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This chapter discusses the two major financial statements the balance sheet and the income statement. In hospitality operations, balance sheets are normally prepared for an overall operation, and income statements are prepared by each of the subordinate operating departments (or divisions). Two basic classifications of costs, direct and indirect, are incurred in a hospitality operation. Departmental income statements report operating costs that are classified as direct costs, that are directly traceable to the department. Indirect costs are costs that are not easily traceable to a specific department, and are usually undistributed costs. Undistributed costs are normally incurred to support the overall facility and will normally appear on a summary income statement. All costs shown in a generic income statement will be shown as cost of sales, and named expenses. Cost of sales was discussed in an example in 1. Calculating the cost of sales will be expanded in this chapter. Four methods of calculating the value of inventory will be discussed and how to adjust the cost of food and beverages used to arrive at net cost of sales will be explained. These adjustments may include interdepartmental transfers, as well as adjustments for employee and promotion meals. Responsibility accounting will be introduced and discussed for profit and cost centers. Allocation methods used to distribute indirect costs to departments will be discussed, as will the effect that a change to sales mix among departments would have on overall profit. A sample balance sheet will be illustrated. An account called retained earnings is demonstrated as the link between the income statement and balance sheet in a corporate

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observed, especially in well-trained athletes (Bramble & Carrier, 1983; Siegmund et al, 1999) However, these relations are not always fixed during an entire race (or trial) To study the transitions in frequency relations between the movement of the rowing handle on a rowing ergometer and breathing, Daffertshofer, Huys, and Beek (2004) computed spectrograms of the handle force and respiration data from a simulated 2 km race by well-trained rowers These were obtained by calculating spectral density estimates over a window that was repetitively shifted in time (as illustrated in Figure 72) Transitions from a 1 1 to a 1 2 frequencylocking ratio between the rowing action and breathing were observed through this technique One drawback of the methods outlined here is that their scope is limited to pairwise comparisons Excellent performance in real-world settings most often comes about through the coordination and cooperation of a variety of components.

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Melvin M. Mark, PhD Department of Psychology Pennsylvania State University University Park, Pennsylvania John J. McArdle, PhD Department of Psychology University of Virginia Charlottesville, Virginia Glenn W. Milligan, PhD Department of Management Sciences Ohio State University Columbus, Ohio Leslie C. Morey, PhD Department of Psychology Texas A&M University College Station, Texas John R. Nesselroade, PhD Department of Psychology University of Virginia Charlottesville, Virginia Randy K. Otto, PhD Department of Mental Health Law & Policy Florida Mental Health Institute University of South Florida Tampa, Florida Aaron L. Pincus, PhD Department of Psychology Pennsylvania State University University Park, Pennsylvania Steven C. Pitts, PhD Department of Psychology University of Maryland, Baltimore County Baltimore, Maryland Karen M. Schmidt, PhD Department of Psychology University of Virginia Charlottesville, Virginia Frank L. Schmidt, PhD Department of Management and Organization University of Iowa Iowa City, Iowa Judith D. Singer, PhD Graduate School of Education Harvard University Cambridge, Massachusetts

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One technique that has successfully been applied to capture the degree of coordination among numerous components is principal component analysis (PCA; see Daffertshofer, Lamoth, Meijer, & Beek, 2005; Haken, 1996) In general, a finite number of time series can be effectively described by fewer (temporal) structures For instance, Haas (1995) studied whole-body movements of people as they learned to ride the pedalo, an odd contraption allowing gait on wheels Early in learning, this could effectively be described as a 5dimensional system At the end of learning, the learner s dynamics could effectively be described by a 2-dimensional system This study and others (eg, Huys, Daffertshofer, & Beek, 2003) suggest that with increasing skill the control structure s dimensionality decreases, with control becoming more effective However, there is evidence that direction of change in dimensionality is task- or skill-dependent (see Newell & Vaillancourt, 2001).

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ideas about the relationships among variables, and they can be directly translated into the equations needed for the analysis. Path Diagrams and Terminology Several conventions are used in developing SEM diagrams. Measured variables, also called observed variables, indicators, or manifest variables, are represented by squares or rectangles. In the gure, they have verbal labels as well as V designations. Factors have two or more indicators and are also called latent variables, constructs, or unobserved variables. Factors are represented by circles or ovals in path diagrams and are shown with verbal labels as well as F designations. Relationships between variables are indicated by lines; lack of a line connecting variables implies that no direct relationship has been hypothesized. Lines have either one or two arrows. A line with one arrow represents a hy-

Principal component analysis allows examination of the structure of variance in a skill; however, due to the makeup of the perceptual-motor system, sport goals may be achieved in various manners Scholz and Sch ner (1999) introduced the uncontrolled manifold (UCM) to address this In this method, the variability across multiple motor executions is decomposed into two distinct subspaces The variability in one subspace, the UCM, does not affect task performance and may thus be released from control In contrast, the variability in the other subspace is critical for task performance, and as such requires control Scholz and Sch ner decomposed the variability in a shooting task into two distinct subspaces (the uncontrolled and the controlled manifold) A skilled marksman can adopt a range of joint combinations, which.

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