'Multiplication rule' presentation slideshows

Basic Probability

Basic Probability. With an Emphasis on Contingency Tables. Students in PSYC 2101. Skip to Slide # 7. . Random Variable. A random variable is real valued function defined on a sample space . The sample space is the set of all distinct outcomes possible for an experiment .

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Probability

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Statistics for Engineers

Statistics for Engineers. Antony Lewis http://cosmologist.info/teaching/STAT/. Starter question Have you previously done any statistics?. Yes No. BOOKS Chatfield C, 1989. Statistics for Technology , Chapman & Hall, 3rd ed.

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Business Statistics for Managerial Decision

Business Statistics for Managerial Decision. Probability Theory. Probability Theory. The mathematics of probability can provide models to describe

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Chapter 4

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Chapter 11 (Part 2)

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Chapter 6 Section 2

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A Survey of Probability Concepts

A Survey of Probability Concepts. Chapter 5. GOALS. Define probability. Describe the classical, empirical, and subjective approaches to probability. Explain the terms experiment, event, outcome, permutations, and combinations. Define the terms conditional probability and joint probability.

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Section 4.2

Section 4.2. Binomial Distributions. Section 4.2 Objectives. Determine if a probability experiment is a binomial experiment Find binomial probabilities using the binomial probability formula Find binomial probabilities using technology and a binomial table Graph a binomial distribution

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Conditional Probability

Conditional Probability. Defining Conditional Probability The Multiplication Rule Independence of Events. Conditional Probability. We begin with a simple example: Toss a balanced die once and record the number on the top face. Let E be the event that a 1 shows on the top face.

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Discrete Mathematics Lecture 6

Discrete Mathematics Lecture 6. Alexander Bukharovich New York University. Counting and Probability. Coin tossing Random process Sample space is the set of all possible outcomes of a random process. An event is a subset of a sample space

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Counting and Probability

Counting and Probability. The outcome of a random process is sure to occur, but impossible to predict. Examples: fair coin tossing, rolling a pair of dice, card drawing from a well shuffled card deck, etc . Sample space is the set of all possible outcomes of a random process.

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Chapter 5 Probability

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Pinochle Poker: An Activity to Solidify Concepts in Counting and Probability

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Statistics in Bioinformatics

Statistics in Bioinformatics. May 12, 2005 Quiz 3-on May 12 Learning objectives-Understand equally likely outcomes, counting techniques (Example, genetic code, PCR primer), E value Homework 10-due May 14, 2005. Statistical analysis of results underlies bioinformatics.

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Probability Rules

Probability Rules. Rule 1. The probability of any event (A) is a number between zero and one. 0 < P(A) < 1. Probability Rules. Rule 2. The sum of the probabilities of all basic outcomes in the sample space must equal one P(S)=P(e 1 )+P(e 2 )+P(e 3 )+....+P(e n )=1. Probability Rules.

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Probability Rules

Probability Rules. Rule 1. The probability of any event (A) is a number between zero and one. 0 < P(A) < 1. Probability Rules. Rule 2. The sum of the probabilities of all basic outcomes in the sample space must equal one P(S)=P(e 1 )+P(e 2 )+P(e 3 )+....+P(e n )=1. Probability Rules.

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Multiplication of Exponents

Multiplication of Exponents. By: Terri LeGault. Multiplication of Exponents. Mulitplication Property: Whenever you multiply two terms with the same base you can add the exponents. x m * x n =x (m+n) , x raised to m times x raised to n equals x raised to m plus n. Example:

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Statistics 1: Elementary Statistics

Statistics 1: Elementary Statistics. Section 4-6. Probability. Chapter 3 Section 2: Fundamentals Section 3: Addition Rule Section 4: Multiplication Rule #1 Section 5: Multiplication Rule #2 Section 6: Simulating Probabilities Section 7: Counting. Simulating Probabilities.

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