STATISTICS and PROBABILITY

Similar documents
STATISTICS and PROBABILITY

continuous rv Note for a legitimate pdf, we have f (x) 0 and f (x)dx = 1. For a continuous rv, P(X = c) = c f (x)dx = 0, hence

Lecture 23. STAT 225 Introduction to Probability Models April 4, Whitney Huang Purdue University. Normal approximation to Binomial

Normal Distribution. Notes. Normal Distribution. Standard Normal. Sums of Normal Random Variables. Normal. approximation of Binomial.

ME3620. Theory of Engineering Experimentation. Spring Chapter III. Random Variables and Probability Distributions.

Probability Theory and Simulation Methods. April 9th, Lecture 20: Special distributions

ECE 340 Probabilistic Methods in Engineering M/W 3-4:15. Lecture 10: Continuous RV Families. Prof. Vince Calhoun

Chapter 4 Continuous Random Variables and Probability Distributions

4-2 Probability Distributions and Probability Density Functions. Figure 4-2 Probability determined from the area under f(x).

UQ, STAT2201, 2017, Lectures 3 and 4 Unit 3 Probability Distributions.

Central Limit Theorem, Joint Distributions Spring 2018

Statistics for Business and Economics

Chapter 5 Discrete Probability Distributions. Random Variables Discrete Probability Distributions Expected Value and Variance

Chapter 4 Continuous Random Variables and Probability Distributions

MA : Introductory Probability

STAT 509: Statistics for Engineers Dr. Dewei Wang. Copyright 2014 John Wiley & Sons, Inc. All rights reserved.

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.

What was in the last lecture?

Homework: Due Wed, Feb 20 th. Chapter 8, # 60a + 62a (count together as 1), 74, 82

Lecture 12. Some Useful Continuous Distributions. The most important continuous probability distribution in entire field of statistics.

Review of the Topics for Midterm I

Business Statistics 41000: Probability 3

Probability Distributions II

Discrete Random Variables

CS 237: Probability in Computing

Homework: Due Wed, Nov 3 rd Chapter 8, # 48a, 55c and 56 (count as 1), 67a

The Normal Distribution

2011 Pearson Education, Inc

Normal Distribution. Definition A continuous rv X is said to have a normal distribution with. the pdf of X is

Chapter 3 Discrete Random Variables and Probability Distributions

Section 7.5 The Normal Distribution. Section 7.6 Application of the Normal Distribution

Chapter 7: SAMPLING DISTRIBUTIONS & POINT ESTIMATION OF PARAMETERS

Tutorial 6. Sampling Distribution. ENGG2450A Tutors. 27 February The Chinese University of Hong Kong 1/6

No, because np = 100(0.02) = 2. The value of np must be greater than or equal to 5 to use the normal approximation.

Chapter 7 Sampling Distributions and Point Estimation of Parameters

Random Variables and Probability Distributions

Chapter 3 Discrete Random Variables and Probability Distributions

Topic 6 - Continuous Distributions I. Discrete RVs. Probability Density. Continuous RVs. Background Reading. Recall the discrete distributions

Chapter 3 - Lecture 5 The Binomial Probability Distribution

Probability Theory. Mohamed I. Riffi. Islamic University of Gaza

Section 7.1: Continuous Random Variables

4.2 Probability Distributions

Much of what appears here comes from ideas presented in the book:

. (i) What is the probability that X is at most 8.75? =.875

Statistics vs. statistics

Chapter 7 1. Random Variables

Department of Quantitative Methods & Information Systems. Business Statistics. Chapter 6 Normal Probability Distribution QMIS 120. Dr.

Chapter 3 Common Families of Distributions. Definition 3.4.1: A family of pmfs or pdfs is called exponential family if it can be expressed as

The Normal Distribution. (Ch 4.3)

Central Limit Theorem (cont d) 7/28/2006

NORMAL RANDOM VARIABLES (Normal or gaussian distribution)

Chapter 5. Continuous Random Variables and Probability Distributions. 5.1 Continuous Random Variables

MAS1403. Quantitative Methods for Business Management. Semester 1, Module leader: Dr. David Walshaw

6. Continous Distributions

ECO220Y Continuous Probability Distributions: Normal Readings: Chapter 9, section 9.10

Binomial Random Variables. Binomial Random Variables

STA258H5. Al Nosedal and Alison Weir. Winter Al Nosedal and Alison Weir STA258H5 Winter / 41

Random Variables Handout. Xavier Vilà

IEOR 165 Lecture 1 Probability Review

Chapter 8: The Binomial and Geometric Distributions

Econ 250 Fall Due at November 16. Assignment 2: Binomial Distribution, Continuous Random Variables and Sampling

Chapter 5: Probability models

Chapter 8. Variables. Copyright 2004 Brooks/Cole, a division of Thomson Learning, Inc.

Class 13. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

Lecture 6: Chapter 6

Exam 2 Spring 2015 Statistics for Applications 4/9/2015

Statistics, Their Distributions, and the Central Limit Theorem

Basic Data Analysis. Stephen Turnbull Business Administration and Public Policy Lecture 4: May 2, Abstract

Elementary Statistics Lecture 5

LECTURE CHAPTER 3 DESCRETE RANDOM VARIABLE

Introduction to Business Statistics QM 120 Chapter 6

Lecture 3: Probability Distributions (cont d)

Tutorial 11: Limit Theorems. Baoxiang Wang & Yihan Zhang bxwang, April 10, 2017

Statistics 6 th Edition

Class 12. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

The graph of a normal curve is symmetric with respect to the line x = µ, and has points of

Discrete Random Variables

MAS187/AEF258. University of Newcastle upon Tyne

Math489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5

ECON 214 Elements of Statistics for Economists 2016/2017

Engineering Statistics ECIV 2305

Chapter 3 Discrete Random Variables and Probability Distributions

Chapter 3 Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2013 John Wiley & Sons, Inc.

Continuous Probability Distributions & Normal Distribution

Lecture 9. Probability Distributions

Chapter Learning Objectives. Discrete Random Variables. Chapter 3: Discrete Random Variables and Probability Distributions.

4.3 Normal distribution

Statistical Tables Compiled by Alan J. Terry

Standard Normal, Inverse Normal and Sampling Distributions

Midterm Exam III Review

MA 1125 Lecture 12 - Mean and Standard Deviation for the Binomial Distribution. Objectives: Mean and standard deviation for the binomial distribution.

STOR Lecture 7. Random Variables - I

Data Analysis and Statistical Methods Statistics 651

Mathematics of Randomness

Lecture 8. The Binomial Distribution. Binomial Distribution. Binomial Distribution. Probability Distributions: Normal and Binomial

Statistical Methods in Practice STAT/MATH 3379

Econ 6900: Statistical Problems. Instructor: Yogesh Uppal

Binomial Distributions

Sampling Distribution

Chapter 7. Sampling Distributions and the Central Limit Theorem

Transcription:

Introduction to Statistics Atatürk University STATISTICS and PROBABILITY LECTURE: PROBABILITY DISTRIBUTIONS Prof. Dr. İrfan KAYMAZ Atatürk University Engineering Faculty Department of Mechanical Engineering P.S. These lecture notes are mainly based on the reference given in the last page.

objectives of this lecture Introduction tostatistics Atatürk University After carefully listening of this lecture, you should be able to do the following: Determine means and variances for discrete random variables. Determine means and variances for continuous random variables. Binomial distribution Uniform Distribution Normal Distribution Standart Normal Distribution

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Mean Defined or E X The mean or expected value of the discrete random variable X, denoted as E X x f x x, is The mean is the weighted average of the possible values of X, the weights being the probabilities where the beam balances. It represents the center of the distribution. It is also called the arithmetic mean. If f(x) is the probability mass function representing the loading on a long, thin beam, then E(X) is the fulcrum or point of balance for the beam. The mean value may, or may not, be a given value of x.

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Variance Defined 2 The variance of X, denoted as or V X, is 2 2 2 2 2 V X E X x f x x f x x The variance is the measure of dispersion or scatter in the possible values for X. It is the average of the squared deviations from the distribution mean. x Figure : The mean is the balance point. Distributions (a) & (b) have equal mean, but (a) has a larger variance.

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Mean & Variance Suppose X x probability density function f. The mean or expected value The variance is a continuous random variable with E X xf x dx of X, denoted as or E X, is 2 of X, denoted as V X or, is 2 (4-4) 2 2 2 V X x f x dx x f x dx The standard deviat of i 2 ion X s.

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Example: Electric Current For the copper wire current measurement, the PDF is f(x) = 0.05 for 0 x 20. Find the mean and variance. 20 2 0.05x E X x f xdx 10 2 0 0 20 2 0.05 x 10 V X x 10 f xdx 33.33 3 0 20 3 20 0

Binomial Distribution Definition The random variable X that equals the number of trials that result in a success is a binomial random variable with parameters 0 < p < 1 and n = 0, 1,... The probability mass function is: If X is a binomial random variable with parameters p and n, μ = E(X) = np and σ 2 = V(X) = np(1-p). John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University

Example What is the probability of getting 4 heads if the experiments are repeated 10 times? What is the probability of getting 6 for 12 times if a die is thrown 20 times? John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University

Continuous Uniform Distribution This is the simplest continuous distribution and analogous to its discrete counterpart. A continuous random variable X with probability density function f(x) = 1 / (b-a) for a x b Figure : Continuous uniform PDF John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Mean & Variance Mean & variance are: 2 a b b a E X 2 12 2 and V X (4-7)

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Example: Uniform Current Let the continuous random variable X denote the current measured in a thin copper wire in ma. Recall that the PDF is F(x) = 0.05 for 0 x 20. What is the probability that the current measurement is between 5 & 10 ma? 10 P 5 x 10 0.05dx 5 0.05 0.25 5

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Continuous Uniform CDF F x x a 1 x a du b a b a The CDF is completely described as 0 x a F x x a b a a x b 1 b x Figure : Graph of the Cumulative Uniform CDF

Normal Distribution The most widely used distribution is the normal distribution, also known as the Gaussian distribution. Random variation of many physical measurements are normally distributed. The location and spread of the normal are independently determined by mean (μ) and standard deviation (σ). Figure : Normal probability density functions John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University

Normal Probability Density Function John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University

Empirical Rule P(μ σ < X < μ + σ) = 0.6827 P(μ 2σ < X < μ + 2σ) = 0.9545 P(μ 3σ < X < μ + 3σ) = 0.9973 Figure : Probabilities associated with a normal distribution John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University 15

Standard Normal Distribution A normal random variable with μ = 0 and σ 2 = 1 is called a standard normal random variable and is denoted as Z. The cumulative distribution function of a standard normal random variable is denoted as: Φ(z) = P(Z z) = F(z) John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University 16

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Standardizing 2 Suppose X is a normal random variable with mean and variance. X x Then, P X x P P Z z (4-11) where Z is a standard normal random variabl e, and x z is the z-value obtainedby standardizing X. The probability is obtained by using Appendix Table III with z x.

Example Assume Z is a standard normal random variable. a) Find P(Z 1.50) b) Find P(Z 1.53) c) Find P(Z 0.02) John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University

Example: Standard Normal Exercises John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University 1. P(Z > 1.26) = 0.1038 2. P(Z < -0.86) = 0.195 3. P(Z > -1.37) = 0.915 4. P(-1.25 < 0.37) = 0.5387 5. P(Z -4.6) 0 6. Find z for P(Z z) = 0.05, z = -1.65 7. Find z for (-z < Z < z) = 0.99, z = 2.58 Figure : Graphical displays for standard normal distributions.

Example: Normally Distributed Current-1 With μ = 10 and σ = 2 ma, what is the probability that the current measurement is between 9 and 11 ma? John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Example: Normally Distributed Current-1 Answer: 9 10 x 10 1110 P9 X 11 P 2 2 2 P 0.5 z 0.5 0.5 P z 0.5 P z 0.69146 0.30854 0.38292 Figure: Standardizing a normal random variable.

John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Example: Shaft Diameter-1 The diameter of the shaft is normally distributed with μ = 0.2508 inch and σ = 0.0005 inch. The specifications on the shaft are 0.2500 ± 0.0015 inch. What proportion of shafts conform to the specifications? Let X denote the shaft diameter in inches.

Example: Shaft Diameter-1 Answer: P 0.2485 X 0.2515 0.2485 0.2508 0.2515 0.2508 P Z 0.0005 0.0005 P 4.6 Z 1.4 1.4 P Z 4.6 P Z 0.91924 0.0000 0.91924 Sec 4-6 Normal Distribution 23 John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University

Next Week John Wiley & Sons, Inc. Applied Statistics and Probability for Engineers, by Montgomery and Runger. Atatürk University Joint probability functions.

Atatürk University References Douglas C. Montgomery, George C. Runger Applied Statistics and Probability for Engineers, John Wiley & Sons, Inc.