Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2010 » Dec » 03 »

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 03 Dec 2010 19:34:21 +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/03/t1291405173z5iul2ffkof1lfd.htm/, Retrieved Fri, 03 Dec 2010 20:39:43 +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/03/t1291405173z5iul2ffkof1lfd.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 «

162556 1081 213118 6282929 29790 309 81767 4324047 87550 458 153198 4108272 84738 588 -26007 -1212617 54660 299 126942 1485329 42634 156 157214 1779876 40949 481 129352 1367203 42312 323 234817 2519076 37704 452 60448 912684 16275 109 47818 1443586 25830 115 245546 1220017 12679 110 48020 984885 18014 239 -1710 1457425 43556 247 32648 -572920 24524 497 95350 929144 6532 103 151352 1151176 7123 109 288170 790090 20813 502 114337 774497 37597 248 37884 990576 17821 373 122844 454195 12988 119 82340 876607 22330 84 79801 711969 13326 102 165548 702380 16189 295 116384 264449 7146 105 134028 450033 15824 64 63838 541063 26088 267 74996 588864 11326 129 31080 -37216 8568 37 32168 783310 14416 361 49857 467359 3369 28 87161 688779 11819 85 106113 608419 6620 44 80570 696348 4519 49 102129 597793 2220 22 301670 821730 18562 155 102313 377934 10327 91 88577 651939 5336 81 112477 697458 2365 79 191778 700368 4069 145 79804 225986 7710 816 128294 348695 13718 61 96448 373683 45 etc...

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	11 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Wealth[t] = -88821.7832784671 + 28.4192263508631Costs[t] -324.547038190327Trades[t] + 4.432023324069Dividends[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-88821.7832784671	133149.674196	-0.6671	0.50632	0.25316
Costs	28.4192263508631	3.868836	7.3457	0	0
Trades	-324.547038190327	443.562453	-0.7317	0.466145	0.233072
Dividends	4.432023324069	1.124363	3.9418	0.000154	7.7e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.744645919140252
R-squared	0.55449754489223
Adjusted R-squared	0.540575593170113
F-TEST (value)	39.8290093199577
F-TEST (DF numerator)	3
F-TEST (DF denominator)	96
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	593204.53405958
Sum Squared Residuals	33781595445968.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6282929	5124602.57390763	1158326.42609237
2	4324047	1019895.18605208	3304151.81394792
3	4108272	2929616.04944915	1178655.95055085
4	-1212617	2013269.33019600	-3225886.33019600
5	1485329	1930143.46944477	-444814.469444769
6	1779876	1768950.28987672	10925.7101232770
7	1367203	1492101.07220845	-124898.072208452
8	2519076	2049538.24963169	469537.750368313
9	912684	1103908.41168577	-191224.411685770
10	1443586	550255.989729416	893330.010270584
11	1220017	1696189.52310429	-476172.523104285
12	984885	448631.173444984	536253.826555016
13	1457425	337976.658194335	1119448.34180567
14	-572920	1213539.61871092	-1786459.61871092
15	929144	869424.869719486	59719.1302805138
16	1151176	734179.852456258	416996.147543742
17	790090	1355408.90015295	-565318.900152948
18	774497	846489.21239458	-71992.2123945796
19	990576	1067070.97597276	-76494.9759727618
20	454195	841028.677497204	-386833.677497204
21	876607	606598.831525736	270008.168474264
22	711969	872197.483212349	-160228.483212349
23	702380	990501.626430696	-288121.626430696
24	264449	791332.298397956	-526883.298397956
25	450033	674199.791293136	-224166.791293136
26	541063	623044.549015327	-81981.5490153266
27	588864	898308.955777911	-309444.955777911
28	-37216	328935.091356921	-366151.091356921
29	783310	285235.233971338	498074.766028662
30	467359	424675.689876976	42683.3101230244
31	688779	384134.85817744	304644.141822560
32	608419	689773.84570314	-81354.84570314
33	696348	442121.544704112	254226.455295888
34	597793	476340.0057936	121452.994206400
35	821730	1304137.34055216	-482407.340552156
36	377934	841844.707682225	-463910.707682225
37	651939	567705.116747636	84233.8832523637
38	697458	535035.585857631	162422.414142369
39	700368	802715.040067593	-102347.040067593
40	225986	333449.9175596	-107463.9175596
41	348695	434062.069061489	-85367.0690614887
42	373683	708695.58003287	-335012.58003287
43	501709	382200.125382412	119508.874617588
44	413743	593163.824560216	-179420.824560216
45	379825	329094.780974815	50730.2190251847
46	336260	458349.0398683	-122089.039868300
47	636765	871932.602364987	-235167.602364987
48	481231	626675.123870018	-145444.123870018
49	469107	563551.177529618	-94444.177529618
50	211928	303997.555475467	-92069.5554754674
51	563925	445007.021057619	118917.978942381
52	511939	577690.98147728	-65751.98147728
53	521016	917978.912582567	-396962.912582567
54	543856	538861.721568651	4994.2784313485
55	329304	686366.849765579	-357062.849765579
56	423262	219344.356522514	203917.643477486
57	509665	237924.928107729	271740.071892271
58	455881	431174.86291879	24706.1370812102
59	367772	353907.957984328	13864.0420156722
60	406339	530908.137005598	-124569.137005598
61	493408	472261.061311671	21146.9386883287
62	232942	209636.246182206	23305.7538177940
63	416002	408448.090180069	7553.90981993126
64	337430	457169.267209198	-119739.267209198
65	361517	223125.151709636	138391.848290364
66	360962	383028.424885145	-22066.424885145
67	235561	270238.251459067	-34677.2514590669
68	408247	397398.973668749	10848.0263312513
69	450296	502248.943613902	-51952.9436139022
70	418799	449826.297548700	-31027.2975487004
71	247405	100018.230191678	147386.769808322
72	378519	213775.190088605	164743.809911395
73	326638	414528.343324262	-87890.343324262
74	328233	274745.419948628	53487.5800513718
75	386225	482830.407216473	-96605.407216473
76	283662	286234.988643715	-2572.98864371539
77	370225	433339.215850412	-63114.215850412
78	269236	248278.473754515	20957.5262454848
79	365732	217453.323188823	148278.676811177
80	420383	397006.035125351	23376.9648746492
81	345811	212232.172441151	133578.827558849
82	431809	417904.772180079	13904.2278199207
83	418876	532847.897120853	-113971.897120853
84	297476	104088.544499944	193387.455500056
85	416776	376197.08945801	40578.9105419902
86	357257	334281.812675811	22975.1873241892
87	458343	386813.283084124	71529.7169158756
88	388386	382734.085186548	5651.91481345217
89	358934	422561.600990134	-63627.6009901338
90	407560	381749.351068290	25810.6489317105
91	392558	374652.90330183	17905.0966981699
92	373177	349108.116299717	24068.8837002828
93	428370	224922.236156705	203447.763843295
94	369419	634968.074665044	-265549.074665044
95	358649	116578.057862086	242070.942137914
96	376641	251059.539786421	125581.460213579
97	467427	417108.345486622	50318.6545133783
98	364885	198159.814196427	166725.185803573
99	436230	553688.593639298	-117458.593639298
100	329118	271429.169156339	57688.8308436605

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	1	1.62336210351231e-19	8.11681051756157e-20
8	1	1.89514848719594e-24	9.47574243597968e-25
9	1	1.22906304159547e-23	6.14531520797737e-24
10	1	3.87458480111444e-26	1.93729240055722e-26
11	1	3.03956811830951e-28	1.51978405915476e-28
12	1	1.52051214142206e-28	7.6025607071103e-29
13	1	3.18822294155307e-33	1.59411147077654e-33
14	1	1.15052452018687e-43	5.75262260093435e-44
15	1	6.93226009983493e-44	3.46613004991747e-44
16	1	1.22030625960227e-46	6.10153129801135e-47
17	1	4.16413802377642e-47	2.08206901188821e-47
18	1	1.17164926325578e-46	5.85824631627888e-47
19	1	1.75291684756292e-46	8.76458423781458e-47
20	1	8.64966041910391e-46	4.32483020955196e-46
21	1	9.49556547518535e-47	4.74778273759268e-47
22	1	5.16843423697256e-46	2.58421711848628e-46
23	1	3.09576850189213e-45	1.54788425094607e-45
24	1	2.35397046646772e-45	1.17698523323386e-45
25	1	1.95792843072467e-44	9.78964215362336e-45
26	1	1.80936275411794e-43	9.04681377058969e-44
27	1	1.59097017108618e-42	7.95485085543091e-43
28	1	2.33787774387899e-44	1.16893887193950e-44
29	1	1.26286989836058e-46	6.31434949180291e-47
30	1	1.03956246300896e-45	5.1978123150448e-46
31	1	1.81490818968447e-46	9.07454094842233e-47
32	1	9.85627692114693e-46	4.92813846057347e-46
33	1	7.1754911198025e-47	3.58774555990125e-47
34	1	1.23341449683296e-46	6.16707248416482e-47
35	1	5.83041277125122e-46	2.91520638562561e-46
36	1	1.94974552108695e-45	9.74872760543477e-46
37	1	8.24999474910211e-46	4.12499737455105e-46
38	1	3.15037121230465e-47	1.57518560615233e-47
39	1	3.14153417880109e-47	1.57076708940054e-47
40	1	5.25834929356343e-47	2.62917464678172e-47
41	1	4.10080919761868e-46	2.05040459880934e-46
42	1	3.29434509030749e-45	1.64717254515374e-45
43	1	1.25504088278451e-44	6.27520441392255e-45
44	1	1.50337567661167e-43	7.51687838305834e-44
45	1	1.97157584528449e-42	9.85787922642246e-43
46	1	1.42056335876742e-41	7.1028167938371e-42
47	1	5.95487995103109e-41	2.97743997551554e-41
48	1	7.0307907860611e-40	3.51539539303055e-40
49	1	7.93873310049888e-39	3.96936655024944e-39
50	1	6.56489171198985e-39	3.28244585599492e-39
51	1	4.16577051693753e-39	2.08288525846877e-39
52	1	2.51136097195419e-38	1.25568048597709e-38
53	1	2.919764795537e-37	1.4598823977685e-37
54	1	3.64741624289181e-37	1.82370812144590e-37
55	1	6.60318225613464e-37	3.30159112806732e-37
56	1	3.64941212412581e-36	1.82470606206291e-36
57	1	6.53808564892334e-37	3.26904282446167e-37
58	1	5.01346295623121e-36	2.50673147811561e-36
59	1	7.6587489547504e-35	3.8293744773752e-35
60	1	1.13360194106297e-33	5.66800970531486e-34
61	1	3.28216837410445e-33	1.64108418705223e-33
62	1	5.69045826616496e-33	2.84522913308248e-33
63	1	7.24907458887198e-32	3.62453729443599e-32
64	1	6.77857306806719e-31	3.38928653403359e-31
65	1	9.5102005851084e-30	4.7551002925542e-30
66	1	7.01928250520036e-29	3.50964125260018e-29
67	1	2.73792346926102e-29	1.36896173463051e-29
68	1	4.17292758378546e-28	2.08646379189273e-28
69	1	5.8877356400622e-27	2.9438678200311e-27
70	1	8.48689483459219e-26	4.24344741729609e-26
71	1	3.243623326978e-25	1.621811663489e-25
72	1	3.87965814409832e-24	1.93982907204916e-24
73	1	2.42324694025938e-23	1.21162347012969e-23
74	1	3.1365261729328e-22	1.5682630864664e-22
75	1	4.55753168957208e-21	2.27876584478604e-21
76	1	1.36179465888522e-20	6.80897329442612e-21
77	1	1.56399259676986e-19	7.81996298384931e-20
78	1	9.91949224546848e-20	4.95974612273424e-20
79	1	1.72917012462424e-18	8.64585062312118e-19
80	1	3.01979388567285e-17	1.50989694283643e-17
81	1	4.70374789211651e-16	2.35187394605826e-16
82	0.999999999999998	4.2358029258758e-15	2.1179014629379e-15
83	0.999999999999969	6.20958427082655e-14	3.10479213541327e-14
84	0.999999999999815	3.69857779263073e-13	1.84928889631536e-13
85	0.999999999996875	6.24912155604845e-12	3.12456077802423e-12
86	0.999999999948873	1.02254561690886e-10	5.11272808454428e-11
87	0.999999999861463	2.77073557761392e-10	1.38536778880696e-10
88	0.99999999800262	3.9947581520865e-09	1.99737907604325e-09
89	0.999999983610695	3.27786098694872e-08	1.63893049347436e-08
90	0.99999973346822	5.33063558668573e-07	2.66531779334286e-07
91	0.999995673586997	8.65282600662877e-06	4.32641300331439e-06
92	0.999933940366812	0.000132119266376143	6.60596331880713e-05
93	0.999587244609272	0.000825510781456029	0.000412755390728014

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	87	1	NOK
5% type I error level	87	1	NOK
10% type I error level	87	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/10pcv21291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/10pcv21291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/1jty81291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/1jty81291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/2t2xb1291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/2t2xb1291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/3t2xb1291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/3t2xb1291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/4t2xb1291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/4t2xb1291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/54bxe1291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/54bxe1291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/64bxe1291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/64bxe1291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/7x2ez1291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/7x2ez1291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/8x2ez1291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/8x2ez1291404848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/9pcv21291404848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291405173z5iul2ffkof1lfd/9pcv21291404848.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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