Lectures related to Model Checking and Residuals

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

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.

Likelihood Estimation and Least Squares

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

Regression: Linear Models

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Linear Regression: Least Squares

Delves into linear regression, emphasizing least squares estimation, residuals, and variance.

Generalized Linear Models: GLMs for Non-Gaussian Data

Explores Generalized Linear Models for non-Gaussian data, covering interpretation of natural link function, MLE asymptotic normality, deviance measures, residuals, and logistic regression.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Logistic Regression: Modeling Binary Response Variables

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Linear Models: Introduction

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Regression and Classification

Explores regression, classification, linear models, decision trees, and random forests in data analysis.

Statistical Inference: Linear Models Log in to Mediaspace to watch this video

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Basics of Linear Regression Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Basics of linear regression model Log in to Mediaspace to watch this video

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

Linear Regression: Beyond the Basics Log in to Mediaspace to watch this video

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

Generalized Linear Models: A Brief Review Log in to Mediaspace to watch this video

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.