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# Two Common Distributions Help

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By — McGraw-Hill Professional
Updated on Apr 25, 2014

## Introduction to Two Common Distributions

The nature of a probability density function can be described in the form of a distribution. Let's get a little bit acquainted with two classical types: the uniform distribution and the normal distribution. These are mere introductions, intended to let you get to know what sorts of animals probability distributions are. There are many types of distributions besides these two. Distributions are sort of like spiders. In one sense, ''when you've seen one, you've seen them all.'' But if you are willing to go deep, you can look at any species endlessly and keep discovering new things about it.

## Uniform Distribution

In a uniform distribution, the value of the function is constant. When graphed, it looks ''flat,'' like a horizontal line (Fig. 3-9).

Let x be a continuous random variable. Let xmin and xmax be the minimum and maximum values that x can attain, respectively. In a uniform distribution, x has a density function of the form:

• f(x) = 1 / (xmaxxmin)

Because the total area under the curve is equal to 1, the probability Pab that any randomly chosen x will be between a and b is:

• Pab = (ba) / (xmaxxmin)

Suppose that the experiment described above reveals that equal numbers of people always have each of the given tested blood-pressure numbers between two limiting values, say xmin = 100 and xmax = 140. Imagine that this is true no matter how many people are tested, and no matter how many different values of blood pressure are specified within the range xmin = 100 and xmax = 140. This is far-fetched and can't represent the real world, but if you play along with the idea, you can see that such a state of affairs produces a uniform probability distribution.

The mean (μ), the variance (σ2), and the standard deviation (σ), which we looked at in the last chapter for discrete random variables, can all be defined for a uniform distribution having a continuous random variable. A detailed analysis of this is beyond the scope of this introductory course. But here are the formulas, in case you're interested:

•     μ = (a + b) / 2
•   σ2 = (ba)2 / 12
• σ = [(ba)2 / 12]1/2

## Normal Distribution

In a normal distribution, the value of the function has a single central peak, and tapers off on either side in a symmetrical fashion. Because of its shape, a graph of this function is often called a bell-shaped curve (Fig. 3-10). The normal distribution isn't just any bell-shaped curve, however. In order to be a true normal distribution, the curve must conform to specific rules concerning its standard deviation.

The symbol σ represents the standard deviation of the function, which is an expression of the extent to which the values of the function are concentrated. It's the same concept you saw in Chapter 2, but generalized for continuous random variables. A small value of σ produces a ''sharp'' curve with a narrow peak and steep sides. A large value of σ produces a ''broad'' curve with less steep sides. As σ approaches 0, the curve becomes narrower and narrower, closing in on a vertical line. If σ becomes arbitrarily large, the curve becomes almost flat and settles down near the horizontal axis. In any normal distribution, the area under the curve is equal to 1, no matter how much or little it is concentrated around the mean.

The symbol μ represents the mean, or average. Again, this is the same mean you learned about in Chapter 2, but generalized for continuous random variables. The value of μ can be found by imagining a moving vertical line that intersects the x axis. When the position of the vertical line is such that the area under the curve to its left is 1/2 (or 50%) and the area under the curve to its right is also 1/2 (50%), then the vertical line intersects the x axis at the point x = μ. In the normal distribution, x = μ at the same point where the function attains its peak, or maximum, value.

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