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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: Mon, 29 Nov 2010 15:41:18 +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/29/t1291045170rzgapm9wpfc3zy2.htm/, Retrieved Mon, 29 Nov 2010 16:39:42 +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/29/t1291045170rzgapm9wpfc3zy2.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 9169 8788 8417 8247 8197 8236 8253 7733 8366 8626 8863 10102 8463 9114 8563 8872 8301 8301 8278 7736 7973 8268 9476 11100 8962 9173 8738 8459 8078 8411 8291 7810 8616 8312 9692 9911 8915 9452 9112 8472 8230 8384 8625 8221 8649 8625 10443 10357 8586 8892 8329 8101 7922 8120 7838 7735 8406 8209 9451 10041 9411 10405 8467 8464 8102 7627 7513 7510 8291 8064 9383 9706 8579 9474 8318 8213 8059 9111 7708 7680 8014 8007 8718 9486 9113 9025 8476 7952 7759 7835 7600 7651 8319 8812 8630

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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Sterftes[t] = + 9560.57142857143 + 960.314153439157M1[t] -474.578042328042M2[t] -79.7202380952388M3[t] -813.362433862435M4[t] -1014.12962962963M5[t] -1276.39682539683M6[t] -1100.03902116402M7[t] -1335.68121693122M8[t] -1585.19841269841M9[t] -1011.21560846561M10[t] -970.857804232804M11[t] -4.23280423280425t + 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)	9560.57142857143	164.960362	57.9568	0	0
M1	960.314153439157	203.617955	4.7163	1e-05	5e-06
M2	-474.578042328042	203.500867	-2.3321	0.02212	0.01106
M3	-79.7202380952388	203.394873	-0.3919	0.696101	0.348051
M4	-813.362433862435	203.299989	-4.0008	0.000136	6.8e-05
M5	-1014.12962962963	203.216231	-4.9904	3e-06	2e-06
M6	-1276.39682539683	203.143613	-6.2832	0	0
M7	-1100.03902116402	203.082147	-5.4167	1e-06	0
M8	-1335.68121693122	203.031842	-6.5787	0	0
M9	-1585.19841269841	202.992708	-7.8091	0	0
M10	-1011.21560846561	202.96475	-4.9822	3e-06	2e-06
M11	-970.857804232804	202.947974	-4.7838	7e-06	4e-06
t	-4.23280423280425	1.506629	-2.8095	0.006187	0.003094

Multiple Linear Regression - Regression Statistics
Multiple R	0.88076124618955
R-squared	0.775740372789369
Adjusted R-squared	0.743317294156507
F-TEST (value)	23.9255618373981
F-TEST (DF numerator)	12
F-TEST (DF denominator)	83
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	405.884762262813
Sum Squared Residuals	13673622.5396826

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	12008	10516.6527777777	1491.34722222225
2	9169	9077.52777777778	91.4722222222215
3	8788	9468.15277777778	-680.152777777779
4	8417	8730.27777777778	-313.277777777776
5	8247	8525.27777777778	-278.277777777776
6	8197	8258.77777777778	-61.7777777777776
7	8236	8430.90277777778	-194.902777777778
8	8253	8191.02777777778	61.9722222222178
9	7733	7937.27777777778	-204.277777777777
10	8366	8507.02777777778	-141.027777777779
11	8626	8543.15277777778	82.8472222222203
12	8863	9509.77777777778	-646.777777777778
13	10102	10465.8591269841	-363.859126984132
14	8463	9026.73412698413	-563.734126984127
15	9114	9417.35912698413	-303.359126984128
16	8563	8679.48412698413	-116.484126984127
17	8872	8474.48412698413	397.515873015872
18	8301	8207.98412698413	93.0158730158723
19	8301	8380.10912698413	-79.1091269841275
20	8278	8140.23412698413	137.765873015873
21	7736	7886.48412698413	-150.484126984128
22	7973	8456.23412698413	-483.234126984128
23	8268	8492.35912698413	-224.359126984127
24	9476	9458.98412698413	17.0158730158728
25	11100	10415.0654761905	684.93452380952
26	8962	8975.94047619048	-13.9404761904765
27	9173	9366.56547619048	-193.565476190477
28	8738	8628.69047619048	109.309523809524
29	8459	8423.69047619048	35.3095238095232
30	8078	8157.19047619048	-79.1904761904767
31	8411	8329.31547619048	81.6845238095236
32	8291	8089.44047619048	201.559523809524
33	7810	7835.69047619048	-25.6904761904767
34	8616	8405.44047619048	210.559523809524
35	8312	8441.56547619048	-129.565476190476
36	9692	9408.19047619048	283.809523809523
37	9911	10364.2718253968	-453.271825396830
38	8915	8925.14682539683	-10.1468253968256
39	9452	9315.77182539683	136.228174603174
40	9112	8577.89682539683	534.103174603175
41	8472	8372.89682539683	99.1031746031742
42	8230	8106.39682539683	123.603174603174
43	8384	8278.52182539683	105.478174603175
44	8625	8038.64682539683	586.353174603175
45	8221	7784.89682539683	436.103174603174
46	8649	8354.64682539683	294.353174603175
47	8625	8390.77182539683	234.228174603175
48	10443	9357.39682539683	1085.60317460317
49	10357	10313.4781746032	43.5218253968213
50	8586	8874.35317460317	-288.353174603174
51	8892	9264.97817460317	-372.978174603175
52	8329	8527.10317460317	-198.103174603174
53	8101	8322.10317460317	-221.103174603175
54	7922	8055.60317460317	-133.603174603175
55	8120	8227.72817460317	-107.728174603174
56	7838	7987.85317460317	-149.853174603174
57	7735	7734.10317460317	0.896825396825363
58	8406	8303.85317460317	102.146825396826
59	8209	8339.97817460317	-130.978174603174
60	9451	9306.60317460317	144.396825396826
61	10041	10262.6845238095	-221.684523809528
62	9411	8823.55952380952	587.440476190477
63	10405	9214.18452380952	1190.81547619048
64	8467	8476.30952380952	-9.30952380952334
65	8464	8271.30952380952	192.690476190476
66	8102	8004.80952380952	97.1904761904763
67	7627	8176.93452380952	-549.934523809524
68	7513	7937.05952380952	-424.059523809523
69	7510	7683.30952380952	-173.309523809524
70	8291	8253.05952380952	37.9404761904766
71	8064	8289.18452380952	-225.184523809523
72	9383	9255.80952380952	127.190476190477
73	9706	10211.8908730159	-505.890873015876
74	8579	8772.76587301587	-193.765873015872
75	9474	9163.39087301587	310.609126984127
76	8318	8425.51587301587	-107.515873015872
77	8213	8220.51587301587	-7.51587301587273
78	8059	7954.01587301587	104.984126984127
79	9111	8126.14087301587	984.859126984127
80	7708	7886.26587301587	-178.265873015872
81	7680	7632.51587301587	47.4841269841275
82	8014	8202.26587301587	-188.265873015872
83	8007	8238.39087301587	-231.390873015872
84	8718	9205.01587301587	-487.015873015872
85	9486	10161.0972222222	-675.097222222226
86	9113	8721.97222222222	391.027777777779
87	9025	9112.59722222222	-87.5972222222217
88	8476	8374.72222222222	101.277777777779
89	7952	8169.72222222222	-217.722222222222
90	7759	7903.22222222222	-144.222222222222
91	7835	8075.34722222222	-240.347222222221
92	7600	7835.47222222222	-235.472222222221
93	7651	7581.72222222222	69.2777777777785
94	8319	8151.47222222222	167.527777777779
95	8812	8187.59722222222	624.402777777779
96	8630	9154.22222222222	-524.222222222221

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.987020014184727	0.0259599716305466	0.0129799858152733
17	0.99321547563839	0.0135690487232213	0.00678452436161064
18	0.987133351840869	0.0257332963182623	0.0128666481591311
19	0.976790272199246	0.0464194556015079	0.0232097278007540
20	0.959403009663849	0.0811939806723024	0.0405969903361512
21	0.935420204405438	0.129159591189124	0.0645797955945622
22	0.92014736539606	0.159705269207881	0.0798526346039403
23	0.888482589270835	0.223034821458331	0.111517410729165
24	0.900965565256397	0.198068869487206	0.0990344347436028
25	0.905826506576499	0.188346986847002	0.0941734934235012
26	0.875987751693859	0.248024496612283	0.124012248306141
27	0.860586032019696	0.278827935960607	0.139413967980304
28	0.822877782747452	0.354244434505096	0.177122217252548
29	0.76744976597903	0.465100468041939	0.232550234020969
30	0.712405518453048	0.575188963093904	0.287594481546952
31	0.650021708427616	0.699956583144768	0.349978291572384
32	0.578692651821352	0.842614696357295	0.421307348178648
33	0.512637385115893	0.974725229768214	0.487362614884107
34	0.484809984751659	0.969619969503317	0.515190015248341
35	0.432314713691071	0.864629427382142	0.567685286308929
36	0.417019103367106	0.834038206734211	0.582980896632894
37	0.65685873372577	0.68628253254846	0.34314126627423
38	0.60328301618002	0.793433967639959	0.396716983819979
39	0.589288509767419	0.821422980465162	0.410711490232581
40	0.597795147391631	0.804409705216738	0.402204852608369
41	0.530189959504277	0.939620080991446	0.469810040495723
42	0.460879826955329	0.921759653910658	0.539120173044671
43	0.39418047575621	0.78836095151242	0.60581952424379
44	0.410858857266978	0.821717714533956	0.589141142733022
45	0.389749072926806	0.779498145853612	0.610250927073194
46	0.340058992320475	0.68011798464095	0.659941007679525
47	0.284727539765528	0.569455079531056	0.715272460234472
48	0.627713892307093	0.744572215385813	0.372286107692907
49	0.664938271041287	0.670123457917427	0.335061728958713
50	0.67560242255838	0.648795154883241	0.324397577441620
51	0.761957337323929	0.476085325352141	0.238042662676070
52	0.741421111660335	0.517157776679329	0.258578888339665
53	0.722945628407211	0.554108743185578	0.277054371592789
54	0.684436115801243	0.631127768397514	0.315563884198757
55	0.642058690125702	0.715882619748596	0.357941309874298
56	0.609483576280738	0.781032847438523	0.390516423719262
57	0.543681355337259	0.912637289325482	0.456318644662741
58	0.473049106543627	0.946098213087254	0.526950893456373
59	0.431582080451853	0.863164160903707	0.568417919548146
60	0.384256763377738	0.768513526755476	0.615743236622262
61	0.375022519268556	0.750045038537113	0.624977480731444
62	0.397144660165167	0.794289320330334	0.602855339834833
63	0.766305573884782	0.467388852230436	0.233694426115218
64	0.705251327541582	0.589497344916836	0.294748672458418
65	0.66576819284512	0.66846361430976	0.33423180715488
66	0.600433695029815	0.799132609940369	0.399566304970185
67	0.754278381359604	0.491443237280791	0.245721618640396
68	0.731138334731414	0.537723330537172	0.268861665268586
69	0.681162108107321	0.637675783785357	0.318837891892679
70	0.597936968161564	0.804126063676873	0.402063031838436
71	0.601724304488221	0.796551391023558	0.398275695511779
72	0.604565079524641	0.790869840950717	0.395434920475359
73	0.553495076281219	0.893009847437563	0.446504923718781
74	0.561280155085894	0.877439689828213	0.438719844914106
75	0.492924162609486	0.985848325218971	0.507075837390514
76	0.40191489946465	0.8038297989293	0.59808510053535
77	0.298253434051579	0.596506868103159	0.70174656594842
78	0.208321261470897	0.416642522941794	0.791678738529103
79	0.778663567912514	0.442672864174971	0.221336432087486
80	0.688651789357795	0.622696421284409	0.311348210642205

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	4	0.0615384615384615	NOK
10% type I error level	5	0.0769230769230769	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/10n1jj1291045270.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/10n1jj1291045270.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/1o82u1291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/1o82u1291045269.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/2o82u1291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/2o82u1291045269.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/3gz1f1291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/3gz1f1291045269.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/4gz1f1291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/4gz1f1291045269.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/5gz1f1291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/5gz1f1291045269.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/69r001291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/69r001291045269.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/7k0h31291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/7k0h31291045269.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/8k0h31291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/8k0h31291045269.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/9k0h31291045269.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/29/t1291045170rzgapm9wpfc3zy2/9k0h31291045269.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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