Home » date » 2008 » Dec » 07 »

dummy

*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 05:19:24 -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/t1228652517g82rga1w7b1vwlv.htm/, Retrieved Sun, 07 Dec 2008 12:21:57 +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/t1228652517g82rga1w7b1vwlv.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 «
160646,8 0 160637,2 0 164997,4 0 167984,4 0 172568,7 0 178880 0 181932,9 0 187714,3 0 188076,3 0 189892,2 0 182166,5 0 196657,6 0 176794,5 0 178830,4 0 168934,5 0 167902,6 0 166530,3 0 169323,4 0 175184,6 0 172493,7 0 158829,4 0 156139,6 0 162112,3 0 186258,3 0 167201,2 0 165990,2 0 165731,8 0 168118,1 0 162460,8 0 151526,9 0 136480,9 0 135780,2 0 114973,3 0 121127,6 0 124920,1 0 122454,3 0 114088,9 0 106266,2 0 104412 0 117742,8 0 121798,2 0 122644,1 0 127011,6 0 129469,6 0 129579,3 0 135693,2 0 136320,4 0 137592,5 0 148244,4 0 154458 0 159692,2 0 163144,1 0 162107,5 0 165131,3 0 164231,2 0 170001,9 0 182144,8 0 188313,5 0 193955,3 0 200918,4 0 206409 0 214390,6 0 203845,4 0 206986,7 0 209341 0 214284 0 222753 0 224041 0 232062 0 229253 0 237632 0 244574 0 257227 0 264083 0 265331 0 269216 0 253965 0 254036 0 259733 0 266302 0 277784 0 282973 0 283398 0 300454 0 304528 0 298325 0 311393 0 316867 0 322725 0 317988 0 274957 1 267993 1 260728 1 286828 1 265985 1 263718 1 239891 1 241398 1 238924 1 246829 1 234725 1 199177 1 189131 1 198293 1 165763 1 124900 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Multiple Linear Regression - Estimated Regression Equation
beurskapitalisatie[t] = + 131973.098732206 -35410.2110676157`Wel(1)_geen(0)_financiële_crisis`[t] -2526.81402860159M1[t] -2951.71708695796M2[t] -4539.40903420326M3[t] -1388.57875922632M4[t] -4915.37070647162M5[t] -10071.2293203836M6[t] -12219.8089267827M7[t] -11386.3008740280M8[t] -17533.0261546066M9[t] -18421.0181018519M10[t] -6803.63999719917M11[t] + 1463.54750280084t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)131973.09873220617647.8458597.478100
`Wel(1)_geen(0)_financiële_crisis`-35410.211067615715247.995633-2.32230.0224240.011212
M1-2526.8140286015921251.948231-0.11890.9056150.452808
M2-2951.7170869579621246.389231-0.13890.8898110.444905
M3-4539.4090342032621242.307384-0.21370.8312570.415628
M4-1388.5787592263221239.703543-0.06540.9480160.474008
M5-4915.3707064716221238.578252-0.23140.817490.408745
M6-10071.229320383621238.931745-0.47420.636490.318245
M7-12219.808926782721282.656219-0.57420.5672560.283628
M8-11386.300874028021277.234673-0.53510.5938440.296922
M9-17533.026154606621273.2882-0.82420.4119690.205984
M10-18421.018101851921270.817622-0.8660.388730.194365
M11-6803.6399971991721854.312576-0.31130.7562630.378132
t1463.54750280084177.2229538.258200


Multiple Linear Regression - Regression Statistics
Multiple R0.686345413966058
R-squared0.471070027272240
Adjusted R-squared0.396329922430273
F-TEST (value)6.30277450464234
F-TEST (DF numerator)13
F-TEST (DF denominator)92
p-value2.42904676373001e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation43707.1879760203
Sum Squared Residuals175749281830.948


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1160646.8130909.83220640629736.9677935942
2160637.2131948.4766508528688.7233491499
3164997.4131824.33220640633173.0677935943
4167984.4136438.70998418331545.6900158165
5172568.7134375.46553973938193.234460261
6178880130683.15442862848196.8455713721
7181932.9129998.12232503051934.7776749703
8187714.3132295.17788058555419.1221194148
9188076.3127612.00010280760464.2998971926
10189892.2128187.55565836361704.644341637
11182166.5141268.48126581740898.0187341835
12196657.6149535.66876581747121.9312341834
13176794.5148472.40224001628322.0977599842
14178830.4149511.04668446029319.3533155397
15168934.5149386.90224001619547.5977599842
16167902.6154001.28001779413901.3199822064
17166530.3151938.03557334914592.2644266508
18169323.4148245.72446223821077.6755377620
19175184.6147560.69235864027623.9076413602
20172493.7149857.74791419522635.9520858047
21158829.4145174.57013641813654.8298635824
22156139.6145750.12569197310389.4743080269
23162112.3158831.0512994273281.24870057334
24186258.3167098.23879942719160.0612005733
25167201.2166034.9722736261166.22772637408
26165990.2167073.616718070-1083.41671807037
27165731.8166949.472273626-1217.67227362594
28168118.1171563.850051404-3445.75005140372
29162460.8169500.605606959-7039.80560695927
30151526.9165808.294495848-14281.3944958482
31136480.9165123.26239225-28642.3623922499
32135780.2167420.317947805-31640.1179478054
33114973.3162737.140170028-47763.8401700277
34121127.6163312.695725583-42185.0957255832
35124920.1176393.621333037-51473.5213330367
36122454.3184660.808833037-62206.5088330368
37114088.9183597.542307236-69508.642307236
38106266.2184636.186751681-78369.9867516805
39104412184512.042307236-80100.042307236
40117742.8189126.420085014-71383.6200850139
41121798.2187063.175640569-65264.9756405694
42122644.1183370.864529458-60726.7645294583
43127011.6182685.83242586-55674.23242586
44129469.6184982.887981416-55513.2879814156
45129579.3180299.710203638-50720.4102036378
46135693.2180875.265759193-45182.0657591933
47136320.4193956.191366647-57635.7913666469
48137592.5202223.378866647-64630.8788666469
49148244.4201160.112340846-52915.7123408462
50154458202198.756785291-47740.7567852906
51159692.2202074.612340846-42382.4123408462
52163144.1206688.990118624-43544.8901186240
53162107.5204625.745674180-42518.2456741795
54165131.3200933.434563068-35802.1345630684
55164231.2200248.40245947-36017.2024594701
56170001.9202545.458015026-32543.5580150257
57182144.8197862.280237248-15717.4802372479
58188313.5198437.835792803-10124.3357928035
59193955.3211518.761400257-17563.4614002570
60200918.4219785.948900257-18867.5489002570
61206409218722.682374456-12313.6823744563
62214390.6219761.326818901-5370.72681890074
63203845.4219637.182374456-15791.7823744563
64206986.7224251.560152234-17264.8601522341
65209341222188.315707790-12847.3157077896
66214284218496.004596679-4212.00459667853
67222753217810.9724930804942.02750691974
68224041220108.0280486363932.97195136419
69232062215424.85027085816637.1497291420
70229253216000.40582641413252.5941735864
71237632229081.3314338678550.66856613287
72244574237348.5189338677225.48106613285
73257227236285.25240806620941.7475919336
74264083237323.89685251126759.1031474891
75265331237199.75240806628131.2475919336
76269216241814.13018584427401.8698141558
77253965239750.88574140014214.1142586003
78254036236058.57463028917977.4253697113
79259733235373.54252669024359.4574733096
80266302237670.59808224628631.4019177540
81277784232987.42030446844796.5796955318
82282973233562.97586002449410.0241399763
83283398246643.90146747736754.0985325228
84300454254911.08896747745542.9110325227
85304528253847.82244167750680.1775583235
86298325254886.46688612143438.533113879
87311393254762.32244167756630.6775583234
88316867259376.70021945457490.2997805457
89322725257313.4557750165411.5442249901
90317988253621.14466389964366.8553361012
91274957217525.90149268557431.0985073152
92267993219822.95704824048170.0429517596
93260728215139.77927046345588.2207295374
94286828215715.33482601871112.6651739818
95265985228796.26043347237188.7395665283
96263718237063.44793347226654.5520665283
97239891236000.1814076713890.81859232901
98241398237038.8258521154359.17414788453
99238924236914.6814076712009.31859232899
100246829241529.0591854495299.9408145512
101234725239465.814741004-4740.81474100434
102199177235773.503629893-36596.5036298933
103189131235088.471526295-45957.471526295
104198293237385.527081851-39092.5270818506
105165763232702.349304073-66939.3493040727
106124900233277.904859628-108377.904859628
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/1tzcu1228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/1tzcu1228652355.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/2rtn31228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/2rtn31228652355.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/3ooxd1228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/3ooxd1228652355.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/42mmy1228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/42mmy1228652355.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/5xg3u1228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/5xg3u1228652355.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/6y6001228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/6y6001228652355.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/7dy9n1228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/7dy9n1228652355.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/8zsg11228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/8zsg11228652355.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/92ih71228652355.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228652517g82rga1w7b1vwlv/92ih71228652355.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')
 




Copyright

Creative Commons License

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.


Privacy Policy

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