Random Data: Analysis and Measurement Procedures (Wiley Series in Probability and Statistics)
A timely update of the classic book on the theory andapplication of random data analysis
First published in 1971, Random Data served as anauthoritative book on the analysis of experimental physical datafor engineering and scientific applications. This FourthEdition features coverage of new developments in random datamanagement and analysis procedures that are applicable to a broadrange of applied fields, from the aerospace and automotiveindustries to oceanographic and biomedical research.
This new edition continues to maintain a balance of classictheory and novel techniques. The authors expand on the treatment ofrandom data analysis theory, including derivations of keyrelationships in probability and random process theory. The bookremains unique in its practical treatment of nonstationary dataanalysis and nonlinear system analysis, presenting the latesttechniques on modern data acquisition, storage, conversion, andqualification of random data prior to its digital analysis. TheFourth Edition also includes: * A new chapter on frequency domain techniques to model andidentify nonlinear systems from measured input/output randomdata * New material on the analysis of multiple-input/single-outputlinear models * The latest recommended methods for data acquisition andprocessing of random data * Important mathematical formulas to design experiments andevaluate results of random data analysis and measurementprocedures * Answers to the problem in each chapter
Comprehensive and self-contained, Random Data, FourthEdition is an indispensible book for courses on random dataanalysis theory and applications at the upper-undergraduate andgraduate level. It is also an insightful reference for engineersand scientists who use statistical methods to investigate and solveproblems with dynamic data.
Why Read This Book
You will get a practical, measurement-oriented treatment of random processes that bridges theory and real-world experiments — showing you how to estimate spectra, compute confidence intervals, and design measurement procedures. The book emphasizes actionable techniques (periodogram/Welch, AR/ARMA modeling, cross-spectral/coherence analysis) so you can turn noisy data into reliable engineering conclusions.
Who Will Benefit
Signal-processing engineers, experimentalists and graduate students who analyze measured time series in communications, radar, audio, biomedical or mechanical systems and need reliable spectral and statistical tools.
Level: Intermediate — Prerequisites: Basic probability and statistics, signals and systems (linear systems, Fourier/FFT), and familiarity with basic numerical tools (MATLAB or similar recommended).
Key Takeaways
- Estimate power spectral densities using FFT-based nonparametric methods (periodogram, Welch) and interpret windowing/averaging tradeoffs.
- Apply parametric spectral modeling (AR, ARMA) for improved resolution and model-based estimation.
- Compute and interpret cross-spectra and coherence to assess linear relationships between signals and identify transfer functions.
- Design measurement procedures and quantify uncertainty using confidence intervals and statistical tests for spectral estimates.
- Use correlation and cross-correlation methods for system identification and time-delay estimation in experimental data.
- Handle practical issues in data acquisition and preprocessing (detrending, window selection, aliasing and leakage).
Topics Covered
- Introduction and The Nature of Random Data
- Probability and Random Processes: Mathematical Background
- Correlation Functions and Power Spectral Density
- Nonparametric Spectral Estimation: Periodogram, Averaging, and Windowing
- FFT Implementation Issues and Practical Spectral Analysis
- Parametric Methods: AR, MA, ARMA Models and Estimation
- Cross-Spectral Analysis, Coherence, and Transfer-Function Estimation
- Statistical Properties: Confidence Intervals and Hypothesis Tests
- Measurement Procedures and Instrumentation Considerations
- Applications to System Identification and Experimental Analysis
- Dealing with Nonstationary and Nonlinear Data (practical approaches)
- Appendices: Tables, Algorithms, and Mathematical Tools
Languages, Platforms & Tools
How It Compares
More measurement- and experiment-focused than Stoica & Moses's Modern Spectral Analysis (which is heavier on advanced theory); complements Kay's Practical Signal Processing texts by emphasizing laboratory procedures and uncertainty quantification.
