Free statistics 1.1.0 software for linux

Free Statistics records and views daily Web site page views (hits) for statistical tracking. This is a Free PHP script to record and view daily website page views (hits) for statistical tracking. Features a chart of daily page views totals displayed with bar graph, total for last x days, most hits in a day for last x days, average hits per day for last x days, projected hits for today, and more. Easy to install.
Edit the values in config.php for MySQL; change the other variables if you want (is optional). Do not edit other files.
Copy the files to the same directory on your server.
Install MySQL table. Execute the following in PhpMyAdmin or other MySQL interface:
CREATE TABLE stats_day (
date date DEFAULT 0000-00-00 NOT NULL,
hits mediumint(8) unsigned DEFAULT 0 NOT NULL,
PRIMARY KEY (date)
);
For php files, you can add this code to each page to record page views to it (be sure to add the path if needed):
If the page is in a different folder than the stats script, you can add the path such as:
You can record stats for non-php pages (and php pages also) by adding this code in the body of the html (remember to add the correct path to the script; you can use a full url here; Note, this only records hits for browsers with images-loading enabled):
Main features:
- Chart of daily page views totals displayed with bar graph, total for last x days, most hits in a day for last x days, average hits per day for last x days, projected hits for today, and more. Easy to install.

RRDStats is a Coyote Linux and BrazilFW add-on package for network traffic monitoring, link quality control, and QOS classes monitoring.
RRD Statistics project is based on RRDtool for storing data to round robin databases, and a slightly modified RRDcgi for visualizing data through a Web interface.
Main features:
- Realtime graphical statistics for bandwidth usage and link quality
- Graphical statistics of QOS priority classes usage
- Historical data stored for one week
Configuration:
All default configuration is stored in /etc/rrd.config. This version supports web based configuration and there is no need to manual configuration for basic package functionality. Just install the packages and browse to your web administration interface (by default its http://192.168.0.1:8180). There should be new link at left menu labeled "RRDStats configuration"
There are some basic options you should set up to fit your configuration. First get sure, the RRDstats package is enabled (its the first option at configuration screen). After that should you set up your line speed (just some basic approximation is good enough). The last this you should set up is your internet gateway IP address. This IP address is used to measure your internet link latency and packet loss.
Ignore other configuration options for now, save your configuration and reboot router. After your system boots up, you can browse RRD statistics.
After system startup, package is initialiazed with /etc/rc.d/pkgs/rc.rrdstats. This file start another copy of tiny webserver which listens by default on port 8080. It reads its homepage files from /var/rrd/www/ directory. After webserver startup there are also started some data gathering threads.
They read transfered data from network interfaces, QOS classes and measure link latency. These values are then stored in RRD databases. RRD databases are by default stored in /var/rrd/data/ directory
For further information how RRD databases work, please visit their homepage. Simply said RRD database has constant size, it does not grow over time and stores average data over period of time.
Last component of RRDStats package are .cgi and template files which display data from RRD databases using web interface. As said before, these files and templates are stored in /var/rrd/www/ and its subdirectories.

Statistics::SPC is a Perl module with calculations for Stastical Process Control (SPC).

Creates thresholds based on the variability of all data, # of samples not meeting spec, and variablity within sample sets, all from training data.
Note: this is only accurate for data which is normally distributed when the process is under control

Recommended usage: at least 15 sample sets, w/ sample size >=2 (5 is good) This module is fudged to work for sample size 1, but its a better idea to use >= 2

Important: the closer the process your are monitoring to how you would like it to be running (steady state), the better the calculated control limits will be.
Example: we take 5 recordings of the CPU utilization at random intervals over the course of a minute. We do this for 15 minutes, keeping all fifteen samples. Using this will be able to tell whether or not CPU use is in steady state.

SYNOPSIS

my $spc = new Statistics::SPC;
$spc->n(5) # set the number of samples per set
$spc->Uspec(.50); # CPU should not be above 50% utilization
$spc->Lspec(.05); # CPU should not be below 5%
# (0 is boring in an example)

# Now feed training data into our object
$return = $spc->history($history); # "train the system";
# $history is ref to 2d array;
# $return > 1 means process not likely to
# meet the constraints of your specified
# upper and lower bounds

# now check to see if the the latest sample of CPU util indicates
# CPU utilization was under control during the time of the sample

$return = $spc->test($data); # check one sample of size n
# $return < 0 there is something wrong with your data
# $return == 0 the sample is "in control"
# $return > 0 there are $return problems with the sample set

Statistics::ROC is a Perl module with receiver-operator-characteristic (ROC) curves with nonparametric confidence bounds.

SYNOPSIS

use Statistics::ROC;

my ($y) = loggamma($x);
my ($y) = betain($x, $p, $q, $beta);
my ($y) = Betain($x, $p, $q);
my ($y) = xinbta($p, $q, $beta, $alpha);
my ($y) = Xinbta($p, $q, $alpha);
my (@rk) = rank($type, @r);
my (@ROC) = roc($model_type,$conf,@val_grp);

This program determines the ROC curve and its nonparametric confidence bounds for data categorized into two groups. A ROC curve shows the relationship of probability of false alarm (x-axis) to probability of detection (y-axis) for a certain test. Expressed in medical terms: the probability of a positive test, given no disease to the probability of a positive test, given disease. The ROC curve may be used to determine an optimal cutoff point for the test.
The main function is roc(). The other exported functions are used by roc(), but might be useful for other nonparametric statistical procedures.

loggamma

This procedure evaluates the natural logarithm of gamma(x) for all x>0, accurate to 10 decimal places. Stirlings formula is used for the central polynomial part of the procedure. For x=0 a value of 743.746924740801 will be returned: this is loggamma(9.9999999999E-324).

betain

Computes incomplete beta function ratio

Remarks:
Complete beta function: B(p,q)=gamma(p)*gamma(q)/gamma(p+q)
log(B(p,q))=ln(gamma(p))+ln(gamma(q))-ln(gamma(p+q))

Incomplete beta function ratio:
I_x(p,q)=1/B(p,q) * int_0^x t^{p-1}*(1-t)^{q-1} dt

--> log(B(p,q)) has to be supplied to calculate I_x(p,q)
log denotes the natural logarithm
$beta = log(B(p,q))
$x = x
$p = p
$q = q
The subroutine returns I_x(p,q). If an error occurs a negative value
{-1,-2} is returned.

Betain

Computes the incomplete beta function by calling loggamma() and betain().

xinbta

Computes inverse of incomplete beta function ratio

Remarks:

Complete beta function: B(p,q)=gamma(p)*gamma(q)/gamma(p+q)
log(B(p,q))=ln(gamma(p))+ln(gamma(q))-ln(gamma(p+q))

Incomplete beta function ratio:
alpha = I_x(p,q) = 1/B(p,q) * int_0^x t^{p-1}*(1-t)^{q-1} dt

--> log(B(p,q)) has to be supplied to calculate I_x(p,q)
log denotes the natural logarithm
$beta = log(B(p,q))
$alpha= I_x(p,q)
$p = p
$q = q
The subroutine returns x. If an error occurs a negative value {-1,-2,-3}
is returned.

Xinbta

Computes the inverse of the incomplete beta function by calling loggamma() and xinbta().

rank

Computes the ranks of the values specified as the second argument (an array). Returns a vector of ranks corresponding to the input vector. Different types of ranking are possible (high, low, mean), and are specified as first argument. These differ in the way ties of the input vector, i.e. identical values, are treated:

high:

replace ranks of identical values with their highest rank

low:

replace ranks of identical values with their lowest rank

mean:

replace ranks of identical values with the mean of their ranks

roc

Determines the ROC curve and its nonparametric confidence bounds. The ROC curve shows the relationship of "probability of false alarm" (x-axis) to "probability of detection" (y-axis) for a certain test. Or in medical terms: the "probability of a positive test, given no disease" to the "probability of a positive test, given disease". The ROC curve may be used to determine an "optimal" cutoff point for the test.

The routine takes three arguments:

(1) type of model: decrease or increase, this states the assumption that a higher (increase) value of the data tends to be an indicator of a positive test result or for the model decrease a lower value.
(2) two-sided confidence interval (usually 0.95 is chosen).
(3) the data stored as a list-of-lists: each entry in this list consits of an "value / true group" pair, i.e. value / disease present. Group values are from {0,1}. 0 stands for disease (or signal) not present (prior knowledge) and 1 for disease (or signal) present (prior knowledge). Example: @s=([2, 0], [12.5, 1], [3, 0], [10, 1], [9.5, 0], [9, 1]); Notice the small overlap of the groups. The optimal cutoff point to separate the two groups would be between 9 and 9.5 if the criterion of optimality is to maximize the probability of detection and simultaneously minimize the probability of false alarm.

Returns a list-of-lists with the three curves: @ROC=([@lower_b], [@roc], [@upper_b]) each of the curves is again a list-of-lists with each entry consisting of one (x,y) pair.

Examples:

$,=" ";
print loggamma(10), "n";
print Xinbta(3,4,Betain(.6,3,4)),"n";

@e=(0.7, 0.7, 0.9, 0.6, 1.0, 1.1, 1,.7,.6);
print rank(low,@e),"n";
print rank(high,@e),"n";
print rank(mean,@e),"n";

@var_grp=([1.5,0],[1.4,0],[1.4,0],[1.3,0],[1.2,0],[1,0],[0.8,0],
[1.1,1],[1,1],[1,1],[0.9,1],[0.7,1],[0.7,1],[0.6,1]);
@curves=roc(decrease,0.95,@var_grp);
print "$curves[0][2][0] $curves[0][2][1] n";

Statistics::SDT Perl package contains signal detection theory measures of sensitivity and response-bias.

SYNOPSIS

use Statistics::SDT;

$sdt = Statistics::SDT->new(
{
hits => 50,
signal_trials => 50,
false_alarms => 17,
noise_trials => 25,
correct => 2,
}
);

$d = $sdt->d_sensitivity();
$c = $sdt->decision_bias();

Signal Detection Theory algorithms (e.g., of d, A, decision bias), as prescribed by Stanislav & Todorov (1999). Both object- and function-oriented interfaces are provided.

KEY VALUES

For both object- and function-oriented styles, the following named parameters must be given as a hash-reference: either to the new constructor method, or (with the function-oriented style) into each function. Basically, either all of the first four parameters are required (in order to calculate the hit-rate and false-alarm-rate), or the required rates are themselves supplied.

hits

The number of hits.

false_alarms

The number of false alarms.

signal_trials

The number of signal trials. The hit-rate is derived by dividing the number of hits by the number of signal trials.

noise_trials

The number of noise trials. The false-alarm-rate is derived by dividing the number of false-alarms by the number of noise trials.

alternatives

The number of response alternatives. Default = 2 (for the classic signal-detection situation of discriminating between signal+noise and noise-only). If the number of alternatives is greater than 2, the measure of sensitivity, when calling d_sensitivity, is based on the Smith (1982) algorithms.

correct

A parameter that indicates whether or not to perform a correction on the number of hits and false-alarms as a corrective when the hit-rate or false-alarm-rate equals 0 or 1 (due, e.g., to strong inducements against false-alarms, or easy discrimination between signals and noise). This is relevant to all functions that make use of the inverse phi function (all except a_sensitivity and griers_bias).
If set to greater than 1, the loglinear transformation is applied, i.e., 0.5 is added to both the number of hits and false-alarms, and 1 is added to the number of signal and noise trials. These adjustments are made irrespective of the extremity of the rates themselves.

If set to 1, extreme rates (of 0 and 1, only) are replaced with the number of signal/noise trials, moderated by a value of 0.5 (specifically, where n = number of signal or noise trials: 0 is replaced with 0.5 / n; 1 is replaced with (n - 0.5) / n.
Stanislav and Todorov (1999) advise that the latter correction is the most common method of handling extreme rates, but that it might bias sensitivity measures and not be as satisfactory as the loglinear transformation applied to all hits and false-alarms.

If set to zero (the default), no correction is performed to the calculation of the rates. This should only be used when you are using (1) the parametric measures and are sure the rates are not at the extremes of 0 and 1; or (2) the nonparametric algorithms (a_sensitivity and griers_bias). An alternative to these corrections is, indeed, to use the nonparametric measures.

hr

This is the hit-rate. Instead of passing the number of hits and signal trials, give the hit-rate directly - but, if doing so, ensure the rate does not equal zero or 1 in order to avoid errors thrown by the inverse-phi function (which will be given as "ndtri domain error").

far

This is the false-alarm-rate. Instead of passing the number of false alarms and noise trials, give the false-alarm-rate directly - but, if doing so, ensure the rate does not equal zero or 1 in order to avoid errors thrown by the inverse-phi function (which will be given as "ndtri domain error").

Statistics::PointEstimation is a Perl module for computing confidence intervals in parameter estimation with Students T distribution.

Statistics::PointEstimation::Sufficient - Perl module for computing the confidence intervals using sufficient statistics

SYNOPSIS

# example for Statistics::PointEstimation
use Statistics::PointEstimation;

my @r=();
for($i=1;$iset_significance(95); #set the significance(confidence) level to 95%
$stat->add_data(@r);
$stat->output_confidence_interval(); #output summary
$stat->print_confidence_interval(); #output the data hash related to confidence interval estimation

#the following is the same as $stat->output_confidence_interval();
print "Summary from the observed values of the sample:n";
print "tsample size= ", $stat->count()," , degree of freedom=", $stat->df(), "n";
print "tmean=", $stat->mean()," , variance=", $stat->variance(),"n";
print "tstandard deviation=", $stat->standard_deviation()," , standard error=", $stat->standard_error(),"n";
print "t the estimate of the mean is ", $stat->mean()," +/- ",$stat->delta(),"nt",
" or (",$stat->lower_clm()," to ",$stat->upper_clm," ) with ",$stat->significance," % of confidencen";
print "t t-statistic=T=",$stat->t_statistic()," , Prob >|T|=",$stat->t_prob(),"n";

#example for Statistics::PointEstimation::Sufficient

use strict;
use Statistics::PointEstimation;
my ($count,$mean,$variance)=(30,3.996,1.235);
my $stat = new Statistics::PointEstimation::Sufficient;
$stat->set_significance(99);
$stat->load_data($count,$mean,$variance);
$stat->output_confidence_interval();
$stat->set_significance(95);
$stat->output_confidence_interval();

Statistics::PointEstimation

This module is a subclass of Statistics::Descriptive::Full. It uses T-distribution for point estimation assuming the data is normally distributed or the sample size is sufficiently large. It overrides the add_data() method in Statistics::Descriptive to compute the confidence interval with the specified significance level (default is 95%). It also computes the t-statistic=T and Prob>|T| in case of hypothesis testing of paired T-tests.

Statistics::PointEstimation::Sufficient

This module is a subclass of Statistics::PointEstimation. Instead of taking the real data points as the input, it will compute the confidence intervals based on the sufficient statistics and the sample size inputted. To use this module, you need to pass the sample size, the sample mean , and the sample variance into the load_data() function. The output will be exactly the same as the Statistics::PointEstimation Module.

Yacas is project is a Yet Another Computer Algebra System.
Yacas is a general purpose, easy to use Computer Algebra System (a CAS is a program that can be used to do symbolic manipulation of mathematical expressions).
It is built on top of its own programming language designed for this purpose, in which new algorithms can easily be implemented.
In addition, it comes with extensive documentation on the functionality implemented and methods used to implement them.
Enhancements:
- The code was cleaned up.
- The Web page was overhauled so that Yacas can now be used online the way one would use it off-line.

Statistics::Hartigan is a Perl extension for the stopping rule proposed by Hartigan J. Hartigan, J. (1975). Clustering Algorithms. John Wiley and Sons, New York, NY, US.

SYNOPSIS

use Statistics::Hartigan;
&hartigan(InputFile, "agglo", 6, 10);

Input file is expected in the "dense" format -
Sample Input file:

6 5
1 1 0 0 1
1 0 0 0 0
1 1 0 0 1
1 1 0 0 1
1 0 0 0 1
1 1 0 0 1

Hartigan J. uses the Within Cluster/Group Sum of Squares (WGSS) to estimate the number of clusters a given data naturally falls into. The is goal is to minimize WG.