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by: Kelsey Marr

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# Week 5 Notes STAT 200

Kelsey Marr
Penn State
GPA 3.59
Elementary Statistics
Andrew Wiesner

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COURSE
Elementary Statistics
PROF.
Andrew Wiesner
TYPE
Class Notes
PAGES
2
WORDS
KARMA
25 ?

## Popular in Statistics

This 2 page Class Notes was uploaded by Kelsey Marr on Friday February 13, 2015. The Class Notes belongs to STAT 200 at Pennsylvania State University taught by Andrew Wiesner in Winter2015. Since its upload, it has received 54 views. For similar materials see Elementary Statistics in Statistics at Pennsylvania State University.

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Date Created: 02/13/15
Stat 200 Wiesner Week 5 Notes Probability Distribution Discrete something counted 0 Ex number of females in class Continuous something measured 0 Ex heights of students age of students Random variable a numerical characteristic of each event in a sample space or equivalently each individual in a population Discrete Random variable countable set of distinct possible values 0 Ex number of heads in 4 ips of a coin Continuous Random Variable such that any value to any number of decimal places within some interval is a possible value 0 Ex heights of individuals Expected value mean value in the long run meaning for many repeats or a large sample size Binomial Random Variable counts how often a particular event occurs in a xed number of tries or trials For a variable to be a binomial random variable all of the following conditions must occun 0 There are a xed number of trials a xed sample size 0 On each trial the event of interest either occurs or does not 0 The probability of occurrence or not is the same on each trial 0 The trials are independent of one another Ex number of correct guesses at 30 true false questions when you randomly guess o N number of trials P probability event of interest occurs on any one trial Special discrete random variable is the binomial ex discrete with 2 outcomes If the following 4 conditions are all satis ed we have what is called a binomial experiment 0 A xed number of trials we will call this n 0 We have 2 possible outcomes for each trial success and failure 0 Probability of success and failure is the same for each trial o The trials are independent 0 For cumulative probability in binomial experiments we want to use software We can use software for exact or cumulative o Minitab calc l probability distribution l binomial Probability use P xx Cumulative use P xlt or equal to x lgnore inverse Events n lnputs x De ate Gate example o If we assume all those footballs are and distributed bw the 2 teams 11 de ated n11 o if randomly divided bw the 2 teams probability of getting a quotbadquot football is 05 p5 Things to Note 0 Sum of all these probabilities is one o The outcomes are mutually exclusive 0 Keep in mind that the means something

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