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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: Thu, 16 Dec 2010 12:06:14 +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/16/t1292501068v65qbo1ruocdjl1.htm/, Retrieved Thu, 16 Dec 2010 13:04:28 +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/16/t1292501068v65qbo1ruocdjl1.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 «

1856 1834 2095 2164 2368 2072 2521 1823 1947 2226 1754 1786 2072 1846 2137 2466 2154 2289 2628 2074 2798 2194 2442 2565 2063 2069 2539 1898 2139 2408 2725 2201 2311 2548 2276 2351 2280 2057 2479 2379 2295 2456 2546 2844 2260 2981 2678 3440 2842 2450 2669 2570 2540 2318 2930 2947 2799 2695 2498 2260 2160 2058 2533 2150 2172 2155 3016 2333 2355 2825 2214 2360 2299 1746 2069 2267 1878 2266 2282 2085 2277 2251 1828 1954 1851 1570 1852 2187 1855 2218 2253 2028 2169 1997 2034 1791 1627 1631 2319 1707 1747 2397 2059 2251 2558 2406 2049 2074 1734

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

vergunningen[t] = + 2413.39057239057 -218.928843995511M1[t] -389.991021324355M2[t] -6.65858585858587M3[t] -104.992817059484M4[t] -173.993714927048M5[t] -12.8835016835017M6[t] + 253.782267115600M7[t] -7.88529741863072M8[t] + 92.8915824915825M9[t] + 167.112906846240M10[t] -91.8879910213244M11[t] -2.11021324354658t + 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)	2413.39057239057	118.621974	20.3452	0	0
M1	-218.928843995511	143.319426	-1.5276	0.12991	0.064955
M2	-389.991021324355	147.270204	-2.6481	0.009463	0.004731
M3	-6.65858585858587	147.211556	-0.0452	0.964017	0.482008
M4	-104.992817059484	147.159061	-0.7135	0.477289	0.238645
M5	-173.993714927048	147.112727	-1.1827	0.23984	0.11992
M6	-12.8835016835017	147.072559	-0.0876	0.930377	0.465189
M7	253.782267115600	147.038562	1.726	0.087572	0.043786
M8	-7.88529741863072	147.01074	-0.0536	0.957335	0.478668
M9	92.8915824915825	146.989098	0.632	0.528914	0.264457
M10	167.112906846240	146.973637	1.137	0.258357	0.129179
M11	-91.8879910213244	146.964359	-0.6252	0.533297	0.266649
t	-2.11021324354658	0.95341	-2.2133	0.029243	0.014622

Multiple Linear Regression - Regression Statistics
Multiple R	0.515437731669643
R-squared	0.265676055228747
Adjusted R-squared	0.173885562132340
F-TEST (value)	2.89437442012333
F-TEST (DF numerator)	12
F-TEST (DF denominator)	96
p-value	0.00188088213908377
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	311.751924658146
Sum Squared Residuals	9330169.2026936

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1856	2192.35151515151	-336.351515151514
2	1834	2019.17912457912	-185.179124579125
3	2095	2400.40134680135	-305.401346801347
4	2164	2299.95690235690	-135.956902356903
5	2368	2228.84579124579	139.154208754209
6	2072	2387.84579124579	-315.845791245791
7	2521	2652.40134680135	-131.401346801347
8	1823	2388.62356902357	-565.623569023569
9	1947	2487.29023569024	-540.290235690236
10	2226	2559.40134680135	-333.401346801347
11	1754	2298.29023569024	-544.290235690236
12	1786	2388.06801346801	-602.068013468013
13	2072	2167.02895622896	-95.0289562289565
14	1846	1993.85656565657	-147.856565656566
15	2137	2375.07878787879	-238.078787878788
16	2466	2274.63434343434	191.365656565657
17	2154	2203.52323232323	-49.5232323232323
18	2289	2362.52323232323	-73.5232323232323
19	2628	2627.07878787879	0.921212121212097
20	2074	2363.30101010101	-289.30101010101
21	2798	2461.96767676768	336.032323232323
22	2194	2534.07878787879	-340.078787878788
23	2442	2272.96767676768	169.032323232323
24	2565	2362.74545454545	202.254545454545
25	2063	2141.70639730640	-78.7063973063975
26	2069	1968.53400673401	100.465993265993
27	2539	2349.75622895623	189.243771043771
28	1898	2249.31178451178	-351.311784511784
29	2139	2178.20067340067	-39.2006734006734
30	2408	2337.20067340067	70.7993265993265
31	2725	2601.75622895623	123.243771043771
32	2201	2337.97845117845	-136.978451178451
33	2311	2436.64511784512	-125.645117845118
34	2548	2508.75622895623	39.2437710437710
35	2276	2247.64511784512	28.3548821548821
36	2351	2337.42289562290	13.5771043771043
37	2280	2116.38383838384	163.616161616161
38	2057	1943.21144781145	113.788552188552
39	2479	2324.43367003367	154.56632996633
40	2379	2223.98922558923	155.010774410774
41	2295	2152.87811447811	142.121885521886
42	2456	2311.87811447811	144.121885521885
43	2546	2576.43367003367	-30.4336700336701
44	2844	2312.65589225589	531.344107744108
45	2260	2411.32255892256	-151.322558922559
46	2981	2483.43367003367	497.56632996633
47	2678	2222.32255892256	455.677441077441
48	3440	2312.10033670034	1127.89966329966
49	2842	2091.06127946128	750.93872053872
50	2450	1917.88888888889	532.111111111111
51	2669	2299.11111111111	369.888888888889
52	2570	2198.66666666667	371.333333333333
53	2540	2127.55555555556	412.444444444445
54	2318	2286.55555555556	31.4444444444444
55	2930	2551.11111111111	378.888888888889
56	2947	2287.33333333333	659.666666666667
57	2799	2386	413
58	2695	2458.11111111111	236.888888888889
59	2498	2197	301
60	2260	2286.77777777778	-26.7777777777778
61	2160	2065.73872053872	94.2612794612793
62	2058	1892.56632996633	165.43367003367
63	2533	2273.78855218855	259.211447811448
64	2150	2173.34410774411	-23.3441077441077
65	2172	2102.23299663300	69.7670033670034
66	2155	2261.23299663300	-106.232996632997
67	3016	2525.78855218855	490.211447811448
68	2333	2262.01077441077	70.9892255892256
69	2355	2360.67744107744	-5.67744107744115
70	2825	2432.78855218855	392.211447811448
71	2214	2171.67744107744	42.3225589225589
72	2360	2261.45521885522	98.5447811447811
73	2299	2040.41616161616	258.583838383838
74	1746	1867.24377104377	-121.243771043771
75	2069	2248.46599326599	-179.465993265993
76	2267	2148.02154882155	118.978451178451
77	1878	2076.91043771044	-198.910437710438
78	2266	2235.91043771044	30.0895622895623
79	2282	2500.46599326599	-218.465993265993
80	2085	2236.68821548822	-151.688215488215
81	2277	2335.35488215488	-58.3548821548822
82	2251	2407.46599326599	-156.465993265993
83	1828	2146.35488215488	-318.354882154882
84	1954	2236.13265993266	-282.13265993266
85	1851	2015.09360269360	-164.093602693603
86	1570	1841.92121212121	-271.921212121212
87	1852	2223.14343434343	-371.143434343434
88	2187	2122.69898989899	64.3010101010102
89	1855	2051.58787878788	-196.587878787879
90	2218	2210.58787878788	7.4121212121212
91	2253	2475.14343434343	-222.143434343434
92	2028	2211.36565656566	-183.365656565657
93	2169	2310.03232323232	-141.032323232323
94	1997	2382.14343434343	-385.143434343434
95	2034	2121.03232323232	-87.0323232323232
96	1791	2210.8101010101	-419.810101010101
97	1627	1989.77104377104	-362.771043771044
98	1631	1816.59865319865	-185.598653198653
99	2319	2197.82087542088	121.179124579125
100	1707	2097.37643097643	-390.376430976431
101	1747	2026.26531986532	-279.26531986532
102	2397	2185.26531986532	211.73468013468
103	2059	2449.82087542088	-390.820875420876
104	2251	2186.04309764310	64.9569023569023
105	2558	2284.70976430976	273.290235690236
106	2406	2356.82087542088	49.1791245791246
107	2049	2095.70976430976	-46.7097643097643
108	2074	2185.48754208754	-111.487542087542
109	1734	1964.44848484848	-230.448484848485

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0528531496479014	0.105706299295803	0.947146850352099
17	0.0910536893600996	0.182107378720199	0.9089463106399
18	0.047958419317942	0.095916838635884	0.952041580682058
19	0.0194851994505992	0.0389703989011983	0.9805148005494
20	0.0119107756006672	0.0238215512013344	0.988089224399333
21	0.178327407972799	0.356654815945597	0.821672592027201
22	0.161477825852336	0.322955651704673	0.838522174147664
23	0.227087509620838	0.454175019241677	0.772912490379162
24	0.309583763115593	0.619167526231186	0.690416236884407
25	0.298855787932989	0.597711575865978	0.701144212067011
26	0.233447200525867	0.466894401051735	0.766552799474133
27	0.175826764398451	0.351653528796902	0.82417323560155
28	0.466325718111298	0.932651436222595	0.533674281888702
29	0.472194328666594	0.944388657333189	0.527805671333406
30	0.404716084719422	0.809432169438844	0.595283915280578
31	0.336123044437038	0.672246088874077	0.663876955562962
32	0.327507802553859	0.655015605107718	0.672492197446141
33	0.340542703889124	0.681085407778248	0.659457296110876
34	0.316408260333021	0.632816520666042	0.683591739666979
35	0.280762585373938	0.561525170747876	0.719237414626062
36	0.254995165859622	0.509990331719244	0.745004834140378
37	0.214819743279490	0.429639486558981	0.78518025672051
38	0.182399482652279	0.364798965304558	0.817600517347721
39	0.147622451313703	0.295244902627406	0.852377548686297
40	0.118202662211109	0.236405324422217	0.881797337788891
41	0.0989373057551088	0.197874611510218	0.901062694244891
42	0.0791049789566526	0.158209957913305	0.920895021043347
43	0.0965543464717458	0.193108692943492	0.903445653528254
44	0.180283987067466	0.360567974134932	0.819716012932534
45	0.278179612759351	0.556359225518702	0.721820387240649
46	0.308085071872153	0.616170143744307	0.691914928127847
47	0.285459216263895	0.57091843252779	0.714540783736105
48	0.835782008011267	0.328435983977466	0.164217991988733
49	0.888593338801811	0.222813322396378	0.111406661198189
50	0.888202446845691	0.223595106308617	0.111797553154309
51	0.862077126057398	0.275845747885204	0.137922873942602
52	0.834709444031573	0.330581111936854	0.165290555968427
53	0.82933482949542	0.341330341009160	0.170665170504580
54	0.845865498045945	0.308269003908110	0.154134501954055
55	0.822903667232404	0.354192665535191	0.177096332767596
56	0.88187514084255	0.236249718314900	0.118124859157450
57	0.860473876757973	0.279052246484054	0.139526123242027
58	0.832168417775691	0.335663164448617	0.167831582224309
59	0.815850693611208	0.368298612777585	0.184149306388792
60	0.862844848252672	0.274310303494655	0.137155151747327
61	0.867791872174628	0.264416255650744	0.132208127825372
62	0.870860565599866	0.258278868800269	0.129139434400134
63	0.866687917514533	0.266624164970934	0.133312082485467
64	0.870728553355494	0.258542893289011	0.129271446644506
65	0.874150905920602	0.251698188158795	0.125849094079398
66	0.894935368365679	0.210129263268642	0.105064631634321
67	0.96273183953576	0.0745363209284786	0.0372681604642393
68	0.954151343236501	0.091697313526998	0.045848656763499
69	0.945640300184601	0.108719399630797	0.0543596998153986
70	0.969737594206653	0.0605248115866943	0.0302624057933472
71	0.965488176294673	0.0690236474106534	0.0345118237053267
72	0.97539785521856	0.0492042895628793	0.0246021447814397
73	0.994186088823337	0.0116278223533263	0.00581391117666316
74	0.994390088658841	0.0112198226823174	0.00560991134115869
75	0.993295765948931	0.0134084681021373	0.00670423405106866
76	0.995464918399726	0.00907016320054797	0.00453508160027399
77	0.995002398893745	0.00999520221250914	0.00499760110625457
78	0.991341734279957	0.0173165314400870	0.00865826572004351
79	0.991472121802698	0.0170557563946047	0.00852787819730237
80	0.986875270408193	0.0262494591836139	0.0131247295918069
81	0.978201330445477	0.0435973391090459	0.0217986695545230
82	0.970806560948915	0.0583868781021698	0.0291934390510849
83	0.962684626191244	0.0746307476175127	0.0373153738087563
84	0.950770083236816	0.0984598335263673	0.0492299167631837
85	0.953826278994837	0.0923474420103255	0.0461737210051627
86	0.929690693682873	0.140618612634253	0.0703093063171265
87	0.937226933458173	0.125546133083653	0.0627730665418267
88	0.98603978762745	0.0279204247450993	0.0139602123725497
89	0.98440287133053	0.0311942573389407	0.0155971286694704
90	0.963027032316658	0.0739459353666832	0.0369729676833416
91	0.987131997972696	0.0257360040546085	0.0128680020273043
92	0.96134910201372	0.0773017959725603	0.0386508979862801
93	0.924060165321722	0.151879669356556	0.0759398346782779

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0256410256410256	NOK
5% type I error level	15	0.192307692307692	NOK
10% type I error level	26	0.333333333333333	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/10m6i21292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/10m6i21292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/1x5391292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/1x5391292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/2x5391292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/2x5391292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/38e3u1292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/38e3u1292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/48e3u1292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/48e3u1292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/58e3u1292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/58e3u1292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/6052f1292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/6052f1292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/7bf101292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/7bf101292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/8bf101292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/8bf101292501164.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/9bf101292501164.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292501068v65qbo1ruocdjl1/9bf101292501164.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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