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M. F. (1986). Some relationships between descriptive comparisons of components from different studies. Multivariate Behav. , 21, 29–40. 2233 FAILURE, RATE OF OCCURRENCE (ROCOF) In a point process* N(t), which describes the occurrence of events in time (interpreted as the failures of a repairable system), the mean function of the process is defined as (t) = E[N(t)], representing the expected number of failures through time t. Since N(t) is a nondecreasing step function (almost surely), (t) is also nondecreasing and right continuous.
1977). Psychometrika, 42, 567–578. 5. Ledermann, W. (1939). Proc. R. Soc. Edinburgh, 60, 1–17. 6. Shapiro, A. (1982). Psychometrika, 47, 187–199. 7. Shapiro, A. (1982). Psychometrika, 47, 243–264. 8. Shapiro, A. (1983). S. Afr. Statist. , 17, 33–81. (Sections 3 and 5 discuss an asymptotic distribution theory of an extremal-value function of the sample covariance matrix. ) 9. Ten Berge, J. M. , Snijders, T. A. , and Zegers, F. E. (1981). Psychometrika, 46, 201–213. See also FACTOR ANALYSIS; FACTOR ANALYSIS BY INSTRUMENTAL VARIABLES (FABIN); and FACTOR ANALYSIS, MINIMUM RANK.
The concept of a factorial experiment is that more than one factor is investigated simultaneously in a single experiment. For example, in Example 1 not only is the temperature important but also the amount of time that the sample is submitted to a temperature. Thus Time could well be a second factor. A third factor might be the Type of lumber. Implicit in a factorial experiment is that the same number of samples occur for each combination of levels of the factors. Hence for the example above the same number of samples of each Type of lumber would be submitted to each Temperature–Time combination.