Abstract
The normal distribution is the best-known of the statistical distributions. It results in the familiar “bell-shaped” or Gaussian curve. In this chapter, we shall develop a relationship between the Gaussian distribution and the mean and standard deviation treated earlier. Once we know the population mean and standard deviation of a randomly distributed continuous variable, we can predict the probability that future measurements will fall within any arbitrarily defined interval of the range of all possible measurements. We can also make statistical judgments whether any individual measurement or observation belongs to the set with a given mean and standard deviation or whether it belongs to some other set. These judgments rely on integration of the Gaussian function. In this book, of course, we do our integration by computer, a technique that is discussed at some length near the end of the chapter.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Bibliography
F. S. Acton, Numerical Methods That Work, Harper and Row, New York, 1970.
L. N. Balaam, Fundamentals of Biometry, George Allen & Unwin, London, 1975.
J. B. Dence, Mathematical Techniques in Chemistry, Wiley-Interscience, New York, 1975.
J. H. Zar, Biostatistical Analysis, Prentice-Hall, Englewood Cliffs, New Jersey, 1974.
Rights and permissions
Copyright information
© 1983 Humana Press Inc.
About this chapter
Cite this chapter
Rogers, D.W. (1983). Using the Normal Distribution. In: BASIC Microcomputing and Biostatistics. Humana Press. https://doi.org/10.1007/978-1-4612-5300-6_7
Download citation
DOI: https://doi.org/10.1007/978-1-4612-5300-6_7
Publisher Name: Humana Press
Print ISBN: 978-1-4612-9776-5
Online ISBN: 978-1-4612-5300-6
eBook Packages: Springer Book Archive