Analysis of Influencing Factors of Life Insurance in Beijing Area

Abstract

Abstract-In recent years, people have been increasingly concerned about life security in the future. Based on the data of life insurance in Beijing from 1997 to 2015, this paper analyzes the long-term impact and short-term impact of various factors, using vector error correction model. It indicates that regional final consumption expenditure and GDP have both long-term and short-term effects on the demand for life insurance. In conclusion, in long run national consumption needs to be further stimulated; in short run, proper measures should be used for publicity. Market strategy should be constantly innovated, and computer applications also need to upgrade.

Motivation Differences Between Different Groups of International Business Students in Guangdong Communication Polytechnic

“…This internal drive includes the needs, interests, targets, attitudes, and the efficiencies of studying (Figure 1). Scholars have researched the major factors that influence the internal drive, which can be divided into two parts: internal (desire, awareness of the importance of studying, and the sense of achievement from learning) and external (needs of self enhancement, the learning environment, work requirements, family support, the future benefits of learning, emphasis from leaders, competition from society, and the opportunity to communicate with others; Zhang, 2011). The internal drive can be influenced by both internal (willing, interesting, and the characteristic) and the external aspects (the social environment and different locations; Ren, 2015).…”

Analysis of the Hedging Rate of Used Cars in Beijing by Linear Regression Model

“…She used 22 indices about the basic configurations, car conditions, and market characteristics. Zhang predicted the hedging rate of used cars in Beijing by using a multiple linear regression model and random forest model regression model [3]. Sameerchand used the combination of multiple linear regression, K-nearest neighbor, decision trees, and Naive Bayes to predict the price of used cars [4].…”