Explores standardizing variables and effects in multilinear regression analysis.
Explores multilinear regression, variance, correlation, optimization, ANOVA, and design procedures.
Covers modeling response surfaces in Matlab, including building the model, defining the domain, computing values, and analyzing residuals.
Covers the basics of multilinear regression and its application in analyzing material properties.
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Explores linear regression estimation, hypothesis testing, and practical applications in statistics.
Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.
Covers simple and multiple linear regression, including least squares estimation and model diagnostics.
Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Covers the Recursive Least-Squares algorithm with weighted formulation for real-time data updating.
