Business Forecasting 관련 PPT 레포트(report) .
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Business Forecasting 관련 PPT 레포트(report) .
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Business Forecasting 관련 PPT 레포트(report) .
순서
레포트/경영경제
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Business Forecasting
경영학과
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Continuous Random Variables:Continuous Probability Distributions
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Probability density function (pdf)
- Unlike a discrete random variable, a continuous random
variable is one that can assume an uncountable number of
values.
- We cannot list the possible values because there is an
infinite number of them.
- Because there is an infinite number of values, the
probability of each individual value is virtually 0.
Thus, we can determine the probability of a range of values
only. For example, with a discrete random variable like
tossing a die, it is meaningful to talk about P(X=5), say.
In a continuous setting (e.g., with time as a random
variable), the probability the random variable of interest,
say task length, takes exactly 5 minutes is infinitesimally
small, hence P(X=5) = 0.
Introduction
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Probability density function (pdf)
- A function f(x) is called a p…(省略) robability density function (over
the range a ≤ x ≤ b if it meets the following requirements:
(1) f(x) ≥ 0 for all x between a and b, and
(2) The total area under the curve between a and b is 1.0
Introduction
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Uniform distribution
- A random variable X with a flat probability density function
between two points a and b, so that
is said to have a uniform distribution X U(a,b)
The cumulative distribution function, an expected value,
and a variance are:
Uniform Distribution
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Uniform distribution
- Probability density function
Uniform Distribution
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Uniform distribution
- Example
The amount of gasoline sold daily at a service station is
uniformly distributed with a minimum of 2,000 gallons and a
maximum of 5,000 gallons. Find the probability that daily
sales will fall between 2,500 and 3,000 gallons.
Algebraically: what is P(2,500 ≤ X ≤ 3,000)
Uniform Distribution
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Unif
다.
