Q. In regression equation, an evaluation criteria considers the significance of

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Q. The high-low method is used for estimation of

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Q. All the conditions or assumptions of regression analysis in simple regression can give

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Q. Industrial engineering method is used to analyze the relationship between

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Q. The residual term is also considered as

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Q. The percentage of variation in the 'Y' explained by the 'X' is measured by

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Q. The small residual terms and positive slope of line indicate

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Q. An analysis of data over successive past periods, which pertain to some plant, activity and organization is called

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Q. The individual variable coefficient's multicollinearity leads to

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Q. The constant variance is classified in assumptions testing as

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Q. As compared to cumulative average-time learning model, the learning model which predicts more cumulative total time to produce more units is known as

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Q. The model, in which the produced unit doubles every time as the cumulative average time per unit decreases, by a constant percentage can be classified as

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Q. In the specification analysis, the assumptions related to variance state that:

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Q. If the slope coefficient is 0.60 and the difference in machine hours is $50000, then difference in cost can be

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Q. The fifth step in quantitative analysis of estimating cost function is to

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Q. The lower level of production leads to

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Q. In plotting the cost functions, the number of machine hours and batches are represented on

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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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