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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: Sun, 12 Dec 2010 19:30:14 +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/12/t1292182139mkmu2absc8nxzf7.htm/, Retrieved Sun, 12 Dec 2010 20:29:02 +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/12/t1292182139mkmu2absc8nxzf7.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 «

29462 27071 31514 26105 29462 27071 22397 26105 29462 23843 22397 26105 21705 23843 22397 18089 21705 23843 20764 18089 21705 25316 20764 18089 17704 25316 20764 15548 17704 25316 28029 15548 17704 29383 28029 15548 36438 29383 28029 32034 36438 29383 22679 32034 36438 24319 22679 32034 18004 24319 22679 17537 18004 24319 20366 17537 18004 22782 20366 17537 19169 22782 20366 13807 19169 22782 29743 13807 19169 25591 29743 13807 29096 25591 29743 26482 29096 25591 22405 26482 29096 27044 22405 26482 17970 27044 22405 18730 17970 27044 19684 18730 17970 19785 19684 18730 18479 19785 19684 10698 18479 19785 31956 10698 18479 29506 31956 10698 34506 29506 31956 27165 34506 29506 26736 27165 34506 23691 26736 27165 18157 23691 26736 17328 18157 23691 18205 17328 18157 20995 18205 17328 17382 20995 18205 9367 17382 20995 31124 9367 17382 26551 31124 9367 30651 26551 31124 25859 30651 26551 25100 25859 30651 25778 25100 25859 20418 25778 25100 18688 20418 25778 2 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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

X[t] = + 15346.807084182 + 0.289096682910106Y_1[t] + 0.27664390031443Y_2[t] + 149.900108370174M1[t] -3544.55214943836M2[t] -8210.6550195411M3[t] -4712.60659211193M4[t] -9776.45674052903M5[t] -9728.54466247162M6[t] -6124.88659935529M7[t] -3321.58788455124M8[t] -9310.67591569469M9[t] -14255.1507298011M10[t] + 5343.75696282205M11[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)	15346.807084182	3027.53376	5.0691	3e-06	1e-06
Y_1	0.289096682910106	0.109067	2.6506	0.009725	0.004863
Y_2	0.27664390031443	0.108496	2.5498	0.012738	0.006369
M1	149.900108370174	2189.494949	0.0685	0.945592	0.472796
M2	-3544.55214943836	1814.798347	-1.9531	0.05439	0.027195
M3	-8210.6550195411	2325.469822	-3.5308	0.000699	0.00035
M4	-4712.60659211193	2254.619156	-2.0902	0.039858	0.019929
M5	-9776.45674052903	1777.365147	-5.5005	0	0
M6	-9728.54466247162	2295.267893	-4.2385	6.1e-05	3.1e-05
M7	-6124.88659935529	1936.716249	-3.1625	0.002229	0.001114
M8	-3321.58788455124	1717.701493	-1.9337	0.056773	0.028386
M9	-9310.67591569469	1646.975538	-5.6532	0	0
M10	-14255.1507298011	2197.023644	-6.4884	0	0
M11	5343.75696282205	2374.202453	2.2508	0.027217	0.013608

Multiple Linear Regression - Regression Statistics
Multiple R	0.948917019856188
R-squared	0.900443510572749
Adjusted R-squared	0.883850762334874
F-TEST (value)	54.2672918110916
F-TEST (DF numerator)	13
F-TEST (DF denominator)	78
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1936.43101832706
Sum Squared Residuals	292481676.921655

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	29462	32040.9993701206	-2578.99937012064
2	26105	27808.6484320532	-1703.64843205316
3	22397	22833.503563073	-436.503563073006
4	23843	24330.8879169159	-487.887916915945
5	21705	18659.275989621	3045.72401037905
6	18089	18489.1264394712	-400.126439471226
7	20764	20455.9462383124	308.053761687636
8	25316	23032.234236364	2283.76576363604
9	17704	19099.1367391684	-1395.13673916842
10	15548	13213.3410089816	2334.65899101844
11	28029	30083.1428840571	-2054.14288405709
12	29383	27751.1573715582	1631.84262844184
13	36438	31745.286908413	4692.71309158698
14	32034	30464.987589561	1569.01241043898
15	22679	26477.4256446405	-3798.42564464049
16	24319	26052.6348664609	-1733.63486646085
17	18004	18874.8995905748	-870.899590574837
18	17537	17550.8621125706	-13.8621125705934
19	20366	19272.5057942823	1093.49420571772
20	22782	22764.4663235922	17.5336764078221
21	19169	18256.4614723491	912.538527650936
22	13807	12935.8520060481	871.1479939519
23	29743	29985.1088730712	-242.108873071237
24	25591	27765.0320556187	-2174.03205561866
25	29096	31123.1999319568	-2027.19993195683
26	26482	27293.4060736427	-811.406073642702
27	22405	22841.241345015	-436.241345015035
28	27044	24437.4954407978	2606.50455920223
29	17970	19586.8876228187	-1616.88762281872
30	18730	18294.8874537085	435.112546291524
31	19684	19607.9922443833	76.0077556166566
32	19785	22897.3385589226	-3112.3385589226
33	18479	17201.367573653	1277.63242634697
34	10698	11907.2735255978	-1209.27352559778
35	31956	28895.4229946868	3060.57700531324
36	29506	27544.7171288212	1961.28287117885
37	34506	32867.2263969457	1638.77360305427
38	27165	29940.4799979174	-2775.47999791737
39	26736	24535.3378801437	2200.66211985629
40	23691	25878.5209583962	-2187.52095839619
41	18157	19815.6911772829	-1658.69117728294
42	17328	17421.3615356584	-93.3615356583782
43	18205	19254.4111043022	-1049.41110430218
44	20995	22081.9098166577	-1086.90981665772
45	17382	17142.0182314092	239.981768590783
46	9367	11924.8735838259	-2557.87358382585
47	31124	28207.1569510885	2916.84304891152
48	26551	26935.9756573214	-384.975657321442
49	30651	31782.7779738848	-1131.77797388476
50	25859	28008.5295598698	-2149.52955986977
51	25100	23091.315376551	2008.68462344902
52	25778	25044.2618513446	733.738148655385
53	20418	19966.4465336019	451.553466398084
54	18688	18652.3649556743	35.6350443256525
55	20424	20273.0744516708	150.925548329155
56	24776	23099.6510604629	1676.34893953713
57	19814	18848.96560429	965.03439570995
58	12738	13673.9473037521	-935.947303752092
59	31566	29854.4998347431	1711.50016525686
60	30111	27996.3229791277	2114.67702087235
61	30019	32934.2387689837	-2915.23876898372
62	31934	28810.67274139	3123.32725861004
63	25826	24672.7387802311	1153.26121976885
64	26835	26934.7577375475	-99.7577375475158
65	20205	20472.8651990662	-267.865199066176
66	17789	18883.1999648468	-1094.19996484684
67	20520	19954.2513829677	565.748617032315
68	22518	22878.7014756396	-360.701475639571
69	15572	18222.7431087092	-2650.74310870922
70	11509	11822.9372479374	-313.937247937439
71	25447	28325.6765863128	-2878.67658631281
72	24090	25887.3450229143	-1797.34502291428
73	27786	29500.803615158	-1714.80361515797
74	26195	26499.4469246585	-304.446924658505
75	20516	22395.8670876079	-1879.86708760793
76	22759	23811.9950073903	-1052.99500739034
77	19028	17825.528008855	1202.47199114504
78	16971	17415.33263138	-444.332631380031
79	20036	19392.1604256771	643.839574322866
80	22485	22512.4839706539	-27.4839706538754
81	18730	18079.307270421	650.692729579
82	14538	12726.7753238572	1811.22467614282
83	27561	30074.9918760405	-2513.99187604049
84	25985	27336.4497846387	-1351.44978463865
85	34670	30633.4670345373	4036.53296546267
86	32066	29013.8286809075	3052.17131909248
87	27186	25997.5703227377	1188.4296772623
88	29586	27364.4462211468	2221.55377885323
89	21359	21644.4058781795	-285.405878179502
90	21553	19977.8649066901	1575.1350933099
91	19573	21361.6583584042	-1788.65835840418
92	24256	23646.2145577072	609.785442292785

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.872779588206698	0.254440823586603	0.127220411793302
18	0.854667402932053	0.290665194135893	0.145332597067947
19	0.790319792196051	0.419360415607898	0.209680207803949
20	0.753995288575988	0.492009422848025	0.246004711424012
21	0.758626161847582	0.482747676304836	0.241373838152418
22	0.77785172452224	0.444296550955519	0.22214827547776
23	0.76827863239911	0.463442735201779	0.23172136760089
24	0.854824326712867	0.290351346574266	0.145175673287133
25	0.854665875257014	0.290668249485972	0.145334124742986
26	0.804556507275848	0.390886985448303	0.195443492724152
27	0.748133047105861	0.503733905788279	0.251866952894139
28	0.764347860530691	0.471304278938617	0.235652139469309
29	0.786290329505886	0.427419340988229	0.213709670494114
30	0.751018055574037	0.497963888851926	0.248981944425963
31	0.698058760778141	0.603882478443718	0.301941239221859
32	0.780590661784816	0.438818676430369	0.219409338215184
33	0.744028325395568	0.511943349208865	0.255971674604432
34	0.779935431727507	0.440129136544986	0.220064568272493
35	0.842634291580508	0.314731416838985	0.157365708419492
36	0.82880165455173	0.34239669089654	0.17119834544827
37	0.815192966312105	0.36961406737579	0.184807033687895
38	0.846870173079204	0.306259653841591	0.153129826920796
39	0.87672178986537	0.246556420269262	0.123278210134631
40	0.873940863402343	0.252118273195314	0.126059136597657
41	0.857276411020767	0.285447177958466	0.142723588979233
42	0.813896308470807	0.372207383058386	0.186103691529193
43	0.779640723910372	0.440718552179255	0.220359276089628
44	0.740980190385194	0.518039619229612	0.259019809614806
45	0.685468975854404	0.629062048291193	0.314531024145596
46	0.724106896882415	0.551786206235171	0.275893103117585
47	0.798897195390973	0.402205609218053	0.201102804609027
48	0.750035684554144	0.499928630891712	0.249964315445856
49	0.711709949856726	0.576580100286548	0.288290050143274
50	0.776849943498016	0.446300113003968	0.223150056501984
51	0.78037388312536	0.439252233749281	0.219626116874641
52	0.72999331265002	0.540013374699961	0.270006687349981
53	0.67179801561785	0.6564039687643	0.32820198438215
54	0.602974509752846	0.794050980494309	0.397025490247154
55	0.531221396235142	0.937557207529716	0.468778603764858
56	0.505995100147101	0.988009799705799	0.494004899852899
57	0.458784598377408	0.917569196754815	0.541215401622592
58	0.423619734948548	0.847239469897096	0.576380265051452
59	0.527978318770018	0.944043362459965	0.472021681229982
60	0.559557370970512	0.880885258058975	0.440442629029488
61	0.799185933808027	0.401628132383947	0.200814066191973
62	0.812953298687126	0.374093402625748	0.187046701312874
63	0.768929106972722	0.462141786054557	0.231070893027278
64	0.718581566875318	0.562836866249364	0.281418433124682
65	0.652959421624593	0.694081156750813	0.347040578375407
66	0.609683375957244	0.780633248085513	0.390316624042756
67	0.544862737627628	0.910274524744744	0.455137262372372
68	0.452751112261341	0.905502224522682	0.547248887738659
69	0.512132983048984	0.975734033902033	0.487867016951016
70	0.437691515578409	0.875383031156818	0.562308484421591
71	0.380750682587749	0.761501365175499	0.619249317412251
72	0.293638130029898	0.587276260059796	0.706361869970102
73	0.538301867342846	0.923396265314307	0.461698132657154
74	0.570132004296937	0.859735991406127	0.429867995703063
75	0.499160750451448	0.998321500902896	0.500839249548552

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/104qr91292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/104qr91292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/1c6ds1292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/1c6ds1292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/2ngvd1292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/2ngvd1292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/3qyt01292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/3qyt01292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/4qyt01292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/4qyt01292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/5qyt01292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/5qyt01292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/6jpa31292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/6jpa31292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/7ch961292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/7ch961292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/84qr91292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/84qr91292182204.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/94qr91292182204.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182139mkmu2absc8nxzf7/94qr91292182204.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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