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Valery Blokhin
Valery Blokhin

Statistics And Probability For Engineering Appl...

Applied Statistics and Probability for Engineers provides a practical approach to probability and statistical methods. Students learn how the material will be relevant in their careers by including a rich collection of examples and problem sets that reflect realistic applications and situations. This product focuses on real engineering applications and real engineering solutions while including material on the bootstrap, increased emphasis on the use of p-value, coverage of equivalence testing, and combining p-values. The base content, examples, exercises and answers presented in this product have been meticulously checked for accuracy.

Statistics and probability for engineering appl...


The course is designed to give a basic understanding of probability and statistics, as related to engineering problem solving, with an emphasis on marine applications. Topics covered include basic descriptive statistics, probability analysis, probability distributions, uncertainty estimation, time-series analysis, confidence limits, and regression analysis. While the course involves a blend of theory and analytics, practical applications of probability and statistics to engineering in the marine environment will be emphasized. Additionally, this course covers the general topics of the Fundamentals of Engineering (FE) exam.

Midshipmen may seek validation and earn credit for this course under one of two circumstances: (1) if SM219 has been validated or (2) if a probability and statistics course is taken while on Service Academy Exchange or Semester Study Abroad. This must be approved by the Course Coordinator and Midshipman's Academic Adviser. To be considered for validation, the student should complete a self-learning module on random waves as outlined below and submit the completed problem sets to the Course Coordinator:

STAT 311 Elements of Statistical Methods (5) NSc, RSNElements of good study design. Descriptive statistics including correlation and regression. Introductory concepts of probability and sampling; binomial and normal distributions. Basic concepts of hypothesis testing, estimation, and confidence intervals; t-tests and chi-square tests. Experience with computer software. Prerequisite: either STAT 220, STAT 221/CS&SS 221/SOC 221, STAT 290, MATH 120, MATH 124, MATH 125, MATH 126, MATH 134, MATH 135, MATH 136, Q SCI 190, or QMETH 201. Offered: AWSpS.View course details in MyPlan: STAT 311

STAT 340 Introduction to Probability and Mathematical Statistics I (4) RSNFundamentals of probability for statistics; axioms of probability, conditional and joint probability, independence; random variables, univariate and multivariate distributions and densities, moments, and moment generating functions; binomial, negative binomial, geometric, Poisson, uniform, normal, exponential distributions; and transformations of a random variable. Prerequisite: either MATH 126 or MATH 136; and either STAT 311, STAT 390/MATH 390, or Q SCI 381. Offered: A.View course details in MyPlan: STAT 340

STAT 390 Statistical Methods in Engineering and Science (4) NScConcepts of probability and statistics. Conditional probability, independence, random variables, distribution functions. Descriptive statistics, transformations, sampling errors, confidence intervals, least squares and maximum likelihood. Exploratory data analysis and interactive computing. Cannot be taken for credit if credit received for STAT509/CS&SS 509/ECON 580. Prerequisite: either MATH 126 or MATH 136. Offered: AWSpS.View course details in MyPlan: STAT 390

STAT 391 Quantitative Introductory Statistics for Data Science (4)The basic concepts of statistics, machine learning and data science, as well as their computational aspects. Statistical models, likelihood, maximum likelihood and Bayesian estimation, regression, classification, clustering, principal component analysis, model validation, statistical testing. Practical implementation and visualization in data analysis. Assumes knowledge of basic probability, mathematical maturity, and ability to program. Prerequisite: either CSE 312, or STAT 394/MATH 394 and STAT 395/MATH 395. Offered: Sp.View course details in MyPlan: STAT 391

STAT 509 Econometrics I: Introduction to Mathematical Statistics (4)Examines methods, tools, and theory of mathematical statistics. Covers, probability densities, transformations, moment generating functions, conditional expectation. Bayesian analysis with conjugate priors, hypothesis tests, the Neyman-Pearson Lemma. Likelihood ratio tests, confidence intervals, maximum likelihood estimation, Central limit theorem, Slutsky Theorems, and the delta-method. Prerequisite: STAT 311/ECON 311; either MATH 136 or MATH 126 with either MATH 308 or MATH 309. (Credit allowed for only one of STAT 390, STAT 481, and ECON 580.) Offered: jointly with CS&SS 509/ECON 580.View course details in MyPlan: STAT 509

STAT 535 Statistical Learning: Modeling, Prediction, and Computing (3)Covers statistical learning over discrete multivariate domains, exemplified by graphical probability models. Emphasizes the algorithmic and computational aspects of these models. Includes additional topics in probability and statistics of discrete structures, general purpose discrete optimization algorithms like dynamic programming and minimum spanning tree, and applications to data analysis. Prerequisite: experience with programming in a high level language. Offered: A.View course details in MyPlan: STAT 535

ECE 313 (also cross-listed as MATH 362) is an undergraduate course on probability theory and statistics with applications to engineering problems primarily chosen from the areas of communications, control, signal processing, and computer engineering. Students taking ECE 313 might consider taking ECE 314, Probability Lab, at the same time.

IND E 315 Probability and Statistics for Engineers (3) NScApplication of probability theory and statistics to engineering problems, distribution theory and discussion of particular distributions of interest in engineering, statistical estimation and data analysis. Illustrative statistical applications may include quality control, linear regression, and analysis of engineering data sets. Prerequisite: either MATH 135, MATH 136, MATH 207, or AMATH 351. Offered: AWS.View course details in MyPlan: IND E 315

IND E 515 Optimization: Fundamentals and Applications (5)Maximization and minimization of functions of finitely many variables subject to constraints. Basic problem types and examples of applications; linear, convex, smooth, and nonsmooth programming. Optimality conditions. Saddlepoints and dual problems. Penalties, decomposition. Overview of computational approaches. Prerequisite: Proficiency in linear algebra and advanced calculus/analysis; recommended: Strongly recommended: probability and statistics. Desirable: optimization, e.g. Math 408, and scientific programming experience in Matlab, Julia or Python. Offered: jointly with AMATH 515/MATH 515.View course details in MyPlan: IND E 515

IND E 517 Markov Decision Processes (3)Markov Decision Processes (MDPs) encapsulate a broad class of mathematical models for solving sequential decision problems under uncertainty. Combines techniques from linear/convex optimization, probability, statistics, and machine learning to build a modeling, theoretical, and algorithmic foundation for MDPs. Prerequisite: either IND E 508 and IND E 513, other similar classes in optimization and stochastic models, or permission of instructor. Coding experience with languages such as MATLAB or Python; recommended: graduate level optimization, probability, and statistics. Computer programming.View course details in MyPlan: IND E 517

IND E 546 Inferential Data Analysis for Engineers (3)Application of statistical methods to analyze transportation systems, with an emphasis on modeling individual behaviors and drawing sound inferences about cause and effect. Addresses linear regression and common misuses; generalized linear models including logit and negative binomial; multilevel modeling; matching methods. Emphasizes frequentist approaches but introduces Bayesian analysis and extensions of regression modeling to machine learning. Prerequisite: either IND E 315, STAT 390, or equivalent; recommended: standard introductory probability and statistics course. Offered: jointly with CET 521; W.View course details in MyPlan: IND E 546

TMATH 110 Introductory Statistics with Applications (5) NSc, RSNAddresses introductory statistical concepts and analysis in modern society. Includes descriptive statistics, graphical displays of data, the normal distribution, data collection, probability, elements of statistical inference, hypothesis testing, and linear regression and correlation. Practical examples used to demonstrate statistical concepts. Prerequisite: a minimum grade of 2.0 in either TMATH 098, MATH 098, TMATH 109, TMATH 124, or MATH 124, a minimum score of 237 on the UWT modified placement exam based on the ACC-AAF exam, or a minimum score of 200 on the Tacoma Directed Self Placement Math Test.View course details in MyPlan: TMATH 110

TMATH 172 Mathematics for Teachers II: Statistics and Modeling (5) RSNAimed at students planning to be elementary/middle school teachers, but any student interested in exploring essential mathematical concepts, skills, and representations in a humanized way may benefit. Topics include statistics, probability, and mathematical modeling. We will also explore mathematical connections to our identities as math learners.View course details in MyPlan: TMATH 172

TMATH 390 Probability and Statistics in Engineering and Science (5) NSc, RSNInvestigates probability and statistics using exploratory data analysis and interactive computing. Study topics including conditional probability, independence, random variables, distribution functions, descriptive statistics, transformations, sampling errors, confidence intervals, least squares, and maximum likelihood. Prerequisite: a minimum grade of 2.0 in either TMATH 126 or MATH 126.View course details in MyPlan: TMATH 390 041b061a72


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