15%. Multiple Choice 1) Linear regression out put will always provide a straight line solution for any given X (cost driver) and Y (cost). Before usint eh equation to predict future costs one should: A) Consider whether a cause-and-effect relationship makes operational sense. B) Consider how well the line fits the data points (R-squared) C) Consider the likelihood that the result could be due to chance ( p-value) (D) allof the above. 2) In the regression method, residual refers to the A) data points that are not used in creating the line of best fit. B) number of data points that do not fall directly on the line of best fit. C) vertical distance between each known data point and the line of best fit. D) slope of the cost-line. 3 If the (X,Y) relationship is purely variable, A) regression analysis must be used. B) the equation of the line includes $0 cost, when the chosen cost driver =$0. C) the equation of a line that must be used is Y=m(X)+b. D) account analysis must be used. 4) Which of the following metrics would NOT be used to evaluate the validity of the regression model? A) Economic plausibility B) Statistical significance of the slope C) binomial distribution D) R-squared 5) In evaluating the economic plausibility of a regression model, the analyst is determining A) the statistical significance of the relationship of the independent variable, X with the dependent variable, Y. B) whether it makes sense in the circumstances that the independent variable, X, could cause changes in the dependent variable, Y. C) the degree to which the regression line fits the actual observations. D) the correlation of variable costs to fixed costs in the total cost line