Lectures related to Modeling Response Surfaces in Matlab

Statistical Inference for Bandit Data

Explores statistical inference for bandit data, focusing on personalized treatment actions and challenges of standard estimators.

Linear Regression: Maximum Likelihood Approach

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

Geometry and Least Squares

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

Linear Least Squares: Minimizing Error Coefficients

Explores linear least squares, normal equations, and the importance of linear regression in minimizing errors.

Multilinear Regression: Least Square Fit

Covers multilinear regression using least square fit method and the importance of standardizing variables.

Linear Regression: Least Squares

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

Box-Jenkins Methodology: Building Time Series Models

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.

Linear Equations: Least Squares Solution MOOC: Linear Algebra (Part 3)

Explains how to solve linear equations using the least squares method to minimize errors in the system.

Linear Regression Testing

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Regression II

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

Hadamard Design: Introduction and Construction

Introduces Hadamard and Plackett-Burman designs, explaining construction, analysis, and optimization techniques.

Direct LiNGAM Algorithm

Covers the Direct LiNGAM Algorithm, focusing on causal order estimation and residual minimization.

Multilinear Regression: Basics and Applications

Covers the basics of multilinear regression and its application in analyzing material properties.

Singular Value Decomposition: Theory and Applications

Explores Singular Value Decomposition theory, linear system solutions, least squares, and data fitting concepts.

Multilinear Regression: Standardized Variables and Effects

Explores standardizing variables and effects in multilinear regression analysis.

Plackett-Burman Design: Hadamard Matrix

Explores the construction and application of Hadamard matrices for efficient estimation of main effects without interactions in the Plackett-Burman Design.

Heteroskedasticity: Ch. 4a Log in to Mediaspace to watch this video

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

Heteroskedasticity and Autocorrelation Log in to Mediaspace to watch this video

Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.

Least Squares Approximation Log in to Mediaspace to watch this video

Explains least squares approximation for finding best fit lines or curves to data points.

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.