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In data fitting, the five commonly used straight line fitting methods include least squares method, overall hypothesis testing, correlation coefficient, linear regression and principal component regression. Below is a discussion of these five methods and an analysis of their advantages and disadvantages: Least Squares Method: Advantages: The least squares method is a simple and widely used fitting method. It determines the fitted straight line by minimizing the sum of squared residuals between the observed value and the fitted straight line, and the calculation is simple and convenient. Disadvantages: The least squares method is sensitive to outliers, that is, points that deviate far from the expected value may have a greater impact on the fitting results. Overall hypothesis testing: Advantages: The overall hypothesis testing method determines whether there is a correlation and goodness of fit by testing the significance of the data, providing a statistical testing method. Disadvantages: This method requires some assumptions about the data, such as the error satisfying normal distribution, homogeneity of variances, etc., and has higher requirements on data. Correlation coefficient: Advantages: The correlation coefficient method can measure the strength and direction of the line between two variables. It provides a simple way to evaluate correlations between data. Disadvantages: The correlation coefficient can only measure linear correlation and cannot describe non-linear correlation. Furthermore, correlation coefficients cannot illustrate cause-and-effect relationships. Linear Regression: Advantages: Linear regression is a common fitting method that can be used to model and predict data sets. It can consider multiple independent variables and is suitable for complex situations. Disadvantages: Linear regression assumes that the relationship between the data independent variables and the dependent variables is linear. If the relationship is non-linear, the fitting results will be inaccurate. Principal component regression: Advantages: Principal component regression...
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