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costacctg13_sm_ch10

# 31x costdriver restatedengineering machinehours

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Unformatted text preview: tudy. Goodness of fit: The r2 of 0.63 indicates an excellent goodness of fit. Significance of independent variables: The t­value on # of POs is 2.14 while the t­value on # o f Ss is 2.00. These t­values are either significant or border on significance. Specification analysis: Results are available to examine the independence of residuals assumpt ion. The Durbin­Watson statist ic o f 1.90 indicates that the assumpt ion o f independence is not rejected. Regressio n 4 is consistent with the findings in Problem 10­39 that both the number of purchase orders and the number of suppliers are drivers of purchasing department costs. Regressio ns 2, 3, and 4 all sat isfy the four criteria outlined in the text. Regressio n 4 has the best goodness o f fit (0.63 for Regressio n 4 co mpared to 0.42 and 0.39 for Regressio ns 2 and 3, respectively). Most importantly, it is economically plausible that both the number of purchase orders and the number of suppliers drive purchasing department costs. We would recommend that Lee use Regressio n 4 over Regressio ns 2 and 3. 2. Regressio n 5 adds an addit ional independent variable (MP\$) to the two independent variables in Regressio n 4. This addit io nal variable (MP\$) has a t­value o f –0.07, implying its slope coefficient is ins ignificant ly different from zero. The r2 in Regressio n 5 (0.63) is the same as that in Regressio n 4 (0.63), implying the addit ion o f this third independent variable adds close to zero explanatory power. In summary, Regressio n 5 adds very litt le to Regressio n 4. We would recommend that Lee use Regressio n 4 over Regression 5. 3. Budgeted purchasing depart ment costs for the Baltimore store next year are \$485,384 + (\$123.22 ´ 3,900) + (\$2,952 ´ 110) = \$1,290,662 10­38 4. Mult ico llinearit y is a frequently encountered problem in cost accounting; it does not arise in simple regressio n because there is only one independent variable in a simple regressio n. One consequence o f mult icollinearit y is an increase in the standard errors of the coefficients of...
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