Application of multiple regression analysis in tax decision

Abstract

The invention provides application of multiple regression analysis in tax decision. Factors affecting tax decision are analyzed; related data is collected and sorted; decisions or recommendations are made using a regression analysis model; the tax department is assisted in solving decision process problems, such as high emotional influence and lack of data support. The factors affecting tax decision feature complexity, dynamism, finiteness and the like, so that the problems such as variety and high upgrade frequency occur in the acquisition process of basic data. The application of the multiple regression analysis has the advantages that no special requirements on basic data are required, and both discrete variables and continuous variable are available; an analysis result is an event probability more scientific and reasonable, perdition precision is high, and prediction results are stable.

Description

technical field [0001] The present invention relates to the field of taxation decision-making and the field of data statistical analysis. Through information collection, the data is analyzed and mined, and the taxation decision-making is quantitatively evaluated according to the taxation business and policy environment to meet the needs of customers. Specifically, it is a multi-regression-based Application of analytics to tax decision-making. Background technique [0002] In the field of tax decision-making, the main decision-making methods are perceptual subjective decision-making and AHP using statistical means. [0003] Subjective decision-making methods are often affected by factors such as the quality of the decision-making subject and stakes, leading to problems such as wrong direction and low quality of decision-making. Differences in the personal qualities of decision-making subjects directly affect the decision-maker's political ability, level of understanding, leg...

AI Technical Summary

Problems solved by technology

In the decision-making process, the decision-making subject often chooses the decision-making plan that is beneficial to him, rather than the optimal plan, out of consideration of his own interests, which leads to the sub-optimization of decision-making.

[0004] Due to the reasons of the algorithm itself, the AHP has the following disadvantages: First, it cannot provide decision makers with new solutions to problems, but only selects the better one from the alternatives

Second, using the decision-making method of simulating the human brain has more qualitative colors, less quantitative data, and more qualitative components, which is not easy to be convincing

Third, the increase of indicators means constructing a judgment matrix with a deeper level, more quantity, and a larger scale. The amount of data statistics is large, and the difficulty of judging the importance of indicators increases.

Fourth, in the process of solving the eigenvalues ​​and eigenvectors of the judgment matrix, the exact method is complicated

Existing tax decision-making methods only rely on personal knowledge and experience or traditional algorithms, without scientific data support, and lack of flexible change mechanism in the decision-making process, which hinders the development of current tax business

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