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Lecture# Linear Regression: Least Squares and Normal Equations

Description

This lecture covers the concepts of least squares and normal equations in linear regression, focusing on finding the best-fit line for given data points. It discusses the uniqueness and existence of solutions, the importance of linear independence, and the process of determining the least squares solution. The instructor explains how to calculate the regression line and interpret the results using real-world examples, such as the number of Christian churches and murders in different cities. The lecture emphasizes the mathematical techniques involved in regression analysis and the significance of minimizing errors to obtain accurate predictions.

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Related concepts (32)

MATH-111(e): Linear Algebra

L'objectif du cours est d'introduire les notions de base de l'algèbre linéaire et ses applications.

Related lectures (37)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Linear least squares

Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares include inverting the matrix of the normal equations and orthogonal decomposition methods. The three main linear least squares formulations are: Ordinary least squares (OLS) is the most common estimator.

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.

Nonlinear regression

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form, relates a vector of independent variables, , and its associated observed dependent variables, . The function is nonlinear in the components of the vector of parameters , but otherwise arbitrary.

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Explains linear regression using least squares to minimize errors and solve normal equations.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.