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mlr trend seiz

*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: Sat, 11 Dec 2010 15:09:47 +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/11/t1292080241ko8pz82c5i3a7nw.htm/, Retrieved Sat, 11 Dec 2010 16:10:51 +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/11/t1292080241ko8pz82c5i3a7nw.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 «

9769 1579 9321 2146 9939 2462 9336 3695 10195 4831 9464 5134 10010 6250 10213 5760 9563 6249 9890 2917 9305 1741 9391 2359 9928 1511 8686 2059 9843 2635 9627 2867 10074 4403 9503 5720 10119 4502 10000 5749 9313 5627 9866 2846 9172 1762 9241 2429 9659 1169 8904 2154 9755 2249 9080 2687 9435 4359 8971 5382 10063 4459 9793 6398 9454 4596 9759 3024 8820 1887 9403 2070 9676 1351 8642 2218 9402 2461 9610 3028 9294 4784 9448 4975 10319 4607 9548 6249 9801 4809 9596 3157 8923 1910 9746 2228 9829 1594 9125 2467 9782 2222 9441 3607 9162 4685 9915 4962 10444 5770 10209 5480 9985 5000 9842 3228 9429 1993 10132 2288 9849 1580 9172 2111 10313 2192 9819 3601 9955 4665 10048 4876 10082 5813 10541 5589 10208 5331 10233 3075 9439 2002 9963 2306 10158 1507 9225 1992 10474 2487 9757 3490 10490 4647 10281 5594 10444 5611 10640 5788 10695 6204 10786 3013 9832 1931 9747 2549 10411 1504 9511 2090 10402 2702 9701 2939 10540 4500 10112 6208 10915 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	8 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

geboortes[t] = + 9198.18672888628 + 0.0132098392350800huwelijken[t] + 293.354221184417M1[t] -561.53253458797M2[t] + 341.103580197078M3[t] -121.286781733765M4[t] + 198.089975881453M5[t] + 3.56981211022072M6[t] + 575.092941052572M7[t] + 526.837239839928M8[t] + 181.833531630878M9[t] + 379.895626257336M10[t] -364.188821255045M11[t] + 9.27576263272296t + 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)	9198.18672888628	229.712392	40.0422	0	0
huwelijken	0.0132098392350800	0.088226	0.1497	0.881347	0.440674
M1	293.354221184417	166.286891	1.7641	0.081432	0.040716
M2	-561.53253458797	149.699174	-3.7511	0.000327	0.000164
M3	341.103580197078	149.3364	2.2841	0.024949	0.012475
M4	-121.286781733765	169.829129	-0.7142	0.47715	0.238575
M5	198.089975881453	251.173729	0.7887	0.432587	0.216293
M6	3.56981211022072	306.569703	0.0116	0.990738	0.495369
M7	575.092941052572	311.949056	1.8435	0.068862	0.034431
M8	526.837239839928	343.619657	1.5332	0.129076	0.064538
M9	181.833531630878	315.008132	0.5772	0.565363	0.282681
M10	379.895626257336	161.884253	2.3467	0.021353	0.010676
M11	-364.188821255045	153.352033	-2.3749	0.019891	0.009945
t	9.27576263272296	1.120115	8.2811	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.842552367519586
R-squared	0.70989449201286
Adjusted R-squared	0.663902155380753
F-TEST (value)	15.4350603599748
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	297.327119757428
Sum Squared Residuals	7249080.12374632

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9769	9521.6750488556	247.324951144397
2	9321	8683.55403456223	637.445965437768
3	9939	9599.6402211783	339.35977882171
4	9336	9162.81335365702	173.186646342977
5	10195	9506.47225127601	688.527748723985
6	9464	9325.23043142573	138.769568574266
7	10010	9920.77150358716	89.2284964128419
8	10213	9875.31874378205	337.681256217952
9	9563	9546.05040959167	16.9495904083248
10	9890	9709.37308251957	180.626917480431
11	9305	8959.02962669946	345.970373300543
12	9391	9340.6578912345	50.342108765495
13	9928	9632.0859313803	295.914068619703
14	8686	8793.71393014146	-107.713930141458
15	9843	9713.23467495863	129.765325041366
16	9627	9263.18475836305	363.815241636947
17	10074	9612.12759167608	461.872408323924
18	9503	9444.28054881017	58.7194511898327
19	10119	10008.9898561969	110.010143803086
20	10000	9986.48258714314	13.5174128568618
21	9313	9649.14304118013	-336.143041180131
22	9866	9819.74433552655	46.2556644734463
23	9172	9070.61618491607	101.383815083931
24	9241	9452.89173157364	-211.891731573636
25	9659	9738.87731795458	-79.8773179545746
26	8904	8906.27801646147	-2.27801646146474
27	9755	9819.44482860657	-64.4448286065686
28	9080	9372.11613889341	-292.116138893414
29	9435	9722.8555103424	-287.855510342408
30	8971	9551.12477474139	-580.124774741386
31	10063	10119.7309847025	-56.7309847024809
32	9793	10106.3649243994	-313.364924399381
33	9454	9746.83284852144	-292.832848521439
34	9759	9933.40483850307	-174.404838503074
35	8820	9183.57656641313	-363.57656641313
36	9403	9559.45855088092	-156.458550880917
37	9676	9852.59066028803	-176.590660288035
38	8642	9018.43259776519	-376.432597765186
39	9402	9933.55446611708	-531.554466117081
40	9610	9487.92984566525	122.070154334748
41	9294	9839.77884361	-545.778843609993
42	9448	9657.05752176538	-209.057521765384
43	10319	10232.9951925019	86.0048074980517
44	9548	10215.7058099460	-667.705809946029
45	9801	9860.95569587119	-59.9556958711863
46	9596	10046.4708987140	-450.470898714015
47	8923	9295.18954430821	-372.189544308212
48	9746	9672.85485707274	73.1451429272644
49	9829	9967.10980281484	-138.109802814835
50	9125	9133.0309993274	-8.03099932739602
51	9782	10041.7064661326	-259.706466132573
52	9441	9606.88749417504	-165.887494175039
53	9162	9949.7802211184	-787.780221118395
54	9915	9768.194945448	146.805054551997
55	10444	10359.6673871250	84.3326128749778
56	10209	10316.8565951669	-107.856595166928
57	9985	9974.78792675776	10.2120732422376
58	9842	10158.7179488924	-316.717948892381
59	9429	9407.5951125574	21.4048874426006
60	10132	9784.95659901952	347.043400980484
61	9849	10078.2340166582	-229.234016658219
62	9172	9239.63744815238	-67.637448152383
63	10313	10152.6193225482	160.380677451805
64	9819	9718.1173867323	100.882613267696
65	9955	10060.8251759264	-105.825175926369
66	10048	9878.36805086646	169.631949133538
67	10082	10471.5445618048	-389.544561804806
68	10541	10429.6056192362	111.394380763773
69	10208	10090.4695351373	117.530464862751
70	10233	10268.0059950821	-35.0059950820895
71	9439	9519.02315270319	-80.0231527031907
72	9963	9896.50352771842	66.4964722815768
73	10158	10188.5788499867	-30.5788499867338
74	9225	9349.37462887608	-124.374628876084
75	10474	10267.8253767152	206.17462328478
76	9757	9827.96024616989	-70.9602461698854
77	10490	10171.8965504128	318.103449587187
78	10281	9999.16186702992	281.838132970075
79	10444	10580.185325872	-136.185325871996
80	10640	10543.5435288367	96.4564711633162
81	10695	10213.3108763821	481.68912361785
82	10786	10378.4961366422	407.50386335781
83	9832	9629.39440571018	202.605594289824
84	9747	10011.0226702452	-264.022670245223
85	10411	10299.8483720617	111.151627938296
86	9511	9461.9783447138	49.0216552862027
87	10402	10381.9746437434	20.0253562565623
88	9701	9931.99077634403	-230.990776344032
89	10540	10281.2638556379	258.736144362068
90	10112	10118.5818599129	-6.58185991293973
91	10915	10702.1151882097	212.884811790324
92	11183	10653.1221914896	529.877808510436
93	10384	10321.4496665584	62.5503334415937
94	10834	10491.7867641201	342.213235879872
95	9886	9741.57540669237	144.424593307634
96	10216	10120.6541722550	95.3458277449567

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.674174037277755	0.65165192544449	0.325825962722245
18	0.651590928487865	0.69681814302427	0.348409071512135
19	0.520960517994043	0.958078964011914	0.479039482005957
20	0.424097459808012	0.848194919616024	0.575902540191988
21	0.356810078223391	0.713620156446782	0.643189921776609
22	0.276617967954903	0.553235935909805	0.723382032045097
23	0.220717439565314	0.441434879130628	0.779282560434686
24	0.151236161811381	0.302472323622761	0.84876383818862
25	0.105956298318977	0.211912596637955	0.894043701681023
26	0.0833373687296912	0.166674737459382	0.916662631270309
27	0.058084965031223	0.116169930062446	0.941915034968777
28	0.0676380404900322	0.135276080980064	0.932361959509968
29	0.19232213797619	0.38464427595238	0.80767786202381
30	0.198538631024158	0.397077262048315	0.801461368975842
31	0.165481880991178	0.330963761982356	0.834518119008822
32	0.123484952393754	0.246969904787508	0.876515047606246
33	0.0992712622336213	0.198542524467243	0.900728737766379
34	0.081723931639457	0.163447863278914	0.918276068360543
35	0.0617861045773205	0.123572209154641	0.93821389542268
36	0.0667930557001045	0.133586111400209	0.933206944299896
37	0.0587819320346987	0.117563864069397	0.941218067965301
38	0.039704292989184	0.079408585978368	0.960295707010816
39	0.0330084890676288	0.0660169781352575	0.966991510932371
40	0.130179808809247	0.260359617618495	0.869820191190753
41	0.134275196776100	0.268550393552201	0.8657248032239
42	0.154793918578906	0.309587837157811	0.845206081421094
43	0.211633585387076	0.423267170774151	0.788366414612924
44	0.252986157988453	0.505972315976906	0.747013842011547
45	0.313302597781022	0.626605195562044	0.686697402218978
46	0.296132371792149	0.592264743584299	0.70386762820785
47	0.265144965966304	0.530289931932608	0.734855034033696
48	0.405525933802798	0.811051867605596	0.594474066197202
49	0.400224484319547	0.800448968639094	0.599775515680453
50	0.472250566448616	0.944501132897232	0.527749433551384
51	0.441636475963343	0.883272951926685	0.558363524036657
52	0.406476279127113	0.812952558254226	0.593523720872887
53	0.745264711985655	0.509470576028689	0.254735288014345
54	0.823957582423409	0.352084835153183	0.176042417576591
55	0.877676412920507	0.244647174158985	0.122323587079492
56	0.87568741158472	0.248625176830561	0.124312588415281
57	0.875639370221722	0.248721259556556	0.124360629778278
58	0.91148267689142	0.177034646217159	0.0885173231085796
59	0.900537542840175	0.198924914319649	0.0994624571598246
60	0.970880936442703	0.058238127114594	0.029119063557297
61	0.960611289437656	0.0787774211246885	0.0393887105623443
62	0.944310123244037	0.111379753511926	0.0556898767559632
63	0.943084108971944	0.113831782056113	0.0569158910280565
64	0.960750219679635	0.0784995606407301	0.0392497803203651
65	0.956480692632867	0.0870386147342653	0.0435193073671327
66	0.951536797886915	0.0969264042261695	0.0484632021130847
67	0.951486440750347	0.0970271184993065	0.0485135592496532
68	0.937172138808479	0.125655722383043	0.0628278611915213
69	0.91589809402169	0.16820381195662	0.08410190597831
70	0.92987227916865	0.140255441662698	0.070127720831349
71	0.91907691751276	0.161846164974479	0.0809230824872393
72	0.890277767729445	0.219444464541110	0.109722232270555
73	0.841789632586992	0.316420734826015	0.158210367413008
74	0.781389726934172	0.437220546131656	0.218610273065828
75	0.731688743055346	0.536622513889308	0.268311256944654
76	0.628838222438627	0.742323555122745	0.371161777561373
77	0.532288096581563	0.935423806836875	0.467711903418437
78	0.57250624779469	0.854987504410621	0.427493752205310
79	0.412155475515329	0.824310951030659	0.587844524484671

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	8	0.126984126984127	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/10uwk81292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/10uwk81292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/15dmw1292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/15dmw1292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/2gm4z1292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/2gm4z1292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/3gm4z1292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/3gm4z1292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/4gm4z1292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/4gm4z1292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/5rdl21292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/5rdl21292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/6rdl21292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/6rdl21292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/724k51292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/724k51292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/824k51292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/824k51292080178.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/9uwk81292080178.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292080241ko8pz82c5i3a7nw/9uwk81292080178.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)

library(lmtest)

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

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

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

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

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

x <- x1

if (par3 == 'First Differences'){

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

for (i in 1:n-1) {

for (j in 1:k) {

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

}

}

x <- x2

}

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

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

for (i in 1:11){

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

}

x <- cbind(x, x2)

}

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

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

for (i in 1:3){

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

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

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

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

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

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

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

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

j <- j + 1

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

}

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

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

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

}

gqarr

}

bitmap(file='test0.png')

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

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

grid()

dev.off()

bitmap(file='test1.png')

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

grid()

dev.off()

bitmap(file='test2.png')

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

grid()

dev.off()

bitmap(file='test3.png')

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

dev.off()

bitmap(file='test4.png')

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

qqline(mysum$resid)

grid()

dev.off()

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

bitmap(file='test5.png')

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

dum

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

dum1

z <- as.data.frame(dum1)

z

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

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

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

grid()

dev.off()

bitmap(file='test7.png')

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

grid()

dev.off()

bitmap(file='test8.png')

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

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

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

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

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

myeq <- colnames(x)[1]

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

for (i in 1:k){

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

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

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

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

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

}

}

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

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

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

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

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

}

a<-table.end(a)

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

a<-table.start()

a<-table.row.start(a)

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

a<-table.row.end(a)

a<-table.row.start(a)

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

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

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

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

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant1)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant5)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

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

a<-table.element(a,numsignificant10)

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

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

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

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

}

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

Software written by Ed van Stee & Patrick Wessa

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