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Science and Nature

Statistical Methods for Engineers and Scientists

Applied Probability, Reliability Data, and Pattern Recognition for Machine LearningBy Priya Ramanathan

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Length15h 5m

About this audiobook

Stop forcing clean textbook data onto messy engineering reality. Real engineering data arrives censored, correlated, and far from normal. Inspection records stop at the detection limit. Failure times are truncated by test schedules. Measurements carry uncertainty that propagates through every downstream calculation. Statistical Methods for Engineers and Scientists teaches inference on the data you actually have, not the idealized samples in a classroom example. Priya Ramanathan builds each method from an engineering problem, then shows the mathematics that makes it work. You will construct probability models from inspection and failure data, fit Weibull and lognormal distributions by probability plotting and maximum likelihood, and propagate measurement uncertainty through a tolerance stack. The book treats interval estimation, sample size, and power as design decisions rather than afterthoughts, and it insists on residual diagnostics instead of an R-squared value alone. Later chapters move into the methods that quality, reliability, and machine learning work depend on: control charts and capability indices, life data analysis with censoring and accelerated testing, system reliability, and the classification, cross-validation, and mixture-model ideas behind pattern recognition. Each topic connects to the one before it, so the book reads as a single argument about how to reason from data under engineering constraints. What you will learn: • Build probability models from inspection and failure problems, including conditioning and Bayes' theorem • Fit Weibull and lognormal distributions by probability plotting and maximum likelihood • Propagate measurement uncertainty through a tolerance stack using joint distributions • Construct confidence, prediction, and tolerance intervals, and choose a sample size that gives real power • Run analysis of variance and factorial experiments, then check regression models with residual diagnostics • Apply statistical process control, capability indices, and measurement systems analysis • Analyze life data with censoring and accelerated testing, and compute system reliability • Use classification, cross-validation, and mixture models as pattern recognition tools • Connect classical inference to modern machine learning without skipping the assumptions Who it is for: Engineering students who need a statistics text grounded in practice, quality and reliability engineers who work with censored and non-normal data, and applied scientists who want the probabilistic foundations behind machine learning methods. If your data never matches the textbook example, this book is written for you.

Audiobook details

GenreTechnology, Science and Nature

Length15 hrs 5 mins

Narrated byListen with 1,000+ voices

FormateBook with Audio

Publish dateSep 27, 2026

LanguageEnglish

1Statistical Methods for Engineers and Scientists

2Foreword

3Preface

4About This Book

5Chapter 1: Engineering Data and Descriptive Statistics

Show all chapters

61.1 Populations, Samples and Engineering Data Types

71.2 Frequency Distributions and Histograms

81.3 Measures of Location and Spread

91.4 Box Plots, Quantiles and Resistant Statistics

101.5 Probability Plots and Normality Checks

111.6 Data Screening, Rounding and Reporting

12Chapter 2: Probability, Conditioning and Bayes’ Theorem

132.1 Sample Spaces, Events and the Axioms of Probability

142.2 Counting Rules and Combinatorial Probability

152.3 Conditional Probability and Independence

162.4 Total Probability and Bayes’ Theorem

172.5 Reliability of Simple Systems by Probability Rules

18Chapter 3: Discrete Random Variables

193.1 Probability Mass Functions and Cumulative Distribution Functions

203.2 Expectation, Variance and Moment Generating Functions

213.3 Binomial and Geometric Distributions

223.4 Hypergeometric and Negative Binomial Distributions

233.5 The Poisson Process and Poisson Distribution

243.6 Fitting Discrete Models and Checking Fit

25Chapter 4: Continuous Random Variables

264.1 Density and Distribution Functions

274.2 Expectation, Variance and the Uniform Distribution

284.3 The Normal Distribution and Standardization

294.4 Exponential and Gamma Distributions

304.5 Weibull and Lognormal Distributions

314.6 Quantiles, Percentiles and Probability Plotting for Continuous Models

32Chapter 5: Joint Distributions and Error Propagation

335.1 Joint, Marginal and Conditional Distributions

345.2 Covariance, Correlation and Linear Combinations

355.3 Tolerance Stack-Up and Worst-Case Analysis

365.4 Propagation of Measurement Uncertainty

375.5 Bivariate Normal and Conditional Prediction

38Chapter 6: Sampling Distributions and the Central Limit Theorem

396.1 Random Samples and Statistics

406.2 The Central Limit Theorem and the Distribution of the Sample Mean

416.3 Chi-Square, $t$ and $F$ Distributions

426.4 Distribution of the Sample Variance and the Variance Ratio

436.5 Simulating Sampling Distributions

44Chapter 7: Point Estimation

457.1 Estimators, Bias and Mean Squared Error

467.2 Efficiency, Consistency and Sufficiency

477.3 Method of Moments Estimation

487.4 Maximum Likelihood Estimation

497.5 Standard Errors, Information and the Bootstrap

50Chapter 8: Interval Estimation

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