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Chpt_6_outline - CHAPTER 6 FIXED AND VARIABLE COSTS In...

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CHAPTER 6 – FIXED AND VARIABLE COSTS In chapters 3 and 5, we concentrated on allocating indirect costs to products in order to get an estimate of the product costs for GAAP purposes (Ch. 3) and some internal decision-making purposes such as product continuance decisions (Ch. 5). However, the costs we determined in Ch.’s 3 and 5 are not particularly useful for planning or forecasting. Costs generally behave in two ways: Variable costs – Variable costs 0 5000 10000 15000 20000 25000 30000 0 1000 2000 3000 4000 5000 Volume Cost

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Fixed costs Fixed costs 0 5000 10000 15000 20000 25000 30000 0 1000 2000 3000 4000 5000 Volume Cost Mixed costs
Mixed costs 0 5000 10000 15000 20000 25000 30000 35000 40000 0 1000 2000 3000 4000 5000 Volume Cost Mixed costs can be represented by the equation: y = mx + b Where: Because companies have both fixed costs and variable costs, we can use the mixed cost equation line as an estimate of total costs in the company. Relevant range

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Step costs ESTIMATING TOTAL COSTS Account analysis – To estimate total costs, managers have many methods at their disposal. We’ll study two of them: High-low method Regression High – Low Method Very simple, straightforward method of examining costs. It is easy to do by hand. It involves two steps: 1. Find the slope (i.e., variable cost per unit) of the mixed cost line 2. Find the intercept (i.e., fixed cost portion) of the mixed cost line Step 1: To estimate the slope, simply calculate rise over run: ( ) ( ) ( ) ( ) Rise y y high y low Slope Run x x high x low - = = = - For example, consider the following data:
Month # of Units Total Costs Jan 3,675 129,591 Feb 4,133 155,602 Mar 4,406 150,461 Apr 4,999 169,087 May 5,921 214,840 Jun 6,587 221,593 Jul 6,575 229,002 Aug 6,614 226,841 Sep 5,800 211,103 Oct 5,534 188,765 Nov 4,973 186,054 Dec 4,123 160,202 The highest volume point is The lowest volume point is Therefore, the slope is Step 2: Therefore, our fixed costs were about \$8,000. Disadvantages of high-low method:

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Regression Analysis Here’s a scatterplot of our data: Total Costs - 50,000 100,000 150,000 200,000 250,000 - 1,000 2,000 3,000 4,000 5,000 6,000 7,000 # of units Total Costs We can use regression analysis in Excel by following the steps outlined in the book. Here’s the output for our data: SUMMARY OUTPUT Regression Statistics Multiple R 0.976 R Square 0.952 Adjusted R Square 0.947 Standard Error 7797 Observations 12 ANOVA df SS MS F Significance F Regression 1 11,955,940,132 11,955,940,132 196.67 0.00 Residual 10 607,929,250 60,792,925 Total 11 12,563,869,382 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 20870.83 12053.16 1.7316 0.1140 (5,985) 47,727 (5,985) 47,727 # of Units 31.46 2.24 14.0238 0.0000 26.46 36.46 26.46 36.46
Coefficients Note that regression tells us our fixed costs are about \$20,871, which is substantially higher (though not “significantly” higher) than the \$8,001 that we found above. R-square F-test

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XIUMIN COMPANY Xiumin Company is located in St. Louis, MO, and makes Hello Kitty stuffed animals. It uses a traditional assembly line setup. Managers and accountants at the company typically produce accurate production budgets for the year, but they have had difficulty in forecasting indirect labor costs.
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Chpt_6_outline - CHAPTER 6 FIXED AND VARIABLE COSTS In...

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