method of least squares
Học thuậtThân thiện
Definition
- Noun:
- A statistical technique for curve fitting: The method of least squares is a standard approach in regression analysis. It determines the best-fitting line or curve to a set of data points by minimizing the sum of the squares of the vertical distances (residuals) between the observed values and the values predicted by the model.
Usage Examples
- Noun:
- The researcher used the method of least squares to find the linear trend in the experimental data.
- To estimate the parameters of the model, we apply the method of least squares.
Advanced Usage
- "Ordinary least squares (OLS)": This is the most common application of the method of least squares for linear regression, where the goal is to minimize the sum of squared residuals.
- The OLS estimator is derived directly from the method of least squares.
- "Applying the principle of least squares": Refers to using the core idea of minimizing the sum of squared errors.
- The algorithm works by applying the principle of least squares to iteratively improve the fit.
Variants and Related Words
- Least squares estimation (n): The process of calculating parameter estimates using the method of least squares.
- The results of the least squares estimation were highly significant.
- Least squares regression (n): A type of regression analysis that employs the method of least squares.
- A least squares regression line was fitted to the scatterplot.
Synonyms
- Least squares approximation: Emphasizes the aspect of finding an approximate model that fits the data.
- Least squares fitting: Focuses on the action of fitting a curve to data using this principle.
Related Concepts (Not Phrasal Verbs or Idioms)
- Residual: The difference between an observed value and the value predicted by the model. The method minimizes the sum of the squares of these residuals.
- Sum of squared errors (SSE): The quantity that the method of least squares aims to minimize.
- Regression line: The line of best fit produced by applying the method of least squares to linear data.
Noun
- a method of fitting a curve to data points so as to minimize the sum of the squares of the distances of the points from the curve