Introduction to Statistics
Comprehensive overview of basic statistical concepts, including measures of central tendency, data distribution, and graphical representations. Learn to analyze and interpret datasets effectively.
Pragmatically Ambitious Multiscale Global Temperature Reconstruction - Finn Lindgren, Edinburgh
Explore pragmatic approaches to global temperature reconstruction using multiscale models, data sources, and statistical techniques for uncertainty quantification and bias correction.
Statistics Lecture - Confidence Interval for the Sample Mean, Population Standard Deviation Known
Learn to calculate confidence intervals for sample means with known population standard deviation, including margin of error and required sample size determination.
Statistics Lecture - The Central Limit Theorem for Statistics Using Z-Score, Standard Score
Explore the Central Limit Theorem, z-scores, and standard scores in statistics. Learn key concepts and their practical applications through examples and explanations.
Survival Analysis with TCGA Data in R - Create Kaplan-Meier Curves
Learn survival analysis using TCGA data in R, including Kaplan-Meier curves. Covers key concepts, data preparation, and practical implementation using survival and survminer packages.
Sampling and Probability - Learning Statistics: Concepts and Applications in R
Explore sampling, probability, and statistical tools for extracting key information from data. Learn about data collection, probabilistic reasoning, and practical applications of Bayes' Rule in everyday life.
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 - Multilevel Adventures
Explore multilevel modeling techniques for analyzing clustered data, focusing on varying confounds, district-level effects, and urban-rural differences in Bangladesh fertility studies.
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 - Categories & Curves
Explore statistical concepts like categories, posterior contrasts, and curves in this comprehensive lecture. Learn advanced techniques for data analysis and modeling using Bayesian methods.
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 - Measurement Error
Explore measurement error in statistical analysis, covering topics like income recall, divorce rates, misclassification, and floating point issues. Gain insights into modeling and addressing data inaccuracies.
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 - 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 2022 - Elemental Confounds
Explore key concepts in statistical analysis: forks, pipes, colliders, and unobserved confounds. Learn to identify and address elemental confounds for more robust research designs.
Science Before Statistics - Causal Inference
Comprehensive workshop on causal inference, covering key concepts like causal design, graph analysis, and Bayesian inference. Equips researchers with tools to navigate complex causal relationships in data analysis.
Bayesian Statistics Without Frequentist Language
Explore Bayesian statistics from a fresh perspective, focusing on its core concepts and applications without relying on traditional frequentist terminology.
Scaling Up Bayesian Inference for Big and Complex Data
Explore advanced Bayesian inference techniques for large-scale datasets, focusing on computational challenges and innovative solutions in machine learning and deep learning applications.