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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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