Statistical Methods for Engineers and Scientists
Applied Probability, Reliability Data, and Pattern Recognition for Machine LearningBy Priya Ramanathan
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
Table of contents
1Statistical Methods for Engineers and Scientists
2Foreword
3Preface
4About This Book
5Chapter 1: Engineering Data and Descriptive Statistics
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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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