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 12:00:47 -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/t12600397036idn0u5aytoe1zu.htm/, Retrieved Sat, 05 Dec 2009 20:01:55 +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/t12600397036idn0u5aytoe1zu.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 «

8776 0 8823 9051 8255 0 8776 8823 7969 0 8255 8776 8758 0 7969 8255 8693 0 8758 7969 8271 0 8693 8758 7790 0 8271 8693 7769 0 7790 8271 8170 0 7769 7790 8209 0 8170 7769 9395 0 8209 8170 9260 0 9395 8209 9018 0 9260 9395 8501 0 9018 9260 8500 0 8501 9018 9649 0 8500 8501 9319 0 9649 8500 8830 0 9319 9649 8436 0 8830 9319 8169 0 8436 8830 8269 0 8169 8436 7945 0 8269 8169 9144 0 7945 8269 8770 0 9144 7945 8834 0 8770 9144 7837 0 8834 8770 7792 0 7837 8834 8616 0 7792 7837 8518 0 8616 7792 7940 0 8518 8616 7545 0 7940 8518 7531 0 7545 7940 7665 0 7531 7545 7599 0 7665 7531 8444 0 7599 7665 8549 0 8444 7599 7986 0 8549 8444 7335 0 7986 8549 7287 0 7335 7986 7870 0 7287 7335 7839 0 7870 7287 7327 0 7839 7870 7259 0 7327 7839 6964 0 7259 7327 7271 0 6964 7259 6956 0 7271 6964 7608 0 6956 7271 7692 0 7608 6956 7255 0 7692 7608 6804 0 7255 7692 6655 0 6804 7255 7341 0 6655 6804 7602 0 7341 6655 7086 0 7602 7341 6625 0 7086 7602 6272 0 6625 7086 6576 0 6272 6625 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	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = -118.273578215342 + 298.441154509799X[t] + 1.00180797541842Y1[t] + 4.4709957013409e-06Y2[t] -148.968859133025M1[t] -581.425595573577M2[t] + 150.275891664170M3[t] + 885.158588241026M4[t] + 106.834156822982M5[t] -420.644134187442M6[t] -317.677673527403M7[t] -110.768661010202M8[t] + 220.266999976515M9[t] + 121.178068938109M10[t] + 1151.52013035616M11[t] -0.0921664123525376t + 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)	-118.273578215342	201.442387	-0.5871	0.558832	0.279416
X	298.441154509799	112.069947	2.663	0.009427	0.004713
Y1	1.00180797541842	0.119442	8.3874	0	0
Y2	4.4709957013409e-06	0.122257	0	0.999971	0.499985
M1	-148.968859133025	197.182234	-0.7555	0.452261	0.22613
M2	-581.425595573577	208.723488	-2.7856	0.006722	0.003361
M3	150.275891664170	249.464086	0.6024	0.548681	0.274341
M4	885.158588241026	181.710113	4.8713	6e-06	3e-06
M5	106.834156822982	140.33494	0.7613	0.448815	0.224408
M6	-420.644134187442	189.998481	-2.2139	0.029793	0.014896
M7	-317.677673527403	234.405891	-1.3552	0.179302	0.089651
M8	-110.768661010202	223.617533	-0.4953	0.621765	0.310882
M9	220.266999976515	204.060755	1.0794	0.283771	0.141885
M10	121.178068938109	173.437906	0.6987	0.486855	0.243428
M11	1151.52013035616	186.867397	6.1622	0	0
t	-0.0921664123525376	1.683467	-0.0547	0.956481	0.478241

Multiple Linear Regression - Regression Statistics
Multiple R	0.99412317923994
R-squared	0.988280895502124
Adjusted R-squared	0.985997953067473
F-TEST (value)	432.897860454874
F-TEST (DF numerator)	15
F-TEST (DF denominator)	77
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	263.319472494806
Sum Squared Residuals	5338960.13381062

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8776	8571.65763033812	204.342369661884
2	8255	8092.0227332535	162.977266746493
3	7969	8301.6898887491	-332.689888749102
4	8758	8749.96100855518	8.03899144482334
5	8693	8761.96962462514	-68.969624625143
6	8271	8169.28517641578	101.714823584222
7	7790	7849.39621442217	-59.3962144221709
8	7769	7574.34153759057	194.658462409425
9	8170	7884.24491413222	285.755085867781
10	8209	8186.78872093334	22.2112790666617
11	9395	9256.11091984963	138.889080150372
12	9260	9292.6430562962	-32.6430562961964
13	9018	9008.34325667023	9.65674332976637
14	8501	8333.35622018165	167.643779818348
15	8500	8547.02973573476	-47.0297357347634
16	9649	9280.81614641907	368.183853580930
17	9319	9653.47690787344	-334.476907873443
18	8830	8795.31495573665	34.6850442633516
19	8436	8408.30367457615	27.6963254238532
20	8169	8220.40599204924	-51.4059920492399
21	8269	8283.86499561458	-14.8649956145794
22	7945	8284.86350194981	-339.863501949811
23	9144	8990.5280600195	153.471939980491
24	8770	9040.08207717508	-270.082077175078
25	8834	8516.35022954706	317.649770452944
26	7837	8147.91536496854	-310.915364968539
27	7792	7880.7224204455	-88.7224204454935
28	8616	8570.42713413345	45.5728658665474
29	8518	8617.50010685303	-99.500106853028
30	7940	7991.7561519397	-51.7561519397043
31	7545	7515.58499823797	29.4150017620344
32	7531	7326.68510981702	204.314890182977
33	7665	7643.60152669223	21.3984733077729
34	7599	7678.6626353536	-79.6626353535975
35	8444	8642.7938030951	-198.793803095101
36	8549	8337.70895046944	211.291049530560
37	7986	8293.84154033436	-307.841540334364
38	7335	7297.27521677544	37.7247832245617
39	7287	7376.70502843286	-89.7050284328615
40	7870	8063.40586515908	-193.405865159078
41	7839	7869.04310238983	-30.043102389827
42	7327	7310.41920431957	16.5807956804267
43	7259	6900.36767655216	358.632323447837
44	6964	7039.05929117876	-75.0592911787596
45	7271	7074.46912897698	196.530871023018
46	6956	7282.84176103595	-326.841761035947
47	7608	7997.52351638052	-389.52351638052
48	7692	7499.08861122117	192.911388778825
49	7255	7434.18237070014	-179.182370700141
50	6804	6563.84375815303	240.156241846973
51	6655	6843.6357282396	-188.635728239592
52	7341	7429.15485364769	-88.1548536476886
53	7602	7337.97786077597	264.022139224031
54	7086	7071.88235204045	14.1176479595488
55	6625	6657.82489790211	-32.8248979021116
56	6272	6402.80596030529	-130.805960305287
57	6576	6380.10917842793	195.890821572070
58	6491	6585.47612724289	-94.476127242889
59	7649	7530.57370352071	118.426296479288
60	7400	7539.0546622521	-139.054662252098
61	6913	7140.54862824056	-227.548628240556
62	6532	6220.11812808095	311.881871919048
63	6486	6570.03643289702	-84.036432897022
64	7295	7258.74209274291	36.2579072570855
65	7556	7290.78794136022	265.212058639782
66	7088	7323.13413706697	-235.13413706697
67	6952	6957.16346574871	-5.16346574871477
68	6773	7027.73233477067	-254.732334770670
69	6917	7179.35159368972	-262.351593689721
70	7371	7224.43004439099	146.569955609014
71	8221	8709.50140406003	-488.501404060024
72	7953	8409.42791622922	-456.427916229221
73	8027	7991.88615361805	35.1138463819479
74	7287	7633.46984271926	-346.469842719263
75	8076	7623.74159258871	452.258407411291
76	8933	9148.95530682152	-215.955306821525
77	9433	9229.09167154032	203.908328459676
78	9479	9202.42903347007	276.570966529927
79	9199	9351.38873008486	-152.388730084858
80	9469	9277.69954873835	191.300451261648
81	10015	9879.1299447969	135.870055203107
82	10999	10326.9372090934	672.062790906569
83	13009	12342.9685930745	666.031406925494
84	13699	13204.9947263568	494.00527364321
85	13895	13747.1901905515	147.809809448519
86	13248	13510.9987358676	-262.998735867622
87	13973	13594.4391729125	378.560827087544
88	15095	15055.5375925211	39.4624074789058
89	15201	15401.1527845820	-200.152784582048
90	14823	14979.7789890108	-156.778989010801
91	14538	14703.9703424759	-165.970342475870
92	14547	14625.2702255501	-78.2702255500941
93	14407	14965.2287176694	-558.228717669449

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.299635102465501	0.599270204931003	0.700364897534499
20	0.194084207320224	0.388168414640448	0.805915792679776
21	0.152473954387757	0.304947908775515	0.847526045612243
22	0.180442661136544	0.360885322273089	0.819557338863456
23	0.109793568593438	0.219587137186876	0.890206431406562
24	0.111545533822162	0.223091067644324	0.888454466177838
25	0.100169081615200	0.200338163230399	0.8998309183848
26	0.179174978890243	0.358349957780486	0.820825021109757
27	0.127959814686716	0.255919629373432	0.872040185313284
28	0.0858777441237858	0.171755488247572	0.914122255876214
29	0.0602859511891754	0.120571902378351	0.939714048810825
30	0.0368083686930114	0.0736167373860229	0.963191631306989
31	0.0223349986461251	0.0446699972922502	0.977665001353875
32	0.0174874888852450	0.0349749777704900	0.982512511114755
33	0.0103109705600909	0.0206219411201817	0.98968902943991
34	0.00589831985561707	0.0117966397112341	0.994101680144383
35	0.00588569635015595	0.0117713927003119	0.994114303649844
36	0.00644007721961358	0.0128801544392272	0.993559922780386
37	0.00612477746145415	0.0122495549229083	0.993875222538546
38	0.003812182543617	0.007624365087234	0.996187817456383
39	0.00246256071244176	0.00492512142488353	0.997537439287558
40	0.00194205175394190	0.00388410350788379	0.998057948246058
41	0.00104868535085329	0.00209737070170658	0.998951314649147
42	0.000600320364227534	0.00120064072845507	0.999399679635772
43	0.00166835962637618	0.00333671925275236	0.998331640373624
44	0.000895125456363203	0.00179025091272641	0.999104874543637
45	0.000852182595419215	0.00170436519083843	0.99914781740458
46	0.000658275724126272	0.00131655144825254	0.999341724275874
47	0.00166536248944694	0.00333072497889388	0.998334637510553
48	0.00136739981115318	0.00273479962230636	0.998632600188847
49	0.000766507777499719	0.00153301555499944	0.9992334922225
50	0.00103623844555915	0.00207247689111831	0.99896376155444
51	0.000746158644872515	0.00149231728974503	0.999253841355128
52	0.000492915176868759	0.000985830353737519	0.999507084823131
53	0.000947489143841888	0.00189497828768378	0.999052510856158
54	0.000647798985551475	0.00129559797110295	0.999352201014449
55	0.000415281296107953	0.000830562592215905	0.999584718703892
56	0.000283435237137404	0.000566870474274808	0.999716564762863
57	0.0006690299769646	0.0013380599539292	0.999330970023035
58	0.000545738262984044	0.00109147652596809	0.999454261737016
59	0.000521220584946034	0.00104244116989207	0.999478779415054
60	0.000270589354793919	0.000541178709587838	0.999729410645206
61	0.000233924768352102	0.000467849536704204	0.999766075231648
62	0.00109662084339246	0.00219324168678491	0.998903379156608
63	0.0104353566094947	0.0208707132189895	0.989564643390505
64	0.00706783622460844	0.0141356724492169	0.992932163775392
65	0.0066221938041856	0.0132443876083712	0.993377806195814
66	0.00370314515277530	0.00740629030555059	0.996296854847225
67	0.00986919126715436	0.0197383825343087	0.990130808732846
68	0.00568940932784329	0.0113788186556866	0.994310590672157
69	0.0203485741362743	0.0406971482725487	0.979651425863726
70	0.0379147853216333	0.0758295706432666	0.962085214678367
71	0.0362354258309453	0.0724708516618906	0.963764574169055
72	0.109086546221593	0.218173092443185	0.890913453778407
73	0.0781665886749354	0.156333177349871	0.921833411325065
74	0.057065818982896	0.114131637965792	0.942934181017104

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	26	0.464285714285714	NOK
5% type I error level	39	0.696428571428571	NOK
10% type I error level	42	0.75	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/10lotb1260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/10lotb1260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/1h6sp1260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/1h6sp1260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/2kls91260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/2kls91260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/381x01260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/381x01260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/4lup91260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/4lup91260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/5s1en1260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/5s1en1260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/69zih1260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/69zih1260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/71ey11260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/71ey11260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/8v3py1260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/8v3py1260039641.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/9wsfv1260039641.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t12600397036idn0u5aytoe1zu/9wsfv1260039641.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

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