Statistical Methods for Physical Science

This volume of Methods of Experimental Physics provides an extensive introduction to probability and statistics in many areas of the physical sciences, with an emphasis on the emerging area of spatial statistics. The scope of topics covered is wide-ranging-the text discusses a variety of the most commonly used classical methods and addresses newer methods that are applicable or potentially important. The chapter authors motivate readers with their insightful discussions.

Examines basic probability, including coverage of standard distributions, time series models, and Monte Carlo methodsDescribes statistical methods, including basic inference, goodness of fit, maximum likelihood, and least squaresAddresses time series analysis, including filtering and spectral analysisIncludes simulations of physical experimentsFeatures applications of statistics to atmospheric physics and radio astronomyCovers the increasingly important area of modern statistical computing

W.R. Leo, Introduction to Probability Modeling. L. Hodges, Common Univariate Distributions. C. Chatfield, Random Process Models. N. Cressie, Models for Spatial Processes. P. Clifford, Monte Carlo Methods. J. Kitchin, Basic Statistical Inference. V.N. Nair and A.E. Freeny, Methods for Assessing Distributional Assumptions in One- and Two-Sample Problems. W.Q. Meeker and L.A. Escobar, Maximum Likelihood Methods for Fitting ParametricStatistical Models. G.A.F. Seber and C.J. Wild, Least Squares. W.J. Randel, Filtering and Data Preprocessing for Time Series Analysis. D.B. Percival, Spectral Analysis of Univariate and Bivariate Time Series. D.A. Lewis, Weak Periodic Signals in Point Process Data. D. Zimmerman, Statistical Analysis of Spatial Data. H.F. Martz and R.A. Waller, Bayesian Methods. J.M. Hauptman, Simulation of Physical Systems. J.L. Stanford and J.R. Ziemke, Field (Map) Statistics. F.L. Hulting and A.P. Jaworski, Modern Statistical Computing and Graphics. References. Tables. Subject Index.