Introductory Statistics
Comprehensive introduction to statistics using Excel, covering data analysis, probability, distributions, hypothesis testing, and regression with practical exercises and real-world applications.
Physics One
Explore advanced physics concepts, from equations of motion to Lagrangian mechanics, using mathematical language to develop authentic physicist thinking skills.
Introducing Linear Algebra
Comprehensive exploration of linear algebra concepts, from basic operations to advanced topics, with practical applications using Julia and Python programming languages.
Chemistry 361B
Comprehensive exploration of quantum mechanics, covering complex numbers, differential equations, atomic models, Schrödinger equation, and molecular structures. Ideal for advanced chemistry students.
Advanced Bayesian Methods
Explore advanced Bayesian computational techniques including Metropolis Hastings, Gibbs sampling, and convergence assessment for efficient statistical analysis and modeling.
Cross-Classified Models
Learn advanced statistical techniques for analyzing complex hierarchical data structures, focusing on model fitting, extensions, and practical applications.
Agent Based Modeling
Explore agent-based modeling for social research, covering model creation, analysis, and documentation, with insights on time dynamics and data quality assessment.
Exploring Introductory Statistics
Comprehensive introduction to statistics covering probability, distributions, hypothesis testing, and data analysis techniques for various scenarios.
Math in Society
Explore voting systems, fair division, graph theory, growth models, financial math, statistics, and probability in this comprehensive applied mathematics course.
Special Topics in Linear Algebra
Explore advanced linear algebra topics including least squares approximation, error-correcting codes, functional calculus, affine transformations, stochastic maps, and C*-algebras.
Stochastic Maps and Bayes' Theorem
Explore categorical probability theory through diagrammatic representations, focusing on stochastic maps, conditional probabilities, and Bayes' theorem.
Categorical Probability Theory
Explore Kleisli categories, probability theory, and stochastic maps. Learn about the Giry monad, Markov kernels, conditional probabilities, and Bayes' theorem in this advanced mathematical study.
Bayesian Theory and Graphical Models
Explore Bayesian theory and graphical models, covering key concepts, applications, and advanced topics in probabilistic reasoning and statistical inference.
ANOVA - Analysis of Variance
Learn statistical analysis techniques with Anova, covering one-way and two-way methods, calculations, notations, and test statistics for effective data interpretation.
Randomization-Based Hypothesis Testing
Learn randomization-based hypothesis testing in under an hour with Professor Christina Knudson.
Introductory Hypothesis Testing
Learn essential concepts and techniques for statistical hypothesis testing, including p-values, errors, and various test types for means and proportions.
Bootstrap Confidence Intervals
Learn to create and interpret bootstrap confidence intervals for various statistical measures, enhancing your data analysis skills.
Regression
Explore point estimation, maximum likelihood, and distribution for regression coefficients, focusing on ordinary least squares and slope estimator analysis.
Maximum Likelihood Estimation
Explore maximum likelihood estimation through examples of binomial, Poisson, and uniform distributions, enhancing statistical analysis skills.
Transformations of Random Variables
Explore linear transformations, sums, and monotone functions in random variables, enhancing your understanding of probability theory and statistical analysis.
Multiple Random Variables - Joint and Conditional Distributions
Explore joint and conditional distributions of multiple random variables, covering discrete and continuous cases, CDFs, independence, covariance, and variance of sums.