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Optimization of Industrial structure has played a significant role in the process of national economy’s sustainable and stable growth. With six affecting factors including proportion of labor productivity, proportion of employed people, proportion of fixed-asset investment, proportion of the actual utilization of foreign capital, the proportion of total energy consumption, and proportion of resident population of Beijing from 2000 to 2015, this paper first uses the method of multiple linear regression model to study these variables’ influence on proportion of tertiary industry, and finds that the model itself has multi-collinearity. Then, the method of Ridge Regression and variable selection model are utilized to quantify these variables’ influence over proportion of tertiary industry. The study shows that these six factors had significantly affected the development of tertiary industry. The most influential one among of them is energy, then is population and investment. By using the method of ridge regression model to quantitatively analyse these factors, this paper proposes related advice about the development of the tertiary industry and deepening of industrial structure from three perspectives of energy, population and investment. Keywords—Tertiary industry; Ridge regression; Affecting factors

Analysis of Affecting Factors of Proportion of Tertiary Industry Based on Ridge Regression