Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2009 » Dec » 05 »

*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: Sat, 05 Dec 2009 11:01:32 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx.htm/, Retrieved Sat, 05 Dec 2009 19:02:38 +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/2009/Dec/05/t1260036146rbaawoyj5p85gzx.htm/},
    year = {2009},
}
@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 = {2009},
    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 «

9051 0 8823 0 8776 0 8255 0 7969 0 8758 0 8693 0 8271 0 7790 0 7769 0 8170 0 8209 0 9395 0 9260 0 9018 0 8501 0 8500 0 9649 0 9319 0 8830 0 8436 0 8169 0 8269 0 7945 0 9144 0 8770 0 8834 0 7837 0 7792 0 8616 0 8518 0 7940 0 7545 0 7531 0 7665 0 7599 0 8444 0 8549 0 7986 0 7335 0 7287 0 7870 0 7839 0 7327 0 7259 0 6964 0 7271 0 6956 0 7608 0 7692 0 7255 0 6804 0 6655 0 7341 0 7602 0 7086 0 6625 0 6272 0 6576 0 6491 0 7649 0 7400 0 6913 0 6532 0 6486 0 7295 0 7556 0 7088 1 6952 1 6773 1 6917 1 7371 1 8221 1 7953 1 8027 1 7287 1 8076 1 8933 1 9433 1 9479 1 9199 1 9469 1 10015 1 10999 1 13009 1 13699 1 13895 1 13248 1 13973 1 15095 1 15201 1 14823 1 14538 1 14547 1 14407 1

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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 8100.42455233596 + 3072.40963029376X[t] -6.22802678073278t + 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)	8100.42455233596	436.359319	18.5637	0	0
X	3072.40963029376	665.087204	4.6196	1.2e-05	6e-06
t	-6.22802678073278	11.057641	-0.5632	0.574646	0.287323

Multiple Linear Regression - Regression Statistics
Multiple R	0.57996515606376
R-squared	0.336359582248061
Adjusted R-squared	0.321932616644758
F-TEST (value)	23.3146450540544
F-TEST (DF numerator)	2
F-TEST (DF denominator)	92
p-value	6.44035913488494e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1813.08224263671
Sum Squared Residuals	302428584.10794

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9051	8094.1965255552	956.80347444479
2	8823	8087.9684987745	735.031501225507
3	8776	8081.74047199376	694.259528006238
4	8255	8075.51244521303	179.487554786972
5	7969	8069.2844184323	-100.284418432296
6	8758	8063.05639165156	694.943608348437
7	8693	8056.82836487083	636.17163512917
8	8271	8050.6003380901	220.399661909903
9	7790	8044.37231130936	-254.372311309365
10	7769	8038.14428452863	-269.144284528632
11	8170	8031.9162577479	138.083742252101
12	8209	8025.68823096717	183.311769032834
13	9395	8019.46020418643	1375.53979581357
14	9260	8013.2321774057	1246.7678225943
15	9018	8007.00415062497	1010.99584937503
16	8501	8000.77612384424	500.223876155765
17	8500	7994.5480970635	505.451902936498
18	9649	7988.32007028277	1660.67992971723
19	9319	7982.09204350204	1336.90795649796
20	8830	7975.8640167213	854.135983278696
21	8436	7969.63598994057	466.364010059429
22	8169	7963.40796315984	205.592036840162
23	8269	7957.1799363791	311.820063620894
24	7945	7950.95190959837	-5.95190959837275
25	9144	7944.72388281764	1199.27611718236
26	8770	7938.4958560369	831.504143963093
27	8834	7932.26782925617	901.732170743825
28	7837	7926.03980247544	-89.0398024754416
29	7792	7919.8117756947	-127.811775694709
30	8616	7913.58374891398	702.416251086024
31	8518	7907.35572213324	610.644277866757
32	7940	7901.12769535251	38.8723046474895
33	7545	7894.89966857178	-349.899668571778
34	7531	7888.67164179104	-357.671641791045
35	7665	7882.44361501031	-217.443615010312
36	7599	7876.21558822958	-277.215588229579
37	8444	7869.98756144885	574.012438551153
38	8549	7863.75953466811	685.240465331886
39	7986	7857.53150788738	128.468492112619
40	7335	7851.30348110665	-516.303481106648
41	7287	7845.07545432592	-558.075454325916
42	7870	7838.84742754518	31.1525724548173
43	7839	7832.61940076445	6.3805992355501
44	7327	7826.39137398372	-499.391373983717
45	7259	7820.16334720298	-561.163347202984
46	6964	7813.93532042225	-849.935320422252
47	7271	7807.70729364152	-536.707293641519
48	6956	7801.47926686079	-845.479266860786
49	7608	7795.25124008005	-187.251240080053
50	7692	7789.02321329932	-97.0232132993204
51	7255	7782.79518651859	-527.795186518588
52	6804	7776.56715973786	-972.567159737855
53	6655	7770.33913295712	-1115.33913295712
54	7341	7764.11110617639	-423.111106176389
55	7602	7757.88307939566	-155.883079395657
56	7086	7751.65505261492	-665.655052614924
57	6625	7745.42702583419	-1120.42702583419
58	6272	7739.19899905346	-1467.19899905346
59	6576	7732.97097227273	-1156.97097227273
60	6491	7726.74294549199	-1235.74294549199
61	7649	7720.51491871126	-71.5149187112598
62	7400	7714.28689193053	-314.286891930527
63	6913	7708.0588651498	-795.058865149794
64	6532	7701.83083836906	-1169.83083836906
65	6486	7695.60281158833	-1209.60281158833
66	7295	7689.3747848076	-394.374784807596
67	7556	7683.14675802686	-127.146758026863
68	7088	10749.3283615399	-3661.32836153989
69	6952	10743.1003347592	-3791.10033475916
70	6773	10736.8723079784	-3963.87230797843
71	6917	10730.6442811977	-3813.64428119769
72	7371	10724.4162544170	-3353.41625441696
73	8221	10718.1882276362	-2497.18822763623
74	7953	10711.9602008555	-2758.96020085550
75	8027	10705.7321740748	-2678.73217407476
76	7287	10699.5041472940	-3412.50414729403
77	8076	10693.2761205133	-2617.2761205133
78	8933	10687.0480937326	-1754.04809373256
79	9433	10680.8200669518	-1247.82006695183
80	9479	10674.5920401711	-1195.5920401711
81	9199	10668.3640133904	-1469.36401339037
82	9469	10662.1359866096	-1193.13598660963
83	10015	10655.9079598289	-640.9079598289
84	10999	10649.6799330482	349.320066951832
85	13009	10643.4519062674	2365.54809373256
86	13699	10637.2238794867	3061.7761205133
87	13895	10630.9958527060	3264.00414729403
88	13248	10624.7678259252	2623.23217407476
89	13973	10618.5397991445	3354.46020085550
90	15095	10612.3117723638	4482.68822763623
91	15201	10606.0837455830	4594.91625441696
92	14823	10599.8557188023	4223.14428119769
93	14538	10593.6276920216	3944.37230797843
94	14547	10587.3996652408	3959.60033475916
95	14407	10581.1716384601	3825.82836153989

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.0152400832962651	0.0304801665925301	0.984759916703735
7	0.00459133932060204	0.00918267864120409	0.995408660679398
8	0.000861016075447155	0.00172203215089431	0.999138983924553
9	0.000234234850517527	0.000468469701035055	0.999765765149482
10	4.41000920688087e-05	8.82001841376173e-05	0.999955899907931
11	1.14187154927883e-05	2.28374309855766e-05	0.999988581284507
12	3.09248942198712e-06	6.18497884397423e-06	0.999996907510578
13	5.66760875860673e-05	0.000113352175172135	0.999943323912414
14	5.2378818436127e-05	0.000104757636872254	0.999947621181564
15	2.03736113583785e-05	4.0747222716757e-05	0.999979626388642
16	5.75733584987185e-06	1.15146716997437e-05	0.99999424266415
17	1.56855421723328e-06	3.13710843446656e-06	0.999998431445783
18	1.93542067172423e-06	3.87084134344845e-06	0.999998064579328
19	8.02807486677303e-07	1.60561497335461e-06	0.999999197192513
20	2.44863904515184e-07	4.89727809030368e-07	0.999999755136096
21	1.01608723441908e-07	2.03217446883816e-07	0.999999898391277
22	5.76438604762046e-08	1.15287720952409e-07	0.99999994235614
23	2.3849316806935e-08	4.769863361387e-08	0.999999976150683
24	1.46598065737051e-08	2.93196131474101e-08	0.999999985340193
25	7.71855427291519e-09	1.54371085458304e-08	0.999999992281446
26	2.68329386673462e-09	5.36658773346923e-09	0.999999997316706
27	9.96454786358733e-10	1.99290957271747e-09	0.999999999003545
28	9.31352448223389e-10	1.86270489644678e-09	0.999999999068648
29	7.2432908162088e-10	1.44865816324176e-09	0.999999999275671
30	3.01092403092351e-10	6.02184806184701e-10	0.999999999698908
31	1.27504891397803e-10	2.55009782795607e-10	0.999999999872495
32	7.60692079812584e-11	1.52138415962517e-10	0.999999999923931
33	8.0822823247571e-11	1.61645646495142e-10	0.999999999919177
34	6.92797311046796e-11	1.38559462209359e-10	0.99999999993072
35	4.25429197232622e-11	8.50858394465245e-11	0.999999999957457
36	2.65041834052225e-11	5.3008366810445e-11	0.999999999973496
37	2.17731838560015e-11	4.35463677120031e-11	0.999999999978227
38	2.52989059418776e-11	5.05978118837553e-11	0.9999999999747
39	1.93765499263936e-11	3.87530998527871e-11	0.999999999980623
40	2.42046559033218e-11	4.84093118066436e-11	0.999999999975795
41	2.91829157100462e-11	5.83658314200924e-11	0.999999999970817
42	3.08161338130817e-11	6.16322676261633e-11	0.999999999969184
43	3.936135483189e-11	7.872270966378e-11	0.999999999960639
44	5.7417751275504e-11	1.14835502551008e-10	0.999999999942582
45	9.0589802801297e-11	1.81179605602594e-10	0.99999999990941
46	1.79356867989256e-10	3.58713735978513e-10	0.999999999820643
47	2.97140150866394e-10	5.94280301732788e-10	0.99999999970286
48	5.51310641766996e-10	1.10262128353399e-09	0.99999999944869
49	1.59131876718285e-09	3.1826375343657e-09	0.999999998408681
50	7.41528441648192e-09	1.48305688329638e-08	0.999999992584716
51	2.33051164277520e-08	4.66102328555040e-08	0.999999976694883
52	6.31995357892066e-08	1.26399071578413e-07	0.999999936800464
53	1.51426139160157e-07	3.02852278320314e-07	0.99999984857386
54	6.03562723134238e-07	1.20712544626848e-06	0.999999396437277
55	5.52118504879695e-06	1.10423700975939e-05	0.999994478814951
56	1.87545504059333e-05	3.75091008118667e-05	0.999981245449594
57	3.65766268202646e-05	7.31532536405291e-05	0.99996342337318
58	5.51557518443365e-05	0.000110311503688673	0.999944844248156
59	5.83800449889991e-05	0.000116760089977998	0.999941619955011
60	4.83345956117284e-05	9.66691912234569e-05	0.999951665404388
61	0.000186165504700086	0.000372331009400171	0.9998138344953
62	0.000343284408219095	0.000686568816438191	0.99965671559178
63	0.000264939982423	0.000529879964846	0.999735060017577
64	0.000160998018454701	0.000321996036909403	0.999839001981545
65	0.000100811452401330	0.000201622904802660	0.999899188547599
66	6.73755909427882e-05	0.000134751181885576	0.999932624409057
67	5.77299034565234e-05	0.000115459806913047	0.999942270096543
68	0.000102334295999245	0.00020466859199849	0.999897665704
69	0.000115305913570740	0.000230611827141481	0.99988469408643
70	8.16865735954318e-05	0.000163373147190864	0.999918313426405
71	4.97001889223254e-05	9.94003778446507e-05	0.999950299811078
72	4.02249273909967e-05	8.04498547819934e-05	0.99995977507261
73	0.000184341531282795	0.000368683062565590	0.999815658468717
74	0.000219674129220574	0.000439348258441149	0.99978032587078
75	0.000204866261566980	0.000409732523133960	0.999795133738433
76	0.000146614978204057	0.000293229956408114	0.999853385021796
77	0.000121834560674017	0.000243669121348035	0.999878165439326
78	0.000230259787039446	0.000460519574078891	0.99976974021296
79	0.000614267526291938	0.00122853505258388	0.999385732473708
80	0.00112779634753602	0.00225559269507205	0.998872203652464
81	0.00270467511957741	0.00540935023915482	0.997295324880423
82	0.0162885882479345	0.032577176495869	0.983711411752066
83	0.181862438963686	0.363724877927372	0.818137561036314
84	0.809265163540811	0.381469672918378	0.190734836459189
85	0.906384652759437	0.187230694481126	0.0936153472405632
86	0.918905529607947	0.162188940784106	0.081094470392053
87	0.902110731923369	0.195778536153262	0.097889268076631
88	0.958146491177433	0.0837070176451333	0.0418535088225666
89	0.998239895817243	0.0035202083655135	0.00176010418275675

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	76	0.904761904761905	NOK
5% type I error level	78	0.928571428571429	NOK
10% type I error level	79	0.94047619047619	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/10hlnd1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/10hlnd1260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/10nmo1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/10nmo1260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/21fye1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/21fye1260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/36b6y1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/36b6y1260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/4wc0k1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/4wc0k1260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/5czx71260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/5czx71260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/684vx1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/684vx1260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/7cxag1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/7cxag1260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/8xdic1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/8xdic1260036087.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/9fi1c1260036087.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260036146rbaawoyj5p85gzx/9fi1c1260036087.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by