x21123 - Normal distribution The normal distribution is the...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
Normal distribution The normal distribution is the most widely known and used of all distributions. Because the normal distribution approximates many natural phenomena so well, it has developed into a standard of reference for many probability problems. I. Characteristics of the Normal distribution Symmetric, bell shaped Continuous for all values of X between - and so that each conceivable interval of real numbers has a probability other than zero. - X Two parameters, μ and σ . Note that the normal distribution is actually a family of distributions, since μ and σ determine the shape of the distribution. The rule for a normal density function is e 2 1 = ) , f(x; 2 2 /2 ) - -(x 2 2 σ µ π The notation N(μ, σ 2 ) means normally distributed with mean μ and variance σ 2 . If we say X N(μ, σ 2 ) we mean that X is distributed N(μ, σ 2 ). About 2/3 of all cases fall within one standard deviation of the mean, that is P(μ - σ X μ + σ ) = .6826. About 95% of cases lie within 2 standard deviations of the mean, that is P(μ - 2 σ X μ + 2 σ ) = .9544 Normal distribution - Page 1
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
II. Why is the normal distribution useful? Many things actually are normally distributed, or very close to it. For example, height and intelligence are approximately normally distributed; measurement errors also often have a normal distribution The normal distribution is easy to work with mathematically. In many practical cases, the methods developed using normal theory work quite well even when the distribution is not normal. There is a very strong connection between the size of a sample N and the extent to which a sampling distribution approaches the normal form. Many sampling distributions based on large N can be approximated by the normal distribution even though the population distribution itself is definitely not normal. III. The standardized normal distribution. a. General Procedure . As you might suspect from the formula for the normal density function, it would be difficult and tedious to do the calculus every time we had a new set of parameters for µ and σ . So instead, we usually work with the standardized normal distribution, where μ = 0 and σ = 1, i.e. N(0,1). That is, rather than directly solve a problem involving a normally distributed variable X with mean μ and standard deviation σ , an indirect approach is used. 1. We first convert the problem into an equivalent one dealing with a normal variable measured in standardized deviation units, called a standardized normal variable. To do this, if X N(μ, σ 5 ), then 1) N(0, - X = Z ~ σ µ 2. A table of standardized normal values (Appendix E, Table I ) can then be used to obtain an answer in terms of the converted problem. 3. If necessary, we can then convert back to the original units of measurement. To do this, simply note that, if we take the formula for Z, multiply both sides by σ , and then add μ to both sides, we get + Z = X 4. The interpetation of Z values is straightforward. Since σ = 1, if Z = 2, the corresponding X value is exactly 2 standard deviations above the mean.
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 10

x21123 - Normal distribution The normal distribution is the...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online