Statistics for Nuclear and Particle Physicists

ISBN-10: 0521379342
ISBN-13: 9780521379342
Edition: N/A
Authors: Louis Lyons
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Description: Written by a non-statistician for non-statisticians, the book emphasizes the practical approach to those problems in statistics that arise regularly in data analysis situations in nuclear and high energy physics experiments. Rather than concentrate  More...

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Book details

List price: $69.99
Publisher: Cambridge University Press
Publication date: 4/6/1989
Binding: Paperback
Pages: 240
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 0.748

Written by a non-statistician for non-statisticians, the book emphasizes the practical approach to those problems in statistics that arise regularly in data analysis situations in nuclear and high energy physics experiments. Rather than concentrate on proofs and theorems, the author provides an abundance of simple examples that illustrate the general ideas presented. This allows the reader to obtain maximum information in the simplest manner. Possible difficulties with the various techniques, and pitfalls to be avoided, are also discussed. This commonsense approach to statistical formalism enables nuclear physicists to better understand how to do justice to their analysis and interpretation of data.

Lyons, Department of Nuclear Physics, University of Oxford.

Preface
Experimental errors
Why do we do experiments?
Why estimate errors?
Random and systematic errors
The meaning of [sigma]
Distributions
Mean and variance
Gaussian distribution
Combining errors
Linear situations
Non-linear situations
Combining results of different experiments
Probability and statistics
Probability
Rules of probability
Statistics
Distributions
Binomial
Poisson
Gaussian distribution
Gaussian distribution in two variables
Using the error matrix
Specific examples of error matrix manipulations
Parameter fitting and hypothesis testing
Normalisation
Interpretation of estimates
Meaning of error estimates
Upper limits
Estimates outside the physical range
The method of moments
Maximum likelihood method
What is it? Some simple examples
The logarithm of the likelihood function, and error estimates
Comments on the maximum likelihood method
Several parameters
Extended maximum likelihood method
Least squares
What is it?
Notes on the least squares method
Correlated errors for y[subscript i]
Hypothesis testing
Use of weighted sum of squared deviations
Types of hypothesis testing
Minimisation
Detailed examples of fitting procedures
Least squares fitting
Function linear in parameters - the straight line
General least squares case
Straight line fit to points with errors on x and y
Kinematic fitting
General comments
Actual fit in a simplified case
Fit involving unmeasured variables
Maximum likelihood determination of Breit-Wigner parameters
Monte Carlo calculations
Introduction
What is it?
Random numbers
Why do Monte Carlo calculations?
Accuracy of Monte Carlo calculations
More than one dimension
An integral by Monte Carlo methods
Very simple applications of Monte Carlo calculations
Evaluation of [pi]
Mean transverse momentum
Non-uniform distributions and correlated variables
Typical uses of the Monte Carlo technique
Designing experiments
Testing programs
Contamination estimates
Geometrical correction factors
Do theory and experiment agree?
Resonance or statistical fluctuation?
Phase space distributions
Matrix elements
Parameter determination
Detailed examples
Geometrical correction factors
Physics
Non-physics applications
How to save yourself a fortune
The Garden of Eden problem
Background subtraction procedures
The number of constraints in kinematic fitting
Bibliography
Index

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