MGMT 2340
Section
W01
Business Statistics I
Instructor:
E. Mark Leany
contact via
Blackboard
online.uen.org
alternately:
[email protected]
Descriptive versus Inferential
z
Chapters 2 - 4
–
Describing collected data
–
Events had already happened
–
DESCRIPTIVE Statistics
z
Chapter 5
–
Probabilty: Something will "probably" happen
–
INFERENTIAL Statistics

Discrete Probability Distributions
Chapter 6
182
1.Define the terms probability distribution andrandomvariable.
2.
Distinguish between
discrete
and
continuous probability
distributions
.
3.
Calculate the
mean, variance
, and
standard deviation
of a
discrete probability distribution.
4.Describe the characteristics of and compute probabilities using the binomial probability distribution.
5.Describe the characteristics of and compute probabilities using the hypergeometric probability distribution.
6.Describe the characteristics of and compute probabilities using the Poisson probability distribution.

1.The probability of a particular outcome is between
0 and 1 inclusive.
2. The outcomes are mutually exclusive events.
3. The list is exhaustive. So the sum of the probabilities
of the various events is equal to 1.
183
PROBABILITY DISTRIBUTION
A listing of all the outcomes of
an experiment and
the probability associated with each outcome.
Example of a Probability Distribution
Experiment:
Toss a coin three times.
Observe the number of
heads. The possible
results are: Zero heads,
One head,
Two heads, and
Three heads.
What is the probability
distribution for the
number of heads?
183
? # of Possible Results
Multiplication rule: (m)(n)... = (2)*(2)*(2)
We will be counting # of heads

Probability Distribution of Number of
Heads Observed in 3 Tosses of a Coin
184
Random Variables
RANDOM VARIABLE A quantity resulting from an experiment
that, by chance, can
assume different values.
185

Types of Random Variables
DISCRETE RANDOM VARIABLE
A random variable that can assume
only certain clearly separated values. It is usually the result of counting
something.
CONTINUOUS RANDOM VARIABLE
can assume an infinite number
of values within a given range. It is usually the result of some type of
measurement
186
Discrete Random Variables
EXAMPLES
1.
The number of students in a class.
2.
The number of children in a family.
3.
The number of cars entering a carwash in a hour.
4.
Number of home mortgages approved by Coastal Federal
Bank last week.
DISCRETE RANDOM VARIABLE
A random variable that can assume
only certain clearly separated values. It is usually the result of counting
something.

#### You've reached the end of your free preview.

Want to read all 22 pages?

- Spring '11
- Leany
- Poisson Distribution, Probability distribution, Probability theory, Binomial distribution, Discrete probability distribution