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by: Kara Lyles

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# Exam 3 Study Guide STAT 110 - 002

Kara Lyles
USC

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Exam 3 study guide
COURSE
Introduction to Statistical Reasoning
PROF.
Gail Ward-Besser (P)
TYPE
Study Guide
PAGES
3
WORDS
CONCEPTS
stat, 110
KARMA
50 ?

## Popular in Statistics

This 3 page Study Guide was uploaded by Kara Lyles on Saturday April 16, 2016. The Study Guide 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 100 views. For similar materials see Introduction to Statistical Reasoning in Statistics at University of South Carolina.

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Date Created: 04/16/16
Study Guide A random outcome is not predictable in the short run but begins form a regular predictable pattern in the long run Types of probabilities Empirical Probability: Experimental “you have to do something” Theoretical: Based on set theories Personal: Based on personal judgement “you believe it “ Null hypothesis  Assume null hypothesis is true until you find evidence to reject  Status quo, no change , =  Mean of normal distribution Alternative hypothesis  Tells us what probability we are calculating under the curve  Uses signs <, >, =  Experimental hypothesis *P value is the evidence that we have against Ho * If p is less than the level of significance you can reject Ho and find favor in Ha * If p is greater than the level of significance then you can fail to reject Ho and not find favor in Ha Probability Rules  Probability needs to be between 0 and 1  Probabilities must total to 1  Outcome must occur every trial  Egain = expected value=a1*p1+a2*p2+a3*p3 ….. Compliment = 1 – probability of event *Complement think opposite  IF asked for an intersection (AND) you multiply  IF asked for a union (OR) you add  Independent probabilities occurs when one event doesn’t affect the other  If disjoint there is no intersection  Tree diagram- representation of presenting complicated probabilities ODDS  Odds for: A / (A+B)  Odds against: B/ (A+B)  If only given a part of the odd : O / (1+ O) Steps in assimilation: 1. Give a model 2. Assign digits to rep the outcomes 3. Simulate repetitions (Random digits table)  As variability decrease the sample size increase & vice versa  As confident level increase the confidence interval get wider P+-Z*square root of [p-hat(1-p-hat)/n] Standard error , standard deviation MOE: Margin of Error Probability of an outcome= # of ways for outcome to occur/ total number of outcomes Interpreting  Must be confident  Talk about population not Sample Sample distribution: Normal distribution with a large enough sample size Law of Large Numbers: The more random outcomes are repeated (Independently) the mean of the observed outcomes (sample) will be closer to the expected value (Population) Central Limit Theorem: The sampling distribution for the statistic is normally distributed when you take many, many samples of the large sample size.

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