Random Variables A random variable is a variable (usually we use x), that has a single numerical value, determined by chance, for each outcome of a procedure.

Slides:



Advertisements
Similar presentations
Probability Basic Probability Concepts Probability Distributions Sampling Distributions.
Advertisements

Unit 4 Sections 4-1 & & 4-2: Sample Spaces and Probability  Probability – the chance of an event occurring.  Probability event – a chance process.
probability distributions
1 Set #3: Discrete Probability Functions Define: Random Variable – numerical measure of the outcome of a probability experiment Value determined by chance.
Sections 4.1 and 4.2 Overview Random Variables. PROBABILITY DISTRIBUTIONS This chapter will deal with the construction of probability distributions by.
Chapter 5 Basic Probability Distributions
Random Variables A Random Variable assigns a numerical value to all possible outcomes of a random experiment We do not consider the actual events but we.
Slide Slide 1 Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley. Created by Tom Wegleitner, Centreville, Virginia Section 5-2.
Slide 1 Statistics Workshop Tutorial 4 Probability Probability Distributions.
Lecture Slides Elementary Statistics Twelfth Edition
Slide 1 Statistics Workshop Tutorial 7 Discrete Random Variables Binomial Distributions.
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Discrete Distributions Chapter 5.
Mean, Variance, and Standard Deviation
5-2 Probability Distributions This section introduces the important concept of a probability distribution, which gives the probability for each value of.
Chapter 6: Random Variables
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 4 and 5 Probability and Discrete Random Variables.
Week71 Discrete Random Variables A random variable (r.v.) assigns a numerical value to the outcomes in the sample space of a random phenomenon. A discrete.
1 Overview This chapter will deal with the construction of probability distributions by combining the methods of Chapter 2 with the those of Chapter 4.
Chapter 5 Probability Distributions
Chapter 6 Random Variables. Make a Sample Space for Tossing a Fair Coin 3 times.
Statistics 5.2.
1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Random Variables  Random variable a variable (typically represented by x)
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Review and Preview This chapter combines the methods of descriptive statistics presented in.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Chapter 5 Discrete Probability Distributions 5-1 Review and Preview 5-2.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
+ The Practice of Statistics, 4 th edition – For AP* STARNES, YATES, MOORE Chapter 6: Random Variables Section 6.1 Discrete and Continuous Random Variables.
1 Chapter 4. Section 4-1 and 4-2. Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman M ARIO F. T RIOLA E IGHTH E DITION.
Chapter 5: Probability Distribution What is Probability Distribution? It is a table of possible outcomes of an experiment and the probability of each outcome.
Slide Slide 1 Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley. Lecture Slides Elementary Statistics Tenth Edition and the.
DISCRETE PROBABILITY DISTRIBUTIONS Chapter 5. Outline  Section 5-1: Introduction  Section 5-2: Probability Distributions  Section 5-3: Mean, Variance,
Chapter 6 Random Variables
1 Chapter 4. Section 4-1 and 4-2. Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman M ARIO F. T RIOLA E IGHTH E DITION.
DISCRETE PROBABILITY DISTRIBUTIONS
6.9 – Discrete Random Variables IBHLY2 - Santowski.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Section 5-2 Random Variables.
+ The Practice of Statistics, 4 th edition – For AP* STARNES, YATES, MOORE Chapter 6: Random Variables Section 6.1 Discrete and Continuous Random Variables.
4.1 Probability Distributions Important Concepts –Random Variables –Probability Distribution –Mean (or Expected Value) of a Random Variable –Variance and.
Slide 5-1 Chapter 5 Probability and Random Variables.
Sections 5.1 and 5.2 Review and Preview and Random Variables.
Random Variables Ch. 6. Flip a fair coin 4 times. List all the possible outcomes. Let X be the number of heads. A probability model describes the possible.
Lesson Discrete Random Variables. Objectives Distinguish between discrete and continuous random variables Identify discrete probability distributions.
Probability Distributions, Discrete Random Variables
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 5-1 Review and Preview.
AP STATISTICS Section 7.1 Random Variables. Objective: To be able to recognize discrete and continuous random variables and calculate probabilities using.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by.
Chapter 5 Probability Distributions 5-1 Overview 5-2 Random Variables 5-3 Binomial Probability Distributions 5-4 Mean, Variance and Standard Deviation.
Week 5 Discrete Random Variables and Probability Distributions Statistics for Social Sciences.
Chapter 7 Lesson 7.4a Random Variables and Probability Distributions 7.4: Mean and Standard Deviation of a Random Variable.
Slide 1 Copyright © 2004 Pearson Education, Inc. Chapter 5 Probability Distributions 5-1 Overview 5-2 Random Variables 5-3 Binomial Probability Distributions.
Lesson 96 – Expected Value & Variance of Discrete Random Variables HL2 Math - Santowski.
Probability Distributions ( 확률분포 ) Chapter 5. 2 모든 가능한 ( 확률 ) 변수의 값에 대해 확률을 할당하는 체계 X 가 1, 2, …, 6 의 값을 가진다면 이 6 개 변수 값에 확률을 할당하는 함수 Definition.
Discrete Random Variables Section 6.1. Objectives Distinguish between discrete and continuous random variables Identify discrete probability distributions.
SWBAT: -Distinguish between discrete and continuous random variables -Construct a probability distribution and its graph -Determine if a distribution is.
4.2 Random Variables and Their Probability distributions
Lecture Slides Elementary Statistics Eleventh Edition
Unit 5 Section 5-2.
Discrete and Continuous Random Variables
Random Variables and Probability Distribution (2)
Random Variables.
Chapter 5 Probability 5.2 Random Variables 5.3 Binomial Distribution
Discrete and Continuous Random Variables
Lecture Slides Elementary Statistics Twelfth Edition
Lecture Slides Elementary Statistics Twelfth Edition
12/6/ Discrete and Continuous Random Variables.
Chapter 7: Random Variables
Chapter 6: Random Variables
M248: Analyzing data Block A UNIT A3 Modeling Variation.
Lecture Slides Essentials of Statistics 5th Edition
Section 1 – Discrete and Continuous Random Variables
Lecture Slides Essentials of Statistics 5th Edition
Presentation transcript:

Random Variables A random variable is a variable (usually we use x), that has a single numerical value, determined by chance, for each outcome of a procedure A probability distribution is a graph, table, or formula that gives the probability for each value of the random variable.

Example of a probability distribution and probability histogram Outcome of the toss of two coins x (number of heads)P(x) 0 ¼ 1 ½ 2 ¼

This is very similar to what we did in Chapter 2, except in Chapter 2 we were constructing frequency distributions and histograms based on observed data. In this chapter, we are doing similar things, but to describe what we expect to happen.

Requirements for a Probability Distribution Where x takes on every possible value. Means: Something has to happen, so sum of all probabilities is 1. for each x. Means: the probability of any event is between 0 and 1 (inclusive)

Center For a probability distribution, This gives the mean of the probability distribution. In other words, it tells us what the average (mean) outcome is. Notice how each outcome is weighted by how likely it is to occur. This is also called the Expected Value of the distribution, denoted E or E(x).

Example Outcome of the toss of two coins (number of heads) xP(x)x. P(x) 0 ¼ 0. ¼ = 0 1 ½ 1. ½ = ½ 2 ¼ 2. ¼ = ½

Example Number of matching digits (of a guess to a random 2-digit number) xP(x)x. P(x) (0.81) = (0.18) = (0.01) = 0.02 So on average, we will match 0.2 digits. So over 1000 tries, we would expect to match about 200 digits total.

Variance For a probability distribution This gives the variance of the probability distribution. In other words, it tells us how spread out we expect the outcomes to be. As before, Standard Deviation can be found from the variance:

Example Outcome of the toss of two coins (number of heads) xP(x)x. P(x)x 2. P(x) 0 ¼ 0. ¼ = ¼ = 0 1 ½ 1. ½ = ½1 2. ½ = ½ 2 ¼ 2. ¼ = ½2 2. ¼ = 1

Example Number of matching digits (of a guess to a random 2-digit number) xP(x)x. P(x)x 2. P(x) (0.81) = 00 2 (0.81) = (0.18) = (0.18) = (0.01) = (0.01) = 0.04

You try Number of Girls out of 4 children xP(x) 01/16 = ¼ = /8 = ¼ = /16 =

Homework Part 1 4.2: 3, 5, 7 Read the rest of section 4.2, especially “Identifying Unusual Results with Probabilities”, pg 189.