Lectures related to Omitted-variable bias

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Instrumental Variables: Addressing Measurement Error and Reverse Causality

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Autocorrelation and Instrumental Variables

Explores autocorrelation, instrumental variables, and challenges in regression analysis.

Experimental Design: Variables and Hypotheses

Covers research questions, hypotheses, variables, and experimental designs in learning experiments.

Full Factorial Design Introduction

Covers the basics of full factorial design, coding, graphical outputs, and data analysis.

Advanced Analysis II: Resonant Cases and Variable Coefficients

Covers resonant cases, variable coefficients, and methods for solving differential equations.

Polymer Modeling: Interactions and Scaling Behavior

Explores polymer modeling, interactions, and scaling behavior of real polymers.

Implicit Functions Theorem Corrections

Addresses corrections to the implicit functions theorem proof and clarifies the role of specific variables.

Regression Modeling: Variable Selection

Explores regression modeling, emphasizing the importance of interpretation and prediction in model building.

Variance Reduction Techniques

Covers variance reduction techniques in stochastic simulation to improve output quantity estimation accuracy.

Linear Regression: Basics and Geometric Interpretation

Explores Gaussian linear regression, design matrix, least squares estimation, and geometric interpretation in linear regression analysis.

Radiative Exchange: Specular View Factors

Covers specular view factors, radiative exchange, energy transfer, and numerical integration methods in thermal radiation.

Partial Derivatives: Composition of Functions

Explores partial derivatives in composing functions, continuity proofs, mean value theorem applications, and integral calculus implications.

Linear Systems: Operations and Solutions

Explores linear systems, row operations, and solution methods, emphasizing geometric interpretations and unique solutions.

Partial Differential Equations

Introduces partial differential equations, covering derivatives, special cases, transformations, and the Jacobian matrix.

Propagation of Uncertainty II

Explores the propagation of uncertainty in correlated variables and extreme correlations, Tchebychev inequality, confidence intervals, and Taylor series development.

Linear Approximation and Derivative Parametric

Covers linear approximation, parametric derivatives, and differentiability conditions on intervals.

Linear Regression: Understanding Quantitative Relationships

Covers linear regression, from developing research questions to interpreting R-squared and adding predictors to improve the model.

Thermodynamics and Energetics I

Delves into thermodynamics basics, calculating energy changes, constructing tables, and using Maxwell relations for thermodynamic relations.

Heteroskedasticity: Ch. 4a

Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.