ch2-5 entry

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ch2-5 entry
2015-03-23 03:04:21

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  1. Variance

    Sum(prob. * (x-mean)^2)

  2. Steps for drawing decision tree

    • Identify which decision first?
    • 2nd option depend on scenario?
    • At event node – compute cost
    • After event node – always scenario
    • After decision node – always options, then reject one

  3. Operating income

    (P-VC)Q – FC

  4. E (XY) =

    • E(X) E(Y) + COV (X, Y)
    • COV (X, Y) = 0 if X and Y are independent

  5. Variable rate, or slope of regression, =

    Change in y/change in x

  6. Properties of estimated coefficients using least square regression

    • Unbiased
    • Efficient ( min. variance)

  7. Evaluation of estimated cost function

    • Economic plausibility(sign of coeff)
    • Slope coeff (high t-value)
    • Goodness of fit(high adjusted r2, ie low standard error)
    • Linearity, constant variance of residue, independence of residual (DW stat 1.1-2.9), normal distribution

  8. R2 indicates…while standard error of regression indicates…

    • Percentage of variation in y explained by x
    • On average how large the residuals are

  9. t-test

    • tcal = estimated coeff / standard error
    • if tcal > ttab, reject coeff = 0

  10. …indicates a strong relation between cost driver and costs

    a steep slope

  11. confidence interval

    estimated coeff +- (critical t-value at 5%significance level) * standard error

  12. for multi-variable regression, cost drivers should correlation < ….; otherwise, ….problem arises

    0.7; multicollinearity

  13. Characteristics of good sample

    • Many reliable data
    • Values for cost driver spans a wide range

  14. Problems that arises with wrong cost function is used

    • Cost prediction, eg. CVP analysis
    • Cost control: wrong benchmark
    • Performance evaluation
    • Decision making, eg. Product mix and pricing

  15. Reasons for cost functions to be nonlinear

    • Economies of scale
    • Discounts on large purchase
    • Learning effect
    • Multiple relevant ranges

  16. Cumulative average time learning: formula

    • Y=px^q
    • Y=average time per unit
    • P=time for 1st unit
    • X=total no of units produced
    • Q=coeff of learning

  17. Cumulative average time learning: what if q=0 and q=-1

    Avg time is constant

  18. Learning percentage formula

    Learning % =2^q

  19. Cumulative average time learning: total time for producing 5th-7th units?

    Y = p*7^(q+1) –p*5^(q+1)

  20. Incremental unit-time learning model: m represents

    Time to produce last unit in a sequence of x units

  21. Incremental unit-time learning model: total time for producing first 3 units


  22. Steps to Cal coefficient range; meaning of the range

    • 1. Find the slope of the critical constraint
    • 2. Find the slope of objective function
    • 3. Change the numerator or denominator such that two slopes are equal;
    • Within this range optimal production plan does not change; as one product become more profitable relative to the other, production emphasis change

  23. When constraint is binding, slack equals


  24. What should management do facing bottleneck?

    • Do not overproduce at non-bottleneck
    • Increase efficiency and capacity of bottleneck, eg. Elim. Idle time, reduce setup time, shift production from bottleneck

  25. finding dual price
    • add one more constraint>
    • change RHS-constraint equation>
    • find new optimal solution>
    • find new total CM>
    • dual price = new total CM - old total CM
  26. Throughput margin
    Revenue less direct material costs
  27. slope of constraint/objective
    coeff x/ coeff y
  28. operating leverage
    total CM/OI