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The distribution, however, is no longer uniform if a second die is added, and they are both thrown, as the probability
        of the sums is not the same. The probability of tossing a coin is another straightforward illustration. There can only
        be two outcomes in such a situation. Consequently, 2 is the finite value.

        Discrete uniform distribution can be advantageous for organisations in a number of ways. For instance, it may
        come up when analysing the frequency of inventory sales in inventory management. It may offer a probability
        distribution that will help the company decide how best to distribute its products throughout available space.
        The formula for a discrete uniform distribution is:
               1
          Px =
               n
        Where:
        Px = Probability of a discrete value
        n = Number of values in the range

        Continuous Uniform Distribution
        An infinite number of equally likely measurable values make up a continuous uniform distribution, often known as
        a rectangle distribution.
        A good example of a continuous uniform distribution is an idealized random number generator. Every variable has
        the same chance of occurring in a continuous uniform distribution, just like in a discrete uniform distribution. But
        there are an endless number of possible points.

        Normal Distribution
        A normal distribution is most common distribution function for independent, randomly generated variables. It is
        sometimes called the bell curve or Gaussian distribution, and is a distribution that occurs naturally in many real-life
        situations like IQ scores and represents natural phenomena such as errors, heights, weights, blood pressure, etc.

        Characteristics of Normal Distribution
                                                      Unimodal
                                                                     Symmetric



                                                                         Asymptotic






                                                 Mean = Median = Mode
        Here, we see the four characteristics of a normal distribution:
        1. The mean, median, and mode are equal.

        2.  The normal curve is bell-shaped and is symmetric   Asymptotic                        Total area = 1
           about the mean.

        3. The total area under the normal curve is equal to
        4.  The normal curve approaches, but never touches                           m
           the x-axis as it extends farther and away from the mean.
        A very special kind of continuous distribution is called a Normal distribution. Its density function is:
                  1    –  (x-m) 2
          f(x) =     e   2s 2  , where s>0, – ∞ < 0 < ∞
               s   2p
        where m and s are specific parameters of the function.

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