EXERCISE R.7 R.7*Find E(X 2 ) for X defined in Exercise R.2. [R.2*A random variable X is defined to be the larger of the numbers when two dice are thrown,

Slides:



Advertisements
Similar presentations
Chapter 4 Sampling Distributions and Data Descriptions.
Advertisements

5.1 Rules for Exponents Review of Bases and Exponents Zero Exponents
Author: Julia Richards and R. Scott Hawley
Warm Up Lesson Presentation Lesson Quiz
8-4 Factoring ax2 + bx + c Warm Up Lesson Presentation Lesson Quiz
1 Building-blocks of understanding. 2 © Dr. Charles Smith, 2006 The author asserts and reserves all rights. However, this resource can be freely copied,
Lecture 2 ANALYSIS OF VARIANCE: AN INTRODUCTION
Probability Distributions
Solve Multi-step Equations
Biostatistics Unit 5 Samples Needs to be completed. 12/24/13.
1 Combination Symbols A supplement to Greenleafs QR Text Compiled by Samuel Marateck ©2009.
Break Time Remaining 10:00.
The basics for simulations
Elementary Statistics
MAT 205 F08 Chapter 12 Complex Numbers.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: probability distribution example: x is the sum of two dice Original.
Hypothesis Tests: Two Independent Samples
Chapter 10 Estimating Means and Proportions
Copyright © 2013, 2009, 2006 Pearson Education, Inc.
EC220 - Introduction to econometrics (review chapter)
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: exercise r.10 and r.12 Original citation: Dougherty, C. (2012) EC220.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: exercise r.13 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: exercise r.4 Original citation: Dougherty, C. (2012) EC220 - Introduction.
1.7 Derive, with a proof, the slope coefficient that would have been obtained in Exercise 1.5 if weight and height had been measured in metric units. (Note:
Revision - Algebra I Binomial Product
To find the expected value of a function of a random variable, you calculate all the possible values of the function, weight them by the corresponding.
Definition of, the expected value of a function of X : 1 EXPECTED VALUE OF A FUNCTION OF A RANDOM VARIABLE To find the expected value of a function of.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: exercise r.22 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: exercise r.7 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: exercise r.2 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: exercise r.19 Original citation: Dougherty, C. (2012) EC220 - Introduction.
: 3 00.
Essential Cell Biology
1 Lesson Dividing with Integers. 2 Lesson Dividing with Integers California Standard: Number Sense 2.3 Solve addition, subtraction, multiplication,
Chapter Thirteen The One-Way Analysis of Variance.
Chapter 8 Estimation Understandable Statistics Ninth Edition
Section 4.3 How Atoms Differ.
Clock will move after 1 minute
Intracellular Compartments and Transport
PSSA Preparation.
TASK: Skill Development A proportional relationship is a set of equivalent ratios. Equivalent ratios have equal values using different numbers. Creating.
Copyright © 2013 Pearson Education, Inc. All rights reserved Chapter 11 Simple Linear Regression.
Experimental Design and Analysis of Variance
Essential Cell Biology
Lial/Hungerford/Holcomb/Mullins: Mathematics with Applications 11e Finite Mathematics with Applications 11e Copyright ©2015 Pearson Education, Inc. All.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: expected value of a random variable Original citation: Dougherty,
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: population variance of a discreet random variable Original citation:
Copyright Tim Morris/St Stephen's School
The third sequence defined the expected value of a function of a random variable X. There is only one function that is of much interest to us, at least.
E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 7) Slideshow: exercise 7.5 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Random Variables and Expectation. Random Variables A random variable X is a mapping from a sample space S to a target set T, usually N or R. Example:
1 PROBABILITY DISTRIBUTION EXAMPLE: X IS THE SUM OF TWO DICE red This sequence provides an example of a discrete random variable. Suppose that you.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 2) Slideshow: exercise 2.22 Original citation: Dougherty, C. (2012) EC220 - Introduction.
EXPECTED VALUE OF A RANDOM VARIABLE 1 The expected value of a random variable, also known as its population mean, is the weighted average of its possible.
Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: expected value of a function of a random variable Original citation:
EXERCISE R.4 1 R.4*Find the expected value of X in Exercise R.2. [R.2*A random variable X is defined to be the larger of the numbers when two dice are.
Econ 482 Lecture 1 I. Administration: Introduction Syllabus Thursday, Jan 16 th, “Lab” class is from 5-6pm in Savery 117 II. Material: Start of Statistical.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 1) Slideshow: exercise 1.9 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Week 11 Similar figures, Solutions, Solve, Square root, Sum, Term.
Mean and Standard Deviation of Discrete Random Variables.
Probability Distribution of a Discrete Random Variable If we have a sample probability distribution, we use (x bar) and s, respectively, for the mean.
Definition of, the expected value of a function of X : 1 EXPECTED VALUE OF A FUNCTION OF A RANDOM VARIABLE To find the expected value of a function of.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 2) Slideshow: exercise 2.11 Original citation: Dougherty, C. (2012) EC220 - Introduction.
Explain intuitively why this should be so.
EXERCISE R.13 R.13Let  HT be the correlation between humidity, H, and temperature measured in degrees Fahrenheit, F. Demonstrate that the correlation.
6.4*The table gives the results of multiple and simple regressions of LGFDHO, the logarithm of annual household expenditure on food eaten at home, on LGEXP,
7.2 Means & Variances of Random Variables AP Statistics.
Introduction to Econometrics, 5th edition
Presentation transcript:

EXERCISE R.7 R.7*Find E(X 2 ) for X defined in Exercise R.2. [R.2*A random variable X is defined to be the larger of the numbers when two dice are thrown, or the number if they are the same. Find the probability distribution for X.] 1

Definition of E[g(X)], the expected value of a function of X: To find the expected value of a function of a random variable, you calculate all the possible values of the function, weight them by the corresponding probabilities, and sum the results. 2 EXERCISE R.7

Definition of E[g(X)], the expected value of a function of X: Example: For example, the expected value of X 2 is found by calculating all its possible values, multiplying them by the corresponding probabilities, and summing. 3 EXERCISE R.7

x i p i g(x i ) g(x i ) p i x 1 p 1 g(x 1 )g(x 1 ) p 1 x 2 p 2 g(x 2 ) g(x 2 ) p 2 x 3 p 3 g(x 3 ) g(x 3 ) p 3 ………...……... x n p n g(x n ) g(x n ) p n   g(x i ) p i EXERCISE R.7 4 The calculation of the expected value of a function g(x) is shown in abstract in the table. The expected value is the sum of the terms g(x i )p i.

x i p i g(x i ) g(x i ) p i x i p i x 1 p 1 g(x 1 )g(x 1 ) p 1 11/36 x 2 p 2 g(x 2 ) g(x 2 ) p 2 23/36 x 3 p 3 g(x 3 ) g(x 3 ) p 3 35/36 ………...……... 47/36 ………...……... 59/36 x n p n g(x n ) g(x n ) p n 611/36   g(x i ) p i EXERCISE R.7 In this exercise, X is the random variable defined in Exercise R.2. The 6 possible values of X and the corresponding probabilities are listed. 5

x i p i g(x i ) g(x i ) p i x i p i x i 2 x 1 p 1 g(x 1 )g(x 1 ) p 1 11/361 x 2 p 2 g(x 2 ) g(x 2 ) p 2 23/364 x 3 p 3 g(x 3 ) g(x 3 ) p 3 35/369 ………...……... 47/3616 ………...……... 59/3625 x n p n g(x n ) g(x n ) p n 611/3636   g(x i ) p i EXERCISE R.7 First you calculate the possible values of X 2. 6

x i p i g(x i ) g(x i ) p i x i p i x i 2 x i 2 p i x 1 p 1 g(x 1 )g(x 1 ) p 1 11/3611/36 x 2 p 2 g(x 2 ) g(x 2 ) p 2 23/364 x 3 p 3 g(x 3 ) g(x 3 ) p 3 35/369 ………...……... 47/3616 ………...……... 59/3625 x n p n g(x n ) g(x n ) p n 611/3636   g(x i ) p i EXERCISE R.7 The first value is 1 since x is equal to 1. The probability of X being equal to 1 is 1/36. Hence the weighted square is 1/36. 7

x i p i g(x i ) g(x i ) p i x i p i x i 2 x i 2 p i x 1 p 1 g(x 1 )g(x 1 ) p 1 11/3611/36 x 2 p 2 g(x 2 ) g(x 2 ) p 2 23/36412/36 x 3 p 3 g(x 3 ) g(x 3 ) p 3 35/36945/36 ………...……... 47/ /36 ………...……... 59/ /36 x n p n g(x n ) g(x n ) p n 611/ /36   g(x i ) p i EXERCISE R.7 Similarly for all the other possible values of X. 8

x i p i g(x i ) g(x i ) p i x i p i x i 2 x i 2 p i x 1 p 1 g(x 1 )g(x 1 ) p 1 11/3611/36 x 2 p 2 g(x 2 ) g(x 2 ) p 2 23/36412/36 x 3 p 3 g(x 3 ) g(x 3 ) p 3 35/36945/36 ………...……... 47/ /36 ………...……... 59/ /36 x n p n g(x n ) g(x n ) p n 611/ /36   g(x i ) p i 791/36= EXERCISE R.7 The expected value of X 2 is the sum of its weighted values in the final column. It is equal to It is the average value of the figures in the previous column, taking the differing probabilities into account. 9

x i p i g(x i ) g(x i ) p i x i p i x i 2 x i 2 p i x 1 p 1 g(x 1 )g(x 1 ) p 1 11/3611/36 x 2 p 2 g(x 2 ) g(x 2 ) p 2 23/36412/36 x 3 p 3 g(x 3 ) g(x 3 ) p 3 35/36945/36 ………...……... 47/ /36 ………...……... 59/ /36 x n p n g(x n ) g(x n ) p n 611/ /36   g(x i ) p i 791/36= Note that E(X 2 ) is not the same thing as E(X), squared. In the previous sequence we saw that E(X) for this example was Its square is EXERCISE R.7 10 E(X 2 ) = 21.97E(X) = 4.47 [E(X)] 2 = 19.98

Copyright Christopher Dougherty 1999–2006. This slideshow may be freely copied for personal use