Lectures related to Dependent and independent variables

Experimental Design: Variables and Hypotheses

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

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.

Delves into measuring learning effects in digital education and analytics, covering research questions, variables, experimental design, and bias solutions.

Experimental Design and Analysis

Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.

Autocorrelation and Instrumental Variables

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

Linear Regression: Understanding Quantitative Relationships

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

Canonical Correlation Analysis: Overview

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

Implicit Functions Theorem Corrections

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

Partial Differential Equations

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

Variables: Initialization and Naming

Introduces variables in MATLAB and Octave, covering initialization, naming conventions, and screen output formatting.

Specification of the deterministic part

Covers the specification of the deterministic part in nonlinear transformations of variables and data preprocessing for nonlinearities.

Radiative Exchange: Specular View Factors

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

Geometry and Least Squares

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Measuring Learning: Experimental Design

Explores experimental design for measuring learning outcomes and discusses biases, group equivalence, and within-subjects designs.

Thermodynamics and Energetics I

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

Regression Modeling: Variable Selection

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

Panel data: dynamic model

Explores the dynamic model for panel data analysis and the Markov assumption.

Linear Regression: Basics and Geometric Interpretation

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

Partial Derivatives: Composition of Functions

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