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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: Fri, 24 Dec 2010 15:04:41 +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/24/t1293203002775qmmbez8olq1o.htm/, Retrieved Fri, 24 Dec 2010 16:03:33 +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/24/t1293203002775qmmbez8olq1o.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 «

4,031636 0,5215052 3,702076 0,4248284 3,056176 0,4250311 3,280707 0,4771938 2,984728 0,8280212 3,693712 0,6156186 3,226317 0,366627 2,190349 0,4308883 2,599515 0,2810287 3,080288 0,4646245 2,929672 0,2693951 2,922548 0,5779049 3,234943 0,5661151 2,983081 0,5077584 3,284389 0,7507175 3,806511 0,6808395 3,784579 0,7661091 2,645654 0,4561473 3,092081 0,4977496 3,204859 0,4193273 3,107225 0,6095514 3,466909 0,457337 2,984404 0,5705478 3,218072 0,3478996 2,82731 0,3874993 3,182049 0,5824285 2,236319 0,2391033 2,033218 0,2367445 1,644804 0,2626158 1,627971 0,4240934 1,677559 0,365275 2,330828 0,3750758 2,493615 0,4090056 2,257172 0,3891676 2,655517 0,240261 2,298655 0,1589496 2,600402 0,4393373 3,04523 0,5094681 2,790583 0,3743465 3,227052 0,4339828 2,967479 0,4130557 2,938817 0,3288928 3,277961 0,5186648 3,423985 0,5486504 3,072646 0,5469111 2,754253 0,4963494 2,910431 0,5308929 3,174369 0,5957761 3,068387 0,5570584 3,089543 0,5731325 2,906654 0,5005416 2,931161 0 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	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

firearmsuicide[t] = + 1.75219418227066 + 3.06470349276860firearmhomicide[t] -0.00947626841170947t + 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)	1.75219418227066	0.171696	10.2052	0	0
firearmhomicide	3.06470349276860	0.294062	10.422	0	0
t	-0.00947626841170947	0.001709	-5.5434	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.791646276919036
R-squared	0.626703827759771
Adjusted R-squared	0.618122306558846
F-TEST (value)	73.0294563267214
F-TEST (DF numerator)	2
F-TEST (DF denominator)	87
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.420693121594387
Sum Squared Residuals	15.3974951224442

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	4.031636	3.34097672179593	0.690659278204068
2	3.702076	3.03521472675453	0.666861273245471
3	3.056176	3.02635967374081	0.0298163262591941
4	3.280707	3.17674661421134	0.103960385788663
5	2.984728	4.24245230393855	-1.25772430393855
6	3.693712	3.58202504543371	0.111686954566287
7	3.226317	2.80946335083196	0.416853649168038
8	2.190349	2.99692891298010	-0.806579912980103
9	2.599515	2.52817740502349	0.0713375949765116
10	3.080288	3.08136782612942	-0.00107982612942403
11	2.929672	2.47357133364660	0.456100666353404
12	2.922548	3.40958612684823	-0.487038126848229
13	3.234943	3.36397761719748	-0.129034617197476
14	2.983081	3.17565536646932	-0.192574366469318
15	3.284389	3.91077670042752	-0.626387700427523
16	3.806511	3.68714508134813	0.11936591865187
17	3.784579	3.9389948538834	-0.154415853883402
18	2.645654	2.97957757438685	-0.33392357438685
19	3.092081	3.09760002009235	-0.00551902009234805
20	3.204859	2.84778265495969	0.357076345040308
21	3.107225	3.42128685022675	-0.314061850226745
22	3.466909	2.94531857848536	0.521590421514641
23	2.984404	3.28279984425278	-0.298395844252777
24	3.218072	2.59097285964243	0.627099140357574
25	2.82731	2.70285793013330	0.124452069866695
26	3.182049	3.29078186180418	-0.108732861804184
27	2.236319	2.22911565379700	0.0072033462030029
28	2.033218	2.21241036278655	-0.179192362786545
29	1.644804	2.2822219578473	-0.6374179578473
30	1.627971	2.76762665415948	-1.13965565415948
31	1.677559	2.57788942982871	-0.90033042982871
32	2.330828	2.59844970740893	-0.267621707408928
33	2.493615	2.69295821556616	-0.199343215566159
34	2.257172	2.62268435926491	-0.365512359264906
35	2.655517	2.1568535137369	0.498663486263101
36	2.298655	1.89818191374329	0.400473086256715
37	2.600402	2.74801080885093	-0.147608808850930
38	3.04523	2.95346464814988	0.0917653518501233
39	2.790583	2.52988074026969	0.260702259730314
40	3.227052	2.70317204876377	0.523879951236228
41	2.967479	2.62956042388855	0.337918576111455
42	2.938817	2.3621498218853	0.576667178114699
43	3.277961	2.93426846470327	0.343692535296725
44	3.423985	3.01668916934433	0.407295830655673
45	3.072646	3.00188246214765	0.070763537852355
46	2.754253	2.83744957514562	-0.0831965751456179
47	2.910431	2.93383889183636	-0.0234078918363603
48	3.174369	3.12321039308665	0.0511586069133454
49	3.068387	2.99507585425298	0.0733111457470219
50	3.089543	3.03486193625438	0.0546810637456193
51	2.906654	2.80291608306946	0.103737916930545
52	2.931161	2.92395113230834	0.00720986769165596
53	3.02566	2.96424197097501	0.0614180290249946
54	2.939551	3.35870465584277	-0.419153655842771
55	2.691019	2.58053700561205	0.110481994387952
56	3.19812	2.9612517370816	0.236868262918397
57	3.07639	3.04140333525661	0.0349866647433906
58	2.863873	2.653872292795	0.210000707205001
59	3.013802	3.00860477452321	0.00519722547678875
60	3.053364	3.01494881201152	0.0384151879884777
61	2.864753	3.11536392321037	-0.250610923210366
62	3.057062	3.02099169040478	0.0360703095952252
63	2.959365	3.31461214566509	-0.355247145665086
64	3.252258	2.61838363389842	0.633874366101579
65	3.602988	3.28976097402914	0.313227025970865
66	3.497704	3.27481635517528	0.222887644824722
67	3.296867	2.96556213050387	0.331304869496132
68	3.602417	3.24725261447788	0.355164385522121
69	3.3001	2.92997009253681	0.370129907463189
70	3.40193	3.54779602971628	-0.145866029716284
71	3.502591	2.92344462190622	0.57914637809378
72	3.402348	2.90661858157815	0.495729418421847
73	3.498551	2.90710198657724	0.591449013422757
74	3.199823	3.2013378342989	-0.00151483429890138
75	2.700064	2.57251226585974	0.127551734140256
76	2.801034	2.55850146216013	0.242532537839866
77	2.898628	2.54742296676240	0.351205033237595
78	2.800854	2.85242818255765	-0.0515741825576523
79	2.399942	2.02703958091035	0.37290241908965
80	2.402724	1.83516074177883	0.567563258221167
81	2.202331	1.96821096106112	0.234120038938878
82	2.102594	2.63212452230555	-0.529530522305548
83	1.798293	2.20693287097305	-0.408639870973051
84	1.202484	1.84064911135646	-0.638165111356455
85	1.400201	1.95069045622608	-0.550489456226085
86	1.200832	1.89728595383043	-0.696453953830428
87	1.298083	1.71709221617614	-0.419009216176139
88	1.099742	1.57363446634897	-0.473892466348969
89	1.001377	1.47940995225173	-0.47803295225173
90	0.8361743	1.38744933462460	-0.551275034624598

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.758002406396321	0.483995187207357	0.241997593603679
7	0.626232764363037	0.747534471273927	0.373767235636963
8	0.796458059535445	0.40708388092911	0.203541940464555
9	0.70089117938158	0.59821764123684	0.29910882061842
10	0.742851097892048	0.514297804215904	0.257148902107952
11	0.726138860471718	0.547722279056564	0.273861139528282
12	0.687806562710301	0.624386874579398	0.312193437289699
13	0.713526976232628	0.572946047534744	0.286473023767372
14	0.653705650005102	0.692588699989796	0.346294349994898
15	0.654615337986054	0.690769324027891	0.345384662013946
16	0.777618949535223	0.444762100929553	0.222381050464776
17	0.784061891352471	0.431876217295058	0.215938108647529
18	0.750289765935845	0.499420468128311	0.249710234064155
19	0.693775114960323	0.612449770079354	0.306224885039677
20	0.673625191502776	0.652749616994447	0.326374808497224
21	0.623287557673667	0.753424884652665	0.376712442326333
22	0.657129556221184	0.685740887557631	0.342870443778816
23	0.612974447550693	0.774051104898614	0.387025552449307
24	0.626942230655378	0.746115538689244	0.373057769344622
25	0.564515147018902	0.870969705962196	0.435484852981098
26	0.500880564467734	0.998238871064532	0.499119435532266
27	0.499760173474075	0.99952034694815	0.500239826525925
28	0.514086937231872	0.971826125536255	0.485913062768128
29	0.660785555099176	0.678428889801648	0.339214444900824
30	0.912058524244615	0.175882951510770	0.0879414757553852
31	0.972631597498246	0.0547368050035085	0.0273684025017542
32	0.969485107785229	0.0610297844295426	0.0305148922147713
33	0.966654087006392	0.0666918259872165	0.0333459129936082
34	0.971451100185845	0.0570977996283109	0.0285488998141555
35	0.977169441925801	0.0456611161483971	0.0228305580741985
36	0.973930955792022	0.0521380884159568	0.0260690442079784
37	0.971729058643156	0.0565418827136883	0.0282709413568441
38	0.972229382397655	0.0555412352046896	0.0277706176023448
39	0.968536504571693	0.0629269908566145	0.0314634954283072
40	0.977438158782221	0.0451236824355574	0.0225618412177787
41	0.974779488918926	0.050441022162149	0.0252205110810745
42	0.980742096744915	0.0385158065101701	0.0192579032550851
43	0.979415014241035	0.0411699715179291	0.0205849857589646
44	0.979524136480938	0.040951727038125	0.0204758635190625
45	0.971244807247093	0.0575103855058139	0.0287551927529069
46	0.961344123029066	0.0773117539418672	0.0386558769709336
47	0.948321082687232	0.103357834625535	0.0516789173127675
48	0.932627168971703	0.134745662056595	0.0673728310282973
49	0.91185565154494	0.176288696910119	0.0881443484550597
50	0.887384820043782	0.225230359912435	0.112615179956218
51	0.855304622959124	0.289390754081752	0.144695377040876
52	0.822591051608906	0.354817896782187	0.177408948391094
53	0.784037541061425	0.43192491787715	0.215962458938575
54	0.848897853065627	0.302204293868745	0.151102146934373
55	0.813407617051401	0.373184765897198	0.186592382948599
56	0.775795386455434	0.448409227089133	0.224204613544566
57	0.74825595158433	0.503488096831341	0.251744048415671
58	0.700628262963435	0.598743474073131	0.299371737036565
59	0.685209876424674	0.629580247150652	0.314790123575326
60	0.67641236264439	0.647175274711221	0.323587637355611
61	0.79807890492864	0.403842190142719	0.201921095071359
62	0.850159998725609	0.299680002548782	0.149840001274391
63	0.98140447395596	0.0371910520880818	0.0185955260440409
64	0.986210377628617	0.0275792447427664	0.0137896223713832
65	0.988086678142198	0.0238266437156048	0.0119133218578024
66	0.991920892141472	0.0161582157170560	0.00807910785852801
67	0.9974203560452	0.00515928790960104	0.00257964395480052
68	0.997144072171806	0.00571185565638827	0.00285592782819413
69	0.999081808823895	0.00183638235220950	0.000918191176104748
70	0.999547294152553	0.000905411694894982	0.000452705847447491
71	0.99919579029679	0.00160841940642159	0.000804209703210794
72	0.998438660090822	0.00312267981835599	0.00156133990917799
73	0.997724289175113	0.00455142164977408	0.00227571082488704
74	0.995194061376863	0.0096118772462733	0.00480593862313665
75	0.99692302342067	0.00615395315865997	0.00307697657932999
76	0.994670825628765	0.0106583487424697	0.00532917437123484
77	0.9894992054321	0.0210015891358015	0.0105007945679007
78	0.984292109677268	0.0314157806454648	0.0157078903227324
79	0.967138402348727	0.0657231953025451	0.0328615976512726
80	0.958582915601958	0.0828341687960844	0.0414170843980422
81	0.99734350899934	0.00531298200132188	0.00265649100066094
82	0.99217538889464	0.0156492222107209	0.00782461110536046
83	0.990184156726846	0.0196316865463083	0.00981584327315417
84	0.99512198275817	0.00975603448366028	0.00487801724183014

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	11	0.139240506329114	NOK
5% type I error level	25	0.316455696202532	NOK
10% type I error level	38	0.481012658227848	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/10eoa21293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/10eoa21293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/175v81293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/175v81293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/20xct1293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/20xct1293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/30xct1293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/30xct1293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/40xct1293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/40xct1293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/5aotw1293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/5aotw1293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/6aotw1293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/6aotw1293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/73fsh1293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/73fsh1293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/83fsh1293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/83fsh1293203072.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/9eoa21293203072.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293203002775qmmbez8olq1o/9eoa21293203072.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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