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Regressie sterftes 4

*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, 26 Nov 2010 19:01:11 +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/Nov/26/t1290798160erkdb6bszxf1e15.htm/, Retrieved Fri, 26 Nov 2010 20:02:40 +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/Nov/26/t1290798160erkdb6bszxf1e15.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 0 0 9169 0 0 8788 0 0 8417 0 0 8247 0 0 8197 0 0 8236 0 0 8253 0 0 7733 0 0 8366 0 0 8626 0 0 8863 0 0 10102 0 0 8463 0 0 9114 0 0 8563 0 0 8872 0 0 8301 0 0 8301 0 0 8278 0 0 7736 0 0 7973 0 0 8268 0 0 9476 0 0 11100 0 0 8962 0 0 9173 0 0 8738 0 0 8459 0 0 8078 0 0 8411 0 0 8291 0 0 7810 0 0 8616 0 0 8312 0 0 9692 0 0 9911 0 0 8915 0 0 9452 0 0 9112 0 0 8472 0 0 8230 0 0 8384 0 0 8625 0 0 8221 0 0 8649 0 0 8625 0 0 10443 0 0 10357 1 49 8586 1 50 8892 1 51 8329 1 52 8101 1 53 7922 1 54 8120 1 55 7838 1 56 7735 1 57 8406 1 58 8209 1 59 9451 1 60 10041 1 61 9411 1 62 10405 1 63 8467 1 64 8464 1 65 8102 1 66 7627 1 67 7513 1 68 7510 1 69 8291 1 70 8064 1 71 9383 1 72 9706 1 73 8579 1 74 9474 1 75 8318 1 76 8213 1 77 8059 1 78 9111 1 79 7708 1 80 7680 1 81 8014 1 82 8007 1 83 8718 1 84 9486 1 85 9113 1 86 9025 1 87 8476 1 88 7952 1 89 7759 1 90 7835 1 91 7600 1 92 7651 1 93 8319 1 94 8812 1 95 8630 1 96

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

Sterftes[t] = + 9484.31288744492 + 8.56703263873167Dummy1[t] -4.01529240421240D1_D2[t] + 984.790891776835M1[t] -452.326462021062M2[t] -59.6938158189554M3[t] -795.561169616849M4[t] -998.553523414743M5[t] -1263.04587721264M6[t] -1088.91323101053M7[t] -1326.78058480842M8[t] -1578.52293860632M9[t] -1006.76529240421M10[t] -968.632646202106M11[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)	9484.31288744492	146.492645	64.7426	0	0
Dummy1	8.56703263873167	315.123238	0.0272	0.978377	0.489189
D1_D2	-4.01529240421240	4.200461	-0.9559	0.341924	0.170962
M1	984.790891776835	199.764933	4.9297	4e-06	2e-06
M2	-452.326462021062	199.53295	-2.2669	0.026027	0.013014
M3	-59.6938158189554	199.322828	-0.2995	0.765329	0.382665
M4	-795.561169616849	199.134636	-3.9951	0.00014	7e-05
M5	-998.553523414743	198.968437	-5.0187	3e-06	1e-06
M6	-1263.04587721264	198.824285	-6.3526	0	0
M7	-1088.91323101053	198.702229	-5.4801	0	0
M8	-1326.78058480842	198.602308	-6.6806	0	0
M9	-1578.52293860632	198.524558	-7.9513	0	0
M10	-1006.76529240421	198.469003	-5.0727	2e-06	1e-06
M11	-968.632646202106	198.435663	-4.8813	5e-06	3e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.887804582769805
R-squared	0.788196977187067
Adjusted R-squared	0.754618449180139
F-TEST (value)	23.4732438844382
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	396.849096941053
Sum Squared Residuals	12914114.8709202

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	12008	10469.1037792217	1538.89622077827
2	9169	9031.98642542385	137.013574576145
3	8788	9424.61907162596	-636.619071625962
4	8417	8688.75171782807	-271.751717828069
5	8247	8485.75936403017	-238.759364030174
6	8197	8221.26701023228	-24.2670102322804
7	8236	8395.39965643439	-159.399656434387
8	8253	8157.5323026365	95.4676973635074
9	7733	7905.7899488386	-172.789948838599
10	8366	8477.5475950407	-111.547595040705
11	8626	8515.68024124281	110.319758757189
12	8863	9484.31288744492	-621.312887444917
13	10102	10469.1037792218	-367.103779221751
14	8463	9031.98642542386	-568.986425423856
15	9114	9424.61907162596	-310.619071625962
16	8563	8688.75171782807	-125.751717828068
17	8872	8485.75936403017	386.240635969826
18	8301	8221.26701023228	79.7329897677194
19	8301	8395.39965643439	-94.3996564343863
20	8278	8157.5323026365	120.467697363507
21	7736	7905.7899488386	-169.789948838599
22	7973	8477.5475950407	-504.547595040705
23	8268	8515.68024124281	-247.680241242812
24	9476	9484.31288744492	-8.3128874449172
25	11100	10469.1037792218	630.896220778248
26	8962	9031.98642542386	-69.9864254238556
27	9173	9424.61907162596	-251.619071625962
28	8738	8688.75171782807	49.2482821719321
29	8459	8485.75936403017	-26.7593640301742
30	8078	8221.26701023228	-143.267010232280
31	8411	8395.39965643439	15.6003435656137
32	8291	8157.5323026365	133.467697363507
33	7810	7905.7899488386	-95.7899488385991
34	8616	8477.5475950407	138.452404959295
35	8312	8515.68024124281	-203.680241242812
36	9692	9484.31288744492	207.687112555083
37	9911	10469.1037792218	-558.103779221752
38	8915	9031.98642542386	-116.986425423856
39	9452	9424.61907162596	27.3809283740382
40	9112	8688.75171782807	423.248282171932
41	8472	8485.75936403017	-13.7593640301742
42	8230	8221.26701023228	8.73298976771944
43	8384	8395.39965643439	-11.3996564343863
44	8625	8157.5323026365	467.467697363507
45	8221	7905.7899488386	315.210051161401
46	8649	8477.5475950407	171.452404959295
47	8625	8515.68024124281	109.319758757189
48	10443	9484.31288744492	958.687112555083
49	10357	10280.9214840541	76.0785159459237
50	8586	8839.78883785197	-253.788837851968
51	8892	9228.40619164986	-336.406191649861
52	8329	8488.52354544776	-159.523545447755
53	8101	8281.51589924565	-180.515899245649
54	7922	8013.00825304354	-91.0082530435424
55	8120	8183.12560684144	-63.1256068414367
56	7838	7941.24296063933	-103.24296063933
57	7735	7685.48531443722	49.5146855627757
58	8406	8253.22766823512	152.772331764882
59	8209	8287.34502203301	-78.3450220330117
60	9451	9251.9623758309	199.037624169095
61	10041	10232.7379752035	-191.737975203528
62	9411	8791.60532900142	619.394670998581
63	10405	9180.22268279931	1224.77731720069
64	8467	8440.3400365972	26.6599634027937
65	8464	8233.3323903951	230.6676096049
66	8102	7964.824744193	137.175255807006
67	7627	8134.94209799089	-507.942097990888
68	7513	7893.05945178878	-380.059451788782
69	7510	7637.30180558668	-127.301805586675
70	8291	8205.04415938457	85.9558406154309
71	8064	8239.16151318246	-175.161513182463
72	9383	9203.77886698036	179.221133019644
73	9706	10184.5544663530	-478.554466352979
74	8579	8743.42182015087	-164.42182015087
75	9474	9132.03917394876	341.960826051236
76	8318	8392.15652774666	-74.1565277466577
77	8213	8185.14888154455	27.8511184554486
78	8059	7916.64123534244	142.358764657555
79	9111	8086.75858914034	1024.24141085966
80	7708	7844.87594293823	-136.875942938233
81	7680	7589.11829673613	90.8817032638736
82	8014	8156.86065053402	-142.860650534020
83	8007	8190.97800433191	-183.978004331914
84	8718	9155.5953581298	-437.595358129808
85	9486	10136.3709575024	-650.37095750243
86	9113	8695.23831130032	417.761688699679
87	9025	9083.85566509822	-58.8556650982152
88	8476	8343.97301889611	132.026981103891
89	7952	8136.965372694	-184.965372694003
90	7759	7868.4577264919	-109.457726491897
91	7835	8038.57508028979	-203.575080289790
92	7600	7796.69243408768	-196.692434087684
93	7651	7540.93478788558	110.065212114422
94	8319	8108.67714168347	210.322858316528
95	8812	8142.79449548137	669.205504518635
96	8630	9107.41184927926	-477.411849279259

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.998993830915993	0.00201233816801482	0.00100616908400741
18	0.99719335548807	0.00561328902385856	0.00280664451192928
19	0.99340656158832	0.0131868768233608	0.00659343841168039
20	0.986168659602186	0.0276626807956288	0.0138313403978144
21	0.974462391616407	0.051075216767187	0.0255376083835935
22	0.96931685819292	0.0613662836141594	0.0306831418070797
23	0.956769938747765	0.0864601225044703	0.0432300612522352
24	0.951515031349998	0.096969937300003	0.0484849686500015
25	0.953272816784932	0.093454366430136	0.046727183215068
26	0.931163864314837	0.137672271370327	0.0688361356851633
27	0.918127377432981	0.163745245134037	0.0818726225670185
28	0.88946883898596	0.221062322028079	0.110531161014039
29	0.847595970107357	0.304808059785287	0.152404029892643
30	0.805282418440049	0.389435163119903	0.194717581559951
31	0.751344805284087	0.497310389431825	0.248655194715913
32	0.686926662915767	0.626146674168466	0.313073337084233
33	0.626257859219651	0.747484281560697	0.373742140780349
34	0.600478696223216	0.799042607553568	0.399521303776784
35	0.550665192799582	0.898669614400835	0.449334807200418
36	0.540825366681982	0.918349266636036	0.459174633318018
37	0.773244021362584	0.453511957274832	0.226755978637416
38	0.743016289995157	0.513967420009686	0.256983710004843
39	0.7480645998465	0.503870800307001	0.251935400153501
40	0.739927223176472	0.520145553647057	0.260072776823529
41	0.690062354304001	0.619875291391998	0.309937645695999
42	0.642799861012467	0.714400277975065	0.357200138987533
43	0.611258746335797	0.777482507328407	0.388741253664203
44	0.577148064213883	0.845703871572233	0.422851935786117
45	0.549373811478416	0.901252377043167	0.450626188521584
46	0.525798462399951	0.948403075200098	0.474201537600049
47	0.56324758455762	0.87350483088476	0.43675241544238
48	0.684313557443708	0.631372885112584	0.315686442556292
49	0.668193551681114	0.663612896637773	0.331806448318886
50	0.652655685536589	0.694688628926822	0.347344314463411
51	0.725506548622965	0.548986902754071	0.274493451377035
52	0.678369766286758	0.643260467426483	0.321630233713242
53	0.629979039112496	0.740041921775009	0.370020960887504
54	0.574431836616395	0.85113632676721	0.425568163383605
55	0.521805785377166	0.956388429245668	0.478194214622834
56	0.457653217496842	0.915306434993685	0.542346782503158
57	0.394391228923474	0.788782457846947	0.605608771076526
58	0.334158544215190	0.668317088430379	0.66584145578481
59	0.291775093524355	0.583550187048711	0.708224906475645
60	0.249050052817826	0.498100105635652	0.750949947182174
61	0.233289750143618	0.466579500287236	0.766710249856382
62	0.265105654785672	0.530211309571345	0.734894345214328
63	0.60879871409516	0.78240257180968	0.39120128590484
64	0.555845003031934	0.888309993936133	0.444154996968066
65	0.516125623846188	0.967748752307624	0.483874376153812
66	0.453789152362177	0.907578304724354	0.546210847637823
67	0.647706673646847	0.704586652706306	0.352293326353153
68	0.62864557801836	0.74270884396328	0.37135442198164
69	0.570767626140758	0.858464747718484	0.429232373859242
70	0.480163713926966	0.960327427853931	0.519836286073034
71	0.480379389662131	0.960758779324262	0.519620610337869
72	0.479444955945455	0.95888991189091	0.520555044054545
73	0.423117867810526	0.846235735621053	0.576882132189474
74	0.424745736558214	0.849491473116429	0.575254263441786
75	0.357895405002934	0.715790810005869	0.642104594997066
76	0.270774330568946	0.541548661137892	0.729225669431054
77	0.181650194567191	0.363300389134381	0.81834980543281
78	0.112618841078803	0.225237682157606	0.887381158921197
79	0.607496316271949	0.785007367456103	0.392503683728051

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0317460317460317	NOK
5% type I error level	4	0.0634920634920635	NOK
10% type I error level	9	0.142857142857143	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/10gwsq1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/10gwsq1290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/1k4vz1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/1k4vz1290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/2k4vz1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/2k4vz1290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/3k4vz1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/3k4vz1290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/4uvuk1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/4uvuk1290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/5uvuk1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/5uvuk1290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/6uvuk1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/6uvuk1290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/7nnt51290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/7nnt51290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/8gwsq1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/8gwsq1290798062.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/9gwsq1290798062.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290798160erkdb6bszxf1e15/9gwsq1290798062.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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