PROBABILITY AND STATISTICS WEEK 8 Onur Doğan. Skewness Onur Doğan.

Slides:



Advertisements
Similar presentations
Let X 1, X 2,..., X n be a set of independent random variables having a common distribution, and let E[ X i ] = . then, with probability 1 Strong law.
Advertisements

Chapter 2 Multivariate Distributions Math 6203 Fall 2009 Instructor: Ayona Chatterjee.
Business Statistics for Managerial Decision
Continuous Probability Distributions.  Experiments can lead to continuous responses i.e. values that do not have to be whole numbers. For example: height.
Part V: Continuous Random Variables /statistics-notes-%E2%80%93-properties-of-normal-distribution-2/
5.4 Joint Distributions and Independence
1 Def: Let and be random variables of the discrete type with the joint p.m.f. on the space S. (1) is called the mean of (2) is called the variance of (3)
Probability Densities
Assignment 2 Chapter 2: Problems  Due: March 1, 2004 Exam 1 April 1, 2004 – 6:30-8:30 PM Exam 2 May 13, 2004 – 6:30-8:30 PM Makeup.
Statistics Lecture 20. Last Day…completed 5.1 Today Section 5.2 Next Day: Parts of Section 5.3 and 5.4.
Probability Distributions Random Variables: Finite and Continuous A review MAT174, Spring 2004.
Chapter 4: Joint and Conditional Distributions
Statistics Alan D. Smith.
Copyright © Cengage Learning. All rights reserved. 4 Continuous Random Variables and Probability Distributions.
5-1 Two Discrete Random Variables Example Two Discrete Random Variables Figure 5-1 Joint probability distribution of X and Y in Example 5-1.
5-1 Two Discrete Random Variables Example Two Discrete Random Variables Figure 5-1 Joint probability distribution of X and Y in Example 5-1.
NIPRL Chapter 2. Random Variables 2.1 Discrete Random Variables 2.2 Continuous Random Variables 2.3 The Expectation of a Random Variable 2.4 The Variance.
Continuous Probability Distribution  A continuous random variables (RV) has infinitely many possible outcomes  Probability is conveyed for a range of.
Sampling Distributions  A statistic is random in value … it changes from sample to sample.  The probability distribution of a statistic is called a sampling.
Joint Probability Distributions Leadership in Engineering
Joint Probability Distributions and Random Samples
Pairs of Random Variables Random Process. Introduction  In this lecture you will study:  Joint pmf, cdf, and pdf  Joint moments  The degree of “correlation”
Jointly Distributed Random Variables
PBG 650 Advanced Plant Breeding
PROBABILITY & STATISTICAL INFERENCE LECTURE 3 MSc in Computing (Data Analytics)
6.1B Standard deviation of discrete random variables continuous random variables AP Statistics.
Review of Probability Concepts ECON 4550 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes SECOND.
Modular 11 Ch 7.1 to 7.2 Part I. Ch 7.1 Uniform and Normal Distribution Recall: Discrete random variable probability distribution For a continued random.
Review of Probability Concepts ECON 6002 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes.
Chapters 7 and 10: Expected Values of Two or More Random Variables
Chapter 5. Joint Probability Distributions and Random Sample Weiqi Luo ( 骆伟祺 ) School of Software Sun Yat-Sen University : Office.
1 Lecture 14: Jointly Distributed Random Variables Devore, Ch. 5.1 and 5.2.
Discrete distribution word problems –Probabilities: specific values, >, =, … –Means, variances Computing normal probabilities and “inverse” values: –Pr(X
Random Sampling Approximations of E(X), p.m.f, and p.d.f.
Statistics for Business & Economics
1 Topic 5 - Joint distributions and the CLT Joint distributions –Calculation of probabilities, mean and variance –Expectations of functions based on joint.
Chapter 5 Joint Probability Distributions Joint, n. 1. a cheap, sordid place. 2. the movable place where two bones join. 3. one of the portions in which.
Topic 5 - Joint distributions and the CLT
PROBABILITY AND STATISTICS WEEK 4 Onur Doğan. Random Variable Random Variable. Let S be the sample space for an experiment. A real-valued function that.
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.
Review of Probability Concepts Prepared by Vera Tabakova, East Carolina University.
Section 5 – Expectation and Other Distribution Parameters.
Chapter 2: Probability. Section 2.1: Basic Ideas Definition: An experiment is a process that results in an outcome that cannot be predicted in advance.
Copyright © Cengage Learning. All rights reserved. 5 Joint Probability Distributions and Random Samples.
Chapter 31 Conditional Probability & Conditional Expectation Conditional distributions Computing expectations by conditioning Computing probabilities by.
1 Two Discrete Random Variables The probability mass function (pmf) of a single discrete rv X specifies how much probability mass is placed on each possible.
7.2 Means & Variances of Random Variables AP Statistics.
Copyright © Cengage Learning. All rights reserved. 5 Joint Probability Distributions and Random Samples.
Chapter 5 Joint Probability Distributions and Random Samples  Jointly Distributed Random Variables.2 - Expected Values, Covariance, and Correlation.3.
Random Variables By: 1.
Statistics and probability Dr. Khaled Ismael Almghari Phone No:
Copyright © Cengage Learning. All rights reserved. 4 Continuous Random Variables and Probability Distributions.
Chapter 25: Joint Densities
Expected values, covariance, and correlation
Functions and Transformations of Random Variables
PROBABILITY AND STATISTICS
Jointly distributed random variables
Random Variables and Probability Distribution (2)
Expected Values.
Two Discrete Random Variables
Means and Variances of Random Variables
Onur DOĞAN.
Some Rules for Expectation
Introduction to Econometrics
Probability and Statistics (week-9)
Expectation And Variance of Random Variables
ASV Chapters 1 - Sample Spaces and Probabilities
Chapter 2. Random Variables
PROBABILITY AND STATISTICS
Presentation transcript:

PROBABILITY AND STATISTICS WEEK 8 Onur Doğan

Skewness Onur Doğan

Pearson's skewness coefficient Onur Doğan

Example It’s been understood that, in a hosptial patients’ average hospital stay is 28, median is 25 and mode is 23 (days). And the standard deviation calculated as 4,2. Define the skewness type, find the pearson coefficient and interpret it. Onur Doğan

Joint Probability Distributions Discrete Random Variables (two and multiple) Continious Random Variables (two and multiple) Onur Doğan

Two Discrete Random Variables Onur Doğan

Example A large insurance agency services a number of customers who have purchased both a homeowner’s policy and an automobile policy from the agency. For each type of policy, a deductible amount must be specified. For an automobile policy, the choices are $100 and $250, whereas for a homeowner’s policy, the choices are 0, $100, and $200. Suppose an individual with both types of policy is selected at random from the agency’s files. Let X the deductible amount on the auto policy and Y the deductible amount on the homeowner’s policy. Suppose the joint pmf is given in the accompanying joint probability table: Onur Doğan

Example P(Y ≥100)=? Onur Doğan

Marginal Probability Mass Function Onur Doğan

Example Let us obtain the marginal pmf of X evaluated at, x=100 and x=250. Let us obtain the marginal pmf of Y. Onur Doğan

Remember We already know! Onur Doğan

Conditional Probability Mass Function Recall: P(A/B)=P(A  B)/P(B) Onur Doğan

Example Opinion about school (X) and teachers (Y) surveyed for students as follows: a)Determine the joint probability distribution of X and Y. b)Find the probability student give 1 point for school and 2 or above point for teachers? c)Find the marginal probability distribution, mean and variance of school scores. d) Determine the conditional probability distribution of teacher scores given that school score is 2. And find the (conditional) mean and variance of Y given X=2. e)Check if school opinions and teacher opinions are independent. Onur Doğan Teachers Scores School Scores

Two Continuous Random Variables Onur Doğan

Example A bank operates A and B facilities. On a randomly selected day, let X the proportion of time that A in use and Y the proportion of time that the B in use. Then the set of possible values for (X, Y) is the rectangle D = {(x, y): 0≤ x ≤ 1, 0 ≤ y ≤ 1}. Suppose the joint pdf of (X, Y) is given by, a) Verify that this is a legitimate pdf. b) Find the probability that neither facility is busy more than one- quarter of the time. Onur Doğan

Marginal Probability Density Function Onur Doğan

Example Find the marginal pdf of X and Y for previous question. Find P(1/4≤Y ≤3/4)=? Onur Doğan

Example A nut company markets cans of deluxe mixed nuts containing almonds, cashews, and peanuts. Suppose the net weight of each can is exactly 1 lb, but the weight contribution of each type of nut is random. Because the three weights sum to 1, a joint probability model for any two gives all necessary information about the weight of the third type. Let X = the weight of almonds in a selected can and Y = the weight of cashews. Verify that this is a legitimate pdf. To compute the probability that the two types of nuts together make up at most 50% of the can, Onur Doğan

Conditional Probability Mass Function Onur Doğan

Example The sizes of college books are under study in terms of width (X) and height (Y). Suppose that the joint p.d.f of X and Y is modeled as f (x,y)=cxy, 2<x<5 and x+ 1 <y<x+3 (unit:inch) Determine the value of c. Determine the marginal probability distribution of X. Find also the mean and variance of X. Onur Doğan

Covariance Covariance and correlation between x and y? Onur Doğan

Covariance Shortcut formula: Onur Doğan

Correlation Onur Doğan

Example Onur Doğan