Sentences with LEAST-SQUARES
Check out our example sentences below to help you understand the context.Sentences
1
"The least squares algorithm minimizes the sum of the squares of the differences between the observed and predicted values."
2
"In regression analysis, least squares is used to estimate the coefficients of a linear equation."
3
"The least squares solution provides the line that best fits the given data."
4
"The method of least squares was first introduced by Carl Friedrich Gauss."
5
"By minimizing the sum of squared residuals, least squares provides a measure of how well the model fits the data."
6
"The least squares estimator is unbiased when the underlying assumptions are met."
7
"In statistics, least squares is often used to perform linear regression analysis."
8
"The least squares approximation minimizes the vertical deviations between the data points and the curve."
9
"One of the advantages of least squares regression is its simplicity."
10
"The least squares algorithm is sensitive to outliers in the data."
11
"An important assumption of least squares is that errors are normally distributed."
12
"In computer vision, least squares is used for image alignment and feature matching."
13
"In mathematics, the method of least squares is used in curve fitting."
14
"The least squares fit can be obtained analytically or using numerical optimization techniques."
1
"The least squares technique minimizes the sum of the squared differences between the observed and predicted values."
2
"The linear regression model can be estimated using the least squares method."
3
"In statistics, the least squares estimator provides the best unbiased estimates of the model parameters."
4
"The ordinary least squares approach assumes that errors are normally distributed."
5
"The method of least squares can be used to solve overdetermined systems of linear equations."
6
"Multivariate least squares analysis is applied when there are multiple dependent variables."
7
"The least squares criterion aims to find the model that best fits the given data points."
8
"Some non-linear regression models can be linearized and estimated using the least squares technique."
9
"Weighted least squares accounts for the variability in measurement errors."
