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In data fitting, the five commonly used linear fitting methods include least squares, general hypothesis testing, correlation coefficient, linear regression and principal component regression. Here is a discussion of these five methods and an analysis of their advantages and disadvantages: least squares: advantages: least squares is a simple and widely used fitting method. It is easy to calculate the fitting line by minimizing the sum of residual squares between the observation value and the fitting line. Disadvantages: The least square method is sensitive to anomalies, i.e. points that are far from the expected value may have a greater impact on the fitting result. Overall hypothesis testing: Advantages: The overall hypothesis testing method provides a statistical test method by means of the significance test of the data to determine whether there is correlation and fit excellence. Disadvantages: This method requires some assumptions about data, such as error meeting normal distribution, variance homogeneity, etc., which requires high data. Correlation coefficient: Advantages: Correlation coefficient method can measure the strength and direction of the line between the two variables. It provides a simple way to evaluate correlations between data. Disadvantages: correlation coefficients can measure linear correlations, can not describe non-line***. Moreover, correlation coefficients cannot describe causality. Linear regression: Advantages: Linear regression is a common fit that can model and predict data sets. It can consider multiple independent variables for complex situations. Disadvantages: Linear regression assumes that the relationship between the data independent variable and the dependent variable is linear. If the relationship is nonlinear, the fitting result will be inaccurate. Main component regression: Advantages: Main component return ...
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