A semester-long study of topics in Statistics. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics. See the New and Topics Courses page on the Registrar's webpage for descriptions and applicability to graduation requirements.
A semester-long study of topics in Statistics. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics. See the New and Topics Courses page on the Registrar's webpage for descriptions and applicability to graduation requirements.
An introduction to the fundamental ideas in probability important for data scientists and statisticians. The core concepts of combinatorics, expectation, variance, conditional probability/expectation/distributions, covariance, and correlation will be motivated and explored through discrete and continuous random variables. The course will touch on the law of large numbers and the central limit theorem. Selected topics may be included such as the Poisson process, Linear Models, Markov chains, Bayesian methods.
An introduction to statistical inference based on probability and calculus. Students will learn about classical and Bayesian perspectives on estimation and hypothesis testing in many contexts including numerical data, categorical data, and linear models. Some coding in R will be done for simulations. The class will explore properties of different estimation techniques such as method of moments, maximum likelihood, and Bayesian methods.
This course will discuss how statistical methods are integrated throughout technology development processes to create high quality products. This class will feature methods around defining quality, measuring quality, sampling methods, design of experiments, statistical process control, and Six Sigma methodology. Data scientists are driving advancements in the 4th industrial revolution. Through this class, students will gain skills to tackle complex engineering problems and drive the next wave of innovation in tech.
A semester-long study of topics in Statistics. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics. See the New and Topics Courses page on the Registrar's webpage for descriptions and applicability to graduation requirements.
A semester-long study of topics in Statistics. Topics and emphases will vary according to the instructor. This course may be repeated for credit with different topics. See the New and Topics Courses page on the Registrar's webpage for descriptions and applicability to graduation requirements.
This course provides an in-depth exploration of survey design, with a strong emphasis on sampling methodology and data analysis. Students will learn how to create effective surveys that address research objectives while minimizing bias and error. A significant focus of the course will be on various sampling techniques, including simple random sampling, stratified sampling, cluster sampling, and systematic sampling. In addition to sampling, students will be introduced to key principles of data analysis. Topics include cleaning and coding survey data, handling missing data, and applying descriptive and inferential statistical techniques to draw meaningful conclusions. The course covers the entire survey process from conceptualization to data analysis.
In this course students will use linear algebra to (1) compute least-squares estimates for regression coefficients, (2) construct analysis of variance (ANOVA) and prove partition the partitioning of variability, and (3) perform prediction and statistical inference for regression models. This class will not only prove regression properties rigorously from a theoretical standpoint, but will apply models to real world data using statistical software (R).
Willamette University