Multilevel Statistical Modeling for Proteomic Experiments With Complex Designs and Isobaric Labeling
Learn advanced statistical modeling techniques for complex proteomic experiments using isobaric labeling, focusing on multilevel designs and time-series data analysis.
Regression Series
Comprehensive exploration of regression analysis, covering key concepts, advanced techniques, and model assumptions for data-driven decision-making.
Simple Linear Regression
Learn to analyze relationships between variables using linear regression, covering model interpretation, assumptions, inference, and practical applications.
Linear Regression, Clearly Explained
Clear explanation of linear regression concepts, including least squares, R-squared, multivariable models, p-values, and F-distribution. Ideal for beginners seeking a solid foundation in statistical analysis.
Partial Least Squares for Nonlinear Regression
Explore dimension reduction in PLS regression for nonlinear relationships, challenging traditional views and proposing new insights into PLS-like methods for predictive modeling.
Statistical Rethinking 2023 - Generalized Linear Madness
Explore advanced statistical concepts including allometry, state space models, and ordinary differential equations through practical examples and applications in various fields.
Statistical Rethinking 2023 - Missing Data
Explore missing data in DAGs and learn Bayesian imputation techniques for handling incomplete datasets in statistical analysis, with practical examples and insights.
Statistical Rethinking 2023 - Measurement & Misclassification
Explore measurement error, misclassification, and modeling techniques in statistical analysis. Learn practical applications and gain insights into the Himba culture's unique color perception.
Statistical Rethinking 2023 - Correlated Features
Explore correlated features in statistical modeling, covering multivariate priors, non-centered approaches, and practical coding techniques for improved analysis and interpretation.
Statistical Rethinking 2023 - Ordered Categories
Explore statistical rethinking with ordered categories, ethical considerations, and advanced modeling techniques. Gain insights into participation bias, Dirichlet distributions, and practical applications in data analysis.
Statistical Rethinking 2023 - Modeling Events
Explore statistical modeling for events, covering discrimination, mediation, generative models, and generalized linear models. Learn to analyze and compute interventions, with a bonus on survival analysis.
Statistical Rethinking 2023 - Geocentric Models
Explore geocentric models in statistical thinking, covering Gaussian distributions, workflows, generative models, and analysis techniques for improved statistical reasoning.
Statistical Rethinking 2023 - The Garden of Forking Data
Explore Bayesian statistics through the "Garden of Forking Data" metaphor. Learn about generative models, probability, testing, and posterior distributions for improved statistical reasoning and prediction.
Statistical Rethinking 2022 - Generalized Linear Madness
Explore advanced statistical concepts like geometric models, state-based analysis, and population dynamics through innovative examples and practical applications.
Statistical Rethinking 2022 - Correlated Varying Effects
Explore advanced statistical concepts including varying effects, correlations, and non-centered coding. Learn to analyze complex data structures and improve model interpretability in this comprehensive lecture.
Statistical Rethinking 2022 - Multilevel Models
Explore multilevel models through coffee golems, tadpoles, and predators. Learn regularization techniques and statistical concepts for advanced data analysis.
Statistical Rethinking 2022 - Ordered Categories
Explore advanced statistical concepts like ordered categories, cumulative log-odds, and Dirichlet priors. Learn to handle sample bias, confounding, and complex causal effects in statistical analysis.
Statistical Rethinking 2022 - Modeling Events
Explore statistical modeling of events, covering generative models, generalized linear models, logistic regression, and binomial regression. Learn to analyze gender applications and calculate marginal causal effects.
Statistical Rethinking 2022 - Counts & Confounds
Explore statistical rethinking with focus on counts and confounds. Learn about confounded admissions, sensitivity analysis, proxies, factors, and Poisson GLMs in this comprehensive lecture.
Statistical Rethinking - Markov Chain Monte Carlo
Explore Markov chain Monte Carlo and Hamiltonian Monte Carlo methods, including practical implementation in Stan and diagnostic techniques for statistical modeling.
Statistical Rethinking 2022 - Geocentric Models
Explore Bayesian linear regression, covering model language, generative and statistical approaches, validation, and posterior predictions for a comprehensive understanding of statistical modeling.