Lecture

Linear Regression and Least Squares Method

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Description

This lecture covers the linear regression model and the least squares method, focusing on finding the parameters a and b that minimize the loss function L(a,b) = Σ (Z - atm - b)². The instructor explains the process step by step, emphasizing the importance of minimizing the loss function to obtain the best fit. Various examples and mathematical derivations are provided to illustrate the concepts and practical applications of linear regression. The lecture concludes with a discussion on the significance of the least squares method in statistical modeling and data analysis.

Official source

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related concepts (32)

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.

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.

Least squares

The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each individual equation. The most important application is in data fitting.

Search engine

A search engine is a software system that finds web pages that match a web search. They search the World Wide Web in a systematic way for particular information specified in a textual web search query. The search results are generally presented in a line of results, often referred to as search engine results pages (SERPs). The information may be a mix of hyperlinks to web pages, images, videos, infographics, articles, and other types of files. Some search engines also mine data available in databases or open directories.

Google Search

Google Search (also known simply as Google or Google.com) is a search engine provided and operated by Google. Handling more than 3.5 billion searches per day, it has a 92% share of the global search engine market. It is the most-visited website in the world. Additionally, it is the most searched and used search engine in the entire world. The order of search results returned by Google is based, in part, on a priority rank system called "PageRank".

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