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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: Sat, 27 Nov 2010 14:01:36 +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/Nov/27/t12908664437s6gvcih4ehtm18.htm/, Retrieved Sat, 27 Nov 2010 15:00:46 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18.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 «

1579 0 2146 0 2462 0 3695 0 4831 0 5134 0 6250 0 5760 0 6249 0 2917 0 1741 0 2359 0 1511 1 2059 0 2635 0 2867 0 4403 0 5720 0 4502 0 5749 0 5627 0 2846 0 1762 0 2429 0 1169 0 2154 1 2249 0 2687 0 4359 0 5382 0 4459 0 6398 0 4596 0 3024 0 1887 0 2070 0 1351 0 2218 0 2461 1 3028 0 4784 0 4975 0 4607 0 6249 0 4809 0 3157 0 1910 0 2228 0 1594 0 2467 0 2222 0 3607 1 4685 0 4962 0 5770 0 5480 0 5000 0 3228 0 1993 0 2288 0 1580 0 2111 0 2192 0 3601 0 4665 1 4876 0 5813 0 5589 0 5331 0 3075 0 2002 0 2306 0 1507 0 1992 0 2487 0 3490 0 4647 0 5594 1 5611 0 5788 0 6204 0 3013 0 1931 0 2549 0 1504 0 2090 0 2702 0 2939 0 4500 0 6208 0 6415 1 5657 0 5964 0 3163 0 1997 0 2422 0 1376 0 2202 0 2683 0 3303 0 5202 0 5231 0 4880 0 7998 1 4977 0 3531 0 2025 0 2205 0 1442 0 2238 0 2179 0 3218 0 5139 0 4990 0 4914 0 6084 0 5672 1 3548 0 1793 0 2086 0

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

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 2294.2 + 482.518518518518X[t] -881.151851851853M1[t] -174.751851851852M2[t] + 84.7481481481482M3[t] + 901.048148148148M4[t] + 2379.04814814815M5[t] + 2964.74814814815M6[t] + 2979.64814814815M7[t] + 3732.74814814815M8[t] + 3100.44814814815M9[t] + 856M10[t] -390.1M11[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)	2294.2	122.104163	18.7889	0	0
X	482.518518518518	135.671292	3.5565	0.000561	0.000281
M1	-881.151851851853	173.213511	-5.0871	2e-06	1e-06
M2	-174.751851851852	173.213511	-1.0089	0.315308	0.157654
M3	84.7481481481482	173.213511	0.4893	0.625652	0.312826
M4	901.048148148148	173.213511	5.202	1e-06	0
M5	2379.04814814815	173.213511	13.7348	0	0
M6	2964.74814814815	173.213511	17.1161	0	0
M7	2979.64814814815	173.213511	17.2022	0	0
M8	3732.74814814815	173.213511	21.55	0	0
M9	3100.44814814815	173.213511	17.8996	0	0
M10	856	172.681364	4.9571	3e-06	1e-06
M11	-390.1	172.681364	-2.2591	0.025906	0.012953

Multiple Linear Regression - Regression Statistics
Multiple R	0.974222552850857
R-squared	0.94910958248324
Adjusted R-squared	0.943402245939304
F-TEST (value)	166.296410799834
F-TEST (DF numerator)	12
F-TEST (DF denominator)	107
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	386.127267313284
Sum Squared Residuals	15953086.5222222

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1579	1413.04814814815	165.951851851848
2	2146	2119.44814814815	26.5518518518523
3	2462	2378.94814814815	83.051851851852
4	3695	3195.24814814815	499.751851851852
5	4831	4673.24814814815	157.751851851851
6	5134	5258.94814814815	-124.948148148148
7	6250	5273.84814814815	976.151851851852
8	5760	6026.94814814815	-266.948148148147
9	6249	5394.64814814815	854.351851851852
10	2917	3150.2	-233.200000000001
11	1741	1904.1	-163.099999999998
12	2359	2294.2	64.8000000000003
13	1511	1895.56666666667	-384.566666666666
14	2059	2119.44814814815	-60.4481481481483
15	2635	2378.94814814815	256.051851851852
16	2867	3195.24814814815	-328.248148148148
17	4403	4673.24814814815	-270.248148148148
18	5720	5258.94814814815	461.051851851852
19	4502	5273.84814814815	-771.848148148148
20	5749	6026.94814814815	-277.948148148148
21	5627	5394.64814814815	232.351851851852
22	2846	3150.2	-304.2
23	1762	1904.1	-142.1
24	2429	2294.2	134.8
25	1169	1413.04814814815	-244.048148148148
26	2154	2601.96666666667	-447.966666666666
27	2249	2378.94814814815	-129.948148148148
28	2687	3195.24814814815	-508.248148148148
29	4359	4673.24814814815	-314.248148148148
30	5382	5258.94814814815	123.051851851852
31	4459	5273.84814814815	-814.848148148148
32	6398	6026.94814814815	371.051851851852
33	4596	5394.64814814815	-798.648148148148
34	3024	3150.2	-126.2
35	1887	1904.1	-17.1000000000003
36	2070	2294.2	-224.2
37	1351	1413.04814814815	-62.0481481481477
38	2218	2119.44814814815	98.5518518518517
39	2461	2861.46666666667	-400.466666666667
40	3028	3195.24814814815	-167.248148148148
41	4784	4673.24814814815	110.751851851852
42	4975	5258.94814814815	-283.948148148148
43	4607	5273.84814814815	-666.848148148148
44	6249	6026.94814814815	222.051851851852
45	4809	5394.64814814815	-585.648148148148
46	3157	3150.2	6.80000000000004
47	1910	1904.1	5.89999999999978
48	2228	2294.2	-66.2000000000002
49	1594	1413.04814814815	180.951851851852
50	2467	2119.44814814815	347.551851851852
51	2222	2378.94814814815	-156.948148148148
52	3607	3677.76666666667	-70.7666666666671
53	4685	4673.24814814815	11.7518518518519
54	4962	5258.94814814815	-296.948148148148
55	5770	5273.84814814815	496.151851851852
56	5480	6026.94814814815	-546.948148148148
57	5000	5394.64814814815	-394.648148148148
58	3228	3150.2	77.7999999999999
59	1993	1904.1	88.8999999999997
60	2288	2294.2	-6.20000000000025
61	1580	1413.04814814815	166.951851851852
62	2111	2119.44814814815	-8.44814814814824
63	2192	2378.94814814815	-186.948148148148
64	3601	3195.24814814815	405.751851851852
65	4665	5155.76666666667	-490.766666666667
66	4876	5258.94814814815	-382.948148148148
67	5813	5273.84814814815	539.151851851852
68	5589	6026.94814814815	-437.948148148148
69	5331	5394.64814814815	-63.6481481481484
70	3075	3150.2	-75.1999999999999
71	2002	1904.1	97.8999999999997
72	2306	2294.2	11.8
73	1507	1413.04814814815	93.9518518518523
74	1992	2119.44814814815	-127.448148148148
75	2487	2378.94814814815	108.051851851852
76	3490	3195.24814814815	294.751851851852
77	4647	4673.24814814815	-26.2481481481481
78	5594	5741.46666666667	-147.466666666667
79	5611	5273.84814814815	337.151851851852
80	5788	6026.94814814815	-238.948148148148
81	6204	5394.64814814815	809.351851851852
82	3013	3150.2	-137.2
83	1931	1904.1	26.8999999999998
84	2549	2294.2	254.8
85	1504	1413.04814814815	90.9518518518523
86	2090	2119.44814814815	-29.4481481481483
87	2702	2378.94814814815	323.051851851852
88	2939	3195.24814814815	-256.248148148148
89	4500	4673.24814814815	-173.248148148148
90	6208	5258.94814814815	949.051851851852
91	6415	5756.36666666667	658.633333333334
92	5657	6026.94814814815	-369.948148148148
93	5964	5394.64814814815	569.351851851852
94	3163	3150.2	12.8
95	1997	1904.1	92.8999999999997
96	2422	2294.2	127.8
97	1376	1413.04814814815	-37.0481481481477
98	2202	2119.44814814815	82.5518518518517
99	2683	2378.94814814815	304.051851851852
100	3303	3195.24814814815	107.751851851852
101	5202	4673.24814814815	528.751851851852
102	5231	5258.94814814815	-27.9481481481483
103	4880	5273.84814814815	-393.848148148148
104	7998	6509.46666666667	1488.53333333333
105	4977	5394.64814814815	-417.648148148148
106	3531	3150.2	380.8
107	2025	1904.1	120.9
108	2205	2294.2	-89.2000000000002
109	1442	1413.04814814815	28.9518518518523
110	2238	2119.44814814815	118.551851851852
111	2179	2378.94814814815	-199.948148148148
112	3218	3195.24814814815	22.7518518518518
113	5139	4673.24814814815	465.751851851852
114	4990	5258.94814814815	-268.948148148148
115	4914	5273.84814814815	-359.848148148148
116	6084	6026.94814814815	57.0518518518522
117	5672	5877.16666666667	-205.166666666666
118	3548	3150.2	397.8
119	1793	1904.1	-111.1
120	2086	2294.2	-208.2

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.520500232492503	0.958999535014994	0.479499767507497
17	0.457456814445005	0.91491362889001	0.542543185554995
18	0.491286168166536	0.982572336333073	0.508713831833464
19	0.974591431624298	0.0508171367514047	0.0254085683757023
20	0.956007824382729	0.087984351234542	0.043992175617271
21	0.952822149040614	0.0943557019187721	0.0471778509593861
22	0.927733439176923	0.144533121646155	0.0722665608230773
23	0.891290413685482	0.217419172629037	0.108709586314518
24	0.845139749204356	0.309720501591287	0.154860250795644
25	0.816066368258028	0.367867263483943	0.183933631741972
26	0.778391978223602	0.443216043552796	0.221608021776398
27	0.733235876692563	0.533528246614874	0.266764123307437
28	0.76149940105456	0.47700119789088	0.23850059894544
29	0.720412103270093	0.559175793459814	0.279587896729907
30	0.656423883099852	0.687152233800295	0.343576116900148
31	0.815536100407802	0.368927799184395	0.184463899592198
32	0.836008291541466	0.327983416917068	0.163991708458534
33	0.964296167772983	0.0714076644540345	0.0357038322270172
34	0.95097462942925	0.098050741141501	0.0490253705707505
35	0.932496205739957	0.135007588520085	0.0675037942600426
36	0.917271673948564	0.165456652102871	0.0827283260514357
37	0.890370870323578	0.219258259352843	0.109629129676422
38	0.858356144455053	0.283287711089894	0.141643855544947
39	0.844806705951746	0.310386588096509	0.155193294048254
40	0.807439663288462	0.385120673423076	0.192560336711538
41	0.771088198528791	0.457823602942418	0.228911801471209
42	0.75472668252674	0.49054663494652	0.24527331747326
43	0.803340547175074	0.393318905649852	0.196659452824926
44	0.775296543572673	0.449406912854654	0.224703456427327
45	0.824246598132319	0.351506803735363	0.175753401867681
46	0.789543408303336	0.420913183393327	0.210456591696664
47	0.7449891971262	0.510021605747601	0.255010802873801
48	0.695248539550705	0.609502920898591	0.304751460449295
49	0.64914258529489	0.70171482941022	0.35085741470511
50	0.625952648783099	0.748094702433802	0.374047351216901
51	0.583812340884255	0.83237531823149	0.416187659115745
52	0.580419118037579	0.839161763924842	0.419580881962421
53	0.522982025601529	0.954035948796942	0.477017974398471
54	0.494599714006704	0.989199428013409	0.505400285993296
55	0.568604920749011	0.862790158501978	0.431395079250989
56	0.610712530989107	0.778574938021786	0.389287469010893
57	0.604356923862377	0.791286152275245	0.395643076137623
58	0.555887298927832	0.888225402144336	0.444112701072168
59	0.501962381748649	0.996075236502702	0.498037618251351
60	0.443576505674494	0.887153011348987	0.556423494325506
61	0.392810557763178	0.785621115526355	0.607189442236822
62	0.338260357357019	0.676520714714039	0.66173964264298
63	0.303996858886585	0.60799371777317	0.696003141113415
64	0.29819326257858	0.59638652515716	0.70180673742142
65	0.39264115718513	0.78528231437026	0.60735884281487
66	0.381830148160044	0.763660296320088	0.618169851839956
67	0.452579468213294	0.905158936426588	0.547420531786706
68	0.468225295028881	0.936450590057761	0.531774704971119
69	0.414367106480836	0.828734212961672	0.585632893519164
70	0.367378360505186	0.734756721010371	0.632621639494814
71	0.315610035787561	0.631220071575122	0.684389964212439
72	0.264148757982249	0.528297515964497	0.735851242017751
73	0.218056589421646	0.436113178843293	0.781943410578354
74	0.182945397779109	0.365890795558217	0.817054602220891
75	0.146901774222912	0.293803548445823	0.853098225777088
76	0.129499130022847	0.258998260045693	0.870500869977153
77	0.107092382613771	0.214184765227543	0.892907617386229
78	0.157393485073194	0.314786970146389	0.842606514926806
79	0.175333669509155	0.35066733901831	0.824666330490845
80	0.157277319454865	0.31455463890973	0.842722680545135
81	0.409499627510222	0.818999255020445	0.590500372489778
82	0.385546680608642	0.771093361217284	0.614453319391358
83	0.323818272208159	0.647636544416319	0.67618172779184
84	0.293906123096032	0.587812246192064	0.706093876903968
85	0.240006082274063	0.480012164548127	0.759993917725936
86	0.19268235502326	0.38536471004652	0.80731764497674
87	0.166611573626602	0.333223147253205	0.833388426373398
88	0.145536515345209	0.291073030690419	0.85446348465479
89	0.16838044762938	0.33676089525876	0.83161955237062
90	0.476293492265304	0.952586984530607	0.523706507734696
91	0.496089677071655	0.99217935414331	0.503910322928345
92	0.664811690406729	0.670376619186542	0.335188309593271
93	0.980007747934455	0.039984504131091	0.0199922520655455
94	0.98110802201777	0.0377839559644598	0.0188919779822299
95	0.967484828347247	0.0650303433055052	0.0325151716527526
96	0.958942207293531	0.0821155854129375	0.0410577927064688
97	0.930363179898495	0.13927364020301	0.069636820101505
98	0.885816722291157	0.228366555417687	0.114183277708843
99	0.906491726419531	0.187016547160938	0.0935082735804692
100	0.846354777840099	0.307290444319802	0.153645222159901
101	0.770621085965886	0.458757828068229	0.229378914034114
102	0.685025807869903	0.629948384260194	0.314974192130097
103	0.549583297954495	0.90083340409101	0.450416702045505
104	0.975552912021272	0.0488941759574557	0.0244470879787279

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	3	0.0337078651685393	OK
10% type I error level	10	0.112359550561798	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18/10fd8f1290866485.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18/10fd8f1290866485.ps (open in new window)

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http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18/754sc1290866485.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18/754sc1290866485.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18/854sc1290866485.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18/854sc1290866485.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18/954sc1290866485.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/27/t12908664437s6gvcih4ehtm18/954sc1290866485.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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

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