Free Online Flashcard Deck

8 Quantitative Business Decision Making Free Online FlashCards

Study 8 Quantitative Business Decision Making with 12 free online flashcards. Review key terms, definitions, and concepts with this interactive flashcard deck.

12 cards
01
Front

What four elements frame a business decision?

Back

Specify the available alternatives, possible states of the world, consequences, and the objective and constraints.

02
Front

Which costs belong in an incremental comparison?

Back

Include costs and benefits that differ between alternatives. Exclude sunk costs because they cannot be recovered.

03
Front

How is an alternative’s expected value calculated?

Back

It is the probability-weighted average payoff: EV(a)=∑i=1kpiV(a,si)EV(a)=\sum_{i=1}^{k}p_iV(a,s_i), where each state’s payoff is weighted by its probability.

04
Front

What conditions must state probabilities satisfy?

Back

They must each be nonnegative and sum to 11.

05
Front

What does expected value say about one decision’s outcome?

Back

It is a long-run average across comparable decisions, not a promise about the outcome of any single decision.

06
Front

How are choices compared using a decision tree?

Back

Calculate expected values at chance points, then work backward through the tree to compare choices.

07
Front

Why might a firm reject the highest expected monetary value?

Back

A limited-cash firm may avoid a small chance of severe loss even when an option has higher expected monetary value.

08
Front

At what high-demand probability is expansion preferred?

Back

Expansion is preferred only when p>65140p>\frac{65}{140}, about 0.4640.464. A plausible estimate change across this threshold makes the recommendation sensitive.

09
Front

What is EVPI in the capacity decision example?

Back

EVPI is the expected payoff with perfect advance knowledge minus the best expected payoff without it. In the example, it is 63−33=3063-33=30 thousand dollars.

10
Front

When is the median less affected than the mean?

Back

The median is less affected by unusually large or small values.

11
Front

What can undermine an estimate from a sample?

Back

Nonresponse, selection effects, small samples, or changing conditions can make a precise-looking estimate misleading.

12
Front

What does a small p-value indicate?

Back

Under the test assumptions, it is evidence against the null hypothesis—not the probability that the null is true or a measure of effect size.