Modern Regression: Inference and Models

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Page 3 of 4

Modern Regression: Statistical Models and Data Analysis

Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.

Inference: Model Checking

Covers iterative weighted least squares, generalized linear models, and model checking.

Linear Models: Least Squares

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Linear Regression: Maximum Likelihood Approach

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.

Regression: Linear Models

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Basics of linear regression model

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Likelihood Inference

Covers iterative weighted least squares, Poisson regression, mixed models, and likelihood ratio statistic.

Regression II

Delves into regression analysis, emphasizing distributional checks, weighted least squares, and hypothesis testing.

Linear Regression: Ozone Data Analysis

Explores linear regression analysis of ozone data using statistical models.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.