Notes - Chapter 06 - MGMT 2340 Section W01 Business...

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MGMT 2340 Section W01 Business Statistics I Instructor: E. Mark Leany contact via Blackboard online.uen.org alternately: professorleany@gmail.com 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
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Discrete Probability Distributions Chapter 6 182 1. Define the terms probability distribution and random variable . 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 . GOALS 182
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Characteristics of a Probability Distribution CHARACTERISTICS OF A 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
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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
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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.
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This note was uploaded on 12/05/2011 for the course MGMT 2340 taught by Professor Leany during the Winter '11 term at Utah Valley University.

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Notes - Chapter 06 - MGMT 2340 Section W01 Business...

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