Management Accounting: A Review of Contemporary Developments by Robert W. Scapens

By Robert W. Scapens

Experiences from first variation: 'This a accomplished survey of released study and pronounced improvement in administration accounting over the last twenty years. As such, it's a invaluable and simply assimilated 'state of the paintings' review.' David Fanning, British Accounting evaluate 'For someone who desires to comprehend the place administration accounting has been going, Scapens is a wonderful guide...I suggest it to a person who desires to meet up with what has been occurring in administration accounting conception. in truth, i do know of no larger solution to do so.' David Solomons, Accounting and enterprise learn

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In practice, however, these values will usually be computed using a computer package. e. 3), included an error term, e. The least squares regression model makes specific assumptions about the properties of this error term. It is important to recognise the nature of these properties, because if they are not met in a particular regression problem, the estimates of a and b will be affected. The following are the assumed properties: (i) The mean of the error term is zero. (ii) The values of the error terms e, and ej associated with any two values of X are independent.

Estimated values of the error term can be determined by subtracting the value of Y (computed using the estimated equation) for each observed value of X, from the corresponding observation of Y. Such calculations can be expressed mathematically as follows. 7) A plot of the error terms against time and against the dependent or the independent variable may indicate whether autocorrelation or heteroscedasticity are present in the data, as will be explained below. In addition, runs of e; with the same sign may suggest that the zero mean assumption could be invalid.

12) In summer months, the variable S takes a value of zero and so the parameters a and b, indicate the fixed and variable costs of production, whereas in winter months the variable S takes a value of 1 giving an additional constant term, b2 , which is added to the fixed costs. Such a procedure can remove the autocorrelation associated with seasonal variation in fixed costs. Techniques are also available for adding a dummy variable to the b parameter this would handle seasonal variation in the variable costs.

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