Kowalski C J, Schneiderman E D, Willis S M
Department of Biologic and Materials Sciences, Dental School, University of Michigan, Ann Arbor 48109.
Int J Biomed Comput. 1994 Nov-Dec;37(3):273-86. doi: 10.1016/0020-7101(94)90125-2.
The Johnson-Neyman (JN) procedure, as originally formulated (Stat Res Mem, 1 (1936) 57-93), applies to a situation in which measurements on 1 dependent (response) variable, X, and 2 independent (predictor) variables, Z1 and Z2, are available for the members of 2 groups. The expected value of X is assumed to be a linear function of Z1 and Z2, but not necessarily the same function for both groups. The JN technique is used to obtain a set of values for the Z variables for which one would reject, at a specified level of significance alpha (e.g., alpha = 0.05), the hypothesis that the 2 groups have the same expected X values. This set of values, or 'region of significance,' may then be plotted to obtain a convenient description of those values of Z1 and Z2 for which the 2 groups differ. The technique can thus be described as a generalization of the analysis of covariance (ANCOVA) which does not make the assumption that the regression coefficients for the regression of X on the covariates, Z1 and Z2, are equal in the groups being compared. In this paper we describe, illustrate and make available a menu-driven PC program (TXJN2) implementing the JN procedure.
最初提出的约翰逊 - 奈曼（JN）方法（《统计研究纪要》，1（1936）57 - 93）适用于这样一种情况：对于两组的成员，可获得关于1个因变量（响应变量）X以及2个自变量（预测变量）Z1和Z2的测量值。假设X的期望值是Z1和Z2的线性函数，但两组的函数不一定相同。JN技术用于获取Z变量的一组值，在指定的显著性水平α（例如，α = 0.05）下，人们会拒绝两组具有相同期望X值的假设。然后可以绘制这组值，即“显著区域”，以便方便地描述Z1和Z2的那些值，对于这些值两组存在差异。因此，该技术可描述为协方差分析（ANCOVA）的一种推广，它不假定在被比较的组中X关于协变量Z1和Z2的回归系数相等。在本文中，我们描述、说明并提供了一个实现JN方法的菜单驱动的个人计算机程序（TXJN2）。
