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This study investigates the relationship between overall job satisfaction and satisfaction with various job facets. Linear and nonlinear models for combining facets are examined. The results provide marginal support for an hypothesis that overall job satisfaction is a linear function of satisfaction with various job facets. Specifically, satisfaction with job facets explains 50%-60%o of overall job satisfaction based on a linear combination of facet satisfactions. The results also indicate that facetbased instruments used individually are not sufficient measures of overall job satisfaction. When facets from multiple facet-based instruments are combined, a marginally sufficient measure is obtained.

ABSTRACTThis paper illustrates the use of the Box‐Jenkins methodology by analyzing the Ohio Electrical Power Consumption time series. The three basic analytical steps discussed are the following:1) Model identification ‐ by matching sample autocorrelation functions against theoretical autocorrelation functions.2) Non‐linear estimation of parameters ‐ by minimizing the sum of the squared residuals.3) Diagnostic checking ‐ by analyzing the pattern of the autocorrelation function of the residuals.Regular and adaptive forecasts are then developed using the appropriate model that emerges from the time series analysis.