Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Dummy variable (statistics)

Related lectures (30)

Regression Analysis: Disentangling Data

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Canonical Correlation Analysis: Overview

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Prediction Tests MOOC: Introduction to Discrete Choice Models

Discusses predicting future behavior, identifying outliers, and improving model specification.

Linear Regression Analysis

Introduces linear regression analysis, covering model building, predictors, coefficients, and outcome interpretation.

Multicollinearity and Model Fit Analysis

Explores the dangers of 'big' models, multicollinearity issues, and model fit analysis in statistics for data science.

Logistic Regression: Model Interpretation and Comparison

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

Causal Inference: Understanding Treatment Effects

Explores treatment effects, confounding variables, and challenges in interpreting observational study results.

Practical Aspects of Gaussian Linear Model

Explores practical aspects of the Gaussian linear model, focusing on variable selection and regularization methods.

Linear Regression: Basics and Applications

Covers the basics of linear regression, including 1D regression, derivatives, gradients, and applications in machine learning.

Model Selection: Least Squares

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Multicollinearity: Dangers and Remedies

Explores the dangers of multicollinearity in linear models and discusses diagnostic methods and remedies.

Two-way ANOVA and Chi-square Test

Explores two-way ANOVA, chi-square test, model comparison, and variable transformation for statistical analysis.

Ridge Regression: Penalised Least Squares

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Model Selection Methods in Biostatistics

Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.

Statistics Essentials: Chi-Square Test

Covers the Chi-square test to assess relationships between categorical variables through contingency tables.

Statistics Essentials: The t-test

Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.

Analysis of Variance (ANOVA)

Covers Analysis of Variance (ANOVA) for assessing categorical variable effects on quantitative outcomes.

Penalization in Ridge Regression

Covers penalization in ridge regression, emphasizing the trade-off between bias and variance in regression models.

Model Building: Linear Regression

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.