Concept

Non-linear least squares

Related lectures (30)

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

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

Explores splines, emphasizing the least-squares method for interpolating splines and demonstrating its application using MATLAB.

Recursive Least-Squares: Weighted Formulation

Covers the Recursive Least-Squares algorithm with weighted formulation for real-time data updating.

Multilinear Regression: Least Square Fit

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

Modeling Response Surfaces in Matlab

Covers modeling response surfaces in Matlab, including building the model, defining the domain, computing values, and analyzing residuals.

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.

Statistical Inference for Bandit Data

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

Model Specification in Time Series

Covers the identification and model specification in time series analysis, including AR models and least squares estimation.

Linear Equations: Least Squares Solution

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

Electric Cable Potential Analysis

Covers the analysis of electric cable potential based on temperature data.

Newton's method: Hessian and optimization

Explores Newton's method for optimization, emphasizing the role of the Hessian matrix.

NonLinear Regression

Explores non-linear regression models, likelihood estimation, model fitting, and confidence intervals.

Multilinear Regression: Basics and Applications

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

Hadamard Design: Introduction and Construction

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

Least Squares Approximation

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

Approximation of Data

Covers the least squares method for approximating data and handling errors.

Splines: Fundamentals and Applications

Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.

Statistics for Civil Engineering

Covers the fundamentals of statistics, exploring probabilistic models, variability, uncertainty, and data collection.

Geometry and Least Squares

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