statistics (n)  Linux Man Pages
statistics: Basic statistical functions and procedures
NAME
math::statistics  Basic statistical functions and procedures
SYNOPSIS
package require Tcl 8package require math::statistics 0.5
::math::statistics::mean data
::math::statistics::min data
::math::statistics::max data
::math::statistics::number data
::math::statistics::stdev data
::math::statistics::var data
::math::statistics::pstdev data
::math::statistics::pvar data
::math::statistics::median data
::math::statistics::basicstats data
::math::statistics::histogram limits values
::math::statistics::corr data1 data2
::math::statistics::intervalmeanstdev data confidence
::math::statistics::ttestmean data est_mean est_stdev confidence
::math::statistics::testnormal data confidence
::math::statistics::lillieforsFit data
::math::statistics::quantiles data confidence
::math::statistics::quantiles limits counts confidence
::math::statistics::autocorr data
::math::statistics::crosscorr data1 data2
::math::statistics::meanhistogramlimits mean stdev number
::math::statistics::minmaxhistogramlimits min max number
::math::statistics::linearmodel xdata ydata intercept
::math::statistics::linearresiduals xdata ydata intercept
::math::statistics::test2x2 n11 n21 n12 n22
::math::statistics::print2x2 n11 n21 n12 n22
::math::statistics::controlxbar data ?nsamples?
::math::statistics::controlRchart data ?nsamples?
::math::statistics::testxbar control data
::math::statistics::testRchart control data
::math::statistics::tstat dof ?alpha?
::math::statistics::mvwls wt1 weights_and_values
::math::statistics::mvols values
::math::statistics::pdfnormal mean stdev value
::math::statistics::pdfexponential mean value
::math::statistics::pdfuniform xmin xmax value
::math::statistics::pdfgamma alpha beta value
::math::statistics::pdfpoisson mu k
::math::statistics::pdfchisquare df value
::math::statistics::pdfstudentt df value
::math::statistics::pdfbeta a b value
::math::statistics::cdfnormal mean stdev value
::math::statistics::cdfexponential mean value
::math::statistics::cdfuniform xmin xmax value
::math::statistics::cdfstudentst degrees value
::math::statistics::cdfgamma alpha beta value
::math::statistics::cdfpoisson mu k
::math::statistics::cdfbeta a b value
::math::statistics::randomnormal mean stdev number
::math::statistics::randomexponential mean number
::math::statistics::randomuniform xmin xmax number
::math::statistics::randomgamma alpha beta number
::math::statistics::randomchisquare df number
::math::statistics::randomstudentt df number
::math::statistics::randombeta a b number
::math::statistics::histogramuniform xmin xmax limits number
::math::statistics::incompleteGamma x p ?tol?
::math::statistics::incompleteBeta a b x ?tol?
::math::statistics::filter varname data expression
::math::statistics::map varname data expression
::math::statistics::samplescount varname list expression
::math::statistics::subdivide
::math::statistics::testKruskalWallis confidence args
::math::statistics::analyseKruskalWallis args
::math::statistics::grouprank args
::math::statistics::plotscale canvas xmin xmax ymin ymax
::math::statistics::plotxydata canvas xdata ydata tag
::math::statistics::plotxyline canvas xdata ydata tag
::math::statistics::plottdata canvas tdata tag
::math::statistics::plottline canvas tdata tag
::math::statistics::plothistogram canvas counts limits tag
DESCRIPTION
The math::statistics package contains functions and procedures for basic statistical data analysis, such as:
 •
 Descriptive statistical parameters (mean, minimum, maximum, standard deviation)
 •
 Estimates of the distribution in the form of histograms and quantiles
 •
 Basic testing of hypotheses
 •
 Probability and cumulative density functions
It is meant to help in developing data analysis applications or doing ad hoc data analysis, it is not in itself a full application, nor is it intended to rival with full (non)commercial statistical packages.
The purpose of this document is to describe the implemented procedures and provide some examples of their usage. As there is ample literature on the algorithms involved, we refer to relevant text books for more explanations. The package contains a fairly large number of public procedures. They can be distinguished in three sets: general procedures, procedures that deal with specific statistical distributions, list procedures to select or transform data and simple plotting procedures (these require Tk). Note: The data that need to be analyzed are always contained in a simple list. Missing values are represented as empty list elements.
GENERAL PROCEDURES
The general statistical procedures are: ::math::statistics::mean data

Determine the mean value of the given list of data.

 list data
  List of data

 ::math::statistics::min data

Determine the minimum value of the given list of data.

 list data
  List of data

 ::math::statistics::max data

Determine the maximum value of the given list of data.

 list data
  List of data

 ::math::statistics::number data

Determine the number of nonmissing data in the given list

 list data
  List of data

 ::math::statistics::stdev data

Determine the sample standard deviation of the data in the
given list

 list data
  List of data

 ::math::statistics::var data

Determine the sample variance of the data in the given list

 list data
  List of data

 ::math::statistics::pstdev data

Determine the population standard deviation of the data
in the given list

 list data
  List of data

 ::math::statistics::pvar data

Determine the population variance of the data in the
given list

 list data
  List of data

 ::math::statistics::median data

Determine the median of the data in the given list
(Note that this requires sorting the data, which may be a
costly operation)

 list data
  List of data

 ::math::statistics::basicstats data

Determine a list of all the descriptive parameters: mean, minimum,
maximum, number of data, sample standard deviation, sample variance,
population standard deviation and population variance.
(This routine is called whenever either or all of the basic statistical parameters are required. Hence all calculations are done and the relevant values are returned.)

 list data
  List of data

 ::math::statistics::histogram limits values

Determine histogram information for the given list of data. Returns a
list consisting of the number of values that fall into each interval.
(The first interval consists of all values lower than the first limit,
the last interval consists of all values greater than the last limit.
There is one more interval than there are limits.)

 list limits
  List of upper limits (in ascending order) for the intervals of the histogram.
 list values
  List of data

 ::math::statistics::corr data1 data2

Determine the correlation coefficient between two sets of data.

 list data1
  First list of data
 list data2
  Second list of data

 ::math::statistics::intervalmeanstdev data confidence

Return the interval containing the mean value and one
containing the standard deviation with a certain
level of confidence (assuming a normal distribution)

 list data
  List of raw data values (small sample)
 float confidence
  Confidence level (0.95 or 0.99 for instance)

 ::math::statistics::ttestmean data est_mean est_stdev confidence

Test whether the mean value of a sample is in accordance with the
estimated normal distribution with a certain level of confidence.
Returns 1 if the test succeeds or 0 if the mean is unlikely to fit
the given distribution.

 list data
  List of raw data values (small sample)
 float est_mean
  Estimated mean of the distribution
 float est_stdev
  Estimated stdev of the distribution
 float confidence
  Confidence level (0.95 or 0.99 for instance)

 ::math::statistics::testnormal data confidence

Test whether the given data follow a normal distribution
with a certain level of confidence.
Returns 1 if the data are normally distributed within the level of
confidence, returns 0 if not. The underlying test is the Lilliefors
test.

 list data
  List of raw data values
 float confidence
  Confidence level (one of 0.80, 0.90, 0.95 or 0.99)

 ::math::statistics::lillieforsFit data

Returns the goodness of fit to a normal distribution according to
Lilliefors. The higher the number, the more likely the data are indeed
normally distributed. The test requires at least five data
points.

 list data
  List of raw data values

 ::math::statistics::quantiles data confidence

Return the quantiles for a given set of data

 list data

 List of raw data values
 float confidence

 Confidence level (0.95 or 0.99 for instance)

 ::math::statistics::quantiles limits counts confidence

Return the quantiles based on histogram information (alternative to the
call with two arguments)

 list limits
  List of upper limits from histogram
 list counts
  List of counts for for each interval in histogram
 float confidence
  Confidence level (0.95 or 0.99 for instance)

 ::math::statistics::autocorr data

Return the autocorrelation function as a list of values (assuming
equidistance between samples, about 1/2 of the number of raw data)
The correlation is determined in such a way that the first value is always 1 and all others are equal to or smaller than 1. The number of values involved will diminish as the "time" (the index in the list of returned values) increases

 list data
  Raw data for which the autocorrelation must be determined

 ::math::statistics::crosscorr data1 data2

Return the crosscorrelation function as a list of values (assuming
equidistance between samples, about 1/2 of the number of raw data)
The correlation is determined in such a way that the values can never exceed 1 in magnitude. The number of values involved will diminish as the "time" (the index in the list of returned values) increases.

 list data1
  First list of data
 list data2
  Second list of data

 ::math::statistics::meanhistogramlimits mean stdev number

Determine reasonable limits based on mean and standard deviation
for a histogram
Convenience function  the result is suitable for the histogram function.

 float mean
  Mean of the data
 float stdev
  Standard deviation
 int number
  Number of limits to generate (defaults to 8)

 ::math::statistics::minmaxhistogramlimits min max number

Determine reasonable limits based on a minimum and maximum for a histogram
Convenience function  the result is suitable for the histogram function.

 float min
  Expected minimum
 float max
  Expected maximum
 int number
  Number of limits to generate (defaults to 8)

 ::math::statistics::linearmodel xdata ydata intercept

Determine the coefficients for a linear regression between
two series of data (the model: Y = A + B*X). Returns a list of
parameters describing the fit

 list xdata
  List of independent data
 list ydata
  List of dependent data to be fitted
 boolean intercept

 (Optional) compute the intercept (1, default) or fit
to a line through the origin (0)
The result consists of the following list:

 •
 (Estimate of) Intercept A
 •
 (Estimate of) Slope B
 •
 Standard deviation of Y relative to fit
 •
 Correlation coefficient R2
 •
 Number of degrees of freedom df
 •
 Standard error of the intercept A
 •
 Significance level of A
 •
 Standard error of the slope B
 •
 Significance level of B


 ::math::statistics::linearresiduals xdata ydata intercept

Determine the difference between actual data and predicted from
the linear model.
Returns a list of the differences between the actual data and the predicted values.

 list xdata
  List of independent data
 list ydata
  List of dependent data to be fitted
 boolean intercept
  (Optional) compute the intercept (1, default) or fit to a line through the origin (0)

 ::math::statistics::test2x2 n11 n21 n12 n22

Determine if two set of samples, each from a binomial distribution,
differ significantly or not (implying a different parameter).
Returns the "chisquare" value, which can be used to the determine the significance.

 int n11
  Number of outcomes with the first value from the first sample.
 int n21
  Number of outcomes with the first value from the second sample.
 int n12
  Number of outcomes with the second value from the first sample.
 int n22
  Number of outcomes with the second value from the second sample.

 ::math::statistics::print2x2 n11 n21 n12 n22

Determine if two set of samples, each from a binomial distribution,
differ significantly or not (implying a different parameter).
Returns a short report, useful in an interactive session.

 int n11
  Number of outcomes with the first value from the first sample.
 int n21
  Number of outcomes with the first value from the second sample.
 int n12
  Number of outcomes with the second value from the first sample.
 int n22
  Number of outcomes with the second value from the second sample.

 ::math::statistics::controlxbar data ?nsamples?

Determine the control limits for an xbar chart. The number of data
in each subsample defaults to 4. At least 20 subsamples are required.
Returns the mean, the lower limit, the upper limit and the number of data per subsample.

 list data
  List of observed data
 int nsamples
  Number of data per subsample

 ::math::statistics::controlRchart data ?nsamples?

Determine the control limits for an R chart. The number of data
in each subsample (nsamples) defaults to 4. At least 20 subsamples are required.
Returns the mean range, the lower limit, the upper limit and the number of data per subsample.

 list data
  List of observed data
 int nsamples
  Number of data per subsample

 ::math::statistics::testxbar control data

Determine if the data exceed the control limits for the xbar chart.
Returns a list of subsamples (their indices) that indeed violate the limits.

 list control
  Control limits as returned by the "controlxbar" procedure
 list data
  List of observed data

 ::math::statistics::testRchart control data

Determine if the data exceed the control limits for the R chart.
Returns a list of subsamples (their indices) that indeed violate the limits.

 list control
  Control limits as returned by the "controlRchart" procedure
 list data
  List of observed data

MULTIVARIATE LINEAR REGRESSION
Besides the linear regression with a single independent variable, the statistics package provides two procedures for doing ordinary least squares (OLS) and weighted least squares (WLS) linear regression with several variables. They were written by Eric KempBenedict.In addition to these two, it provides a procedure (tstat) for calculating the value of the tstatistic for the specified number of degrees of freedom that is required to demonstrate a given level of significance.
Note: These procedures depend on the math::linearalgebra package.
Description of the procedures
 ::math::statistics::tstat dof ?alpha?

Returns the value of the tdistribution t* satisfying

P(t*) = 1  alpha/2 P(t*) = alpha/2


for the number of degrees of freedom dof.
Given a sample of normallydistributed data x, with an estimate xbar for the mean and sbar for the standard deviation, the alpha confidence interval for the estimate of the mean can be calculated as

( xbar  t* sbar , xbar + t* sbar)


The return values from this procedure can be compared to
an estimated tstatistic to determine whether the estimated
value of a parameter is significantly different from zero at
the given confidence level.

 int dof
 Number of degrees of freedom
 float alpha
 Confidence level of the tdistribution. Defaults to 0.05.

 ::math::statistics::mvwls wt1 weights_and_values

Carries out a weighted least squares linear regression for
the data points provided, with weights assigned to each point.
The linear model is of the form

y = b0 + b1 * x1 + b2 * x2 ... + bN * xN + error


and each point satisfies

yi = b0 + b1 * xi1 + b2 * xi2 + ... + bN * xiN + Residual_i
The procedure returns a list with the following elements:

 •
 The rsquared statistic
 •
 The adjusted rsquared statistic
 •
 A list containing the estimated coefficients b1, ... bN, b0 (The constant b0 comes last in the list.)
 •
 A list containing the standard errors of the coefficients
 •
 A list containing the 95% confidence bounds of the coefficients, with each set of bounds returned as a list with two values


Arguments:

 list weights_and_values
 A list consisting of: the weight for the first observation, the data for the first observation (as a sublist), the weight for the second observation (as a sublist) and so on. The sublists of data are organised as lists of the value of the dependent variable y and the independent variables x1, x2 to xN.

 ::math::statistics::mvols values

Carries out an ordinary least squares linear regression for
the data points provided.
This procedure simply calls ::mvlinreg::wls with the weights set to 1.0, and returns the same information.
Example of the use:

# Store the value of the unicode value for the "+/" character set pm "\u00B1" # Provide some data set data {{ .67 14.18 60.03 7.5 } { 36.97 15.52 34.24 14.61 } {29.57 21.85 83.36 7. } {16.9 11.79 51.67 6.56 } { 14.09 16.24 36.97 12.84} { 31.52 20.93 45.99 25.4 } { 24.05 20.69 50.27 17.27} { 22.23 16.91 45.07 4.3 } { 40.79 20.49 38.92 .73 } {10.35 17.24 58.77 18.78}} # Call the ols routine set results [::math::statistics::mvols $data] # Prettyprint the results puts "Rsquared: [lindex $results 0]" puts "Adj Rsquared: [lindex $results 1]" puts "Coefficients $pm s.e.  \[95% confidence interval\]:" foreach val [lindex $results 2] se [lindex $results 3] bounds [lindex $results 4] { set lb [lindex $bounds 0] set ub [lindex $bounds 1] puts " $val $pm $se  \[$lb to $ub\]" }
STATISTICAL DISTRIBUTIONS
In the literature a large number of probability distributions can be found. The statistics package supports: •
 The normal or Gaussian distribution
 •
 The uniform distribution  equal probability for all data within a given interval
 •
 The exponential distribution  useful as a model for certain extremevalue distributions.
 •
 The gamma distribution  based on the incomplete Gamma integral
 •
 The chisquare distribution
 •
 The student's T distribution
 •
 The Poisson distribution
 •
 PM  binomial,F.
In principle for each distribution one has procedures for:
 •
 The probability density (pdf*)
 •
 The cumulative density (cdf*)
 •
 Quantiles for the given distribution (quantiles*)
 •
 Histograms for the given distribution (histogram*)
 •
 List of random values with the given distribution (random*)
The following procedures have been implemented:
 ::math::statistics::pdfnormal mean stdev value

Return the probability of a given value for a normal distribution with
given mean and standard deviation.

 float mean
  Mean value of the distribution
 float stdev
  Standard deviation of the distribution
 float value
  Value for which the probability is required

 ::math::statistics::pdfexponential mean value

Return the probability of a given value for an exponential
distribution with given mean.

 float mean
  Mean value of the distribution
 float value
  Value for which the probability is required

 ::math::statistics::pdfuniform xmin xmax value

Return the probability of a given value for a uniform
distribution with given extremes.

 float xmin
  Minimum value of the distribution
 float xmin
  Maximum value of the distribution
 float value
  Value for which the probability is required

 ::math::statistics::pdfgamma alpha beta value

Return the probability of a given value for a Gamma
distribution with given shape and rate parameters

 float alpha
  Shape parameter
 float beta
  Rate parameter
 float value
  Value for which the probability is required

 ::math::statistics::pdfpoisson mu k

Return the probability of a given number of occurrences in the same
interval (k) for a Poisson distribution with given mean (mu)

 float mu
  Mean number of occurrences
 int k
  Number of occurences

 ::math::statistics::pdfchisquare df value

Return the probability of a given value for a chi square
distribution with given degrees of freedom

 float df
  Degrees of freedom
 float value
  Value for which the probability is required

 ::math::statistics::pdfstudentt df value

Return the probability of a given value for a Student's t
distribution with given degrees of freedom

 float df
  Degrees of freedom
 float value
  Value for which the probability is required

 ::math::statistics::pdfbeta a b value

Return the probability of a given value for a Beta
distribution with given shape parameters

 float a
  First shape parameter
 float b
  First shape parameter
 float value
  Value for which the probability is required

 ::math::statistics::cdfnormal mean stdev value

Return the cumulative probability of a given value for a normal
distribution with given mean and standard deviation, that is the
probability for values up to the given one.

 float mean
  Mean value of the distribution
 float stdev
  Standard deviation of the distribution
 float value
  Value for which the probability is required

 ::math::statistics::cdfexponential mean value

Return the cumulative probability of a given value for an exponential
distribution with given mean.

 float mean
  Mean value of the distribution
 float value
  Value for which the probability is required

 ::math::statistics::cdfuniform xmin xmax value

Return the cumulative probability of a given value for a uniform
distribution with given extremes.

 float xmin
  Minimum value of the distribution
 float xmin
  Maximum value of the distribution
 float value
  Value for which the probability is required

 ::math::statistics::cdfstudentst degrees value

Return the cumulative probability of a given value for a Student's t
distribution with given number of degrees.

 int degrees
  Number of degrees of freedom
 float value
  Value for which the probability is required

 ::math::statistics::cdfgamma alpha beta value

Return the cumulative probability of a given value for a Gamma
distribution with given shape and rate parameters

 float alpha
  Shape parameter
 float beta
  Rate parameter
 float value
  Value for which the cumulative probability is required

 ::math::statistics::cdfpoisson mu k

Return the cumulative probability of a given number of occurrences in
the same interval (k) for a Poisson distribution with given mean (mu)

 float mu
  Mean number of occurrences
 int k
  Number of occurences

 ::math::statistics::cdfbeta a b value

Return the cumulative probability of a given value for a Beta
distribution with given shape parameters

 float a
  First shape parameter
 float b
  First shape parameter
 float value
  Value for which the probability is required

 ::math::statistics::randomnormal mean stdev number

Return a list of "number" random values satisfying a normal
distribution with given mean and standard deviation.

 float mean
  Mean value of the distribution
 float stdev
  Standard deviation of the distribution
 int number
  Number of values to be returned

 ::math::statistics::randomexponential mean number

Return a list of "number" random values satisfying an exponential
distribution with given mean.

 float mean
  Mean value of the distribution
 int number
  Number of values to be returned

 ::math::statistics::randomuniform xmin xmax number

Return a list of "number" random values satisfying a uniform
distribution with given extremes.

 float xmin
  Minimum value of the distribution
 float xmax
  Maximum value of the distribution
 int number
  Number of values to be returned

 ::math::statistics::randomgamma alpha beta number

Return a list of "number" random values satisfying
a Gamma distribution with given shape and rate parameters

 float alpha
  Shape parameter
 float beta
  Rate parameter
 int number
  Number of values to be returned

 ::math::statistics::randomchisquare df number

Return a list of "number" random values satisfying
a chi square distribution with given degrees of freedom

 float df
  Degrees of freedom
 int number
  Number of values to be returned

 ::math::statistics::randomstudentt df number

Return a list of "number" random values satisfying
a Student's t distribution with given degrees of freedom

 float df
  Degrees of freedom
 int number
  Number of values to be returned

 ::math::statistics::randombeta a b number

Return a list of "number" random values satisfying
a Beta distribution with given shape parameters

 float a
  First shape parameter
 float b
  Second shape parameter
 int number
  Number of values to be returned

 ::math::statistics::histogramuniform xmin xmax limits number

Return the expected histogram for a uniform distribution.

 float xmin
  Minimum value of the distribution
 float xmax
  Maximum value of the distribution
 list limits
  Upper limits for the buckets in the histogram
 int number
  Total number of "observations" in the histogram

 ::math::statistics::incompleteGamma x p ?tol?

Evaluate the incomplete Gamma integral

1 / x p1 P(p,x) =   dt exp(t) * t Gamma(p) / 0

 float x
  Value of x (limit of the integral)
 float p
  Value of p in the integrand
 float tol
  Required tolerance (default: 1.0e9)

 ::math::statistics::incompleteBeta a b x ?tol?

Evaluate the incomplete Beta integral

 float a
  First shape parameter
 float b
  Second shape parameter
 float x
  Value of x (limit of the integral)
 float tol
  Required tolerance (default: 1.0e9)

TO DO: more function descriptions to be added
DATA MANIPULATION
The data manipulation procedures act on lists or lists of lists: ::math::statistics::filter varname data expression

Return a list consisting of the data for which the logical
expression is true (this command works analogously to the command foreach).

 string varname
  Name of the variable used in the expression
 list data
  List of data
 string expression
  Logical expression using the variable name

 ::math::statistics::map varname data expression

Return a list consisting of the data that are transformed via the
expression.

 string varname
  Name of the variable used in the expression
 list data
  List of data
 string expression
  Expression to be used to transform (map) the data

 ::math::statistics::samplescount varname list expression

Return a list consisting of the counts of all data in the
sublists of the "list" argument for which the expression is true.

 string varname
  Name of the variable used in the expression
 list data
  List of sublists, each containing the data
 string expression
  Logical expression to test the data (defaults to "true").

 ::math::statistics::subdivide

Routine PM  not implemented yet
 ::math::statistics::testKruskalWallis confidence args

Check if the population medians of two or more groups are equal with a
given confidence level, using the KruskalWallis test.

 float confidence
  Confidence level to be used (01)
 list args
  Two or more lists of data

 ::math::statistics::analyseKruskalWallis args

Compute the statistical parameters for the KruskalWallis test.
Returns the KruskalWallis statistic and the probability that that
value would occur assuming the medians of the populations are
equal.

 list args
  Two or more lists of data

 ::math::statistics::grouprank args

Rank the groups of data with respect to the complete set.
Returns a list consisting of the group ID, the value and the rank
(possibly a rational number, in case of ties) for each data item.

 list args
  Two or more lists of data

PLOT PROCEDURES
The following simple plotting procedures are available: ::math::statistics::plotscale canvas xmin xmax ymin ymax

Set the scale for a plot in the given canvas. All plot routines expect
this function to be called first. There is no automatic scaling
provided.

 widget canvas
  Canvas widget to use
 float xmin
  Minimum x value
 float xmax
  Maximum x value
 float ymin
  Minimum y value
 float ymax
  Maximum y value

 ::math::statistics::plotxydata canvas xdata ydata tag

Create a simple XY plot in the given canvas  the data are
shown as a collection of dots. The tag can be used to manipulate the
appearance.

 widget canvas
  Canvas widget to use
 float xdata
  Series of independent data
 float ydata
  Series of dependent data
 string tag
  Tag to give to the plotted data (defaults to xyplot)

 ::math::statistics::plotxyline canvas xdata ydata tag

Create a simple XY plot in the given canvas  the data are
shown as a line through the data points. The tag can be used to
manipulate the appearance.

 widget canvas
  Canvas widget to use
 list xdata
  Series of independent data
 list ydata
  Series of dependent data
 string tag
  Tag to give to the plotted data (defaults to xyplot)

 ::math::statistics::plottdata canvas tdata tag

Create a simple XY plot in the given canvas  the data are
shown as a collection of dots. The horizontal coordinate is equal to the
index. The tag can be used to manipulate the appearance.
This type of presentation is suitable for autocorrelation functions for
instance or for inspecting the timedependent behaviour.

 widget canvas
  Canvas widget to use
 list tdata
  Series of dependent data
 string tag
  Tag to give to the plotted data (defaults to xyplot)

 ::math::statistics::plottline canvas tdata tag

Create a simple XY plot in the given canvas  the data are
shown as a line. See plottdata for an explanation.

 widget canvas
  Canvas widget to use
 list tdata
  Series of dependent data
 string tag
  Tag to give to the plotted data (defaults to xyplot)

 ::math::statistics::plothistogram canvas counts limits tag

Create a simple histogram in the given canvas

 widget canvas
  Canvas widget to use
 list counts
  Series of bucket counts
 list limits
  Series of upper limits for the buckets
 string tag
  Tag to give to the plotted data (defaults to xyplot)

THINGS TO DO
The following procedures are yet to be implemented: •
 Fteststdev
 •
 intervalmeanstdev
 •
 histogramnormal
 •
 histogramexponential
 •
 testhistogram
 •
 testcorr
 •
 quantiles*
 •
 fouriercoeffs
 •
 fourierresiduals
 •
 oneparfunctionfit
 •
 oneparfunctionresiduals
 •
 plotlinearmodel
 •
 subdivide
EXAMPLES
The code below is a small example of how you can examine a set of data:

# Simple example: #  Generate data (as a cheap way of getting some) #  Perform statistical analysis to describe the data # package require math::statistics # # Two auxiliary procs # proc pause {time} { set wait 0 after [expr {$time*1000}] {set ::wait 1} vwait wait } proc printhistogram {counts limits} { foreach count $counts limit $limits { if { $limit != {} } { puts [format "<%12.4g\t%d" $limit $count] set prev_limit $limit } else { puts [format ">%12.4g\t%d" $prev_limit $count] } } } # # Our source of arbitrary data # proc generateData { data1 data2 } { upvar 1 $data1 _data1 upvar 1 $data2 _data2 set d1 0.0 set d2 0.0 for { set i 0 } { $i < 100 } { incr i } { set d1 [expr {10.02.0*cos(2.0*3.1415926*$i/24.0)+3.5*rand()}] set d2 [expr {0.7*$d2+0.3*$d1+0.7*rand()}] lappend _data1 $d1 lappend _data2 $d2 } return {} } # # The analysis session # package require Tk console show canvas .plot1 canvas .plot2 pack .plot1 .plot2 fill both side top generateData data1 data2 puts "Basic statistics:" set b1 [::math::statistics::basicstats $data1] set b2 [::math::statistics::basicstats $data2] foreach label {mean min max number stdev var} v1 $b1 v2 $b2 { puts "$label\t$v1\t$v2" } puts "Plot the data as function of \"time\" and against each other" ::math::statistics::plotscale .plot1 0 100 0 20 ::math::statistics::plotscale .plot2 0 20 0 20 ::math::statistics::plottline .plot1 $data1 ::math::statistics::plottline .plot1 $data2 ::math::statistics::plotxydata .plot2 $data1 $data2 puts "Correlation coefficient:" puts [::math::statistics::corr $data1 $data2] pause 2 puts "Plot histograms" ::math::statistics::plotscale .plot2 0 20 0 100 set limits [::math::statistics::minmaxhistogramlimits 7 16] set histogram_data [::math::statistics::histogram $limits $data1] ::math::statistics::plothistogram .plot2 $histogram_data $limits puts "First series:" printhistogram $histogram_data $limits pause 2 set limits [::math::statistics::minmaxhistogramlimits 0 15 10] set histogram_data [::math::statistics::histogram $limits $data2] ::math::statistics::plothistogram .plot2 $histogram_data $limits d2 puts "Second series:" printhistogram $histogram_data $limits puts "Autocorrelation function:" set autoc [::math::statistics::autocorr $data1] puts [::math::statistics::map $autoc {[format "%.2f" $x]}] puts "Crosscorrelation function:" set crossc [::math::statistics::crosscorr $data1 $data2] puts [::math::statistics::map $crossc {[format "%.2f" $x]}] ::math::statistics::plotscale .plot1 0 100 1 4 ::math::statistics::plottline .plot1 $autoc "autoc" ::math::statistics::plottline .plot1 $crossc "crossc" puts "Quantiles: 0.1, 0.2, 0.5, 0.8, 0.9" puts "First: [::math::statistics::quantiles $data1 {0.1 0.2 0.5 0.8 0.9}]" puts "Second: [::math::statistics::quantiles $data2 {0.1 0.2 0.5 0.8 0.9}]"
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 There is a strong correlation between two time series, as displayed by the raw data and especially by the correlation functions.
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 Both time series show a significant periodic component
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 The histograms are not very useful in identifying the nature of the time series  they do not show the periodic nature.
BUGS, IDEAS, FEEDBACK
This document, and the package it describes, will undoubtedly contain bugs and other problems. Please report such in the category math :: statistics of the Tcllib SF Trackers [http://sourceforge.net/tracker/?group_id=12883]. Please also report any ideas for enhancements you may have for either package and/or documentation.KEYWORDS
data analysis, mathematics, statisticsCATEGORY
Mathematics