costacctg13_sm_ch10

# 90 2539 2430 2700 090 2349 2285 2232 2187 2430

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Unformatted text preview: y purchased. Goodness of Fit: As seen in So lut ion Exhibit 10­28, the regression line fits the data well. The vert ical distance between the regressio n line and observat ions is small. Significance of the Independent Variable: The relatively steep slope of the regression line suggests that the quant it y purchased is correlated with purchasing cost for part #4599. 10­15 SOLUTION EXHIBIT 10­28 Serth Manufacturing Purchase Costs for Part #4599 \$25,000 Cost of Purchase \$20,000 \$15,000 \$10,000 \$5,000 \$0 0 1,000 2,000 Quantity Purchased 3,000 4,000 According to the regression, Pat’s original est imate of fixed cost is too low given all the data points. The original slope is too steep, but only by 16 cents. So, the variable rate is lower but the fixed cost is higher for the regression line than for the high­low cost equation. The regressio n is the more accurate estimate because it uses all available data (all nine data points) while the high­low method only relies on two data points and may therefore miss so me important informat ion contained in the other data. 4. Using the regression equat ion, the purchase costs for each month will be: Purchase Quantity Month Expected Formula Expected cost October 3,000 parts y = \$501.54 + (\$5.84 ´ 3,000) \$18,022 November 3,200 y = \$501.54 + (\$5.84 ´ 3,200) 19,190 December 2,500 y = \$501.54 + (\$5.84 ´ 2,500) 15,102 Alt hough the two equations are different in both fixed element and variable rate, within the relevant range they give similar expected costs. In fact the estimated costs for December vary by only \$2. This implies that the high and low points of the data are a reasonable representation of the total set of points within the relevant range. 10­16 10­29 (20 min.) Learning curve, cumulative average­time learning model. The direct manufacturing labor­hours (DMLH) required to produce the first 2, 4, and 8 units given the assumpt ion of a cumulat ive average­t ime learning curve of 90%, is as fo llows: 90% Learning Curve Cumulative Number of Units (X) (1) 1 2 3 4 5 6...
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## This note was uploaded on 10/11/2010 for the course ACCT 321 taught by Professor Cole during the Spring '10 term at University of Miami.

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