HKUST, Department of Industrial Engineering and Decision Analytics

IEDA5270: Engineering Statistics

This course develops a rigorous statistical toolkit for engineering data analytics, covering probability foundations, random-sample theory, estimation and inference, regression modeling, regularization, generalized linear models, and modern classification methods.

All courses

Course Snapshot

  • Institution: HKUST, Department of Industrial Engineering and Decision Analytics
  • Period: 2019–present
  • Audience: Graduate students in engineering and data analytics, and advanced undergraduates with strong quantitative backgrounds.
  • Prerequisites: Probability theory, linear algebra, calculus, and basic programming/statistical computing.

Learning Outcomes

  • Formulate statistical inference problems and interpret assumptions in engineering contexts.
  • Construct and evaluate point estimators, hypothesis tests, and confidence sets.
  • Build and diagnose linear and generalized linear regression models.
  • Apply model selection, regularization, and cross-validation for predictive performance.
  • Compare and deploy classification methods based on data characteristics and evaluation metrics.

Lecture Modules

Materials

CH1

Review of Probability Theory

Events, conditional probability, random variables, expectation, transformations, and multivariate distributions as the foundation for inference.

Open Handout

CH2

Properties of a Random Sample

Sampling distributions, moment generating functions, order statistics, LLN/CLT, and delta-method approximations.

Open Handout

CH3

Principles of Data Reduction

Sufficient, minimal sufficient, and complete statistics, with factorization and exponential-family perspectives.

Open Handout

CH4

Point Estimation

Method of moments, maximum likelihood, Fisher information, Cramer-Rao bounds, and unbiased estimation under sufficiency/completeness.

Open Handout

CH5

Hypothesis Testing

Test construction, simple and composite hypotheses, UMP tests, likelihood-ratio methods, and sequential testing principles.

Open Handout

CH6

Confidence Set

Confidence-set construction via pivotal quantities and test inversion, plus asymptotic, bootstrap, and Bayesian intervals.

Open Handout

CH7

Regression Models

Multiple linear regression, least squares theory, inference, nested model tests, diagnostics, Box-Cox transforms, spline, and robust methods.

Open Handout

CH8

Model Selection and Regularization

Subset selection, ridge and lasso shrinkage, model criteria (Adjusted R2/Cp/AIC/BIC), and cross-validation workflows.

Open Handout

CH9

Generalized Linear Model

Logistic and Poisson regression, exponential-family formulation, estimation algorithms, and GLM inference/testing.

Open Handout

CH10

Classification Methods

LDA/QDA, k-NN, tree-based ensembles, and SVM methods for supervised classification tasks.

Open Handout