Home » date » 2007 » Dec » 17 » attachments

Multiple regression Totaal

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

Title produced by software: Multiple Regression

Date of computation: Sun, 16 Dec 2007 16:41:47 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510.htm/, Retrieved Mon, 17 Dec 2007 00:28:11 +0100

User-defined keywords:

Multiple regression Totaal

Dataseries X:

» Textbox « » Textfile « » CSV «

174.1 0 180.4 0 182.6 0 207.1 0 213.7 0 186.5 0 179.1 0 168.3 0 156.5 0 144.3 0 138.9 0 137.8 0 136.3 0 140.3 0 149.1 0 149.2 0 140.4 0 129 0 124.7 0 130.8 0 130.1 0 133.2 0 130.1 0 126.6 0 124.8 0 125.3 0 126.9 0 120.1 0 118.7 0 117.7 0 113.4 0 107.5 0 107.6 0 114.3 0 114.9 0 111.2 0 109.9 0 108.6 0 109.2 0 106.4 0 103.7 0 103 0 96.9 0 104.7 0 102.2 0 99 0 95.8 0 94.5 0 102.7 0 103.2 0 105.6 0 103.9 0 107.2 0 100.7 0 92.1 0 90.3 0 93.4 0 98.5 0 100.8 0 102.3 0 104.7 0 101.1 0 101.4 0 99.5 0 98.4 0 96.3 0 100.7 0 101.2 0 100.3 0 97.8 0 97.4 1 98.6 1 99.7 1 99 1 98.1 1 97 1 98.5 1 103.8 1 114.4 1 124.5 1 134.2 1 131.8 1 125.6 1 119.9 1 114.9 1 115.5 1 112.5 1 111.4 1 115.3 1 110.8 1 103.7 1 111.1 1 113 1 111.2 1 117.6 1 121.7 1 127.3 1 129.8 1 137.1 1 141.4 1 137.4 1 130.7 1 117.2 1 110.8 1 111.4 1 108.2 1 108.8 1 110.2 1 109.5 1 109.5 1 116 1 111.2 1 112.1 1 114 1 119.1 1 114.1 1 115.1 1 115.4 1 110.8 1 116 1 119.2 1 126.5 1 127.8 1 131.3 1 140.3 1 137.3 1 143 1 134.5 1 139.9 1 159.3 1 170.4 1 175 1 175.8 1 180.9 1 180.3 1 169.6 1 172.3 1 184.8 1 177.7 1 184.6 1 211.4 1

Text written by user:

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 compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Totaal[t] = + 119.776923076923 + 13.8396794871795`9/11`[t] + 5.55316579254073M1[t] + 7.42940850815842M2[t] + 9.74731789044272M3[t] + 9.98189393939383M4[t] + 10.9164699883448M5[t] + 7.40937937062927M6[t] + 4.80228875291364M7[t] + 4.94519813519803M8[t] + 7.77977418414907M9[t] + 0.966273310023195M10[t] -0.355030594405694M11[t] -0.109576048951049t + 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)	119.776923076923	9.752169	12.2821	0	0
`9/11`	13.8396794871795	9.598784	1.4418	0.151816	0.075908
M1	5.55316579254073	11.810395	0.4702	0.639024	0.319512
M2	7.42940850815842	11.808873	0.6291	0.530389	0.265194
M3	9.74731789044272	11.808531	0.8254	0.410666	0.205333
M4	9.98189393939383	11.809367	0.8453	0.39956	0.19978
M5	10.9164699883448	11.811382	0.9242	0.357118	0.178559
M6	7.40937937062927	11.814574	0.6271	0.531694	0.265847
M7	4.80228875291364	11.818944	0.4063	0.68519	0.342595
M8	4.94519813519803	11.82449	0.4182	0.676495	0.338248
M9	7.77977418414907	11.83121	0.6576	0.512009	0.256004
M10	0.966273310023195	12.079492	0.08	0.936369	0.468184
M11	-0.355030594405694	12.061025	-0.0294	0.976563	0.488281
t	-0.109576048951049	0.117988	-0.9287	0.354805	0.177403

Multiple Linear Regression - Regression Statistics
Multiple R	0.190736931444518
R-squared	0.0363805770168706
Adjusted R-squared	-0.0622576316349459
F-TEST (value)	0.368828444008859
F-TEST (DF numerator)	13
F-TEST (DF denominator)	127
p-value	0.977168178571238
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	28.2842573680236
Sum Squared Residuals	101599.900287296

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	174.1	125.220512820512	48.8794871794878
2	180.4	126.987179487179	53.4128205128206
3	182.6	129.195512820513	53.4044871794872
4	207.1	129.320512820513	77.779487179487
5	213.7	130.145512820513	83.5544871794874
6	186.5	126.528846153846	59.9711538461538
7	179.1	123.812179487179	55.2878205128205
8	168.3	123.845512820513	44.4544871794871
9	156.5	126.570512820513	29.9294871794872
10	144.3	119.647435897436	24.6525641025641
11	138.9	118.216555944056	20.6834440559441
12	137.8	118.462010489511	19.3379895104894
13	136.3	123.905600233100	12.3943997668997
14	140.3	125.672266899767	14.6277331002331
15	149.1	127.880600233100	21.2193997668998
16	149.2	128.005600233100	21.1943997668998
17	140.4	128.830600233100	11.5693997668998
18	129	125.213933566434	3.78606643356643
19	124.7	122.497266899767	2.2027331002331
20	130.8	122.530600233100	8.26939976689976
21	130.1	125.255600233100	4.84439976689975
22	133.2	118.332523310023	14.8674766899767
23	130.1	116.901643356643	13.1983566433566
24	126.6	117.147097902098	9.45290209790197
25	124.8	122.590687645688	2.20931235431228
26	125.3	124.357354312354	0.942645687645686
27	126.9	126.565687645688	0.334312354312359
28	120.1	126.690687645688	-6.59068764568765
29	118.7	127.515687645688	-8.8156876456876
30	117.7	123.899020979021	-6.19902097902098
31	113.4	121.182354312354	-7.78235431235431
32	107.5	121.215687645688	-13.7156876456877
33	107.6	123.940687645688	-16.3406876456877
34	114.3	117.017610722611	-2.71761072261073
35	114.9	115.586730769231	-0.686730769230778
36	111.2	115.832185314685	-4.63218531468544
37	109.9	121.275775058275	-11.3757750582751
38	108.6	123.042441724942	-14.4424417249417
39	109.2	125.250775058275	-16.0507750582751
40	106.4	125.375775058275	-18.9757750582751
41	103.7	126.200775058275	-22.5007750582750
42	103	122.584108391608	-19.5841083916084
43	96.9	119.867441724942	-22.9674417249417
44	104.7	119.900775058275	-15.2007750582751
45	102.2	122.625775058275	-20.4257750582751
46	99	115.702698135198	-16.7026981351981
47	95.8	114.271818181818	-18.4718181818182
48	94.5	114.517272727273	-20.0172727272729
49	102.7	119.960862470863	-17.2608624708625
50	103.2	121.727529137529	-18.5275291375291
51	105.6	123.935862470862	-18.3358624708625
52	103.9	124.060862470862	-20.1608624708625
53	107.2	124.885862470862	-17.6858624708624
54	100.7	121.269195804196	-20.5691958041958
55	92.1	118.552529137529	-26.4525291375292
56	90.3	118.585862470862	-28.2858624708625
57	93.4	121.310862470862	-27.9108624708625
58	98.5	114.387785547786	-15.8877855477856
59	100.8	112.956905594406	-12.1569055944056
60	102.3	113.202360139860	-10.9023601398603
61	104.7	118.64594988345	-13.9459498834500
62	101.1	120.412616550117	-19.3126165501166
63	101.4	122.620949883450	-21.2209498834499
64	99.5	122.745949883450	-23.2459498834499
65	98.4	123.570949883450	-25.1709498834499
66	96.3	119.954283216783	-23.6542832167833
67	100.7	117.237616550117	-16.5376165501166
68	101.2	117.27094988345	-16.0709498834499
69	100.3	119.995949883450	-19.6959498834499
70	97.8	113.072872960373	-15.2728729603730
71	97.4	125.481672494173	-28.0816724941725
72	98.6	125.727127039627	-27.1271270396272
73	99.7	131.170716783217	-31.4707167832168
74	99	132.937383449883	-33.9373834498834
75	98.1	135.145716783217	-37.0457167832168
76	97	135.270716783217	-38.2707167832168
77	98.5	136.095716783217	-37.5957167832167
78	103.8	132.479050116550	-28.6790501165501
79	114.4	129.762383449883	-15.3623834498834
80	124.5	129.795716783217	-5.29571678321678
81	134.2	132.520716783217	1.67928321678322
82	131.8	125.597639860140	6.20236013986016
83	125.6	124.166759906760	1.43324009324009
84	119.9	124.412214452215	-4.51221445221455
85	114.9	129.855804195804	-14.9558041958042
86	115.5	131.622470862471	-16.1224708624708
87	112.5	133.830804195804	-21.3308041958042
88	111.4	133.955804195804	-22.5558041958042
89	115.3	134.780804195804	-19.4808041958041
90	110.8	131.164137529138	-20.3641375291375
91	103.7	128.447470862471	-24.7474708624708
92	111.1	128.480804195804	-17.3808041958042
93	113	131.205804195804	-18.2058041958042
94	111.2	124.282727272727	-13.0827272727273
95	117.6	122.851847319347	-5.25184731934733
96	121.7	123.097301864802	-1.39730186480197
97	127.3	128.540891608392	-1.24089160839167
98	129.8	130.307558275058	-0.50755827505825
99	137.1	132.515891608392	4.58410839160841
100	141.4	132.640891608392	8.7591083916084
101	137.4	133.465891608392	3.93410839160845
102	130.7	129.849224941725	0.850775058275053
103	117.2	127.132558275058	-9.93255827505826
104	110.8	127.165891608392	-16.3658916083916
105	111.4	129.890891608392	-18.4908916083916
106	108.2	122.967814685315	-14.7678146853147
107	108.8	121.536934731935	-12.7369347319347
108	110.2	121.782389277389	-11.5823892773894
109	109.5	127.225979020979	-17.7259790209791
110	109.5	128.992645687646	-19.4926456876457
111	116	131.200979020979	-15.200979020979
112	111.2	131.325979020979	-20.125979020979
113	112.1	132.150979020979	-20.0509790209790
114	114	128.534312354312	-14.5343123543123
115	119.1	125.817645687646	-6.71764568764568
116	114.1	125.850979020979	-11.7509790209790
117	115.1	128.575979020979	-13.4759790209790
118	115.4	121.652902097902	-6.25290209790208
119	110.8	120.222022144522	-9.42202214452215
120	116	120.467476689977	-4.4674766899768
121	119.2	125.911066433566	-6.71106643356648
122	126.5	127.677733100233	-1.17773310023309
123	127.8	129.886066433566	-2.08606643356642
124	131.3	130.011066433566	1.28893356643359
125	140.3	130.836066433566	9.46393356643363
126	137.3	127.219399766900	10.0806002331002
127	143	124.502733100233	18.4972668997669
128	134.5	124.536066433566	9.96393356643357
129	139.9	127.261066433566	12.6389335664336
130	159.3	120.337989510490	38.9620104895105
131	170.4	118.907109557110	51.4928904428904
132	175	119.152564102564	55.8474358974358
133	175.8	124.596153846154	51.2038461538461
134	180.9	126.362820512820	54.5371794871795
135	180.3	128.571153846154	51.7288461538462
136	169.6	128.696153846154	40.9038461538462
137	172.3	129.521153846154	42.7788461538462
138	184.8	125.904487179487	58.8955128205128
139	177.7	123.187820512820	54.5121794871795
140	184.6	123.221153846154	61.3788461538462
141	211.4	125.946153846154	85.4538461538462

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/1s0if1197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/1s0if1197848502.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/23lsm1197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/23lsm1197848502.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/3mu231197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/3mu231197848502.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/4yh901197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/4yh901197848502.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/5hebs1197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/5hebs1197848502.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/65t2o1197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/65t2o1197848502.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/7yx4g1197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/7yx4g1197848502.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/8ytg51197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/8ytg51197848502.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/929hw1197848502.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/17/t1197847681k0p484pz2tu4510/929hw1197848502.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)

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))

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')

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()

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')

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