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math::statistics - Basic statistical functions and procedures
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Category
Mathematics
tcllib 1.6.1 math::statistics(3tcl)
Data Manipulation
The data manipulation procedures act on lists or lists of lists:
::math::statistics::filtervarnamedataexpression
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::mapvarnamedataexpression
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::samplescountvarnamelistexpression
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
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. Note: With version 1.0.1 a mistake in the procs pdf-lognormal, cdf-lognormal and random-lognormal has been corrected. In previous versions the argument for the standard deviation was actually
used as if it was the variance.
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 print-histogram {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 } { $i < 100 } { incr i } {
set d1 [expr {10.0-2.0*cos(2.0*3.1415926*$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::basic-stats $data1]
set b2 [::math::statistics::basic-stats $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::plot-scale .plot1 0 100 0 20
::math::statistics::plot-scale .plot2 0 20 0 20
::math::statistics::plot-tline .plot1 $data1
::math::statistics::plot-tline .plot1 $data2
::math::statistics::plot-xydata .plot2 $data1 $data2
puts "Correlation coefficient:"
puts [::math::statistics::corr $data1 $data2]
pause 2
puts "Plot histograms"
.plot2 delete all
::math::statistics::plot-scale .plot2 0 20 0 100
set limits [::math::statistics::minmax-histogram-limits 7 16]
set histogram_data [::math::statistics::histogram $limits $data1]
::math::statistics::plot-histogram .plot2 $histogram_data $limits
puts "First series:"
print-histogram $histogram_data $limits
pause 2
set limits [::math::statistics::minmax-histogram-limits 0 15 10]
set histogram_data [::math::statistics::histogram $limits $data2]
::math::statistics::plot-histogram .plot2 $histogram_data $limits d2
.plot2 itemconfigure d2 -fill red
puts "Second series:"
print-histogram $histogram_data $limits
puts "Autocorrelation function:"
set autoc [::math::statistics::autocorr $data1]
puts [::math::statistics::map $autoc {[format "%.2f" $x]}]
puts "Cross-correlation function:"
set crossc [::math::statistics::crosscorr $data1 $data2]
puts [::math::statistics::map $crossc {[format "%.2f" $x]}]
::math::statistics::plot-scale .plot1 0 100 -1 4
::math::statistics::plot-tline .plot1 $autoc "autoc"
::math::statistics::plot-tline .plot1 $crossc "crossc"
.plot1 itemconfigure autoc -fill green
.plot1 itemconfigure crossc -fill yellow
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}]"
If you run this example, then the following should be clear:
• There is a strong correlation between two time series, as displayed by the raw data and especially
by the correlation functions.
• Both time series show a significant periodic component
• The histograms are not very useful in identifying the nature of the time series - they do not show
the periodic nature.
General Procedures
The general statistical procedures are:
::math::statistics::meandata
Determine the mean value of the given list of data.
list data
- List of data
::math::statistics::mindata
Determine the minimum value of the given list of data.
list data
- List of data
::math::statistics::maxdata
Determine the maximum value of the given list of data.
list data
- List of data
::math::statistics::numberdata
Determine the number of non-missing data in the given list
list data
- List of data
::math::statistics::stdevdata
Determine the samplestandarddeviation of the data in the given list
list data
- List of data
::math::statistics::vardata
Determine the samplevariance of the data in the given list
list data
- List of data
::math::statistics::pstdevdata
Determine the populationstandarddeviation of the data in the given list
list data
- List of data
::math::statistics::pvardata
Determine the populationvariance of the data in the given list
list data
- List of data
::math::statistics::mediandata
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::basic-statsdata
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::histogramlimitsvalues ?weights?
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.)
Optionally, you can use weights to influence the histogram.
list limits
- List of upper limits (in ascending order) for the intervals of the histogram.
list values
- List of data
list weights
- List of weights, one weight per value
::math::statistics::histogram-altlimitsvalues ?weights?
Alternative implementation of the histogram procedure: the open end of the intervals is at the
lower bound instead of the upper bound.
list limits
- List of upper limits (in ascending order) for the intervals of the histogram.
list values
- List of data
list weights
- List of weights, one weight per value
::math::statistics::corrdata1data2
Determine the correlation coefficient between two sets of data.
list data1
- First list of data
list data2
- Second list of data
::math::statistics::interval-mean-stdevdataconfidence
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::t-test-meandataest_meanest_stdevalpha
Test whether the mean value of a sample is in accordance with the estimated normal distribution
with a certain probability. 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 alpha
- Probability level (0.95 or 0.99 for instance)
::math::statistics::test-normaldatasignificance
Test whether the given data follow a normal distribution with a certain level of significance.
Returns 1 if the data are normally distributed within the level of significance, returns 0 if not.
The underlying test is the Lilliefors test. Smaller values of the significance mean a stricter
testing.
list data
- List of raw data values
float significance
- Significance level (one of 0.01, 0.05, 0.10, 0.15 or 0.20). For compatibility reasons the
values "1-significance", 0.80, 0.85, 0.90, 0.95 or 0.99 are also accepted.
Compatibility issue: the original implementation and documentation used the term "confidence" and used a
value 1-significance (see ticket 2812473fff). This has been corrected as of version 0.9.3.
::math::statistics::lillieforsFitdata
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::test-Duckworthlist1list2significance
Determine if two data sets have the same median according to the Tukey-Duckworth test. The
procedure returns 0 if the medians are unequal, 1 if they are equal, -1 if the test can not be
conducted (the smallest value must be in a different set than the greatest value). # # Arguments:
# list1 Values in the first data set # list2 Values in the second data
set # significance Significance level (either 0.05, 0.01 or 0.001) # # Returns: Test
whether the given data follow a normal distribution with a certain level of significance. Returns
1 if the data are normally distributed within the level of significance, returns 0 if not. The
underlying test is the Lilliefors test. Smaller values of the significance mean a stricter
testing.
list list1
- First list of data
list list2
- Second list of data
float significance
- Significance level (either 0.05, 0.01 or 0.001)
::math::statistics::test-anova-Falphaargs
Determine if two or more groups with normally distributed data have the same means. The procedure
returns 0 if the means are likely unequal, 1 if they are. This is a one-way ANOVA test. The groups
may also be stored in a nested list: The procedure returns a list of the comparison results for
each pair of groups. Each element of this list contains: the index of the first group and that of
the second group, whether the means are likely to be different (1) or not (0) and the confidence
interval the conclusion is based on. The groups may also be stored in a nested list:
test-anova-F 0.05 $A $B $C
#
# Or equivalently:
#
test-anova-F 0.05 [list $A $B $C]
float alpha
- Significance level
list args
- Two or more groups of data to be checked
::math::statistics::test-Tukey-rangealphaargs
Determine if two or more groups with normally distributed data have the same means, using Tukey's
range test. It is complementary to the ANOVA test. The procedure returns a list of the comparison
results for each pair of groups. Each element of this list contains: the index of the first group
and that of the second group, whether the means are likely to be different (1) or not (0) and the
confidence interval the conclusion is based on. The groups may also be stored in a nested list,
just as with the ANOVA test.
float alpha
- Significance level - either 0.05 or 0.01
list args
- Two or more groups of data to be checked
::math::statistics::test-Dunnettalphacontrolargs
Determine if one or more groups with normally distributed data have the same means as the group of
control data, using Dunnett's test. It is complementary to the ANOVA test. The procedure returns
a list of the comparison results for each group with the control group. Each element of this list
contains: whether the means are likely to be different (1) or not (0) and the confidence interval
the conclusion is based on. The groups may also be stored in a nested list, just as with the ANOVA
test.
Note: some care is required if there is only one group to compare the control with:
test-Dunnett-F 0.05 $control [list $A]
Otherwise the group A is split up into groups of one element - this is due to an ambiguity.
float alpha
- Significance level - either 0.05 or 0.01
list args
- One or more groups of data to be checked
::math::statistics::quantilesdataconfidence
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) or a list of confidence levels.
::math::statistics::quantileslimitscountsconfidence
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) or a list of confidence levels.
::math::statistics::autocorrdata
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::crosscorrdata1data2
Return the cross-correlation 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::mean-histogram-limitsmeanstdevnumber
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::minmax-histogram-limitsminmaxnumber
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::linear-modelxdataydataintercept
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::linear-residualsxdataydataintercept
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::test-2x2n11n21n12n22
Determine if two set of samples, each from a binomial distribution, differ significantly or not
(implying a different parameter).
Returns the "chi-square" 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::print-2x2n11n21n12n22
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::control-xbardata ?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::control-Rchartdata ?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::test-xbarcontroldata
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 "control-xbar" procedure
list data
- List of observed data
::math::statistics::test-Rchartcontroldata
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 "control-Rchart" procedure
list data
- List of observed data
::math::statistics::test-Kruskal-Wallisconfidenceargs
Check if the population medians of two or more groups are equal with a given confidence level,
using the Kruskal-Wallis test.
float confidence
- Confidence level to be used (0-1)
list args
- Two or more lists of data
::math::statistics::analyse-Kruskal-Wallisargs
Compute the statistical parameters for the Kruskal-Wallis test. Returns the Kruskal-Wallis
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::test-Levenegroups
Compute the Levene statistic to determine if groups of data have the same variance (are
homoscadastic) or not. The data are organised in groups. This version uses the mean of the data as
the measure to determine the deviations. The statistic is equivalent to an F statistic with
degrees of freedom k-1 and N-k, k being the number of groups and N the total number of data.
list groups
- List of groups of data
::math::statistics::test-Brown-Forsythegroups
Compute the Brown-Forsythe statistic to determine if groups of data have the same variance (are
homoscadastic) or not. Like the Levene test, but this version uses the median of the data.
list groups
- List of groups of data
::math::statistics::group-rankargs
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
::math::statistics::test-Wilcoxonsample_asample_b
Compute the Wilcoxon test statistic to determine if two samples have the same median or not. (The
statistic can be regarded as standard normal, if the sample sizes are both larger than 10.)
Returns the value of this statistic.
list sample_a
- List of data comprising the first sample
list sample_b
- List of data comprising the second sample
::math::statistics::spearman-ranksample_asample_b
Return the Spearman rank correlation as an alternative to the ordinary (Pearson's) correlation
coefficient. The two samples should have the same number of data.
list sample_a
- First list of data
list sample_b
- Second list of data
::math::statistics::spearman-rank-extendedsample_asample_b
Return the Spearman rank correlation as an alternative to the ordinary (Pearson's) correlation
coefficient as well as additional data. The two samples should have the same number of data. The
procedure returns the correlation coefficient, the number of data pairs used and the z-score, an
approximately standard normal statistic, indicating the significance of the correlation.
list sample_a
- First list of data
list sample_b
- Second list of data
::math::statistics::kernel-densitydata opt -optionvalue ...
Return the density function based on kernel density estimation. The procedure is controlled by a
small set of options, each of which is given a reasonable default.
The return value consists of three lists: the centres of the bins, the associated probability
density and a list of computational parameters (begin and end of the interval, mean and standard
deviation and the used bandwidth). The computational parameters can be used for further analysis.
list data
- The data to be examined
list args
- Option-value pairs:
-weightsweights
Per data point the weight (default: 1 for all data)
-bandwidthvalue
Bandwidth to be used for the estimation (default: determined from standard
deviation)
-numbervalue
Number of bins to be returned (default: 100)
-interval{beginend}
Begin and end of the interval for which the density is returned (default: mean +/-
3*standard deviation)
-kernelfunction
Kernel to be used (One of: gaussian, cosine, epanechnikov, uniform, triangular,
biweight, logistic; default: gaussian)
::math::statistics::bootstrapdatasampleSize ?numberSamples?
Create a subsample or subsamples from a given list of data. The data in the samples are chosen
from this list - multiples may occur. If there is only one subsample, the sample itself is
returned (as a list of "sampleSize" values), otherwise a list of samples is returned.
list data
List of values to chose from
int sampleSize
Number of values per sample
int numberSamples
Number of samples (default: 1)
::math::statistics::wasserstein-distanceprob1prob2
Compute the Wasserstein distance or earth mover's distance for two equidstantly spaced histograms
or probability densities. The histograms need not to be normalised to sum to one, but they must
have the same number of entries.
Note: the histograms are assumed to be based on the same equidistant intervals. As the bounds are
not passed, the value is expressed in the length of the intervals.
list prob1
List of values for the first histogram/probability density
list prob2
List of values for the second histogram/probability density
::math::statistics::kl-divergenceprob1prob2
Compute the Kullback-Leibler (KL) divergence for two equidstantly spaced histograms or probability
densities. The histograms need not to be normalised to sum to one, but they must have the same
number of entries.
Note: the histograms are assumed to be based on the same equidistant intervals. As the bounds are
not passed, the value is expressed in the length of the intervals.
Note also that the KL divergence is not symmetric and that the second histogram should not contain
zeroes in places where the first histogram has non-zero values.
list prob1
List of values for the first histogram/probability density
list prob2
List of values for the second histogram/probability density
::math::statistics::logistic-modelxdataydata
Estimate the coefficients of the logistic model that fits the data best. The data consist of
independent x-values and the outcome 0 or 1 for each of the x-values. The result can be used to
estimate the probability that a certain x-value gives 1.
list xdata
List of values for which the success (1) or failure (0) is known
list ydata
List of successes or failures corresponding to each value in xdata.
::math::statistics::logistic-probabilitycoeffsx
Calculate the probability of success for the value x given the coefficients of the logistic model.
list coeffs
List of coefficients as determine by the logistic-model command
float x
X-value for which the probability needs to be determined
Keywords
data analysis, mathematics, statistics
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 Kemp-Benedict.
In addition to these two, it provides a procedure (tstat) for calculating the value of the t-statistic
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.
Descriptionoftheprocedures::math::statistics::tstatdof ?alpha?
Returns the value of the t-distribution t* satisfying
P(t*) = 1 - alpha/2
P(-t*) = alpha/2
for the number of degrees of freedom dof.
Given a sample of normally-distributed 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 t-statistic 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 t-distribution. Defaults to 0.05.
::math::statistics::mv-wlsweights_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 r-squared statistic
• The adjusted r-squared 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.
Exampleoftheuse: The weight factors are quite simple: 0.2 for negative values to indicate we
put less trust in these observations and 1.0 for all positive values.
# Store the value of the unicode value for the "+/-" character
set pm "\u00B1"
# Provide some data
set data { 0.2 { -.67 14.18 60.03 -7.5 }
1.0 { 36.97 15.52 34.24 14.61 }
0.2 {-29.57 21.85 83.36 -7. }
0.2 {-16.9 11.79 51.67 -6.56 }
1.0 { 14.09 16.24 36.97 -12.84}
1.0 { 31.52 20.93 45.99 -25.4 }
1.0 { 24.05 20.69 50.27 17.27}
1.0 { 22.23 16.91 45.07 -4.3 }
1.0 { 40.79 20.49 38.92 -.73 }
0.2 {-10.35 17.24 58.77 18.78}}
# Call the ols routine
set results [::math::statistics::mv-ols $data]
# Pretty-print the results
puts "R-squared: [lindex $results 0]"
puts "Adj R-squared: [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 -- \[$lb to $ub\]"
}
::math::statistics::mv-olsvalues
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.
Exampleoftheuse:
# 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::mv-ols $data]
# Pretty-print the results
puts "R-squared: [lindex $results 0]"
puts "Adj R-squared: [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 -- \[$lb to $ub\]"
}
Name
math::statistics - Basic statistical functions and procedures
Plot Procedures
The following simple plotting procedures are available:
::math::statistics::plot-scalecanvasxminxmaxyminymax
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::plot-xydatacanvasxdataydatatag
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::plot-xylinecanvasxdataydatatag
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::plot-tdatacanvastdatatag
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 time-dependent 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::plot-tlinecanvastdatatag
Create a simple XY plot in the given canvas - the data are shown as a line. See plot-tdata 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::plot-histogramcanvascountslimitstag
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)
Statistical Distributions
In the literature a large number of probability distributions can be found. The statistics package
supports:
• The normal or Gaussian distribution as well as the log-normal distribution
• The uniform distribution - equal probability for all data within a given interval
• The exponential distribution - useful as a model for certain extreme-value distributions.
• The gamma distribution - based on the incomplete Gamma integral
• The beta distribution
• The chi-square distribution
• The student's T distribution
• The Poisson distribution
• The Pareto distribution
• The Gumbel distribution
• The Weibull distribution
• The Cauchy distribution
• The F distribution (only the cumulative density function)
• PM - binomial.
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::pdf-normalmeanstdevvalue
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::pdf-lognormalmeanstdevvalue
Return the probability of a given value for a log-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::pdf-exponentialmeanvalue
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::pdf-uniformxminxmaxvalue
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::pdf-triangularxminxmaxvalue
Return the probability of a given value for a triangular distribution with given extremes. If the
argument min is lower than the argument max, then smaller values have higher probability and vice
versa. In the first case the probability density function is of the form f(x)=2(1-x) and the
other case it is of the form f(x)=2x.
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::pdf-symmetric-triangularxminxmaxvalue
Return the probability of a given value for a symmetric triangular 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::pdf-gammaalphabetavalue
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::pdf-poissonmuk
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::pdf-chisquaredfvalue
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::pdf-student-tdfvalue
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::pdf-gammaabvalue
Return the probability of a given value for a Gamma distribution with given shape and rate
parameters
float a
- Shape parameter
float b
- Rate parameter
float value
- Value for which the probability is required
::math::statistics::pdf-betaabvalue
Return the probability of a given value for a Beta distribution with given shape parameters
float a
- First shape parameter
float b
- Second shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-weibullscaleshapevalue
Return the probability of a given value for a Weibull distribution with given scale and shape
parameters
float location
- Scale parameter
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-gumbellocationscalevalue
Return the probability of a given value for a Gumbel distribution with given location and shape
parameters
float location
- Location parameter
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-paretoscaleshapevalue
Return the probability of a given value for a Pareto distribution with given scale and shape
parameters
float scale
- Scale parameter
float shape
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-cauchylocationscalevalue
Return the probability of a given value for a Cauchy distribution with given location and shape
parameters. Note that the Cauchy distribution has no finite higher-order moments.
float location
- Location parameter
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-laplacelocationscalevalue
Return the probability of a given value for a Laplace distribution with given location and shape
parameters. The Laplace distribution consists of two exponential functions, is peaked and has
heavier tails than the normal distribution.
float location
- Location parameter (mean)
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::pdf-kumaraswamyabvalue
Return the probability of a given value for a Kumaraswamy distribution with given parameters a and
b. The Kumaraswamy distribution is related to the Beta distribution, but has a tractable
cumulative distribution function.
float a
- Parameter a
float b
- Parameter b
float value
- Value for which the probability is required
::math::statistics::pdf-negative-binomialrpvalue
Return the probability of a given value for a negative binomial distribution with an allowed
number of failures and the probability of success.
int r - Allowed number of failures (at least 1)
float p
- Probability of success
int value
- Number of successes for which the probability is to be returned
::math::statistics::cdf-normalmeanstdevvalue
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::cdf-lognormalmeanstdevvalue
Return the cumulative probability of a given value for a log-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::cdf-exponentialmeanvalue
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::cdf-uniformxminxmaxvalue
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::cdf-triangularxminxmaxvalue
Return the cumulative probability of a given value for a triangular distribution with given
extremes. If xmin < xmax, then lower values have a higher probability and vice versa, see also
pdf-triangular
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::cdf-symmetric-triangularxminxmaxvalue
Return the cumulative probability of a given value for a symmetric triangular 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::cdf-students-tdegreesvalue
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::cdf-gammaalphabetavalue
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::cdf-poissonmuk
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::cdf-betaabvalue
Return the cumulative probability of a given value for a Beta distribution with given shape
parameters
float a
- First shape parameter
float b
- Second shape parameter
float value
- Value for which the probability is required
::math::statistics::cdf-weibullscaleshapevalue
Return the cumulative probability of a given value for a Weibull distribution with given scale and
shape parameters.
float scale
- Scale parameter
float shape
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::cdf-gumbellocationscalevalue
Return the cumulative probability of a given value for a Gumbel distribution with given location
and scale parameters.
float location
- Location parameter
float scale
- Scale parameter
float value
- Value for which the probability is required
::math::statistics::cdf-paretoscaleshapevalue
Return the cumulative probability of a given value for a Pareto distribution with given scale and
shape parameters
float scale
- Scale parameter
float shape
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::cdf-cauchylocationscalevalue
Return the cumulative probability of a given value for a Cauchy distribution with given location
and scale parameters.
float location
- Location parameter
float scale
- Scale parameter
float value
- Value for which the probability is required
::math::statistics::cdf-Fnf1nf2value
Return the cumulative probability of a given value for an F distribution with nf1 and nf2 degrees
of freedom.
float nf1
- Degrees of freedom for the numerator
float nf2
- Degrees of freedom for the denominator
float value
- Value for which the probability is required
::math::statistics::cdf-laplacelocationscalevalue
Return the cumulative probability of a given value for a Laplace distribution with given location
and shape parameters. The Laplace distribution consists of two exponential functions, is peaked
and has heavier tails than the normal distribution.
float location
- Location parameter (mean)
float scale
- Shape parameter
float value
- Value for which the probability is required
::math::statistics::cdf-kumaraswamyabvalue
Return the cumulative probability of a given value for a Kumaraswamy distribution with given
parameters a and b. The Kumaraswamy distribution is related to the Beta distribution, but has a
tractable cumulative distribution function.
float a
- Parameter a
float b
- Parameter b
float value
- Value for which the probability is required
::math::statistics::cdf-negative-binomialrpvalue
Return the cumulative probability of a given value for a negative binomial distribution with an
allowed number of failures and the probability of success.
int r - Allowed number of failures (at least 1)
float p
- Probability of success
int value
- Greatest number of successes
::math::statistics::empirical-distributionvalues
Return a list of values and their empirical probability. The values are sorted in increasing
order. (The implementation follows the description at the corresponding Wikipedia page)
list values
- List of data to be examined
::math::statistics::random-normalmeanstdevnumber
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::random-lognormalmeanstdevnumber
Return a list of "number" random values satisfying a log-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::random-exponentialmeannumber
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::random-uniformxminxmaxnumber
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::random-triangularxminxmaxnumber
Return a list of "number" random values satisfying a triangular distribution with given extremes.
If xmin < xmax, then lower values have a higher probability and vice versa (see also pdf-triangular.
float xmin
- Minimum value of the distribution
float xmax
- Maximum value of the distribution
int number
- Number of values to be returned
::math::statistics::random-symmetric-triangularxminxmaxnumber
Return a list of "number" random values satisfying a symmetric triangular 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::random-gammaalphabetanumber
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::random-poissonmunumber
Return a list of "number" random values satisfying a Poisson distribution with given mean.
float mu
- Mean of the distribution
int number
- Number of values to be returned
::math::statistics::random-chisquaredfnumber
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::random-student-tdfnumber
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::random-betaabnumber
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::random-weibullscaleshapenumber
Return a list of "number" random values satisfying a Weibull distribution with given scale and
shape parameters.
float scale
- Scale parameter
float shape
- Shape parameter
int number
- Number of values to be returned
::math::statistics::random-gumbellocationscalenumber
Return a list of "number" random values satisfying a Gumbel distribution with given location and
scale parameters.
float location
- Location parameter
float scale
- Scale parameter
int number
- Number of values to be returned
::math::statistics::random-paretoscaleshapenumber
Return a list of "number" random values satisfying a Pareto distribution with given scale and
shape parameters.
float scale
- Scale parameter
float shape
- Shape parameter
int number
- Number of values to be returned
::math::statistics::random-cauchylocationscalenumber
Return a list of "number" random values satisfying a Cauchy distribution with given location and
scale parameters.
float location
- Location parameter
float scale
- Scale parameter
int number
- Number of values to be returned
::math::statistics::random-laplacelocationscalenumber
Return a list of "number" random values satisfying a Laplace distribution with given location and
shape parameters. The Laplace distribution consists of two exponential functions, is peaked and
has heavier tails than the normal distribution.
float location
- Location parameter (mean)
float scale
- Shape parameter
int number
- Number of values to be returned
::math::statistics::random-kumaraswamyabnumber
Return a list of "number" random values satisying a Kumaraswamy distribution with given parameters
a and b. The Kumaraswamy distribution is related to the Beta distribution, but has a tractable
cumulative distribution function.
float a
- Parameter a
float b
- Parameter b
int number
- Number of values to be returned
::math::statistics::random-negative-binomialrpnumber
Return a list of "number" random values satisying a negative binomial distribution.
int r - Allowed number of failures (at least 1)
float p
- Probability of success
int number
- Number of values to be returned
::math::statistics::histogram-uniformxminxmaxlimitsnumber
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::incompleteGammaxp ?tol?
Evaluate the incomplete Gamma integral
1 / x p-1
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: 1.0e-9)
::math::statistics::incompleteBetaabx ?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: 1.0e-9)
::math::statistics::estimate-paretovalues
Estimate the parameters for the Pareto distribution that comes closest to the given values.
Returns the estimated scale and shape parameters, as well as the standard error for the shape
parameter.
list values
- List of values, assumed to be distributed according to a Pareto distribution
::math::statistics::estimate-exponentialvalues
Estimate the parameter for the exponential distribution that comes closest to the given values.
Returns an estimate of the one parameter and of the standard error.
list values
- List of values, assumed to be distributed according to an exponential distribution
::math::statistics::estimate-laplacevalues
Estimate the parameters for the Laplace distribution that comes closest to the given values.
Returns an estimate of respectively the location and scale parameters, based on maximum
likelihood.
list values
- List of values, assumed to be distributed according to an exponential distribution
::math::statistics::estimante-negative-binomialrvalues
Estimate the probability of success for the negative binomial distribution that comes closest to
the given values. The allowed number of failures must be given.
int r - Allowed number of failures (at least 1)
int number
- List of values, assumed to be distributed according to a negative binomial distribution.
TO DO: more function descriptions to be added
Synopsis
package require Tcl8.59
package require math::statistics1.6.1::math::statistics::meandata::math::statistics::mindata::math::statistics::maxdata::math::statistics::numberdata::math::statistics::stdevdata::math::statistics::vardata::math::statistics::pstdevdata::math::statistics::pvardata::math::statistics::mediandata::math::statistics::basic-statsdata::math::statistics::histogramlimitsvalues ?weights?
::math::statistics::histogram-altlimitsvalues ?weights?
::math::statistics::corrdata1data2::math::statistics::interval-mean-stdevdataconfidence::math::statistics::t-test-meandataest_meanest_stdevalpha::math::statistics::test-normaldatasignificance::math::statistics::lillieforsFitdata::math::statistics::test-Duckworthlist1list2significance::math::statistics::test-anova-Falphaargs::math::statistics::test-Tukey-rangealphaargs::math::statistics::test-Dunnettalphacontrolargs::math::statistics::quantilesdataconfidence::math::statistics::quantileslimitscountsconfidence::math::statistics::autocorrdata::math::statistics::crosscorrdata1data2::math::statistics::mean-histogram-limitsmeanstdevnumber::math::statistics::minmax-histogram-limitsminmaxnumber::math::statistics::linear-modelxdataydataintercept::math::statistics::linear-residualsxdataydataintercept::math::statistics::test-2x2n11n21n12n22::math::statistics::print-2x2n11n21n12n22::math::statistics::control-xbardata ?nsamples?
::math::statistics::control-Rchartdata ?nsamples?
::math::statistics::test-xbarcontroldata::math::statistics::test-Rchartcontroldata::math::statistics::test-Kruskal-Wallisconfidenceargs::math::statistics::analyse-Kruskal-Wallisargs::math::statistics::test-Levenegroups::math::statistics::test-Brown-Forsythegroups::math::statistics::group-rankargs::math::statistics::test-Wilcoxonsample_asample_b::math::statistics::spearman-ranksample_asample_b::math::statistics::spearman-rank-extendedsample_asample_b::math::statistics::kernel-densitydata opt -optionvalue ...
::math::statistics::bootstrapdatasampleSize ?numberSamples?
::math::statistics::wasserstein-distanceprob1prob2::math::statistics::kl-divergenceprob1prob2::math::statistics::logistic-modelxdataydata::math::statistics::logistic-probabilitycoeffsx::math::statistics::tstatdof ?alpha?
::math::statistics::mv-wlsweights_and_values::math::statistics::mv-olsvalues::math::statistics::pdf-normalmeanstdevvalue::math::statistics::pdf-lognormalmeanstdevvalue::math::statistics::pdf-exponentialmeanvalue::math::statistics::pdf-uniformxminxmaxvalue::math::statistics::pdf-triangularxminxmaxvalue::math::statistics::pdf-symmetric-triangularxminxmaxvalue::math::statistics::pdf-gammaalphabetavalue::math::statistics::pdf-poissonmuk::math::statistics::pdf-chisquaredfvalue::math::statistics::pdf-student-tdfvalue::math::statistics::pdf-gammaabvalue::math::statistics::pdf-betaabvalue::math::statistics::pdf-weibullscaleshapevalue::math::statistics::pdf-gumbellocationscalevalue::math::statistics::pdf-paretoscaleshapevalue::math::statistics::pdf-cauchylocationscalevalue::math::statistics::pdf-laplacelocationscalevalue::math::statistics::pdf-kumaraswamyabvalue::math::statistics::pdf-negative-binomialrpvalue::math::statistics::cdf-normalmeanstdevvalue::math::statistics::cdf-lognormalmeanstdevvalue::math::statistics::cdf-exponentialmeanvalue::math::statistics::cdf-uniformxminxmaxvalue::math::statistics::cdf-triangularxminxmaxvalue::math::statistics::cdf-symmetric-triangularxminxmaxvalue::math::statistics::cdf-students-tdegreesvalue::math::statistics::cdf-gammaalphabetavalue::math::statistics::cdf-poissonmuk::math::statistics::cdf-betaabvalue::math::statistics::cdf-weibullscaleshapevalue::math::statistics::cdf-gumbellocationscalevalue::math::statistics::cdf-paretoscaleshapevalue::math::statistics::cdf-cauchylocationscalevalue::math::statistics::cdf-Fnf1nf2value::math::statistics::cdf-laplacelocationscalevalue::math::statistics::cdf-kumaraswamyabvalue::math::statistics::cdf-negative-binomialrpvalue::math::statistics::empirical-distributionvalues::math::statistics::random-normalmeanstdevnumber::math::statistics::random-lognormalmeanstdevnumber::math::statistics::random-exponentialmeannumber::math::statistics::random-uniformxminxmaxnumber::math::statistics::random-triangularxminxmaxnumber::math::statistics::random-symmetric-triangularxminxmaxnumber::math::statistics::random-gammaalphabetanumber::math::statistics::random-poissonmunumber::math::statistics::random-chisquaredfnumber::math::statistics::random-student-tdfnumber::math::statistics::random-betaabnumber::math::statistics::random-weibullscaleshapenumber::math::statistics::random-gumbellocationscalenumber::math::statistics::random-paretoscaleshapenumber::math::statistics::random-cauchylocationscalenumber::math::statistics::random-laplacelocationscalenumber::math::statistics::random-kumaraswamyabnumber::math::statistics::random-negative-binomialrpnumber::math::statistics::histogram-uniformxminxmaxlimitsnumber::math::statistics::incompleteGammaxp ?tol?
::math::statistics::incompleteBetaabx ?tol?
::math::statistics::estimate-paretovalues::math::statistics::estimate-exponentialvalues::math::statistics::estimate-laplacevalues::math::statistics::estimante-negative-binomialrvalues::math::statistics::filtervarnamedataexpression::math::statistics::mapvarnamedataexpression::math::statistics::samplescountvarnamelistexpression::math::statistics::subdivide::math::statistics::plot-scalecanvasxminxmaxyminymax::math::statistics::plot-xydatacanvasxdataydatatag::math::statistics::plot-xylinecanvasxdataydatatag::math::statistics::plot-tdatacanvastdatatag::math::statistics::plot-tlinecanvastdatatag::math::statistics::plot-histogramcanvascountslimitstag
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Things To Do
The following procedures are yet to be implemented:
• F-test-stdev
• interval-mean-stdev
• histogram-normal
• histogram-exponential
• test-histogram
• test-corr
• quantiles-*
• fourier-coeffs
• fourier-residuals
• onepar-function-fit
• onepar-function-residuals
• plot-linear-model
• subdivide
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