From: Jorge B. <fic...@us...> - 2007-01-01 10:33:13
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Update of /cvsroot/maxima/maxima/doc/info In directory sc8-pr-cvs7.sourceforge.net:/tmp/cvs-serv28771 Modified Files: distrib.texi Log Message: distrib.texi -> adjusting *nominal form* to *noun form* Index: distrib.texi =================================================================== RCS file: /cvsroot/maxima/maxima/doc/info/distrib.texi,v retrieving revision 1.2 retrieving revision 1.3 diff -u -d -r1.2 -r1.3 --- distrib.texi 20 Jul 2006 10:38:18 -0000 1.2 +++ distrib.texi 1 Jan 2007 10:33:08 -0000 1.3 @@ -590,7 +590,7 @@ @deffn {Function} pdf_chi2 (@var{x},@var{n}) Returns the value at @var{x} of the density function of a Chi-square random variable @math{Chi^2(n)}, with @math{n>0}. -The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a nominal form based on the gamma density is returned. +The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a noun form based on the gamma density is returned. @c ===beg=== @c load (distrib)$ @@ -659,7 +659,7 @@ @deffn {Function} mean_chi2 (@var{n}) Returns the mean of a Chi-square random variable @math{Chi^2(n)}, with @math{n>0}. -The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a nominal form based on the gamma mean is returned. +The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a noun form based on the gamma mean is returned. @c ===beg=== @c load (distrib)$ @@ -681,7 +681,7 @@ @deffn {Function} var_chi2 (@var{n}) Returns the variance of a Chi-square random variable @math{Chi^2(n)}, with @math{n>0}. -The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a nominal form based on the gamma variance is returned. +The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a noun form based on the gamma variance is returned. @c ===beg=== @c load (distrib)$ @@ -703,7 +703,7 @@ @deffn {Function} std_chi2 (@var{n}) Returns the standard deviation of a Chi-square random variable @math{Chi^2(n)}, with @math{n>0}. -The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a nominal form based on the gamma standard deviation is returned. +The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a noun form based on the gamma standard deviation is returned. @c ===beg=== @c load (distrib)$ @@ -725,7 +725,7 @@ @deffn {Function} skewness_chi2 (@var{n}) Returns the skewness coefficient of a Chi-square random variable @math{Chi^2(n)}, with @math{n>0}. -The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a nominal form based on the gamma skewness coefficient is returned. +The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a noun form based on the gamma skewness coefficient is returned. @c ===beg=== @c load (distrib)$ @@ -749,7 +749,7 @@ @deffn {Function} kurtosis_chi2 (@var{n}) Returns the kurtosis coefficient of a Chi-square random variable @math{Chi^2(n)}, with @math{n>0}. -The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a nominal form based on the gamma kurtosis coefficient is returned. +The @math{Chi^2(n)} random variable is equivalent to the @math{Gamma(n/2,2)}, therefore when Maxima has not enough information to get the result, a noun form based on the gamma kurtosis coefficient is returned. @c ===beg=== @c load (distrib)$ @@ -919,7 +919,7 @@ @deffn {Function} pdf_exp (@var{x},@var{m}) Returns the value at @var{x} of the density function of an @math{Exponential(m)} random variable, with @math{m>0}. -The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull density is returned. +The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull density is returned. @c ===beg=== @c load (distrib)$ @@ -942,7 +942,7 @@ @deffn {Function} cdf_exp (@var{x},@var{m}) Returns the value at @var{x} of the distribution function of an @math{Exponential(m)} random variable, with @math{m>0}. -The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull distribution is returned. +The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull distribution is returned. @c ===beg=== @c load (distrib)$ @@ -965,7 +965,7 @@ @deffn {Function} quantile_exp (@var{q},@var{m}) Returns the @var{q}-quantile of an @math{Exponential(m)} random variable, with @math{m>0}; in other words, this is the inverse of @code{cdf_exp}. Argument @var{q} must be an element of @math{[0,1]}. -The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull quantile is returned. +The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull quantile is returned. @c ===beg=== @c load (distrib)$ @@ -987,7 +987,7 @@ @deffn {Function} mean_exp (@var{m}) Returns the mean of an @math{Exponential(m)} random variable, with @math{m>0}. -The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull mean is returned. +The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull mean is returned. @c ===beg=== @c load (distrib)$ @@ -1011,7 +1011,7 @@ @deffn {Function} var_exp (@var{m}) Returns the variance of an @math{Exponential(m)} random variable, with @math{m>0}. -The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull variance is returned. +The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull variance is returned. @c ===beg=== @c load (distrib)$ @@ -1036,7 +1036,7 @@ @deffn {Function} std_exp (@var{m}) Returns the standard deviation of an @math{Exponential(m)} random variable, with @math{m>0}. -The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull standard deviation is returned. +The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull standard deviation is returned. @c ===beg=== @c load (distrib)$ @@ -1060,7 +1060,7 @@ @deffn {Function} skewness_exp (@var{m}) Returns the skewness coefficient of an @math{Exponential(m)} random variable, with @math{m>0}. -The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull skewness coefficient is returned. +The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull skewness coefficient is returned. @c ===beg=== @c load (distrib)$ @@ -1082,7 +1082,7 @@ @deffn {Function} kurtosis_exp (@var{m}) Returns the kurtosis coefficient of an @math{Exponential(m)} random variable, with @math{m>0}. -The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull kurtosis coefficient is returned. +The @math{Exponential(m)} random variable is equivalent to the @math{Weibull(1,1/m)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull kurtosis coefficient is returned. @c ===beg=== @c load (distrib)$ @@ -1567,7 +1567,7 @@ @deffn {Function} pdf_rayleigh (@var{x},@var{b}) Returns the value at @var{x} of the density function of a @math{Rayleigh(b)} random variable, with @math{b>0}. -The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull density is returned. +The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull density is returned. @c ===beg=== @c load (distrib)$ @@ -1591,7 +1591,7 @@ @deffn {Function} cdf_rayleigh (@var{x},@var{b}) Returns the value at @var{x} of the distribution function of a @math{Rayleigh(b)} random variable, with @math{b>0}. -The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull distribution is returned. +The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull distribution is returned. @c ===beg=== @c load (distrib)$ @@ -1615,7 +1615,7 @@ @deffn {Function} quantile_rayleigh (@var{q},@var{b}) Returns the @var{q}-quantile of a @math{Rayleigh(b)} random variable, with @math{b>0}; in other words, this is the inverse of @code{cdf_rayleigh}. Argument @var{q} must be an element of @math{[0,1]}. -The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull quantile is returned. +The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull quantile is returned. @c ===beg=== @c load (distrib)$ @@ -1639,7 +1639,7 @@ @deffn {Function} mean_rayleigh (@var{b}) Returns the mean of a @math{Rayleigh(b)} random variable, with @math{b>0}. -The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull mean is returned. +The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull mean is returned. @c ===beg=== @c load (distrib)$ @@ -1663,7 +1663,7 @@ @deffn {Function} var_rayleigh (@var{b}) Returns the variance of a @math{Rayleigh(b)} random variable, with @math{b>0}. -The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull variance is returned. +The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull variance is returned. @c ===beg=== @c load (distrib)$ @@ -1690,7 +1690,7 @@ @deffn {Function} std_rayleigh (@var{b}) Returns the standard deviation of a @math{Rayleigh(b)} random variable, with @math{b>0}. -The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull standard deviation is returned. +The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull standard deviation is returned. @c ===beg=== @c load (distrib)$ @@ -1716,7 +1716,7 @@ @deffn {Function} skewness_rayleigh (@var{b}) Returns the skewness coefficient of a @math{Rayleigh(b)} random variable, with @math{b>0}. -The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull skewness coefficient is returned. +The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull skewness coefficient is returned. @c ===beg=== @c load (distrib)$ @@ -1745,7 +1745,7 @@ @deffn {Function} kurtosis_rayleigh (@var{b}) Returns the kurtosis coefficient of a @math{Rayleigh(b)} random variable, with @math{b>0}. -The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a nominal form based on the Weibull kurtosis coefficient is returned. +The @math{Rayleigh(b)} random variable is equivalent to the @math{Weibull(2,1/b)}, therefore when Maxima has not enough information to get the result, a noun form based on the Weibull kurtosis coefficient is returned. @c ===beg=== @c load (distrib)$ @@ -2107,7 +2107,7 @@ @deffn {Function} pdf_bernoulli (@var{x},@var{p}) Returns the value at @var{x} of the probability function of a @math{Bernoulli(p)} random variable, with @math{0<p<1}. -The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a nominal form based on the binomial probability function is returned. +The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a noun form based on the binomial probability function is returned. @c ===beg=== @c load (distrib)$ @@ -2137,7 +2137,7 @@ @deffn {Function} mean_bernoulli (@var{p}) Returns the mean of a @math{Bernoulli(p)} random variable, with @math{0<p<1}. -The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a nominal form based on the binomial mean is returned. +The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a noun form based on the binomial mean is returned. @c ===beg=== @c load (distrib)$ @@ -2157,7 +2157,7 @@ @deffn {Function} var_bernoulli (@var{p}) Returns the variance of a @math{Bernoulli(p)} random variable, with @math{0<p<1}. -The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a nominal form based on the binomial variance is returned. +The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a noun form based on the binomial variance is returned. @c ===beg=== @c load (distrib)$ @@ -2177,7 +2177,7 @@ @deffn {Function} std_bernoulli (@var{p}) Returns the standard deviation of a @math{Bernoulli(p)} random variable, with @math{0<p<1}. -The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a nominal form based on the binomial standard deviation is returned. +The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a noun form based on the binomial standard deviation is returned. @c ===beg=== @c load (distrib)$ @@ -2197,7 +2197,7 @@ @deffn {Function} skewness_bernoulli (@var{p}) Returns the skewness coefficient of a @math{Bernoulli(p)} random variable, with @math{0<p<1}. -The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a nominal form based on the binomial skewness coefficient is returned. +The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a noun form based on the binomial skewness coefficient is returned. @c ===beg=== @c load (distrib)$ @@ -2219,7 +2219,7 @@ @deffn {Function} kurtosis_bernoulli (@var{p}) Returns the kurtosis coefficient of a @math{Bernoulli(p)} random variable, with @math{0<p<1}. -The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a nominal form based on the binomial kurtosis coefficient is returned. +The @math{Bernoulli(p)} random variable is equivalent to the @math{Binomial(1,p)}, therefore when Maxima has not enough information to get the result, a noun form based on the binomial kurtosis coefficient is returned. @c ===beg=== @c load (distrib)$ |