The Application Research of Least Square Linear Fitting Based on Logarithmic Transform
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DOI: 10.23977/iset.2019.018
Corresponding Author
Mengqiu Kong
ABSTRACT
This paper theoretically proves that the least square method for linear fitting after logarithmic transformation of data is essentially based on the principle of reducing relative error. Through empirical analysis and comparison with the traditional least square method, it is found that the least square method after logarithmic transformation of data has a higher precision in model fitting effect, and at the same time makes it possible to use the least square method after logarithmic transformation of data to fit the model accurately. Fitting the straight line gives better consideration to the information of all observation points.
KEYWORDS
Linearity after logarithmic, transformation, least square method