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knowledge covered in exam No.2

by: danqisandy

knowledge covered in exam No.2 STT212

Marketplace > University of Rochester > Statistics > STT212 > knowledge covered in exam No 2
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this is a general list of all the informations that have been covered in this portion of the lecture. Specific formulas can be found in the formula cheat sheet on class website. Make sure to refer ...
Statistics for Biological and Physical Sciences
Prof. McDermott
Study Guide
50 ?




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This 1 page Study Guide was uploaded by danqisandy on Saturday March 19, 2016. The Study Guide belongs to STT212 at University of Rochester taught by Prof. McDermott in Spring 2016. Since its upload, it has received 25 views. For similar materials see Statistics for Biological and Physical Sciences in Statistics at University of Rochester.

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Date Created: 03/19/16
Exam 2 Coverage for STT212: PART 1 Probability: Chance experiment - outcome is not certain sample space - all possible outcomes of a chance experiment event - any collection of outcomes from sample space e.g. roll a dice sample space {1,2,3,4,5,6} event: want a 1 event sample space: {1} using vein diagram to find Union/ Complement/ Intersection of A &B disjoint events - nothing in common probability - classic definition: # of outcomes favorable to A / the total number of outcomes relative frequency definition: # of times A occurs/ the total # of trials rules: the addition rule & complement rule & multiplication rule & Bayes’ Rule independent event - when P(A|B)= P(A) PART 2 Random Variable (x): Discrete random variable - usually counts continuous random variable - usually measurements rules for probability distribution of a discrete random variable: 1)sum of P(x) =1; 2) P(X) range 0-1 rules for probability distribution of a continuous random variable: probability = area under density curve, range 0-1. (*equal signs do not matter when calculate the area.) mean of a discrete random variable (expected value) standard deviation of a discrete random variable population mean vs. sample mean; population s.d. vs. sample s.d. combination of two random variables via linear equation. PART 3 Binomial & Normal distribution: binomial distribution: 1) fixed number of observations; 2) each observation is independent;3)only two outcomes, success/failure; 4)P(success) is always the same. X~ Bin (n,p), x= number of successes normal probability plot: x- theoretical quantiles; y - sample quantiles. The data’s normal when the graph is a straight line. Distribution of sample mean: always normal when x>/= 30(Central Limit Theorem). sample mean = population mean, sample mean standard deviation equals population standard deviation/ square root n. (* standard error= sample mean standard deviation.) Distribution of sample proportion: when x~ bin(n,p); proportion of successes in a sample= x/n; always normal when n*p >/=10 and n*(1-p) >/=10 (* standard error= sample proportion standard deviation.)


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