Q. An error term, disturbance term or residual term is calculated as

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Q. The cost function, in which the graph of total cost would not result in straight line is classified as

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Q. An assumption, which states that there must be linear relationship between independent variable and dependent variable is

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Q. The vertical difference, which measures distance between estimated and actual cost for every single observation is classified as

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Q. In cost accounting, the conference, quantitative analysis and account analysis methods are considered as

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Q. The component of total cost, which never changes with change in level of production is classified as

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Q. The strength of relationship between cost driver and cost is considered as

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Q. In the regression equation Y = $6000 + (per unit cost 'x' number of units), the '$6000' is a

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Q. In linear cost function, the fixed cost is considered as

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Q. The description in mathematical form to represent changes in cost, with level of activity related to that cost is classified as

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Q. In step cost functions, the cost is increased by

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Q. The individual activities such as machine setups, material handling, customer service and distribution of products are a part of

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Q. A problem which arises because of the presence of systematic pattern in residual value sequence is considered as

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Q. If the difference between costs linked to highest and lowest observation of cost driver, is $8000 and observation of cost driver is 40 machine hours, then slope coefficient will be

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Q. The standard error of regression analysis is known as

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Q. An analysis and estimation method of cost, by classifying cost accounts as fixed or variable with respect for specific output level is considered as

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Q. An estimation of relationship between two or more independent variables and the dependent variables are classified as

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Q. The second step for estimation of cost function by using quantitative analysis is to

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Q. An estimation of relationship between one independent variable and the dependent variable is known as

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Q. The goodness of fit predicted values is also known as

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