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by: runnergal

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# STAT 110: Notes for Week of 10/4/16 STAT 110 - 002

Marketplace > University of South Carolina > Statistics > STAT 110 - 002 > STAT 110 Notes for Week of 10 4 16
runnergal
USC

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These notes cover what was discussed in class during the week of 10/4/16.
COURSE
Introduction to Statistical Reasoning
PROF.
Gail Ward-Besser (P)
TYPE
Class Notes
PAGES
1
WORDS
CONCEPTS
Math, Statistics, intro to statistics
KARMA
25 ?

## Popular in Statistics

This 1 page Class Notes was uploaded by runnergal on Sunday October 9, 2016. The Class Notes belongs to STAT 110 - 002 at University of South Carolina taught by Gail Ward-Besser (P) in Spring 2016. Since its upload, it has received 6 views. For similar materials see Introduction to Statistical Reasoning in Statistics at University of South Carolina.

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Date Created: 10/09/16
STAT 110 – Notes for Week of 10/04/16  Chapter 19 o Simulation: a strategy used to figure out patterns in chance behavior when it is  not possible to perform an experiement. o How to conduct a simulation: 1. Design a valid probability model using the probability rules. 2. Assign digits to outcomes. 3. Simulate numerous repetitions of the simulation to discover the pattern in  the chance behavior. o Independence: when one outcome does not affect another outcome in the same  experiment. o When two events are independent, you can find the probability of both events  occurring by multiplying their probabilities. P(A&B) = P(A) x P(B) o If P(A&B) does not equal (P[A] x P[B]), then the events are not independent.  Chapter 20 o Expected value: the average of all possible values in the long run; the  expectation of the first value.  o The expected value does NOT have to be one of the outcomes. o Do not round the expected value. o Possible outcomes: a , a1… 2 k o Probabilities: p ,1p 2 a k o To find the expected value, multiply each outcome by its corresponding  probability, and then add those numbers together. o Expected value = a p +1 1   2 2 + a p k k  o Law of Large Numbers: in a long­run experiment with many trials and  outcomes, the average of the observed outcomes approaches the expected value. o You can find the exact expected value of a theoretical probability. o You can find the approximate expected value of an experimental probability.

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