Notes - Chapter 06 - MGMT 2340 Section W01 Business Statistics I Instructor E Mark Leany contact via Blackboard online.uen.org alternately

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: [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.

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

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