'Countable number' diaporamas de présentation

Service Capacity Planning & Waiting Lines

Service Capacity Planning & Waiting Lines. Learning Objectives. Identify or Define: The assumptions of the two basic waiting-line models. Explain or be able to use: How to apply waiting-line models? How to conduct an economic analysis of queues?.

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Chapter 7: Counting Principles

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Chapter 7 Section 7.1

Chapter 7 Section 7.1. Definition: a random variable is a variable whose value is a numerical outcome of a random phenomenon. Discrete random variable.

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Chapter 4

Chapter 4. Discrete Probability Distributions. Chapter Outline. 4.1 Probability Distributions 4.2 Binomial Distributions 4.3 More Discrete Probability Distributions. Section 4.1. Probability Distributions. Section 4.1 Objectives.

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Set Theory

Set Theory. Set Notation. Set - is a collection or aggregate of definite, distinct objects. A well-defined set means that it is possible to determine whether an object belongs to a given set. Elements of a Set - the objects or members of a set. Symbol:

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AP Statistics Section 7.1 Probability Distributions

AP Statistics Section 7.1 Probability Distributions. Consider tossing two 4-sided dice. What is the sample space ?. 1-1 2-1 3-1 4-1 1-2 2-2 3-2 4-2 1-3 2-3 3-3 4-3 1-4 2-4 3-4 4-4.

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Energy Localization

Energy Localization. The key to Understanding Energy in Nanotechnology & Nature. Nature assembles ordered structures out of chaos without a temperature gradient or energy input. Systems generally go from ordered to chaotic. How does nature go from the chaotic to ordered?.

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Nonregular Languages

Nonregular Languages. Section 2.4 Wed, Oct 6, 2004. Countability of the Set of DFAs. Theorem: The set of all DFAs (over an alphabet ? ) is countable. Proof: For a given n > 0, let S n be the set of all DFAs with exactly n states. How many DFAs are in S n ?

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Nonregular Languages

Nonregular Languages. Section 2.4 Wed, Oct 5, 2005. Countability of the Set of DFAs. Theorem: The set of all DFAs (over an alphabet ? ) is countable. Proof: For a given n > 0, let S n be the set of all DFAs with exactly n states. How many DFAs are in S n ?

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Section 6.1

Section 6.1. Discrete and Continuous Random Variables. Sample Spaces. In Chapter 6, we studied sample spaces. If we toss two coins, the sample space is {HH, HT, TH, or TT}.

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Nonregular Languages

Nonregular Languages. Section 2.4 Wed, Oct 5, 2005. Countability of the Set of DFAs. Theorem: The set of all DFAs (over an alphabet ? ) is countable. Proof: For a given n > 0, let S n be the set of all DFAs with exactly n states. How many DFAs are in S n ?

146 views • 14 slides

Set Theory

Set Theory. Prepared by: Still John F. Reyes. Set Notation. Set - is a collection or aggregate of definite, distinct objects. A well-defined set means that it is possible to determine whether an object belongs to a given set. Elements of a Set - the objects or members of a set.

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5-Minute Check on section 7-1a

5-Minute Check on section 7-1a. Convert these statements into discrete probability expressions Probability of less than 4 green bulbs Probability of more than 2 green lights Probability of 3 or more If x is a discrete variable x?[1,3], P(x=1) = 0.23, and P(x=2) = 0.3 Find P(x<3) Find P(x=3)

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Lesson 7 - 1

Lesson 7 - 1. Discrete and Continuous Random Variables. Objectives. Define statistics and statistical thinking Understand the process of statistics Distinguish between qualitative and quantitative variables Distinguish between discrete and continuous variables. Vocabulary.

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Introductory Statistics Lesson 4.1 A Objective:

Introductory Statistics Lesson 4.1 A Objective: SSBAT distinguish between discrete random variables and continuous random variables Standards: S2.5B. Random Variable, x Represents a numerical value associated with each outcome of a probability experiment.

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Chapter 7

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Random Variables

Random Variables. A random variable, usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. Random Variables. example. Flip a coin three times; X = the total number of heads.The values of X are. X= 0, 1, 2, 3.

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Discrete and Continuous Random Variables

Discrete and Continuous Random Variables. What is a random variable?. A variable whose value is a numerical outcome of a random phenomena Example: Flipping a coin 3 times. Let X be the number of heads. HHH X = 3 HTT X = 1 X can be 0, 1, 2, or 3 for this phenomena. Discrete Variables.

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Learn Discrete Probability Distribution with CK-12 FlexBook

CK-12 FlexBooks explain that Data can be classified into two groups: discrete and continuous. To Learn basis of Random variables and Discrete Probability Distribution visit

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