6.05897435897436
70	1.7	-3.85897435897436	5.55897435897436
71	-7.2	-3.85897435897436	-3.34102564102564
72	-4.8	-3.85897435897436	-0.94102564102564
73	-2.9	-3.85897435897436	0.95897435897436
74	-2.4	-3.85897435897436	1.45897435897436
75	-2.5	-3.85897435897436	1.35897435897436
76	-5.3	-3.85897435897436	-1.44102564102564
77	-7.1	-3.85897435897436	-3.24102564102564
78	-8	-3.85897435897436	-4.14102564102564
79	-8.9	5.50526315789474	-14.4052631578947
80	-7.7	5.50526315789474	-13.2052631578947
81	-1.1	5.50526315789474	-6.60526315789474
82	4	5.50526315789474	-1.50526315789474
83	9.6	5.50526315789474	4.09473684210526
84	10.9	5.50526315789474	5.39473684210526
85	13	5.50526315789474	7.49473684210526
86	14.9	5.50526315789474	9.39473684210526
87	20.1	5.50526315789473	14.5947368421053
88	10.8	5.50526315789474	5.29473684210526
89	11	5.50526315789474	5.49473684210526
90	3.8	5.50526315789474	-1.70526315789474
91	10.8	5.50526315789474	5.29473684210526
92	7.6	5.50526315789474	2.09473684210526
93	10.2	5.50526315789474	4.69473684210526
94	2.2	5.50526315789474	-3.30526315789474
95	-0.1	5.50526315789474	-5.60526315789474
96	-1.7	5.50526315789474	-7.20526315789474
97	-4.8	5.50526315789474	-10.3052631578947

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.272483475203204	0.544966950406408	0.727516524796796
6	0.384385095769802	0.768770191539605	0.615614904230198
7	0.550363619223262	0.899272761553476	0.449636380776738
8	0.567522717778277	0.864954564443445	0.432477282221723
9	0.463242773117027	0.926485546234053	0.536757226882973
10	0.395805892703793	0.791611785407586	0.604194107296207
11	0.464811644393386	0.929623288786772	0.535188355606614
12	0.483760993385719	0.967521986771438	0.516239006614281
13	0.493457117251009	0.986914234502019	0.506542882748991
14	0.493927192730059	0.987854385460119	0.506072807269941
15	0.54536347571719	0.90927304856562	0.45463652428281
16	0.508501262818807	0.982997474362385	0.491498737181193
17	0.558076925882297	0.883846148235407	0.441923074117703
18	0.601382996995582	0.797234006008837	0.398617003004418
19	0.755059830615604	0.489880338768792	0.244940169384396
20	0.738609718190161	0.522780563619678	0.261390281809839
21	0.67595083344257	0.64809833311486	0.32404916655743
22	0.660969212687504	0.678061574624991	0.339030787312496
23	0.695900591154335	0.608198817691329	0.304099408845665
24	0.657685455552032	0.684629088895936	0.342314544447968
25	0.629717479134588	0.740565041730824	0.370282520865412
26	0.5968234700449	0.806353059910201	0.403176529955101
27	0.649943676448422	0.700112647103156	0.350056323551578
28	0.69175935751064	0.616481284978721	0.308240642489361
29	0.750559828836884	0.498880342326232	0.249440171163116
30	0.794128852642095	0.411742294715811	0.205871147357905
31	0.87710991900825	0.245780161983499	0.122890080991750
32	0.920129629410716	0.159740741178569	0.0798703705892843
33	0.931547943065013	0.136904113869975	0.0684520569349873
34	0.91589173056765	0.168216538864700	0.0841082694323502
35	0.900620679659072	0.198758640681855	0.0993793203409276
36	0.880434167300181	0.239131665399639	0.119565832699819
37	0.86778606785313	0.264427864293739	0.132213932146870
38	0.84973419549321	0.300531609013579	0.150265804506789
39	0.893758198596738	0.212483602806524	0.106241801403262
40	0.877987074518678	0.244025850962643	0.122012925481322
41	0.873402165638457	0.253195668723087	0.126597834361543
42	0.88371905485176	0.232561890296481	0.116280945148241
43	0.971193343754146	0.0576133124917084	0.0288066562458542
44	0.993495932809298	0.0130081343814034	0.00650406719070169
45	0.995627645428335	0.00874470914332979	0.00437235457166489
46	0.993370460523732	0.0132590789525354	0.00662953947626769
47	0.990419725220553	0.0191605495588938	0.0095802747794469
48	0.987216086174366	0.0255678276512674	0.0127839138256337
49	0.981953311314618	0.0360933773707641	0.0180466886853820
50	0.974443947350043	0.0511121052999131	0.0255560526499565
51	0.964821369209488	0.0703572615810237	0.0351786307905119
52	0.956766906365371	0.0864661872692571	0.0432330936346286
53	0.9424083027929	0.115183394414199	0.0575916972070997
54	0.935863118080434	0.128273763839131	0.0641368819195657
55	0.961951426405276	0.0760971471894491	0.0380485735947245
56	0.97301856974412	0.0539628605117614	0.0269814302558807
57	0.96340724732551	0.0731855053489789	0.0365927526744894
58	0.955943600757538	0.0881127984849248	0.0440563992424624
59	0.955201222823542	0.0895975543529157	0.0447987771764578
60	0.941046234042933	0.117907531914134	0.0589537659570668
61	0.922406267917687	0.155187464164627	0.0775937320823134
62	0.907517541001794	0.184964917996412	0.0924824589982062
63	0.922126558849351	0.155746882301297	0.0778734411506485
64	0.89885132382293	0.202297352354139	0.101148676177070
65	0.881196574857354	0.237606850285292	0.118803425142646
66	0.933012426763677	0.133975146472647	0.0669875732363235
67	0.96327911861604	0.0734417627679197	0.0367208813839599
68	0.988280072766776	0.0234398544664488	0.0117199272332244
69	0.985605126401657	0.0287897471966851	0.0143948735983425
70	0.982496908768554	0.0350061824628920	0.0175030912314460
71	0.97410946518444	0.0517810696311229	0.0258905348155614
72	0.961096584982312	0.0778068300353755	0.0389034150176877
73	0.943901619249767	0.112196761500467	0.0560983807502334
74	0.922493400600756	0.155013198798489	0.0775065993992443
75	0.896867469627567	0.206265060744867	0.103132530372433
76	0.860007269327171	0.279985461345658	0.139992730672829
77	0.813394494229158	0.373211011541684	0.186605505770842
78	0.757654899494111	0.484690201011778	0.242345100505889
79	0.841153096326266	0.317693807347468	0.158846903673734
80	0.911756684268169	0.176486631463663	0.0882433157318313
81	0.913443217262875	0.173113565474249	0.0865567827371246
82	0.884893741588529	0.230212516822943	0.115106258411471
83	0.844285411669281	0.311429176661438	0.155714588330719
84	0.797670640009427	0.404658719981145	0.202329359990573
85	0.76268061508868	0.474638769822641	0.237319384911321
86	0.758641675801803	0.482716648396393	0.241358324198197
87	0.902122117324744	0.195755765350511	0.0978778826752555
88	0.879095237017142	0.241809525965716	0.120904762982858
89	0.86575366447976	0.268492671040480	0.134246335520240
90	0.771295952066124	0.457408095867752	0.228704047933876
91	0.76486148883173	0.470277022336541	0.235138511168270
92	0.691178925688152	0.617642148623696	0.308821074311848

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.0113636363636364	NOK
5% type I error level	9	0.102272727272727	NOK
10% type I error level	21	0.238636363636364	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/10w5w91227777240.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/10w5w91227777240.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/1i83i1227777240.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/1i83i1227777240.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/2r53h1227777240.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/2r53h1227777240.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/3fczx1227777240.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/3fczx1227777240.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/4tmlg1227777240.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/4tmlg1227777240.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/5tgy11227777240.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/5tgy11227777240.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/6dlvr1227777240.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227777306e9keunckb5zzgnr/6dlvr1227777240.ps (open in new window)

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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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