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WS10, Multiple Regression (MR):

*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, 10 Dec 2010 13:41:51 +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/10/t12919886264j7dj8805eyew5k.htm/, Retrieved Fri, 10 Dec 2010 14:43:46 +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/10/t12919886264j7dj8805eyew5k.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 «

12008.00 4.00 9169.00 5.90 8788.00 7.10 8417.00 10.50 8247.00 15.10 8197.00 16.80 8236.00 15.30 8253.00 18.40 7733.00 16.10 8366.00 11.30 8626.00 7.90 8863.00 5.60 10102.00 3.40 8463.00 4.80 9114.00 6.50 8563.00 8.50 8872.00 15.10 8301.00 15.70 8301.00 18.70 8278.00 19.20 7736.00 12.90 7973.00 14.40 8268.00 6.20 9476.00 3.30 11100.00 4.60 8962.00 7.10 9173.00 7.80 8738.00 9.90 8459.00 13.60 8078.00 17.10 8411.00 17.80 8291.00 18.60 7810.00 14.70 8616.00 10.50 8312.00 8.60 9692.00 4.40 9911.00 2.30 8915.00 2.80 9452.00 8.80 9112.00 10.70 8472.00 13.90 8230.00 19.30 8384.00 19.50 8625.00 20.40 8221.00 15.30 8649.00 7.90 8625.00 8.30 10443.00 4.50 10357.00 3.20 8586.00 5.00 8892.00 6.60 8329.00 11.10 8101.00 12.80 7922.00 16.30 8120.00 17.40 7838.00 18.90 7735.00 15.80 8406.00 11.70 8209.00 6.40 9451.00 2.90 10041.00 4.70 9411.00 2.40 10405.00 7.20 8467.00 10.70 8464.00 13.40 8102.00 18.30 7627.00 18.40 7513.00 16.80 7510.00 16.60 8291.00 14.10 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Sterftegevallen[t] = + 9702.03898923781 -96.5432003817131Temperatuur[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)	9702.03898923781	136.447244	71.1047	0	0
Temperatuur	-96.5432003817131	10.99678	-8.7792	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.671217338794934
R-squared	0.450532715898953
Adjusted R-squared	0.44468731925958
F-TEST (value)	77.0747895642017
F-TEST (DF numerator)	1
F-TEST (DF denominator)	94
p-value	7.22755189030977e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	596.998497817072
Sum Squared Residuals	33502277.401209

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	12008	9315.86618771094	2692.13381228906
2	9169	9132.4341069857	36.5658930142965
3	8788	9016.58226652765	-228.582266527648
4	8417	8688.33538522982	-271.335385229823
5	8247	8244.23666347394	2.76333652605708
6	8197	8080.11322282503	116.886777174970
7	8236	8224.9280233976	11.0719766023999
8	8253	7925.64410221429	327.355897785710
9	7733	8147.69346309223	-414.69346309223
10	8366	8611.10082492445	-245.100824924453
11	8626	8939.34770622228	-313.347706222278
12	8863	9161.39706710022	-298.397067100218
13	10102	9373.79210793999	728.207892060013
14	8463	9238.63162740559	-775.631627405588
15	9114	9074.50818675668	39.4918132433241
16	8563	8881.42178599325	-318.421785993250
17	8872	8244.23666347394	627.763336526057
18	8301	8186.31074324492	114.689256755085
19	8301	7896.68114209978	404.318857900224
20	8278	7848.40954190892	429.590458091081
21	7736	8456.63170431371	-720.631704313712
22	7973	8311.81690374114	-338.816903741142
23	8268	9103.47114687119	-835.47114687119
24	9476	9383.44642797816	92.553572021842
25	11100	9257.94026748193	1842.05973251807
26	8962	9016.58226652765	-54.582266527648
27	9173	8949.00202626045	223.997973739551
28	8738	8746.26130545885	-8.26130545885119
29	8459	8389.05146404651	69.9485359534874
30	8078	8051.15026271052	26.8497372894836
31	8411	7983.57002244332	427.429977556683
32	8291	7906.33546213795	384.664537862053
33	7810	8282.85394362663	-472.853943626628
34	8616	8688.33538522982	-72.3353852298233
35	8312	8871.76746595508	-559.767465955078
36	9692	9277.24890755827	414.751092441727
37	9911	9479.98962835987	431.010371640129
38	8915	9431.71802816901	-516.718028169014
39	9452	8852.45882587874	599.541174121264
40	9112	8669.02674515348	442.973254846519
41	8472	8360.088503932	111.911496068001
42	8230	7838.75522187075	391.244778129252
43	8384	7819.4465817944	564.553418205595
44	8625	7732.55770145086	892.442298549137
45	8221	8224.9280233976	-3.92802339760014
46	8649	8939.34770622228	-290.347706222278
47	8625	8900.7304260696	-275.730426069592
48	10443	9267.5945875201	1175.40541247990
49	10357	9393.10074801633	963.89925198367
50	8586	9219.32298732925	-633.322987329246
51	8892	9064.8538667185	-172.853866718505
52	8329	8630.4094650008	-301.409465000795
53	8101	8466.28602435188	-365.286024351883
54	7922	8128.38482301589	-206.384823015887
55	8120	8022.187302596	97.8126974039973
56	7838	7877.37250202343	-39.3725020234331
57	7735	8176.65642320674	-441.656423206744
58	8406	8572.48354477177	-166.483544771768
59	8209	9084.16250679485	-875.162506794847
60	9451	9422.06370813084	28.9362918691568
61	10041	9248.28594744376	792.71405255624
62	9411	9470.3353083217	-59.3353083216996
63	10405	9006.92794648948	1398.07205351052
64	8467	8669.02674515348	-202.026745153481
65	8464	8408.36010412286	55.6398958771448
66	8102	7935.29842225246	166.701577747539
67	7627	7925.64410221429	-298.64410221429
68	7513	8080.11322282503	-567.11322282503
69	7510	8099.42186290137	-589.421862901373
70	8291	8340.77986385566	-49.779863855656
71	8064	9113.12546690936	-1049.12546690936
72	9383	9364.13778790181	18.8622120981847
73	9706	9537.9155485889	168.084451411101
74	8579	9479.98962835987	-900.989628359871
75	9474	9267.5945875201	206.405412479898
76	8318	8804.18722568788	-486.187225687879
77	8213	8331.12554381748	-118.125543817485
78	8059	8031.84162263417	27.1583773658261
79	9111	7481.54538045841	1629.45461954159
80	7708	8128.38482301589	-420.384823015887
81	7680	7925.64410221429	-245.644102214290
82	8014	8331.12554381748	-317.125543817485
83	8007	8823.49586576422	-816.495865764222
84	8718	9132.4341069857	-414.434106985704
85	9486	9006.92794648948	479.072053510523
86	9113	9045.54522664216	67.454773357838
87	9025	8929.6933861841	95.3066138158938
88	8476	8321.47122377931	154.528776220687
89	7952	8292.5082636648	-340.508263664799
90	7759	8012.53298255783	-253.532982557831
91	7835	8041.49594267235	-206.495942672345
92	7600	8041.49594267235	-441.495942672345
93	7651	8340.77986385566	-689.779863855656
94	8319	8697.989705268	-378.989705267995
95	8812	9045.54522664216	-233.545226642162
96	8630	9306.21186767279	-676.211867672787

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.996912310741485	0.00617537851702912	0.00308768925851456
6	0.995768887043949	0.00846222591210209	0.00423111295605104
7	0.990641676620488	0.0187166467590244	0.00935832337951222
8	0.988192124098861	0.0236157518022770	0.0118078759011385
9	0.981018507081119	0.0379629858377623	0.0189814929188811
10	0.974725748582996	0.0505485028340079	0.0252742514170039
11	0.976276517658776	0.0474469646824473	0.0237234823412236
12	0.9772666221658	0.0454667556684016	0.0227333778342008
13	0.968662011127835	0.0626759777443297	0.0313379888721649
14	0.985551594726278	0.0288968105474443	0.0144484052737222
15	0.977074808856398	0.0458503822872048	0.0229251911436024
16	0.969635077995494	0.0607298440090116	0.0303649220045058
17	0.970307320035769	0.0593853599284626	0.0296926799642313
18	0.956016005673547	0.0879679886529058	0.0439839943264529
19	0.94585584243252	0.108288315134960	0.0541441575674798
20	0.933240774084873	0.133518451830255	0.0667592259151273
21	0.943498353199587	0.113003293600825	0.0565016468004126
22	0.928769028149861	0.142461943700278	0.0712309718501388
23	0.94881836449902	0.102363271001962	0.0511816355009808
24	0.928669061412515	0.142661877174970	0.0713309385874852
25	0.99499295462712	0.0100140907457608	0.00500704537288041
26	0.992362956591972	0.0152740868160564	0.0076370434080282
27	0.988719500805337	0.0225609983893251	0.0112804991946626
28	0.983210160040659	0.0335796799186828	0.0167898399593414
29	0.97548758780039	0.0490248243992211	0.0245124121996106
30	0.964980706524795	0.0700385869504099	0.0350192934752049
31	0.95854056673585	0.0829188665283006	0.0414594332641503
32	0.949446868367021	0.101106263265958	0.050553131632979
33	0.943489006521618	0.113021986956763	0.0565109934783815
34	0.9248359168361	0.150328166327799	0.0751640831638995
35	0.924269320360756	0.151461359278489	0.0757306796392444
36	0.910331892954537	0.179336214090927	0.0896681070454634
37	0.896307482055044	0.207385035889911	0.103692517944956
38	0.8938164730616	0.212367053876802	0.106183526938401
39	0.892462669285516	0.215074661428969	0.107537330714484
40	0.878897694172366	0.242204611655268	0.121102305827634
41	0.847490152391585	0.30501969521683	0.152509847608415
42	0.826287014756035	0.347425970487930	0.173712985243965
43	0.821712638514576	0.356574722970848	0.178287361485424
44	0.866643256083942	0.266713487832117	0.133356743916058
45	0.833710717545181	0.332578564909637	0.166289282454819
46	0.805439235238106	0.389121529523789	0.194560764761894
47	0.772858359525751	0.454283280948498	0.227141640474249
48	0.887423153628927	0.225153692742146	0.112576846371073
49	0.937572811942139	0.124854376115723	0.0624271880578614
50	0.939184377556533	0.121631244886934	0.060815622443467
51	0.92146246346294	0.15707507307412	0.07853753653706
52	0.903368405261874	0.193263189476252	0.0966315947381258
53	0.885371462697106	0.229257074605789	0.114628537302894
54	0.857224259840224	0.285551480319551	0.142775740159776
55	0.823154116744612	0.353691766510776	0.176845883255388
56	0.781742537901232	0.436514924197536	0.218257462098768
57	0.757676417837725	0.484647164324549	0.242323582162275
58	0.710950670981379	0.578098658037242	0.289049329018621
59	0.757542653709268	0.484914692581463	0.242457346290732
60	0.71090325249807	0.578193495003861	0.289096747501930
61	0.780500983007377	0.438998033985246	0.219499016992623
62	0.737674607516003	0.524650784967994	0.262325392483997
63	0.956494512378875	0.0870109752422502	0.0435054876211251
64	0.940510906860677	0.118978186278647	0.0594890931393234
65	0.921646020844378	0.156707958311244	0.0783539791556219
66	0.89937432330034	0.201251353399321	0.100625676699661
67	0.873697019698274	0.252605960603452	0.126302980301726
68	0.867707774176624	0.264584451646751	0.132292225823376
69	0.866543513452468	0.266912973095065	0.133456486547532
70	0.826032853536911	0.347934292926178	0.173967146463089
71	0.877745460911515	0.244509078176970	0.122254539088485
72	0.853633389909257	0.292733220181486	0.146366610090743
73	0.859576215502806	0.280847568994388	0.140423784497194
74	0.859703546078978	0.280592907842043	0.140296453921022
75	0.864726570241063	0.270546859517873	0.135273429758937
76	0.829777395785757	0.340445208428486	0.170222604214243
77	0.775339278800012	0.449321442399976	0.224660721199988
78	0.710490686957133	0.579018626085734	0.289509313042867
79	0.995043055227032	0.00991388954593584	0.00495694477296792
80	0.991192514544408	0.0176149709111831	0.00880748545559155
81	0.983788781086638	0.0324224378267241	0.0162112189133620
82	0.971184123973519	0.0576317520529624	0.0288158760264812
83	0.980733487451556	0.0385330250968875	0.0192665125484437
84	0.970805841674142	0.0583883166517164	0.0291941583258582
85	0.987951429761491	0.0240971404770173	0.0120485702385086
86	0.984743744407795	0.0305125111844100	0.0152562555922050
87	0.9887079972916	0.0225840054167991	0.0112920027083995
88	0.996299269824961	0.00740146035007708	0.00370073017503854
89	0.987896363193067	0.0242072736138666	0.0121036368069333
90	0.966251686272309	0.0674966274553823	0.0337483137276912
91	0.933999347301896	0.132001305396208	0.0660006526981042

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.0459770114942529	NOK
5% type I error level	23	0.264367816091954	NOK
10% type I error level	34	0.390804597701149	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/10q4d71291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/10q4d71291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/1jlyv1291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/1jlyv1291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/2jlyv1291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/2jlyv1291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/3cufg1291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/3cufg1291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/4cufg1291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/4cufg1291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/5cufg1291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/5cufg1291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/6n3x11291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/6n3x11291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/7gvw41291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/7gvw41291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/8gvw41291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/8gvw41291988502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/9gvw41291988502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t12919886264j7dj8805eyew5k/9gvw41291988502.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)

library(lmtest)

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

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

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

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

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

x <- x1

if (par3 == 'First Differences'){

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

for (i in 1:n-1) {

for (j in 1:k) {

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

}

}

x <- x2

}

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

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

for (i in 1:11){

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

}

x <- cbind(x, x2)

}

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

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

for (i in 1:3){

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

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

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

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

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

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

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

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

j <- j + 1

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

}

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

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

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

}

gqarr

}

bitmap(file='test0.png')

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

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

grid()

dev.off()

bitmap(file='test1.png')

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

grid()

dev.off()

bitmap(file='test2.png')

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

grid()

dev.off()

bitmap(file='test3.png')

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

dev.off()

bitmap(file='test4.png')

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

qqline(mysum$resid)

grid()

dev.off()

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

bitmap(file='test5.png')

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

dum

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

dum1

z <- as.data.frame(dum1)

z

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

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

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

grid()

dev.off()

bitmap(file='test7.png')

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

grid()

dev.off()

bitmap(file='test8.png')

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

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

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

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

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

myeq <- colnames(x)[1]

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

for (i in 1:k){

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

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

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

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

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

}

}

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

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant1)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant5)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant10)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

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

}

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

Software written by Ed van Stee & Patrick Wessa

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