Concept
Count data
Related lectures (23)
Inference: Model Checking
Covers iterative weighted least squares, generalized linear models, and model checking.
Modern Regression: Spring Barley Data
Covers inference, weighted least squares, spring barley data analysis, and smoothing techniques.
Modern Regression: Overdispersion and Model Assessment
Explores overdispersion, model assessment, and regression techniques for count data.
Estimation and Analysis of Deviance
Covers estimation of unknown parameters, analyzing model fit, bird prey response, and model diagnostics.
Modern Regression: Spring Barley Data
Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.
Marginal Models: Interpretation and Application
Explores marginal models in modern regression, emphasizing interpretation and application in statistical analysis.
Modern Regression: Statistical Models and Data Analysis
Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.
Quadrature Proof: Exam Blanc
Explores the proof of quadrature, accuracy, exactitude, and interpolation in mathematical calculations.
Binary Response: Link Functions
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Happiness and Growth: Understanding the Relationship
Examines the complex relationship between happiness and economic growth, highlighting the impact of consumption satisfaction, relative prosperity, and key determinants of happiness.