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UA / Economics / STAT 260 / probability study guide

probability study guide

probability study guide

Description

School: University of Alabama - Tuscaloosa
Department: Economics
Course: Statistical Data Analysis
Term: Fall 2015
Tags: Statistical, data, and analysis
Cost: 50
Name: ST 260 Exam 2 Study Guide
Description: This study guide will cover conditional probability, events, random and discrete random variables, probability distribution, etc.
Uploaded: 10/16/2017
3 Pages 7 Views 13 Unlocks
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ST 260 EXAM 2


what is random variable?



Study Guide

Note: U = intersection, is upside down U = union

Conditional Probability 

­is the probability of event A will occur, given that event B has already occurred. P(A | B) = P(A U B) / P(B)

We assume that P(B) does not equal 0

-For example,

A= “it rains tomorrow” B= “it rains today”

P(B)=0.8 P(A | B)= 0.9

What is the chance it will rain both days?

P(A U B) =P(B) x P(A | B) = (0.8) x (0.9) = 0.72

Disjoint Events (mutually exclusive events) 

­this is when A and B are two exclusively different events

­if two events A and B are mutually exclusively, the probability of the union of A and B equals  the sum of the probabilities of A and B; that is, P(A U B) = P(A) + P(B)

­For example,

You toss a coin 3 times

S = {HHH,HHT,HTH,THH,HTT,THT,TTH,TTT}

P(HHH) = 1/8

P(THT) = 1/8


what is discrete random variable?



P(HHH U THT)= 1/8 + 1/8 = 2/8

Independent Events

­Events A and B are independent events if the occurrence of B does not alter the probability that  A has occurred; that is, events A and B are independent if 

P(A | B) = P(A)

When events A and B are independent, it is also true that

P(B | A) = P(B) or P(A U B) = P(B) x P(A)

Events that are not independent are said to be dependent.

­For example, toss a coin 2 times

Event A = {HEAD on the 1st toss}

Event B = {HEAD on the 2nd toss}

P(A | B) = P(A U B) ÷ P(B) =1/4 ÷ ½ P(A

P(A | B) =P(A) thus, events are independent.

Discrete Probability Distributions 

-Random variable: a variable that can take on different numeric values  according to the outcome of an experiment.

Example: Toss a fair die

X= # spots S= (1, 2, 3, 4, 5, 6)


what are the two parameters of binomal?



Example: Toss a coin

S= {H, T} X= # of heads

X is the random variable in both examples

-Discrete random variable: a random variable whose possible values are  associated with a countable set of numbers. If you want to learn more check out What are the types of archaeology?

Examples:

1. The number of sales made by a salesperson in a given week: x =0,1,2, …

2. The number of consumers in a sample of 500 who favor a particular  product over all competitors: x= 0,1,2,….,500

3. The number of errors on a page of an accountant’s ledger: x= 0,1,2,….

4. The number of customers waiting to be served in a restaurant at a  particular time: x=0,1,2,…..

Intuitive Interpretation 

If we repeat an experiment many, many times under the same conditions,  and if we average the results, then the average will represent the expected  value.

Bernouilli Distribution 

“X” is a discrete, binary random

Binary means there can only be two possible outcomes, either 0 or 1 Example: Take one card at random. Success is picking a “diamond.” If “diamond” appears, X=1 If “no diamond” appears, X=0 We also discuss several other topics like one of the most common editing techniques designed to hide the instantaneous and potentially jarring shift from one camera viewpoint to another is:
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If you want to learn more check out math 227 binghamton

Bernouilli only has one parameter: p= the probability of success We also discuss several other topics like sta 13 study guides

Binomial Distribution 

X is a discrete random variable If you want to learn more check out psy 350 exam 3

X= a count of the number of successes in n independent Bernouilli trials It has to have:

1. A fixed number of trials, n

2. All n trials must be independent of each other

3. The probability of success remains the same for all trials Binomial has two paramaters:

n = the fixed number of trials

p=the probability of success for each trial

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