What Happens When Maths Goes Wrong? - With Matt Parker
Explore real-world consequences of mathematical errors with comedian Matt Parker. From bridge collapses to plane crashes, discover how small miscalculations can lead to big disasters in this entertaining and eye-opening talk.
The Power of Mathematical Visualization - The Power of a Mathematical Picture
Discover the power of visual mathematics with Professor James S. Tanton, exploring innovative problem-solving techniques across various mathematical fields through engaging graphics and practical projects.
Multiblock Data Fusion
Explore multiblock data analysis techniques for integrating diverse data sources in natural and life sciences, covering key methods, applications, and interpretation strategies.
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.
But What Is the Central Limit Theorem?
Visual exploration of the Central Limit Theorem, explaining its significance in probability and statistics through animations, simulations, and real-world examples.
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.
Statistics Lecture - Introduction to Probability
Comprehensive introduction to probability concepts, covering sample spaces, events, observed and classical probabilities, and subjective probability, with practical examples and applications.
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.
Practical Statistics for Data Scientists - Statistical Experiments Significance Testing
Learn statistical experiments and significance testing for data science, covering A/B testing, hypothesis testing, resampling, and ANOVA, with practical examples and insights.
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 - 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 - 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 - 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 - Markov Chain Monte Carlo
Explore Markov Chain Monte Carlo methods, from basic concepts to advanced techniques like Hamiltonian Monte Carlo, with practical applications and diagnostics for statistical modeling.
Statistical Rethinking 2023 - Good & Bad Controls
Explore causal inference, do-calculus, and the backdoor criterion. Learn to distinguish good and bad controls in statistical analysis, with insights on the Table 2 Fallacy.
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.