Least Squares Regression Analysis The management of Digger Inc., is trying to develop a cost formula for its major manufacturing overhead activities. Digger's manufacturing process is highly automated and power costs are a significant manufacturing cost. Cost analysts have decided that power costs are mixed. The costs must be separated into their fixed and variable components so that the cost behavior of the power usage activity can be better understood. Analysts have determined that machine hours drive power usage; thus machine hours are the cost driver for power costs. Nine months of data have been collected and are presented in the chart below: Machine Period Hours Power Cost January 24,000 $32,500 February 30,000 $47,500 March 36,000 $53,125 April 26,400 $46,250 May 25,200 $42,500 June 21,600 $36,250 July 28,800 $45,000 August 33,600 $50,000 September 31,200 $ $40,000 Note: For the following requirements, round the variable cost per unit to the nearest cent and the total fixed cost to the nearest dollar. Required a. Use the high and low points to estimate a power cost formula. Power cost = $ + ($ x machine hours) b. Use the method of least squares in Excel or a similar computer program to estimate a power cost formula. Power cost = $ + ($ x machine hours) C. Evaluate R2 from requirement b. Are machine hours a good predictor of power costs? ONo, since 65% of variability in power costs is explained by machine hours, implies 35% of power costs is driven by some other activity. A common rule thumb is that a "good" independent variable should explain approximately 80% of the variability of a dependent variable. No, since 35% of variability in power costs is explained by machine hours, implies 65% of power costs is driven by some other activity. A common rule of thumb is that a "good" independent variable should explain approximately 80% of the variability of a dependent variable. OYes, since 81% of variability in power costs is explained by machine hours, implies 19% of power costs is driven by some other activity. A common rule of thumb is that a "good" independent variable should explain approximately 80% of the variability of a dependent variable. OYes, since 19% of variability in power costs is explained by machine hours, implies 81% of power costs is driven by some other activity. A common rule of thumb is that a "good" independent variable should explain approximately 80% of the variability of a dependent variable