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Regressie Sterftes2

*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 18:38:01 +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/t12907966107bp9ijdg4nizb5x.htm/, Retrieved Fri, 26 Nov 2010 19:36:53 +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/t12907966107bp9ijdg4nizb5x.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 1 9169 1 8788 1 8417 1 8247 1 8197 1 8236 1 8253 1 7733 1 8366 1 8626 1 8863 1 10102 1 8463 1 9114 1 8563 1 8872 1 8301 1 8301 1 8278 1 7736 1 7973 1 8268 1 9476 1 11100 1 8962 1 9173 1 8738 1 8459 1 8078 1 8411 1 8291 1 7810 1 8616 1 8312 1 9692 1 9911 1 8915 1 9452 1 9112 1 8472 1 8230 1 8384 1 8625 1 8221 1 8649 1 8625 1 10443 1 10357 0 8586 0 8892 0 8329 0 8101 0 7922 0 8120 0 7838 0 7735 0 8406 0 8209 0 9451 0 10041 0 9411 0 10405 0 8467 0 8464 0 8102 0 7627 0 7513 0 7510 0 8291 0 8064 0 9383 0 9706 0 8579 0 9474 0 8318 0 8213 0 8059 0 9111 0 7708 0 7680 0 8014 0 8007 0 8718 0 9486 0 9113 0 9025 0 8476 0 7952 0 7759 0 7835 0 7600 0 7651 0 8319 0 8812 0 8630 0

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

Sterftes[t] = + 9190.72916666667 + 282.541666666667Dummy1[t] + 1006.875M1[t] -432.25M2[t] -41.6249999999997M3[t] -779.5M4[t] -984.5M5[t] -1251M6[t] -1078.875M7[t] -1318.75M8[t] -1572.5M9[t] -1002.75M10[t] -966.625M11[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)	9190.72916666667	145.960641	62.9672	0	0
Dummy1	282.541666666667	80.964396	3.4897	0.000777	0.000388
M1	1006.875	198.321457	5.077	2e-06	1e-06
M2	-432.25	198.321457	-2.1795	0.032125	0.016062
M3	-41.6249999999997	198.321457	-0.2099	0.834271	0.417135
M4	-779.5	198.321457	-3.9305	0.000175	8.7e-05
M5	-984.5	198.321457	-4.9642	4e-06	2e-06
M6	-1251	198.321457	-6.3079	0	0
M7	-1078.875	198.321457	-5.44	1e-06	0
M8	-1318.75	198.321457	-6.6496	0	0
M9	-1572.5	198.321457	-7.929	0	0
M10	-1002.75	198.321457	-5.0562	3e-06	1e-06
M11	-966.625	198.321457	-4.874	5e-06	3e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.88647432177585
R-squared	0.785836723167954
Adjusted R-squared	0.754873357842838
F-TEST (value)	25.3795643631328
F-TEST (DF numerator)	12
F-TEST (DF denominator)	83
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	396.642914858732
Sum Squared Residuals	13058024.9583334

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	12008	10480.1458333333	1527.85416666668
2	9169	9041.02083333333	127.979166666667
3	8788	9431.64583333333	-643.645833333333
4	8417	8693.77083333333	-276.770833333333
5	8247	8488.77083333333	-241.770833333333
6	8197	8222.27083333333	-25.2708333333336
7	8236	8394.39583333333	-158.395833333334
8	8253	8154.52083333333	98.4791666666663
9	7733	7900.77083333333	-167.770833333334
10	8366	8470.52083333333	-104.520833333334
11	8626	8506.64583333333	119.354166666666
12	8863	9473.27083333333	-610.270833333333
13	10102	10480.1458333333	-378.145833333337
14	8463	9041.02083333333	-578.020833333333
15	9114	9431.64583333333	-317.645833333333
16	8563	8693.77083333333	-130.770833333333
17	8872	8488.77083333333	383.229166666666
18	8301	8222.27083333333	78.7291666666664
19	8301	8394.39583333333	-93.3958333333336
20	8278	8154.52083333333	123.479166666666
21	7736	7900.77083333333	-164.770833333334
22	7973	8470.52083333333	-497.520833333334
23	8268	8506.64583333333	-238.645833333334
24	9476	9473.27083333333	2.72916666666678
25	11100	10480.1458333333	619.854166666664
26	8962	9041.02083333333	-79.0208333333336
27	9173	9431.64583333333	-258.645833333334
28	8738	8693.77083333333	44.2291666666666
29	8459	8488.77083333333	-29.7708333333337
30	8078	8222.27083333333	-144.270833333334
31	8411	8394.39583333333	16.6041666666664
32	8291	8154.52083333333	136.479166666666
33	7810	7900.77083333333	-90.770833333334
34	8616	8470.52083333333	145.479166666666
35	8312	8506.64583333333	-194.645833333334
36	9692	9473.27083333333	218.729166666667
37	9911	10480.1458333333	-569.145833333336
38	8915	9041.02083333333	-126.020833333334
39	9452	9431.64583333333	20.3541666666664
40	9112	8693.77083333333	418.229166666666
41	8472	8488.77083333333	-16.7708333333337
42	8230	8222.27083333333	7.72916666666644
43	8384	8394.39583333333	-10.3958333333336
44	8625	8154.52083333333	470.479166666666
45	8221	7900.77083333333	320.229166666666
46	8649	8470.52083333333	178.479166666666
47	8625	8506.64583333333	118.354166666666
48	10443	9473.27083333333	969.729166666667
49	10357	10197.6041666667	159.395833333331
50	8586	8758.47916666667	-172.479166666667
51	8892	9149.10416666667	-257.104166666667
52	8329	8411.22916666667	-82.2291666666665
53	8101	8206.22916666667	-105.229166666666
54	7922	7939.72916666667	-17.7291666666664
55	8120	8111.85416666667	8.14583333333378
56	7838	7871.97916666667	-33.9791666666663
57	7735	7618.22916666667	116.770833333334
58	8406	8187.97916666667	218.020833333334
59	8209	8224.10416666667	-15.1041666666663
60	9451	9190.72916666667	260.270833333334
61	10041	10197.6041666667	-156.604166666669
62	9411	8758.47916666667	652.520833333333
63	10405	9149.10416666667	1255.89583333333
64	8467	8411.22916666667	55.7708333333334
65	8464	8206.22916666667	257.770833333334
66	8102	7939.72916666667	162.270833333334
67	7627	8111.85416666667	-484.854166666666
68	7513	7871.97916666667	-358.979166666666
69	7510	7618.22916666667	-108.229166666666
70	8291	8187.97916666667	103.020833333334
71	8064	8224.10416666667	-160.104166666666
72	9383	9190.72916666667	192.270833333334
73	9706	10197.6041666667	-491.604166666669
74	8579	8758.47916666667	-179.479166666667
75	9474	9149.10416666667	324.895833333333
76	8318	8411.22916666667	-93.2291666666666
77	8213	8206.22916666667	6.77083333333367
78	8059	7939.72916666667	119.270833333334
79	9111	8111.85416666667	999.145833333334
80	7708	7871.97916666667	-163.979166666666
81	7680	7618.22916666667	61.7708333333338
82	8014	8187.97916666667	-173.979166666666
83	8007	8224.10416666667	-217.104166666666
84	8718	9190.72916666667	-472.729166666666
85	9486	10197.6041666667	-711.604166666669
86	9113	8758.47916666667	354.520833333333
87	9025	9149.10416666667	-124.104166666667
88	8476	8411.22916666667	64.7708333333334
89	7952	8206.22916666667	-254.229166666667
90	7759	7939.72916666667	-180.729166666666
91	7835	8111.85416666667	-276.854166666666
92	7600	7871.97916666667	-271.979166666666
93	7651	7618.22916666667	32.7708333333338
94	8319	8187.97916666667	131.020833333334
95	8812	8224.10416666667	587.895833333334
96	8630	9190.72916666667	-560.729166666666

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.998214961866922	0.00357007626615658	0.00178503813307829
17	0.997406392186752	0.00518721562649635	0.00259360781324817
18	0.99384496274348	0.0123100745130403	0.00615503725652017
19	0.98718352538778	0.025632949224441	0.0128164746122205
20	0.975602664569233	0.0487946708615346	0.0243973354307673
21	0.958254234060627	0.0834915318787462	0.0417457659393731
22	0.951497905669108	0.0970041886617832	0.0485020943308916
23	0.934564887121657	0.130870225756686	0.065435112878343
24	0.928552846511199	0.142894306977602	0.071447153488801
25	0.930007344563152	0.139985310873696	0.0699926554368482
26	0.901647012630178	0.196705974739645	0.0983529873698225
27	0.886549608732805	0.226900782534389	0.113450391267195
28	0.852087352065333	0.295825295869334	0.147912647934667
29	0.803099860475067	0.393800279049867	0.196900139524933
30	0.75552790509111	0.488944189817779	0.244472094908889
31	0.696406929288356	0.607186141423287	0.303593070711644
32	0.627904730812756	0.744190538374489	0.372095269187244
33	0.56473595628344	0.870528087433121	0.435264043716561
34	0.539860529918245	0.92027894016351	0.460139470081755
35	0.488510748431167	0.977021496862334	0.511489251568833
36	0.480673196359352	0.961346392718703	0.519326803640648
37	0.731816048288399	0.536367903423201	0.268183951711601
38	0.702388218487081	0.595223563025838	0.297611781512919
39	0.711504838756837	0.576990322486325	0.288495161243163
40	0.70361965750328	0.592760684993442	0.296380342496721
41	0.653000544443662	0.693998911112676	0.346999455556338
42	0.60606287076347	0.78787425847306	0.39393712923653
43	0.575973615338095	0.84805276932381	0.424026384661905
44	0.541742917718796	0.916514164562408	0.458257082281204
45	0.515493805125211	0.969012389749579	0.484506194874789
46	0.493481997584234	0.986963995168469	0.506518002415766
47	0.53196193907859	0.93607612184282	0.46803806092141
48	0.659379378756896	0.681241242486207	0.340620621243104
49	0.658659864882797	0.682680270234406	0.341340135117203
50	0.631392785757094	0.737214428485812	0.368607214242906
51	0.671169845683446	0.657660308633108	0.328830154316554
52	0.608814999512382	0.782370000975237	0.391185000487618
53	0.544410259526641	0.911179480946718	0.455589740473359
54	0.476546061285968	0.953092122571936	0.523453938714032
55	0.411036336440253	0.822072672880507	0.588963663559747
56	0.360485872199469	0.720971744398938	0.639514127800531
57	0.304927029591729	0.609854059183457	0.695072970408271
58	0.264768256124237	0.529536512248473	0.735231743875763
59	0.211296264220883	0.422592528441765	0.788703735779117
60	0.206606814462309	0.413213628924618	0.793393185537691
61	0.203040839107482	0.406081678214964	0.796959160892518
62	0.271574386191307	0.543148772382614	0.728425613808693
63	0.736247245212497	0.527505509575006	0.263752754787503
64	0.67015322481706	0.659693550365881	0.32984677518294
65	0.634709688811434	0.730580622377132	0.365290311188566
66	0.56741023012911	0.86517953974178	0.43258976987089
67	0.706633203184126	0.586733593631749	0.293366796815874
68	0.663731886760018	0.672536226479964	0.336268113239982
69	0.591225667709053	0.817548664581894	0.408774332290947
70	0.508878475963899	0.982243048072203	0.491121524036101
71	0.457773206381942	0.915546412763883	0.542226793618058
72	0.531435494456006	0.937129011087989	0.468564505543994
73	0.486258273629986	0.972516547259973	0.513741726370014
74	0.464238773822614	0.928477547645228	0.535761226177386
75	0.423112286175011	0.846224572350022	0.576887713824989
76	0.325065728372609	0.650131456745219	0.67493427162739
77	0.242385130854867	0.484770261709734	0.757614869145133
78	0.172066527117203	0.344133054234406	0.827933472882797
79	0.673169625587178	0.653660748825645	0.326830374412822
80	0.510700949568135	0.97859810086373	0.489299050431865

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0307692307692308	NOK
5% type I error level	5	0.0769230769230769	NOK
10% type I error level	7	0.107692307692308	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/1097ua1290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/1097ua1290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/1k6fy1290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/1k6fy1290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/2k6fy1290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/2k6fy1290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/3vye11290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/3vye11290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/4vye11290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/4vye11290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/5vye11290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/5vye11290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/65pv41290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/65pv41290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/7yyvp1290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/7yyvp1290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/8yyvp1290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/8yyvp1290796671.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/9yyvp1290796671.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/26/t12907966107bp9ijdg4nizb5x/9yyvp1290796671.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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