Home » date » 2008 » Dec » 07 »

dummy3

*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: Sun, 07 Dec 2008 05:55:51 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy.htm/, Retrieved Sun, 07 Dec 2008 12:57:34 +0000

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/2008/Dec/07/t12286546543aaxe2dliqwy0cy.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
test

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

86,9 0 99,7 0 109,1 0 94,6 0 111,2 0 112,8 0 53,5 0 107,5 0 105,2 0 122,8 0 103,4 0 76,9 0 89,6 0 92,8 0 107,6 0 104,6 0 103 0 106,9 0 56,3 0 93,4 0 109,1 0 113,8 0 97,4 0 72,5 0 82,7 0 88,9 0 105,9 0 100,8 0 94 0 105 0 58,5 0 87,6 0 113,1 0 112,5 0 89,6 0 74,5 0 82,7 0 90,1 0 109,4 0 96 0 89,2 0 109,1 0 49,1 0 92,9 0 107,7 0 103,5 0 91,1 0 79,8 0 71,9 0 82,9 0 90,1 0 100,7 0 90,7 0 108,8 0 44,1 0 93,6 0 107,4 0 96,5 0 93,6 0 76,5 0 76,7 0 84 0 103,3 0 88,5 0 99 0 105,9 0 44,7 0 94 0 107,1 0 104,8 0 102,5 0 77,7 0 85,2 0 91,3 0 106,5 0 92,4 0 97,5 0 107 0 51,1 1 98,6 1 102,2 1 114,3 1 99,4 1 72,5 1 92,3 1 99,4 1 85,9 1 109,4 1 97,6 1 104,7 1 56,9 1 86,7 1 108,5 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Bouwnijverheid[t] = + 80.4038607474862 + 6.13955289951306`Wel(1)_geen(0)_financiële_crisis`[t] + 7.26429888909968M1[t] + 15.0165803716352M2[t] + 26.2188618541706M3[t] + 22.4836433367061M4[t] + 21.9984248192416M5[t] + 31.863206301777M6[t] -24.5394563281267M7[t] + 18.0878251544088M8[t] + 31.4526066369443M9[t] + 33.7418656063576M10[t] + 20.8280756603217M11[t] -0.114781482535467t + 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)	80.4038607474862	2.48594	32.3434	0	0
`Wel(1)_geen(0)_financiële_crisis`	6.13955289951306	2.119123	2.8972	0.004869	0.002435
M1	7.26429888909968	2.972805	2.4436	0.016772	0.008386
M2	15.0165803716352	2.971779	5.0531	3e-06	1e-06
M3	26.2188618541706	2.971033	8.8248	0	0
M4	22.4836433367061	2.970567	7.5688	0	0
M5	21.9984248192416	2.970381	7.4059	0	0
M6	31.863206301777	2.970474	10.7266	0	0
M7	-24.5394563281267	2.977639	-8.2412	0	0
M8	18.0878251544088	2.976659	6.0766	0	0
M9	31.4526066369443	2.975958	10.5689	0	0
M10	33.7418656063576	3.068186	10.9973	0	0
M11	20.8280756603217	3.067779	6.7893	0	0
t	-0.114781482535467	0.028839	-3.98	0.000152	7.6e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.948844244850798
R-squared	0.900305400986481
Adjusted R-squared	0.883899960642484
F-TEST (value)	54.8784660520207
F-TEST (DF numerator)	13
F-TEST (DF denominator)	79
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.7390358528424
Sum Squared Residuals	2601.98606909663

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	86.9	87.5533781540505	-0.65337815405051
2	99.7	95.1908781540505	4.50912184594954
3	109.1	106.278378154050	2.82162184594954
4	94.6	102.428378154050	-7.82837815405046
5	111.2	101.828378154050	9.37162184594954
6	112.8	111.578378154050	1.22162184594953
7	53.5	55.0609340416113	-1.56093404161133
8	107.5	97.5734340416113	9.92656595838867
9	105.2	110.823434041611	-5.62343404161132
10	122.8	112.997911528489	9.80208847151078
11	103.4	99.9693400999178	3.43065990008222
12	76.9	79.0264829570607	-2.12648295706064
13	89.6	86.1760003636249	3.42399963637512
14	92.8	93.8135003636249	-1.01350036362487
15	107.6	104.901000363625	2.69899963637513
16	104.6	101.051000363625	3.54899963637513
17	103	100.451000363625	2.54899963637514
18	106.9	110.201000363625	-3.30100036362486
19	56.3	53.6835562511857	2.61644374881427
20	93.4	96.1960562511857	-2.79605625118573
21	109.1	109.446056251186	-0.34605625118574
22	113.8	111.620533738064	2.17946626193638
23	97.4	98.5919623094922	-1.19196230949218
24	72.5	77.649105166635	-5.14910516663505
25	82.7	84.7986225731993	-2.09862257319925
26	88.9	92.4361225731993	-3.53612257319926
27	105.9	103.523622573199	2.37637742680074
28	100.8	99.6736225731993	1.12637742680073
29	94	99.0736225731993	-5.07362257319926
30	105	108.823622573199	-3.82362257319927
31	58.5	52.3061784607601	6.19382153923987
32	87.6	94.8186784607601	-7.21867846076014
33	113.1	108.068678460760	5.03132153923986
34	112.5	110.243155947638	2.25684405236199
35	89.6	97.2145845190666	-7.61458451906659
36	74.5	76.2717273762094	-1.77172737620945
37	82.7	83.4212447827737	-0.721244782773657
38	90.1	91.0587447827737	-0.958744782773674
39	109.4	102.146244782774	7.25375521722633
40	96	98.2962447827737	-2.29624478277367
41	89.2	97.6962447827737	-8.49624478277366
42	109.1	107.446244782774	1.65375521722633
43	49.1	50.9288006703345	-1.82880067033453
44	92.9	93.4413006703345	-0.541300670334532
45	107.7	106.691300670335	1.00869932966547
46	103.5	108.865778157212	-5.36577815721242
47	91.1	95.837206728641	-4.737206728641
48	79.8	74.8943495857838	4.90565041421615
49	71.9	82.043866992348	-10.1438669923481
50	82.9	89.681366992348	-6.78136699234806
51	90.1	100.768866992348	-10.6688669923481
52	100.7	96.918866992348	3.78113300765194
53	90.7	96.318866992348	-5.61886699234806
54	108.8	106.068866992348	2.73113300765193
55	44.1	49.5514228799089	-5.45142287990893
56	93.6	92.063922879909	1.53607712009106
57	107.4	105.313922879909	2.08607712009107
58	96.5	107.488400366787	-10.9884003667868
59	93.6	94.4598289382154	-0.859828938215399
60	76.5	73.5169717953582	2.98302820464175
61	76.7	80.6664892019225	-3.96648920192246
62	84	88.3039892019225	-4.30398920192247
63	103.3	99.3914892019225	3.90851079807753
64	88.5	95.5414892019225	-7.04148920192247
65	99	94.9414892019225	4.05851079807753
66	105.9	104.691489201922	1.20851079807754
67	44.7	48.1740450894833	-3.47404508948333
68	94	90.6865450894833	3.31345491051666
69	107.1	103.936545089483	3.16345491051666
70	104.8	106.111022576361	-1.31102257636123
71	102.5	93.0824511477898	9.4175488522102
72	77.7	72.1395940049326	5.56040599506735
73	85.2	79.2891114114969	5.91088858850314
74	91.3	86.9266114114969	4.37338858850312
75	106.5	98.0141114114969	8.48588858850312
76	92.4	94.1641114114969	-1.76411141149687
77	97.5	93.5641114114969	3.93588858850313
78	107	103.314111411497	3.68588858850313
79	51.1	52.9362201985708	-1.8362201985708
80	98.6	95.4487201985708	3.1512798014292
81	102.2	108.698720198571	-6.4987201985708
82	114.3	110.873197685449	3.42680231455132
83	99.4	97.8446262568773	1.55537374312274
84	72.5	76.9017691140201	-4.40176911402011
85	92.3	84.0512865205843	8.24871347941568
86	99.4	91.6887865205843	7.71121347941568
87	85.9	102.776286520584	-16.8762865205843
88	109.4	98.9262865205843	10.4737134794157
89	97.6	98.3262865205843	-0.726286520584335
90	104.7	108.076286520584	-3.37628652058433
91	56.9	51.5588424081452	5.3411575918548
92	86.7	94.0713424081452	-7.3713424081452
93	108.5	107.321342408145	1.1786575918548

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/1ispd1228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/1ispd1228654540.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/255f21228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/255f21228654540.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/3ce6t1228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/3ce6t1228654540.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/45fof1228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/45fof1228654540.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/5dsew1228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/5dsew1228654540.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/6rtt11228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/6rtt11228654540.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/7olh01228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/7olh01228654540.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/801cs1228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/801cs1228654540.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/9lzd61228654540.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t12286546543aaxe2dliqwy0cy/9lzd61228654540.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