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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: Tue, 21 Dec 2010 19:41:47 +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/21/t1292960383pvwrau67g6movyp.htm/, Retrieved Tue, 21 Dec 2010 20:39:46 +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/21/t1292960383pvwrau67g6movyp.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 «

320 324 343 295 301 367 196 182 342 361 333 330 345 323 365 323 316 358 235 169 430 409 407 341 326 374 364 349 300 385 304 196 443 414 325 388 356 386 444 387 327 448 225 182 460 411 342 361 377 331 428 340 352 461 221 198 422 329 320 375 364 351 380 319 322 386 221 187 343 342 365 313 356 337 389 326 343 357 220 218 391 425 332 298 360 336 325 393 301 426 265 210 429 440 357 431 442 422 544 420 396 482 261 211 448 468 464 425

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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

Vl[t] = + 326.861111111111 + 4.74583333333336M1[t] -2.73611111111112M2[t] + 40.8930555555555M3[t] -7.47777777777778M4[t] -29.6263888888889M5[t] + 48.8916666666666M6[t] -120.8125M7[t] -165.294444444444M8[t] + 51.3347222222222M9[t] + 38.6305555555556M10[t] -1.29583333333333M11[t] + 0.593055555555555t + 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)	326.861111111111	14.43192	22.6485	0	0
M1	4.74583333333336	17.861868	0.2657	0.791049	0.395524
M2	-2.73611111111112	17.853769	-0.1533	0.878525	0.439263
M3	40.8930555555555	17.846438	2.2914	0.024151	0.012075
M4	-7.47777777777778	17.839875	-0.4192	0.676045	0.338022
M5	-29.6263888888889	17.834083	-1.6612	0.099966	0.049983
M6	48.8916666666666	17.829062	2.7422	0.007293	0.003646
M7	-120.8125	17.824812	-6.7778	0	0
M8	-165.294444444444	17.821334	-9.2751	0	0
M9	51.3347222222222	17.818628	2.881	0.004901	0.00245
M10	38.6305555555556	17.816696	2.1682	0.03264	0.01632
M11	-1.29583333333333	17.815536	-0.0727	0.942169	0.471085
t	0.593055555555555	0.117368	5.053	2e-06	1e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.88286179482399
R-squared	0.779444948759838
Adjusted R-squared	0.751585363340028
F-TEST (value)	27.9776219572028
F-TEST (DF numerator)	12
F-TEST (DF denominator)	95
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	37.791638668049
Sum Squared Residuals	135679.755555556

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	320	332.2	-12.1999999999998
2	324	325.311111111111	-1.31111111111113
3	343	369.533333333333	-26.5333333333333
4	295	321.755555555556	-26.7555555555556
5	301	300.2	0.800000000000013
6	367	379.311111111111	-12.3111111111111
7	196	210.2	-14.2
8	182	166.311111111111	15.688888888889
9	342	383.533333333333	-41.5333333333333
10	361	371.422222222222	-10.4222222222223
11	333	332.088888888889	0.911111111111104
12	330	333.977777777778	-3.97777777777779
13	345	339.316666666667	5.68333333333328
14	323	332.427777777778	-9.42777777777779
15	365	376.65	-11.65
16	323	328.872222222222	-5.87222222222223
17	316	307.316666666667	8.68333333333333
18	358	386.427777777778	-28.4277777777778
19	235	217.316666666667	17.6833333333333
20	169	173.427777777778	-4.42777777777778
21	430	390.65	39.35
22	409	378.538888888889	30.4611111111111
23	407	339.205555555556	67.7944444444445
24	341	341.094444444444	-0.0944444444444565
25	326	346.433333333333	-20.4333333333334
26	374	339.544444444444	34.4555555555556
27	364	383.766666666667	-19.7666666666667
28	349	335.988888888889	13.0111111111111
29	300	314.433333333333	-14.4333333333333
30	385	393.544444444444	-8.54444444444445
31	304	224.433333333333	79.5666666666667
32	196	180.544444444444	15.4555555555555
33	443	397.766666666667	45.2333333333333
34	414	385.655555555556	28.3444444444444
35	325	346.322222222222	-21.3222222222222
36	388	348.211111111111	39.7888888888889
37	356	353.55	2.44999999999997
38	386	346.661111111111	39.3388888888889
39	444	390.883333333333	53.1166666666667
40	387	343.105555555556	43.8944444444445
41	327	321.55	5.44999999999999
42	448	400.661111111111	47.3388888888889
43	225	231.55	-6.55000000000001
44	182	187.661111111111	-5.66111111111114
45	460	404.883333333333	55.1166666666666
46	411	392.772222222222	18.2277777777778
47	342	353.438888888889	-11.4388888888889
48	361	355.327777777778	5.67222222222221
49	377	360.666666666667	16.3333333333333
50	331	353.777777777778	-22.7777777777778
51	428	398	30
52	340	350.222222222222	-10.2222222222222
53	352	328.666666666667	23.3333333333333
54	461	407.777777777778	53.2222222222222
55	221	238.666666666667	-17.6666666666667
56	198	194.777777777778	3.22222222222221
57	422	412	10
58	329	399.888888888889	-70.8888888888889
59	320	360.555555555556	-40.5555555555556
60	375	362.444444444444	12.5555555555556
61	364	367.783333333333	-3.78333333333336
62	351	360.894444444444	-9.89444444444444
63	380	405.116666666667	-25.1166666666667
64	319	357.338888888889	-38.3388888888889
65	322	335.783333333333	-13.7833333333333
66	386	414.894444444444	-28.8944444444444
67	221	245.783333333333	-24.7833333333333
68	187	201.894444444444	-14.8944444444445
69	343	419.116666666667	-76.1166666666667
70	342	407.005555555556	-65.0055555555555
71	365	367.672222222222	-2.67222222222222
72	313	369.561111111111	-56.5611111111111
73	356	374.9	-18.9
74	337	368.011111111111	-31.0111111111111
75	389	412.233333333333	-23.2333333333333
76	326	364.455555555556	-38.4555555555555
77	343	342.9	0.0999999999999954
78	357	422.011111111111	-65.0111111111111
79	220	252.9	-32.9
80	218	209.011111111111	8.98888888888888
81	391	426.233333333333	-35.2333333333333
82	425	414.122222222222	10.8777777777778
83	332	374.788888888889	-42.7888888888889
84	298	376.677777777778	-78.6777777777778
85	360	382.016666666667	-22.0166666666667
86	336	375.127777777778	-39.1277777777778
87	325	419.35	-94.35
88	393	371.572222222222	21.4277777777778
89	301	350.016666666667	-49.0166666666667
90	426	429.127777777778	-3.12777777777777
91	265	260.016666666667	4.98333333333333
92	210	216.127777777778	-6.12777777777776
93	429	433.35	-4.34999999999999
94	440	421.238888888889	18.7611111111111
95	357	381.905555555556	-24.9055555555556
96	431	383.794444444444	47.2055555555556
97	442	389.133333333333	52.8666666666667
98	422	382.244444444444	39.7555555555556
99	544	426.466666666667	117.533333333333
100	420	378.688888888889	41.3111111111112
101	396	357.133333333333	38.8666666666667
102	482	436.244444444444	45.7555555555556
103	261	267.133333333333	-6.13333333333333
104	211	223.244444444444	-12.2444444444444
105	448	440.466666666667	7.53333333333333
106	468	428.355555555556	39.6444444444444
107	464	389.022222222222	74.9777777777778
108	425	390.911111111111	34.0888888888889

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0163631957965692	0.0327263915931383	0.98363680420343
17	0.0031422543592835	0.00628450871856699	0.996857745640716
18	0.00332543441256295	0.00665086882512589	0.996674565587437
19	0.00235813714201857	0.00471627428403714	0.997641862857981
20	0.00207307551225139	0.00414615102450277	0.997926924487749
21	0.0267174961242895	0.053434992248579	0.97328250387571
22	0.016731102581678	0.0334622051633559	0.983268897418322
23	0.0226398240520937	0.0452796481041873	0.977360175947906
24	0.0126129746360377	0.0252259492720754	0.987387025363962
25	0.0152990943327828	0.0305981886655656	0.984700905667217
26	0.009407738500772	0.018815477001544	0.990592261499228
27	0.00629867821536238	0.0125973564307248	0.993701321784638
28	0.00323235589623535	0.0064647117924707	0.996767644103765
29	0.00334871793879673	0.00669743587759346	0.996651282061203
30	0.00169889433990631	0.00339778867981262	0.998301105660094
31	0.005757800454449	0.011515600908898	0.994242199545551
32	0.00332025977472417	0.00664051954944833	0.996679740225276
33	0.00259033304014202	0.00518066608028404	0.997409666959858
34	0.0014880678512139	0.00297613570242781	0.998511932148786
35	0.00648567633487582	0.0129713526697516	0.993514323665124
36	0.00509792410818931	0.0101958482163786	0.99490207589181
37	0.003080294901277	0.00616058980255399	0.996919705098723
38	0.00215913200103689	0.00431826400207378	0.997840867998963
39	0.00326904038383283	0.00653808076766567	0.996730959616167
40	0.00275763685522208	0.00551527371044417	0.997242363144778
41	0.00192393608711706	0.00384787217423412	0.998076063912883
42	0.00214057164883158	0.00428114329766316	0.997859428351168
43	0.00453671987840097	0.00907343975680195	0.9954632801216
44	0.00434839184777029	0.00869678369554058	0.99565160815223
45	0.00567064004051311	0.0113412800810262	0.994329359959487
46	0.00516085967280305	0.0103217193456061	0.994839140327197
47	0.00629286185353926	0.0125857237070785	0.99370713814646
48	0.00546169287573623	0.0109233857514725	0.994538307124264
49	0.0038792464601244	0.0077584929202488	0.996120753539876
50	0.00629315819976494	0.0125863163995299	0.993706841800235
51	0.00591312435134517	0.0118262487026903	0.994086875648655
52	0.00533933184748116	0.0106786636949623	0.994660668152519
53	0.00487717516064119	0.00975435032128238	0.99512282483936
54	0.0118339661965672	0.0236679323931344	0.988166033803433
55	0.0170475108841258	0.0340950217682517	0.982952489115874
56	0.0164276907879249	0.0328553815758499	0.983572309212075
57	0.0286593147280754	0.0573186294561507	0.971340685271925
58	0.0861987242397985	0.172397448479597	0.913801275760202
59	0.093783766362822	0.187567532725644	0.906216233637178
60	0.122855710391857	0.245711420783714	0.877144289608143
61	0.107594000850936	0.215188001701871	0.892405999149064
62	0.107721620687649	0.215443241375297	0.892278379312351
63	0.0985776533049916	0.197155306609983	0.901422346695008
64	0.0908028703432874	0.181605740686575	0.909197129656713
65	0.0841145158187243	0.168229031637449	0.915885484181276
66	0.0818754383693631	0.163750876738726	0.918124561630637
67	0.0840036809454011	0.168007361890802	0.915996319054599
68	0.083399798297251	0.166799596594502	0.91660020170275
69	0.127307396036015	0.254614792072031	0.872692603963985
70	0.129252812892148	0.258505625784295	0.870747187107852
71	0.138527599041966	0.277055198083932	0.861472400958034
72	0.132058815710125	0.26411763142025	0.867941184289875
73	0.101775708005127	0.203551416010254	0.898224291994873
74	0.0810556261635575	0.162111252327115	0.918944373836442
75	0.0607252389840625	0.121450477968125	0.939274761015938
76	0.0447173589177772	0.0894347178355543	0.955282641082223
77	0.0491555697219611	0.0983111394439222	0.950844430278039
78	0.0460586845371484	0.0921173690742967	0.953941315462852
79	0.0348112756065954	0.0696225512131907	0.965188724393405
80	0.0599498223458444	0.119899644691689	0.940050177654156
81	0.049446833668272	0.098893667336544	0.950553166331728
82	0.0608911647389749	0.12178232947795	0.939108835261025
83	0.0433428841638824	0.0866857683277648	0.956657115836118
84	0.0431976241961339	0.0863952483922679	0.956802375803866
85	0.028549337212298	0.057098674424596	0.971450662787702
86	0.0192834434042907	0.0385668868085813	0.98071655659571
87	0.587656397827859	0.824687204344282	0.412343602172141
88	0.500322257108205	0.99935548578359	0.499677742891795
89	0.592341947174198	0.815316105651603	0.407658052825802
90	0.516016034896484	0.967967930207032	0.483983965103516
91	0.433521646612237	0.867043293224473	0.566478353387763
92	0.341246791277446	0.682493582554892	0.658753208722554

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	20	0.25974025974026	NOK
5% type I error level	41	0.532467532467532	NOK
10% type I error level	51	0.662337662337662	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/104mw21292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/104mw21292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/1flz81292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/1flz81292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/28uyb1292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/28uyb1292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/38uyb1292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/38uyb1292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/48uyb1292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/48uyb1292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/504gw1292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/504gw1292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/604gw1292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/604gw1292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/7tvfz1292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/7tvfz1292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/8tvfz1292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/8tvfz1292960498.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/94mw21292960498.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292960383pvwrau67g6movyp/94mw21292960498.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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