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#### 52.2.1 Normal Random Variable

Normal random variables (also called Gaussian) is denoted by \({\it Normal}(m, s)\) where \(m\) is the mean and \(s > 0\) is the standard deviation.

Function: pdf_normal (x,m,s) ¶

  Returns the value at x of the density function of a \({\it Normal}(m, s)\) random variable, with \(s>0\). To make use of this function, write first `load("distrib")`.

  The pdf is $$ f(x; m, s) = {1\over s\sqrt{2\pi}} e^{\displaystyle -{(x-m)^2\over 2s^2}} $$

  Categories: Package distrib ·

Function: cdf_normal (x,m,s) ¶

  Returns the value at x of the distribution function of a \({\it Normal}(m, s)\) random variable, with \(s>0\). This function is defined in terms of Maxima’s built-in error function `erf`.

  The cdf can be written analytically: $$ F(x; m, s) = {1\over 2} + {1\over 2} {\rm erf}\left(x-m\over s\sqrt{2}\right) $$

      (%i1) load ("distrib")$

      (%i2) cdf_normal(x,m,s);
                                   x - m
                             erf(---------)
                                 sqrt(2) s    1
      (%o2)                  -------------- + -
                                   2          2

  See also `erf`.

  Categories: Package distrib ·

Function: quantile_normal (q,m,s) ¶

  Returns the q-quantile of a \({\it Normal}(m, s)\) random variable, with \(s>0\); in other words, this is the inverse of `cdf_normal`. Argument q must be an element of \([0,1]\). To make use of this function, write first `load("distrib")`.

      (%i1) load ("distrib")$

      (%i2) quantile_normal(95/100,0,1);
                                               9
      (%o2)                sqrt(2) inverse_erf(--)
                                               10

      (%i3) float(%);
      (%o3)                   1.644853626951472

  Categories: Package distrib ·

Function: mean_normal (m,s) ¶

  Returns the mean of a \({\it Normal}(m, s)\) random variable, with \(s>0\). To make use of this function, write first `load("distrib")`.

  The mean is $$ E[X] = m $$

  Categories: Package distrib ·

Function: var_normal (m,s) ¶

  Returns the variance of a \({\it Normal}(m, s)\) random variable, with \(s>0\). To make use of this function, write first `load("distrib")`.

  The variance is $$ V[X] = s^2 $$

  Categories: Package distrib ·

Function: std_normal (m,s) ¶

  Returns the standard deviation of a \({\it Normal}(m, s)\) random variable, with \(s>0\), namely s. To make use of this function, write first `load("distrib")`.

  The standard deviation is $$ D[X] = s $$

  Categories: Package distrib ·

Function: skewness_normal (m,s) ¶

  Returns the skewness coefficient of a \({\it Normal}(m, s)\) random variable, with \(s>0\). To make use of this function, write first `load("distrib")`.

  The skewness coefficient is $$ SK[X] = 0 $$

  Categories: Package distrib ·

Function: kurtosis_normal (m,s) ¶

  Returns the kurtosis coefficient of a \({\it Normal}(m, s)\) random variable, with \(s>0\), which is always equal to 0. To make use of this function, write first `load("distrib")`.

  The kurtosis coefficient is $$ KU[X] = 0 $$

  Categories: Package distrib ·

Function: random_normal (m,s)
`random_normal` (m,s,n) ¶

  Returns a \({\it Normal}(m, s)\) random variate, with \(s>0\). Calling `random_normal` with a third argument n, a random sample of size n will be simulated.

  This is an implementation of the Box-Mueller algorithm, as described in Knuth, D.E. (1981) Seminumerical Algorithms. The Art of Computer Programming. Addison-Wesley.

  To make use of this function, write first `load("distrib")`.

  Categories: Package distrib · Random numbers ·

Next: Student’s t Random Variable, Previous: Functions and Variables for continuous distributions, Up: Functions and Variables for continuous distributions [Contents][Index]

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