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[section:chi_squared_dist Chi Squared Distribution]
``#include <boost/math/distributions/chi_squared.hpp>``
namespace boost{ namespace math{
template <class RealType = double,
class ``__Policy`` = ``__policy_class`` >
class chi_squared_distribution;
typedef chi_squared_distribution<> chi_squared;
template <class RealType, class ``__Policy``>
class chi_squared_distribution
{
public:
typedef RealType value_type;
typedef Policy policy_type;
// Constructor:
chi_squared_distribution(RealType i);
// Accessor to parameter:
RealType degrees_of_freedom()const;
// Parameter estimation:
static RealType find_degrees_of_freedom(
RealType difference_from_mean,
RealType alpha,
RealType beta,
RealType sd,
RealType hint = 100);
};
}} // namespaces
The Chi-Squared distribution is one of the most widely used distributions
in statistical tests. If [chi][sub i] are [nu]
independent, normally distributed
random variables with means [mu][sub i] and variances [sigma][sub i][super 2],
then the random variable:
[equation chi_squ_ref1]
is distributed according to the Chi-Squared distribution.
The Chi-Squared distribution is a special case of the gamma distribution
and has a single parameter [nu] that specifies the number of degrees of
freedom. The following graph illustrates how the distribution changes
for different values of [nu]:
[graph chi_squared_pdf]
[h4 Member Functions]
chi_squared_distribution(RealType v);
Constructs a Chi-Squared distribution with /v/ degrees of freedom.
Requires v > 0, otherwise calls __domain_error.
RealType degrees_of_freedom()const;
Returns the parameter /v/ from which this object was constructed.
static RealType find_degrees_of_freedom(
RealType difference_from_variance,
RealType alpha,
RealType beta,
RealType variance,
RealType hint = 100);
Estimates the sample size required to detect a difference from a nominal
variance in a Chi-Squared test for equal standard deviations.
[variablelist
[[difference_from_variance][The difference from the assumed nominal variance
that is to be detected: Note that the sign of this value is critical, see below.]]
[[alpha][The maximum acceptable risk of rejecting the null hypothesis when it is
in fact true.]]
[[beta][The maximum acceptable risk of falsely failing to reject the null hypothesis.]]
[[variance][The nominal variance being tested against.]]
[[hint][An optional hint on where to start looking for a result: the current sample
size would be a good choice.]]
]
Note that this calculation works with /variances/ and not /standard deviations/.
The sign of the parameter /difference_from_variance/ is important: the Chi
Squared distribution is asymmetric, and the caller must decide in advance
whether they are testing for a variance greater than a nominal value (positive
/difference_from_variance/) or testing for a variance less than a nominal value
(negative /difference_from_variance/). If the latter, then obviously it is
a requirement that `variance + difference_from_variance > 0`, since no sample
can have a negative variance!
This procedure uses the method in Diamond, W. J. (1989).
Practical Experiment Designs, Van-Nostrand Reinhold, New York.
See also section on Sample sizes required in
[@http://www.itl.nist.gov/div898/handbook/prc/section2/prc232.htm the NIST Engineering Statistics Handbook, Section 7.2.3.2].
[h4 Non-member Accessors]
All the [link math_toolkit.dist_ref.nmp usual non-member accessor functions]
that are generic to all distributions are supported: __usual_accessors.
(We have followed the usual restriction of the mode to degrees of freedom >= 2,
but note that the maximum of the pdf is actually zero for degrees of freedom from 2 down to 0,
and provide an extended definition that would avoid a discontinuity in the mode
as alternative code in a comment).
The domain of the random variable is \[0, +[infin]\].
[h4 Examples]
Various [link math_toolkit.stat_tut.weg.cs_eg worked examples]
are available illustrating the use of the Chi Squared Distribution.
[h4 Accuracy]
The Chi-Squared distribution is implemented in terms of the
[link math_toolkit.sf_gamma.igamma incomplete gamma functions]:
please refer to the accuracy data for those functions.
[h4 Implementation]
In the following table /v/ is the number of degrees of freedom of the distribution,
/x/ is the random variate, /p/ is the probability, and /q = 1-p/.
[table
[[Function][Implementation Notes]]
[[pdf][Using the relation: pdf = __gamma_p_derivative(v / 2, x / 2) / 2 ]]
[[cdf][Using the relation: p = __gamma_p(v / 2, x / 2) ]]
[[cdf complement][Using the relation: q = __gamma_q(v / 2, x / 2) ]]
[[quantile][Using the relation: x = 2 * __gamma_p_inv(v / 2, p) ]]
[[quantile from the complement][Using the relation: x = 2 * __gamma_q_inv(v / 2, p) ]]
[[mean][v]]
[[variance][2v]]
[[mode][v - 2 (if v >= 2)]]
[[skewness][2 * sqrt(2 / v) == sqrt(8 / v)]]
[[kurtosis][3 + 12 / v]]
[[kurtosis excess][12 / v]]
]
[h4 References]
* [@http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm NIST Exploratory Data Analysis]
* [@http://en.wikipedia.org/wiki/Chi-square_distribution Chi-square distribution]
* [@http://mathworld.wolfram.com/Chi-SquaredDistribution.html Weisstein, Eric W. "Chi-Squared Distribution." From MathWorld--A Wolfram Web Resource.]
[endsect] [/section:chi_squared_dist Chi Squared]
[/ chi_squared.qbk
Copyright 2006 John Maddock and Paul A. Bristow.
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE_1_0.txt or copy at
http://www.boost.org/LICENSE_1_0.txt).
]
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