Least Squares Regression Analysis

The management of Digger Inc., is trying to develop a cost formula for its major manufacturing overhead activities. Diggers 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:

Period	Machine Hours	Power Cost
January	36,000	$45,000
February	45,000	$60,300
March	54,000	$67,500
April	39,600	$53,064
May	37,800	$47,250
June	32,400	$43,416
July	43,200	$54,000
August	50,400	$67,536
September	46,800	$58,500

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 = $Answer

+ ($Answer

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 = $Answer

+ ($Answer

x machine hours)

c. Evaluate R² from requirement b. Are machine hours a good predictor of power costs?

No, since 64% of variability in power costs is explained by machine hours, implies 36% 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.

No, since 36% of variability in power costs is explained by machine hours, implies 64% 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.

Yes, since 95% of variability in power costs is explained by machine hours, implies 5% 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.

Yes, since 5% of variability in power costs is explained by machine hours, implies 95% 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.

d. Using the cost formula from requirement b, estimate power costs when 55,800 machine hours are used.

(Note: Round to the nearest dollar.)

$Answer

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 36,000 $45,000 February 45,000 $60,300 March 54,000 $67,500 April 39,600 $53,064 May 37,800 $47,250 June 32,400 $43,416 July 43,200 $54,000 August 50,400 $67,536 September 46,800 $58,500 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 = $ 1,494.43 X + ($ 1.25 X 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 = $ 1,494.43 X + ($ 1.25 x machine hours) c. Evaluate R2 from requirement b. Are machine hours a good predictor of power costs? No, since 64% of variability in power costs is explained by machine hours, implies 36% 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. No, since 36% of variability in power costs is explained by machine hours, implies 64% 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. Yes, since 95% of variability in power costs is explained by machine hours, implies 5% 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. Yes, since 5% of variability in power costs is explained by machine hours, implies 95% 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. d. Using the cost formula from requirement b, estimate power costs when 55,800 machine hours are used. (Note: Round to the nearest dollar.) $ 71,244.43 X 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 36,000 $45,000 February 45,000 $60,300 March 54,000 $67,500 April 39,600 $53,064 May 37,800 $47,250 June 32,400 $43,416 July 43,200 $54,000 August 50,400 $67,536 September 46,800 $58,500 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 = $ 1,494.43 X + ($ 1.25 X 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 = $ 1,494.43 X + ($ 1.25 x machine hours) c. Evaluate R2 from requirement b. Are machine hours a good predictor of power costs? No, since 64% of variability in power costs is explained by machine hours, implies 36% 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. No, since 36% of variability in power costs is explained by machine hours, implies 64% 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. Yes, since 95% of variability in power costs is explained by machine hours, implies 5% 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. Yes, since 5% of variability in power costs is explained by machine hours, implies 95% 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. d. Using the cost formula from requirement b, estimate power costs when 55,800 machine hours are used. (Note: Round to the nearest dollar.) $ 71,244.43 X