Costing MCQ Questions and Solutions with Explanations | Commerce

Statistical method used to measure average change in dependent variable, with respect to change of one unit in independent variable is called regression method. Regression analysis is a set of statistical processes for estimating the relationships among variables.

Line which uses to join observations with lower and highest values of cost driver is called straight line.

A technique which minimizes sum of squared vertical difference, to determine regression line is considered as least square technique. The least squares method is a form of mathematical regression analysis that finds the line of best fit for a set of data, providing a visual demonstration of the relationship between the data points.

Predicted cost = Observed cost value - Disturbance error
= 85 - 25 = 60

Better fit between estimated cost and actual observations is represented by smaller residual terms.

Coefficient of determination = Unexplained variation ÷ Total variation
= 456870 ÷ 955000 = 0.4783

Regression Analysis: In regression analysis, we often make certain assumptions about the data and the relationship between variables.

Testing Assumptions: Testing whether these assumptions hold true involves various statistical tests and procedures.

Significance Analysis: One of the key aspects of this process is significance analysis, where we assess the statistical significance of the regression coefficients and other parameters to determine if the relationships observed in the data are meaningful.

Other Options:

Weighted analysis and average analysis do not specifically pertain to the testing of assumptions in regression.

Specification analysis involves checking if the model is correctly specified, but it is not the primary term used for testing assumptions.

Therefore, the correct term for testing the assumptions in regression analysis is significance analysis.

In estimation of cost functions, variations in a single activity level represents the related total costs. Total cost refers to the aggregation of all types of costs related to a cost object, which means fixed costs, variable costs, and mixed costs.

An estimated coefficient, which indicates degree by which estimated values are affected by random factors is known as standard error of estimated coefficient.

Regression Analysis Method of quantitative analysis of cost function considers all data points.