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Accounting managerial > BDM FROM TEXTBOOKS > Flashcards

BDM FROM TEXTBOOKS Flashcards

Business Decision Models

1
Q

Quantitative analysis

A

uses a scientific approach to managerial decision making

- the approach starts with data

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2
Q

Qualitative factors in quantitative analysis

A

the weather, new technological breakthroughs, federal ligislation (in most cases, it will be an aid to decision making).

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3
Q

POM

A

production/operations management

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4
Q

Business analytics

A

is a data driven-approach to decision making, allows companies to make better decisions.
- involves the use of large amounts of data

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5
Q

3 categories of Business analytics

A

1) descriptive
2) predictive
3) prescriptive

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6
Q

Descriptive analytics

A

involves the study and consolidation of historical data for a business and an industry (measures how company has performed in the past and how it is performing now)

ex: statistical quality control

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7
Q

Predictive analytics

A

is aimed at forecasting future outcomes based on patterns in the past data

ex: decision trees, regression models, forecasting, etc

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8
Q

Prescriptive analytics

A

Involves the use of optimization methods to provide new and better ways to operate, based on specific business objectives

ex: economic order quantity, linear programming

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9
Q

The Quantitative Analysis Approach

A

defining a problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing the results, implementing the results

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10
Q

The Quantitative Analysis Approach: Defining the problem

A

develop a clear, concise statement of the problem.

  • selecting the problem that creating the greatest increase in profits or reduction in costs
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11
Q

The Quantitative Analysis Approach: Developing a model

A

2nd step is to develop a model. physical, scale, schematic, and mathematical models

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12
Q

schematic model

A

a picture, drawing or chart of reality

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13
Q

A Mathematical model

A

is a set of mathematical relationships

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14
Q

Variable

A

a measurable quantity that may vary or is subject to change. can be either controllable (decision variable) or uncontrollable

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15
Q

parameter

A

a measurable quantity that is inherent in the problem. In most cases, theses are known

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16
Q

Acquiring Input Data

A

“garbage in, garbage out” -

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17
Q

Developing a solution

A

involves manipulating the model to arrive a the best optimal solution to the problem. The input data and model determine the accuracy of the solution

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18
Q

Testing the Solution

A

testing the data and model is done before the results are analyzed

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19
Q

Analyzing the results and sensitivity analysis

A

sensitivity analysis determines how the solution wills change with a different model or input data

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20
Q

Profit formula

A

revenue - expenses

sX - f - vX 
s = selling price per unit 
f = fixed cost 
v = variable costs per unit
X = number of units sold
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21
Q

Expenses include…

A

fixed and variable costs

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22
Q

BEP

A

Break-even-point. the number of units sold that will result in $0 profits

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23
Q

BEP formula

A

= fixed costs
_____________
(SP per unit - VC per unit)

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24
Q

Advantages of Mathematical modeling

A
  1. accurately represent reality
  2. help decision makers formulate problems
  3. give us insight and information
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25
Q

Deterministic model

A

A model in which all values used in the model are known with complete certainty

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26
Q

Probabilistic model

A

means models that involve change or risk (often measured as a probability value

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27
Q

Possible problems in the Quantitative analysis approach

A

Defining the problem :
All viewpoints should be considered before formally defining the problem (conflicting viewpoints, impact on other departments, beginning assumptions)

Developing solution:
Hard to understand Mathematics
Assumptions should be reviewed

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28
Q

Implementation problems

A
  • Lack of commitment and resistance to change

- Lack of commitment by quantitative analysts (technical)

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29
Q

Mathematical model

A

a model that uses mathematical equations and statements to represent the relationships within the model

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30
Q

A probability

A

is a numerical statement about the chance that an event will occur

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31
Q

Two basic rules of probability

A

Regardless of how probabilities are determined, they must adhere to these two rules:

1) the probability of any event or state of nature occurring is greater than or equal to 0 and less than or equal to 1 (probability of 0 indicates that an event is never expected to occur
2) the sum of simple probabilities for all possible outcomes of an activity must equal 1

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32
Q

2 different ways to determine probability

A

objective approach

subjective approach

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33
Q

the Relative frequency approach

A 
an objective probability assessment 
in general: 
P(event) = number of occurrences of the e vent 
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
total number of trails or outcomes
34
Q

Determining objective probabilities

A

classical or logical method

35
Q

Mutually exclusive

A

if only one of the events can occur on any one trail

36
Q

Collectively exhaustive

A

if the list of outcomes includes every possible outcome

37
Q

Intersection of two events

A

is the set of all outcomes that are common to both events (the word and is commonly associated with the intersection)

38
Q

joint probability

A

implies that both events are occurring at the same time or jointly

39
Q

The union of two events

A

the set of all outcomes that are contained in either of these two events. the word or is commonly associated with the union, as in the symbol

40
Q

Probability of the union of two events (additive rule)

A

on formula sheet

P(A or B) = P(A) + P(B) - P(A and B)

41
Q

Conditional Probability

A

is the probability of an event occurring given that another event has already happened

Equation on formula sheet

42
Q

Two events are independent

A

Independent if the occurrence of one has no impact on the occurrence of the other

independent if :
P(A/B) = P(A)

probability of the intersection is:
P(A and B) = P(A)P(B)

43
Q

Bayes Theorem

A

used to incorporate additional information as it is made available and help create revised or posterior probabilities from the original prior probabilities.

44
Q

revised or posterior probabilities

A

two conditional probabilities

45
Q

Thomas Bayes

A

did the work leading to the Bayes theorem

46
Q

Random Variables

A

assigns a real number to every possible outcome or event in an experiment

47
Q

Types of random variables

A

Discrete random variables

continuous random variable

48
Q

discrete random variables

A

if it can assume only a finite or limited set of values. Probability value assigned to each event - must be between 0 and 1 and must sum to 1

49
Q

continuous random variable

A

is a random variable that has a infinite or unlimited set of values

50
Q

3 rules request of all probability distributions

A

1) events are mutually exclusive and collectively exhaustive
2) individual probability values are between 0 and 1 inclusive
3) the total of the probability values is 1

51
Q

Expected value

A

the central tendency of the distribution

52
Q

Variance

A

amount of variability or spread of the distribution

53
Q

Expected value of a discrete probability distribution

A

the expected value of a discrete distribution is the weighted average of the values of the random variable.

Given on Formula sheet

54
Q

Variance of a discrete probability distribution

A

is a number that reveals the overall spread or dispersion of the distribution. For a discrete probability distribution, it can be computed using the following equation.

Given on Forumla sheet

55
Q

Standard deviation

A

a relative measure of dispersion or spread (square root of the variance)

56
Q

Probability distribution of Continuous Random Variable

A

used the probability density function

57
Q

Probability density function

A

f(X), is a mathematical way of describing the probability distribution. Shaded area under the graph represents probability.

58
Q

The Binomial Distribution

A

a discrete distribution that describes the number of successes in independent trails of a Bernoulli process

Used to find the probability of a specific number of success out of n trials of a Bernoulli process.

Bernouli process, following characteristics

  1. each trail has only two possible outcomes - success and a failure
  2. probability stays the same from one trail to the next
  3. the trails are statistically independent
  4. the number of trails is a positive integer
59
Q

!

A

symbol used in binomial distribution, means factorial (ex: 4! = (4) (3) (2) (1) = 24

60
Q

The normal distribution

A

continuous bell-shaped distribution that is a function of two parameters, mean and standard deviation of the distribution

affects a large number of processes in our lives (e.g. filling boxes of cereal with 32 ounces of corn flakes). Each normal distribution depends on the mean and standard deviation

as standard deviation becomes smaller, normal distribution becomes steeper, vise versa
formula is on formula sheet

61
Q

Area under the normal curve

A

normal distribution is symmetrical, the highest point is at the mean. Area under the curve (in a continuous distribution) describes the probability that a random variable has a value in a specified interval

62
Q

The Empirical Rule

A

This rule states that for a normal distribution:

68% of all the values will be within +/- 1 standard deviation of the mean

95% of the values will be within +/- 2 standard deviations of the mean

about 99.7% of the values will be within +/- standard deviations of the mean

63
Q

the F distribution

A

is a continuous probability distribution that is helpful in testing hypotheses about variances. Used when regression models are tested for significance.

64
Q

Finding the F value

A

associated with a particular probability and degrees of freedom

65
Q

The exponential Distribution

A

used in dealing with queuing problems. A continuous distribution, Function is given on sheet.

66
Q

The Poisson distribution

A

this distribution is used in many queuing models to represent arrival patterns

formula is given

67
Q

Bernouli Process

A

A process with two outcomes in each of a series of independent trials in which the probabilities of the outcomes do not change

68
Q

objective approach

A

the method of determining probability vales based on historical data or logic

69
Q

Decision Theory

A

is an analytic and systematic way to tackle problems. a good decision is based on logic

70
Q

6 steps in decision making

A
  1. clearly define the problem at hand
  2. list the possible alternatives
  3. identity the possible outcomes or states of nature
  4. list the payoff (typically profit) of each combination of alternative and outcomes
  5. select one of the mathematical decision theory models
  6. apply the model and make your decision
71
Q

Types of decision-making environments

A

there decision-making environments:

  • decision making under certainty
  • decision making under uncertainty
  • decision making under risk
72
Q

decision-making under certainty

A

decision makers know with certainty the consequence of every alternative or decision choice (maximize their well-being)

73
Q

decision-making under uncertainty

A

the decision maker does not know the probabilities of the various outcomes. sometimes it is impossible to assess the probability of success of a new undertaking or product

Several criter for making decisions under conditions of uncertainty

  1. optimistic
  2. pessimist
  3. criterion of realism (HUrwicz)
  4. Equally Likely (Laplace
  5. Minimax regret
74
Q

decision making under risk

A

there are several possible outcomes for each alternative, and the decision maker knows the probability of ocurence of each outcome .

selecting the altnerative with the highest expected monetary value (or simply expected value)

75
Q

optimistic (maximax)

A

the best (maximum) payoff for each alternative is considered and the alternative with the best (maximum) of these is selected

76
Q

Pessimistic

A

the worst (minimum) payoff for each altnerative I s considered, and the alternative with the best (maximum) of these is selected. (Maximin)

77
Q

Criterion of Realism (Hurwicz Criterion)

A

comprised of an optimistic and a pessimistic decision. Uses the weighted average approach

78
Q

weighted average formula

A

Weighted average = a(best in a row) - (1-a)(worst in row)

79
Q

Equally Likely (Laplace)

A

Involves finding the average payoff for each altnerative and selecting the altnerative with the best or highest average

80
Q

Minimax regret

A

Opportunity loss refers to the difference between the optimal profit or payoff for a given state of nature and the actual payoff received for a particular decision for that state of nature

81
Q

Expected Monetary Value

A

is the weighted sum of possible payoffs for each altnerative

Accounting managerial (7 decks)

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