Probability and Statistics for Physical Sciences
- 2nd book:metaData.edition - September 5, 2023
- book:metaData.latestEdition
- common:contributors.authors Brian Martin, Mark Hurwitz
- publicationLanguages:language
Probability and Statistics for Physical Sciences, Second Edition is an accessible guide to commonly used concepts and methods in statistical analysis used in the physical scienc… seeMoreDescription
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Probability and Statistics for Physical Sciences, Second Edition is an accessible guide to commonly used concepts and methods in statistical analysis used in the physical sciences. This brief yet systematic introduction explains the origin of key techniques, providing mathematical background and useful formulas. The text does not assume any background in statistics and is appropriate for a wide-variety of readers, from first-year undergraduate students to working scientists across many disciplines.
promoMetaData.keyFeatures
promoMetaData.keyFeatures
- Provides a collection of useful formulas with mathematical background
- Includes worked examples throughout and end-of-chapter problems for practice
- Offers a logical progression through topics and methods in statistics and probability
promoMetaData.readership
promoMetaData.readership
Students in undergraduate and graduate courses introducing or utilizing basic statistics (ie, Intro Stats for non-Math/Stats majors), especially in physical science programs (physics, chemistry, astronomy, earth science, etc)Professionals, researchers, academics across Physical Sciences applying statistics in their work, who require a refresher to the subject
promoMetaData.tableOfContents
promoMetaData.tableOfContents
1. Statistics, Experiments, and Data
1.1 Experiments and Observations
1.2 Random Variables and Sampling
1.3 Displaying Data
1.4 Summarizing Data Numerically
1.4.1 Measures of location
1.4.2 Measures of spread
1.4.3 More than one variable: correlation
1.5 Large Samples
1.6 Experimental Errors
2. Probability
2.1 Sample Spaces and Events
2.2 Axioms and Calculus of Probability
2.3 Conditional and Marginal Probabilities
2.4 Permutations and Combinations
2.5 The Meaning of Probability
2.5.1 Frequency interpretation
2.5.2 Subjective interpretation
3. Probability Distributions I : Basic Concepts
3.1 Random Variables
3.2 Single Variable
3.2.1 Probability distributions
3.2.2 Expectation values
3.2.3 Moment-generating, and characteristic functions
3.3 Several Variables
3.3.1 Joint probability distributions
3.3.2 Marginal and conditional distributions
3.3.3 Expectation values of sums and products
3.3.4 Moments and expectation values
3.4 Functions of a Random Variable
4. Probability Distributions II : Examples
4.1 Continuous Variables
4.1.1 Uniform distribution
4.1.2 Univariate normal (Gaussian) distribution
4.1.3 Multivariate normal distribution
4.1.4 Bivariate normal distribution
4.1.5 Exponential and gamma distributions
4.1.6 Cauchy distribution
4.2 Discrete Variables
4.2.1 Binomial distribution
4.2.2 Multinomial distribution
4.2.3 Poisson distribution
5. Sampling and Estimation
5.1 Random Samples and Estimators
5.1.1 Samplng distributions
5.1.2 Properties of point estimators
5.2 Estimators for the Mean, Variance, and Covariance
5.3 Laws of Large Numbers and the Central Limit Theorem
5.4 Experimental Errors
5.4.1 Propagation of errors
5.5 Monte Carlo Method and Simulations
5.5.1 Monte Carlo method for integration
5.5.2 Simulations
6. Sampling Distributions Associated with the Normal Distribution
6.1 Chi-Squared Distribution
6.2 Student’s t Distribution
6.3 F Distribution
6.4 Relations Between X2, t and F Distributions
7. Parameter Estimation I: Maximum Likelihood and Minimum Variance
7.1 Estimation of a Single Parameter
7.2 Variance of an Estimator
7.2.1 Approximate methods
7.3 Simultaneous Estimation of Several Parameters
7.4 Minimum Variance
7.4.1 Parameter estimation
7.4.2 Minimum variance bound
8. Parameter Estimation II : Least-Squares and Other Methods
8.1 Unconstrained Linear Least-Squares
8.1.1 General solution for the parameters
8.1.2 Errors on the parameter estimates
8.1.3 Quality of the fit
8.1.4 Orthogonal polynomials
8.1.5 Fitting a straight line
8.1.6 Combining experimental data
8.2 Linear Least-Squares with Constraints
8.3 Non-Linear Least-Squares
8.4 Other Methods
8.4.1 Minimum chi-squared
8.4.2 Method of moments
8.4.3 Bayes’ estimators
9. Interval Estimation
9.1 Confidence Intervals : Basic Ideas
9.2 Confidence Intervals : General Method
9.3 Normal Distribution
9.3.1 Confidence intervals for the mean
9.3.2 Confidence intervals for the variance
9.3.3 Confidence intervals for the ratio of variances
9.3.4 Confidence regions for the mean and variance
9.4 Poisson Distribution
9.5 Large Samples
9.6 Confidence Intervals Near Boundaries
9.7 Bayesian Confidence Intervals
10. Hypothesis Testing I : Parameters
10.1 Statistical Hypotheses
10.2 General Hypotheses : Likelihood Ratios
10.2.1 Simple hypothesis : one simple alternative
10.2.2 Composite hypotheses
10.3 Normal Distribution
10.3.1 Basic ideas
10.3.2 Specific tests
10.4 Other Distributions
10.5 Analysis of Variance
10.6 Bayesian Hypothesis Testing
11. Hypothesis Testing II : Other Tests
11.1 Goodness-Of-Fit Tests
11.1.1 Discrete distributions
11.1.2 Continuous distributions
11.1.3 Linear hypotheses
11.2 Tests for Independence
11.3 Nonparametric Tests
11.3.1 Sign test
11.3.2 Signed-rank test
11.3.3 Rank-sum test
11.3.4 Run test
11.3.5 Rank correlation coefficient
Appendix A. Miscellaneous Mathematics
A.1 Matrix Algebra
A.2 Classical Theory of Minima
A.3 Delta function
A.4 Distribution of the Mean of a Poisson Sample
A.5 The Chebyshev Inequality
Appendix B. Optimization of Nonlinear Functions
B.1 General Principles
B.2 Unconstrained Minimization of Functions of One variable
B.3 Unconstrained Minimization of Multivariable Functions
B.3.1 Direct search methods
B.3.2 Gradient methods
B.4 Constrained Optimization
Appendix C. Statistical Tables
C.1 Normal Distribution
C.2 Binomial Distribution
C.3 Poisson Distribution
C.4 Chi-squared Distribution
C.5 Student’s t Distribution
C.6 F distribution
C.7 Signed-Rank Test
C.8 Rank-Sum Test
C.9 Run Test
C.10 Rank Correlation Coefficient
Appendix D. Answers to Selected Problems
1.1 Experiments and Observations
1.2 Random Variables and Sampling
1.3 Displaying Data
1.4 Summarizing Data Numerically
1.4.1 Measures of location
1.4.2 Measures of spread
1.4.3 More than one variable: correlation
1.5 Large Samples
1.6 Experimental Errors
2. Probability
2.1 Sample Spaces and Events
2.2 Axioms and Calculus of Probability
2.3 Conditional and Marginal Probabilities
2.4 Permutations and Combinations
2.5 The Meaning of Probability
2.5.1 Frequency interpretation
2.5.2 Subjective interpretation
3. Probability Distributions I : Basic Concepts
3.1 Random Variables
3.2 Single Variable
3.2.1 Probability distributions
3.2.2 Expectation values
3.2.3 Moment-generating, and characteristic functions
3.3 Several Variables
3.3.1 Joint probability distributions
3.3.2 Marginal and conditional distributions
3.3.3 Expectation values of sums and products
3.3.4 Moments and expectation values
3.4 Functions of a Random Variable
4. Probability Distributions II : Examples
4.1 Continuous Variables
4.1.1 Uniform distribution
4.1.2 Univariate normal (Gaussian) distribution
4.1.3 Multivariate normal distribution
4.1.4 Bivariate normal distribution
4.1.5 Exponential and gamma distributions
4.1.6 Cauchy distribution
4.2 Discrete Variables
4.2.1 Binomial distribution
4.2.2 Multinomial distribution
4.2.3 Poisson distribution
5. Sampling and Estimation
5.1 Random Samples and Estimators
5.1.1 Samplng distributions
5.1.2 Properties of point estimators
5.2 Estimators for the Mean, Variance, and Covariance
5.3 Laws of Large Numbers and the Central Limit Theorem
5.4 Experimental Errors
5.4.1 Propagation of errors
5.5 Monte Carlo Method and Simulations
5.5.1 Monte Carlo method for integration
5.5.2 Simulations
6. Sampling Distributions Associated with the Normal Distribution
6.1 Chi-Squared Distribution
6.2 Student’s t Distribution
6.3 F Distribution
6.4 Relations Between X2, t and F Distributions
7. Parameter Estimation I: Maximum Likelihood and Minimum Variance
7.1 Estimation of a Single Parameter
7.2 Variance of an Estimator
7.2.1 Approximate methods
7.3 Simultaneous Estimation of Several Parameters
7.4 Minimum Variance
7.4.1 Parameter estimation
7.4.2 Minimum variance bound
8. Parameter Estimation II : Least-Squares and Other Methods
8.1 Unconstrained Linear Least-Squares
8.1.1 General solution for the parameters
8.1.2 Errors on the parameter estimates
8.1.3 Quality of the fit
8.1.4 Orthogonal polynomials
8.1.5 Fitting a straight line
8.1.6 Combining experimental data
8.2 Linear Least-Squares with Constraints
8.3 Non-Linear Least-Squares
8.4 Other Methods
8.4.1 Minimum chi-squared
8.4.2 Method of moments
8.4.3 Bayes’ estimators
9. Interval Estimation
9.1 Confidence Intervals : Basic Ideas
9.2 Confidence Intervals : General Method
9.3 Normal Distribution
9.3.1 Confidence intervals for the mean
9.3.2 Confidence intervals for the variance
9.3.3 Confidence intervals for the ratio of variances
9.3.4 Confidence regions for the mean and variance
9.4 Poisson Distribution
9.5 Large Samples
9.6 Confidence Intervals Near Boundaries
9.7 Bayesian Confidence Intervals
10. Hypothesis Testing I : Parameters
10.1 Statistical Hypotheses
10.2 General Hypotheses : Likelihood Ratios
10.2.1 Simple hypothesis : one simple alternative
10.2.2 Composite hypotheses
10.3 Normal Distribution
10.3.1 Basic ideas
10.3.2 Specific tests
10.4 Other Distributions
10.5 Analysis of Variance
10.6 Bayesian Hypothesis Testing
11. Hypothesis Testing II : Other Tests
11.1 Goodness-Of-Fit Tests
11.1.1 Discrete distributions
11.1.2 Continuous distributions
11.1.3 Linear hypotheses
11.2 Tests for Independence
11.3 Nonparametric Tests
11.3.1 Sign test
11.3.2 Signed-rank test
11.3.3 Rank-sum test
11.3.4 Run test
11.3.5 Rank correlation coefficient
Appendix A. Miscellaneous Mathematics
A.1 Matrix Algebra
A.2 Classical Theory of Minima
A.3 Delta function
A.4 Distribution of the Mean of a Poisson Sample
A.5 The Chebyshev Inequality
Appendix B. Optimization of Nonlinear Functions
B.1 General Principles
B.2 Unconstrained Minimization of Functions of One variable
B.3 Unconstrained Minimization of Multivariable Functions
B.3.1 Direct search methods
B.3.2 Gradient methods
B.4 Constrained Optimization
Appendix C. Statistical Tables
C.1 Normal Distribution
C.2 Binomial Distribution
C.3 Poisson Distribution
C.4 Chi-squared Distribution
C.5 Student’s t Distribution
C.6 F distribution
C.7 Signed-Rank Test
C.8 Rank-Sum Test
C.9 Run Test
C.10 Rank Correlation Coefficient
Appendix D. Answers to Selected Problems
promoMetaData.reviewQuotes
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"...remains a stalwart example of an introductory text for statistical methods. A real highlight of this text is that it is never boring, contrived or difficult.... From the beginning, the reader is treated to a gentle, but quantitative introduction to the basics of statistical measurement, before delving into a very nice introduction of probability. [It] explores various topics from sampling, estimators and hypothesis testing. Fundamentally, a refreshing aspect of this text is that it empowers the reader (from even a first read) to confidently apply the techniques within to problems that the reader may find in real life (be it as a part of a lecture course or research problem).... Furthermore, there are numerous detailed examples through the book, which in opposition to other texts, are easy to follow and most importantly instructive.... a nicely written self-contained guide and introduction to statistical methods applicable to the everyday experimental scientist, which for a reader with a basic grip on multivariate calculus, becomes a pleasurable self-paced exploration into statistics." Review by Dr Kymani Armstrong-Williams, Physics Book Reviews, September 2025
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- productDetails.edition: 2
- book:metaData.latestEdition
- productDetails.published: October 26, 2023
- publicationLanguages:languageTitle: publicationLanguages:en
promoMetaData.aboutTheAuthors
promoMetaData.aboutTheAuthors
BM
Brian Martin
Prof. Brian R. Martin graduated from Birmingham University with a BSc in Physics and then moved to University College London (1962-1965) to take a PhD in Theoretical Physics. He was a Ford Foundation Fellow at the Institute for Theoretical Physics, Copenhagen University, Copenhagen; a NATO Postdoctoral Fellowship at the Neils Bohr Institute, Copenhagen; and a Research Associate in the Physics Department of Brookhaven National Laboratory, New York. Returning to University College London, he served as a Lecturer, then a Reader and Professor, before becoming Head of Department (1993-2004). Professor Martin retired as Professor Emeritus in October 2005.
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Professor Emeritus, University College London, UKMH
Mark Hurwitz
Dr. Mark F. Hurwitz graduated from Northwestern University with a BS in Mechanical Engineering and then worked as an engineer at Xerox Corporation while earning an MS in Mechanical and Aerospace Engineering at the University of Rochester. He earned a PhD at Cornell University in Theoretical and Applied Mechanics during an extensive R&D career in the filtration and separations industry at Pall Corporation, where he was inventor of 12 US patents with multiple foreign cognates. Returning to Cornell University, he was an Adjunct Professor in the Robert Frederick Smith School of Chemical and Biomolecular Engineering, before moving to administration and becoming the Chief Research Compliance Officer.
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Chief Research Compliance Officer and Research Integrity Officer, Cornell University, NY, USA