Statistical Analysis Research On Influential Factors Of College Tutors’ Self-Effcacy

The influence factors of self-efficacy has got a good research, but to work in a line of the instructor, and from two aspects of the study of literature is rare. Research on this kind of counselors self-efficacy and its influence factors, countermeasures have certain practical significance.First of all, based on the research situation of self-efficacy, in turn, self-efficacy level and influence factors in two aspects, and puts forward the description of the current situation of the self-efficacy function model, the multivariate linear regression model. In the self-efficacy of understanding of the basis of the use of self-efficacy of each affecting factor score to measure its level, so as to ensure the multiple linear regression model is reasonable.Secondly, the paper build multivariate linear regression model to describe their efficacy, and by using questionnaire survey data of the regression model to estimate the parameters. In order to further shows the accuracy of the models and rationality, and from two aspects to verify analysis:all the factors proportion of the Angle and tectonic comparative matrix to me to weight vectors Angle. Through the analysis of the two aspects of the calculation results, find out the results and linear regression results, it also suggests that model is accurate and reasonable. Through the multivariate linear regression model, can not only predict don’t take measures, the trend of the development of self-efficacy, also can judge the effect in all the self-efficacy factors, which factors for proportion is bigger, this is the university provides an important clue, also is the university can use the results to make corresponding measures to promote their self-efficacy.Finally, this paper in order to further explain the accuracy of the questionnaire investigation, interview and literature research method were used to analyze the analysis the influence factors of self-efficacy. Finally the results from the multiple linear regression model with the same results.