In-Class Exercise: Bayes’ Theorem

Slides:



Advertisements
Similar presentations
MEGN 537 – Probabilistic Biomechanics Ch.2 – Mathematics of Probability Anthony J Petrella, PhD.
Advertisements

Statistical Estimation and Sampling Distributions
Ch 5 Probability: The Mathematics of Randomness
Probability & Statistical Inference Lecture 6 MSc in Computing (Data Analytics)
13-1 Experimental and Theoretical Probability. Outcome: the possible result of a situation or experiment Even: may be a single outcome or a group of outcomes.
Exercise Exercise3.1 8 Exercise3.1 9 Exercise
Bayes Theorem Mutually exclusive events A collection of events (B1, B2, …, Bk) is said to be mutually exclusive if no two of them overlap. If mutually.
Transparency Masters to accompany Operations Management, 5E (Heizer & Render) 4-20 © 1998 by Prentice Hall, Inc. A Simon & Schuster Company Upper Saddle.
CIVL 181 Tutorial 2 Total probability Bayes theorem.
Exercise Exercise Exercise Exercise
ENM 207 Lecture 7. Example: There are three columns entitled “Art” (A),“Books” (B) and “Cinema” (C) in a new magazine. Reading habits of a randomly selected.
Exercise Exercise Exercise Exercise
Exercise Exercise6.1 7 Exercise6.1 8 Exercise6.1 9.
Discrete Probability Distributions
Sampling Distributions
MA305 Conditional Probability Bayes’ Theorem
Bayes’ Theorem.
Conditional Probabilities
An outcome is a possible result An event is a specific outcome Random means all outcomes are equally likely to occur or happen. random = fair A favorable.
Steps in Using the and R Chart
Lecture 06 Prof. Dr. M. Junaid Mughal
STATISTIC :PROBABILITY Experiment Any operation or procedure whose outcome cannot be predicted with certainty. Outcomes for an experiment is called sample.
Aim: How do we find the probability with two outcomes? Do Now: John takes a multiple choice test on a topic for which he has learned nothing. Each question.
Permutation An arrangement of r objects from n objects, the order of which is important. The possible number of such arrangements is denoted by n P r Combination.
Ch Counting Techniques Product Rule If the first element or object of an ordered pair can be used in n 1 ways, and for each of these n1 ways.
Binomial Distributions
Math 4030 – 4a Discrete Distributions
4.3 Binomial Distributions. Red Tiles and Green Tiles in a Row You have 4 red tiles and 3 green tiles. You need to select 4 tiles. Repeated use of a tiles.
IES 331 Quality Control Chapter 6 Control Charts for Attributes
ExperimentExperimental Outcome (Possibilities) Toss a CoinHead, Tail Select a part for inspectionDefective, Non-Defective Conduct a SalesPurchase, No Purchase.
2-1 Sample Spaces and Events Random Experiments Figure 2-1 Continuous iteration between model and physical system.
2-1 Sample Spaces and Events Random Experiments Figure 2-1 Continuous iteration between model and physical system.
Bayes’ Theorem Susanna Kujanpää OUAS Bayes’ Theorem This is a theorem with two distinct interpretations. 1) Bayesian interpretation: it shows.
1 Problem 6.15: A manufacturer wishes to maintain a process average of 0.5% nonconforming product or less less. 1,500 units are produced per day, and 2.
Baye’s Theorem Working with Conditional Probabilities.
Chapter 2: Probability · An Experiment: is some procedure (or process) that we do and it results in an outcome. A random experiment: is an experiment we.
1 Chapter 3: Attribute Measurement Systems Analysis (Optional) 3.1 Introduction to Attribute Measurement Systems Analysis 3.2 Conducting an Attribute MSA.
Chapter 2. Conditional Probability Weiqi Luo ( 骆伟祺 ) School of Data & Computer Science Sun Yat-Sen University :
Onur DOĞAN.  A small life insurance company has determined that on the average it receives 3 death claims per day. Find the probability that the company.
S TATISTICAL R EVIEW MMRP-8 Indria Purwantiningrum Food Science & Technology 2013.
Probability of Simple Events
Binomial Distribution If you flip a coin 3 times, what is the probability that you will get exactly 1 tails? There is more than one way to do this problem,
1 CHAPTER (7) Attributes Control Charts. 2 Introduction Data that can be classified into one of several categories or classifications is known as attribute.
Lesson 7.8 Simple Probability Essential Question: How do you find the probability of an event?
2.5 Additive Rules: Theorem 2.10: If A and B are any two events, then: P(A  B)= P(A) + P(B)  P(A  B) Corollary 1: If A and B are mutually exclusive.
1. Binomial Trials: Success/Failure 2. Probability of k Successes 1.
HL2 Math - Santowski Lesson 93 – Bayes’ Theorem. Bayes’ Theorem  Main theorem: Suppose we know We would like to use this information to find if possible.
Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 1 In-Jeong Chung Intelligent Information System lab. Department.
Sampling Distributions
Bayes’ Theorem.
Copyright © Cengage Learning. All rights reserved.
HW 11 Key.
Chapter 5 Bayes’ Theorem.
Conditional probability
Section 7.6 Bayes’ Theorem.
Tech 31: Unit 4 Control Charts for Attributes
Chapter 2: Probability · An Experiment: is some procedure (or process) that we do and it results in an outcome. A random experiment: is an experiment we.
In-Class Exercise: Discrete Distributions
Lecture Slides Elementary Statistics Twelfth Edition
Lesson 10-5 Experimental Probability
Problem 6.15: A manufacturer wishes to maintain a process average of 0.5% nonconforming product or less less. 1,500 units are produced per day, and 2 days’
Lesson 10-5 Experimental Probability
Problem Markov Chains 1 A manufacturer has one key machine at the core of its production process. Because of heavy use, the machine.
P-VALUE.
Problem Markov Chains 1 A manufacturer has one key machine at the core of its production process. Because of heavy use, the machine.
Steps in Using the and R Chart
MAS2317 Presentation Question 1
Hypergeometric Distribution
Lecture Slides Essentials of Statistics 5th Edition
Chapter 5 – Probability Rules
Presentation transcript:

In-Class Exercise: Bayes’ Theorem 2-98. An inspector working for a manufacturing company has a 99% chance of correctly identifying defective items and a 0.5% chance of incorrectly classifying a good item as defective. The company has evidence that its line produces 0.9% of nonconforming items. (a) What is the probability that an item selected for inspection is classified as defective? (b) If an item selected at random is classified as nondefective, what is the probability that it is indeed good?