Exam 2 - Review Chapters 14 - 18.

Slides:



Advertisements
Similar presentations
Chapter Six Sampling Distributions McGraw-Hill/Irwin Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Advertisements

Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 6-1 Introduction to Statistics Chapter 7 Sampling Distributions.
Chapter 7 The Normal Probability Distribution 7.5 Sampling Distributions; The Central Limit Theorem.
Mutually Exclusive: P(not A) = 1- P(A) Complement Rule: P(A and B) = 0 P(A or B) = P(A) + P(B) - P(A and B) General Addition Rule: Conditional Probability:
Sample Distribution Models for Means and Proportions
Normal and Sampling Distributions A normal distribution is uniquely determined by its mean, , and variance,  2 The random variable Z = (X-  /  is.
Chapter 6 Sampling and Sampling Distributions
Chapter 5 Discrete Probability Distribution I. Basic Definitions II. Summary Measures for Discrete Random Variable Expected Value (Mean) Variance and Standard.
AP Statistics Chapter 9 Notes.
June 10, 2008Stat Lecture 9 - Proportions1 Introduction to Inference Sampling Distributions for Counts and Proportions Statistics Lecture 9.
Binomial Distributions Calculating the Probability of Success.
Copyright ©2011 Nelson Education Limited The Normal Probability Distribution CHAPTER 6.
Chap 6-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 6 Introduction to Sampling.
Day 2 Review Chapters 5 – 7 Probability, Random Variables, Sampling Distributions.
Sampling and sampling distibutions. Sampling from a finite and an infinite population Simple random sample (finite population) – Population size N, sample.
Chapter 10 – Sampling Distributions Math 22 Introductory Statistics.
Bernoulli Trials Two Possible Outcomes –Success, with probability p –Failure, with probability q = 1  p Trials are independent.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 7 Sampling Distributions.
Determination of Sample Size: A Review of Statistical Theory
Chapter 7 Sampling and Sampling Distributions ©. Simple Random Sample simple random sample Suppose that we want to select a sample of n objects from a.
Estimation Chapter 8. Estimating µ When σ Is Known.
Chapter 5 Discrete Probability Distributions. Random Variable A numerical description of the result of an experiment.
Using the Tables for the standard normal distribution.
AP Statistics Semester One Review Part 2 Chapters 4-6 Semester One Review Part 2 Chapters 4-6.
Sampling Distributions Chapter 18. Sampling Distributions A parameter is a measure of the population. This value is typically unknown. (µ, σ, and now.
Conditions and Formulas Are you confident?. 1 proportion z-interval what variable(s) need to be defined? write the formula.
Probability Models Chapter 17. Bernoulli Trials  The basis for the probability models we will examine in this chapter is the Bernoulli trial.  We have.
1 7.3 RANDOM VARIABLES When the variables in question are quantitative, they are known as random variables. A random variable, X, is a quantitative variable.
1 Sampling distributions The probability distribution of a statistic is called a sampling distribution. : the sampling distribution of the mean.
Lecture 5 Introduction to Sampling Distributions.
Chapter 5 Sampling Distributions. Introduction Distribution of a Sample Statistic: The probability distribution of a sample statistic obtained from a.
Chapter 22 Comparing Two Proportions. Comparing 2 Proportions How do the two groups differ? Did a treatment work better than the placebo control? Are.
Central Limit Theorem Let X 1, X 2, …, X n be n independent, identically distributed random variables with mean  and standard deviation . For large n:
An Example of {AND, OR, Given that} Using a Normal Distribution By Henry Mesa.
Slide 17-1 Copyright © 2004 Pearson Education, Inc.
Copyright © 2010 Pearson Education, Inc. Chapter 17 Probability Models.
Sampling Distributions Chapter 18. Sampling Distributions A parameter is a number that describes the population. In statistical practice, the value of.
Sampling and Sampling Distributions
Ch5.4 Central Limit Theorem
Chapter 7 Review.
Chapter 7 Sampling and Sampling Distributions
Basic Business Statistics (8th Edition)
CHAPTER 6 Random Variables
CHAPTER 7 Sampling Distributions
Discrete Probability Distributions
Review of Hypothesis Testing
MATH 2311 Section 4.4.
Using the Tables for the standard normal distribution
Chapter 16 Probability Models
ASV Chapters 1 - Sample Spaces and Probabilities
Lecture Slides Elementary Statistics Twelfth Edition
Chapter 17 Probability Models.
CHAPTER 7 Sampling Distributions
Sampling Distribution Models
Sampling Distributions
The estimate of the proportion (“p-hat”) based on the sample can be a variety of values, and we don’t expect to get the same value every time, but the.
CHAPTER 7 Sampling Distributions
CHAPTER 7 Sampling Distributions
CHAPTER 7 Sampling Distributions
CHAPTER 7 Sampling Distributions
CHAPTER 7 Sampling Distributions
Chapter 17 Probability Models Copyright © 2009 Pearson Education, Inc.
Chapter 17 – Probability Models
Quantitative Methods Varsha Varde.
Sample Proportions Section 9.2.
Introduction to Sampling Distributions
Central Limit Theorem cHapter 18 part 2.
The Geometric Distributions
Review of Hypothesis Testing
MATH 2311 Section 4.4.
Presentation transcript:

Exam 2 - Review Chapters 14 - 18

Chapter 14: Randomness & Probability P(A) = 0 ≤ P(A) ≤ 1 P(A) = 1 – P(Ac) A,B disjoint: P(A or B) = P(A) + P(B) A,B independent: P(A and B) = P(A) x P(B)

Chapter 15: Probability Rules P(A or B) = P(A) + P(B) – P(A and B) P(A and B) = P(A) x P(B | A) Independence occurs when P(B | A) = P(B)

Chapter 16: Random Variables Probability Model using table µ = E(X) = σ2 = Var(X) = σ = SD(X) = Impact of shift/stretch on mean and variance

Chapter 17: Binomial Model Binom(n,p): P(X = x) = nCx px qn-x Expected Value: µ = np Standard Deviation: σ = Success/Failure condition: Binomial model can be approximated by Normal if we expect at least 10 successes and 10 failures 10% Condition: sample size must be no more than 10% of population to assume independence

Chapter 18: Sampling Distribution Models Central Limit Theorem Sampling Distribution can be described using Normal model Conditions: Randomization 10% Condition Success/Failure Condition Large enough sample Proportions: Means: Mean: µ