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目的：为预测人为飞行事件易发者，积极预防或减少此类事故的发生。方法：随机抽取健康疗养男性歼（ 强） 击机飞行员１６０ 人，年龄２１ ～４７ 岁。依据有无事故征候、等级事故，把１６０ 人分为无飞行事故组 Ａ 和飞行事故组 Ｂ。用艾森克个性问卷测定 Ｎ、 Ｅ、 Ｐ三个量表的得分及年龄和飞行时间，将上述５ 项变量输入微机进行逐步判别分析，对判别函数贡献率大的变量，再做 Ｆｉｓｈｅｒ 两类判别和二值回归判别分析，获得三种等效判别函数式。结果：三种方法的两类总体判别效力检验极其显著（ Ｆ＝ ３３ ．９５ ， Ｐ＜ ０ ．００１） 。内部和外部检验符合率分别为７３ ．７５ ％ 和７２ ．５０ ％ 。另１００ 名男性现役歼（ 强） 击飞行员跟踪观察２ ．５ ～３ 年，预测阴性和阳性符合率分别为７９ ．６９ ％ 和６９ ．４４ ％ 。结论：用数学模式预测飞行人员的人为飞行事故，对保障新生选拔和飞行训练的安全、降低飞行事故将产生良好效果。
Purpose: To prevent or minimize the occurrence of such incidents in order to predict the vulnerability of man-made flight incidents. Methods: A total of 160 male F-76 pilots were selected randomly from the age of 21 to 47 years old. Based on whether there was an incident or a grade accident, 160 people were divided into flight-free group A and flight accident group B. Eysenck personality questionnaire was used to measure the scores of N, E, P three scales and the age and flight time. The above five variables were input to the microcomputer for stepwise discriminant analysis. For the variables with large contribution rate of discriminant function, Fisher Discriminant and binary regression discriminant analysis, to obtain three equivalent discriminant function. Results: Two kinds of overall discriminant validity test of the three methods were extremely significant (F = 33.95, P <0.001). The internal and external test coincidence rates were 73. 75% and 72. 50%. Another 100 men active duty J (strong) pilots follow the observation 2. 5 to 3 years, the negative and positive predictive rates were 79, respectively. 69% and 69. 44%. Conclusion: Predicting the manned flight accidents of pilots with mathematical models will have a good effect on the safety of freshmen selection and flight training and the reduction of flight accidents.