Suppose you roll two dice, and let X be sum of the dice. Then X is

Slides:

Advertisements

Similar presentations

6.2 Construct and Interpret Binomial Distributions

Advertisements

Random Variables Probability Continued Chapter 7.

EXAMPLE 1 Construct a probability distribution

Working with Random Variables. What is a Random Variable? A random variable is a variable that has a numerical value which arises by chance (ie – from.

EXAMPLE 1 Construct a probability distribution Let X be a random variable that represents the sum when two six-sided dice are rolled. Make a table and.

MA 102 Statistical Controversies Monday, April 1, 2002 Today: Randomness and probability Probability models and rules Reading (for Wednesday): Chapter.

Section 5.7 Suppose X 1, X 2, …, X n is a random sample from a Bernoulli distribution with success probability p. Then Y = X 1 + X 2 + … + X n has a distribution.

Random Variables November 23, Discrete Random Variables A random variable is a variable whose value is a numerical outcome of a random phenomenon.

1 Continuous random variables f(x) x. 2 Continuous random variables A discrete random variable has values that are isolated numbers, e.g.: Number of boys.

The moment generating function of random variable X is given by Moment generating function.

Determine whether each curve below is the graph of a function of x. Select all answers that are graphs of functions of x:

 The Law of Large Numbers – Read the preface to Chapter 7 on page 388 and be prepared to summarize the Law of Large Numbers.

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.

LECTURE UNIT 4.3 Normal Random Variables and Normal Probability Distributions.

Introduction to Normal Distributions and the Standard Distribution

Chapter 9 Introducing Probability - A bridge from Descriptive Statistics to Inferential Statistics.

Lesson Means and Variances of Random Variables.

L7.1b Continuous Random Variables CONTINUOUS RANDOM VARIABLES NORMAL DISTRIBUTIONS AD PROBABILITY DISTRIBUTIONS.

Normal distribution and intro to continuous probability density functions...

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.

Copyright © 2010 Pearson Addison-Wesley. All rights reserved. Chapter 6 Some Continuous Probability Distributions.

Chapter 7 Lesson 7.3 Random Variables and Probability Distributions 7.3 Probability Distributions for Continuous Random Variables.

§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.

MATH 2400 Ch. 10 Notes. So…the Normal Distribution. Know the 68%, 95%, 99.7% rule Calculate a z-score Be able to calculate Probabilities of… X < a(X is.

Chapter 7 Random Variables. Usually notated by capital letters near the end of the alphabet, such as X or Y. As we progress from general rules of probability.

Statistics 101 Discrete and Continuous Random Variables.

Discrete Distributions. Random Variable - A numerical variable whose value depends on the outcome of a chance experiment.

INTRODUCTORY MATHEMATICAL ANALYSIS For Business, Economics, and the Life and Social Sciences  2011 Pearson Education, Inc. Chapter 16 Continuous Random.

Continuous Random Variables Section Starter A 9-sided die has three faces that show 1, two faces that show 2, and one face each showing 3,

Modeling Discrete Variables Lecture 22, Part 1 Sections 6.4 Fri, Oct 13, 2006.

Discrete and Continuous Random Variables. Yesterday we calculated the mean number of goals for a randomly selected team in a randomly selected game.

AP STATISTICS Section 7.1 Random Variables. Objective: To be able to recognize discrete and continuous random variables and calculate probabilities using.

Chap 7.1 Discrete and Continuous Random Variables.

1 1 Slide Continuous Probability Distributions n The Uniform Distribution  a b   n The Normal Distribution n The Exponential Distribution.

Chapter 7.2. The __________ of a discrete random variable, X, is its _________ _____________. Each value of X is weighted by its probability. To find.

Chapter 5 Joint Probability Distributions and Random Samples  Jointly Distributed Random Variables.2 - Expected Values, Covariance, and Correlation.3.

Probability Distributions Section 7.6. Definitions Random Variable: values are numbers determined by the outcome of an experiment. (rolling 2 dice: rv’s.

AP Statistics Section 7.1 Probability Distributions.

Random Variable 2013.

Normal Probabilities Find the probability P(x ≤ x0), where x is a normally distributed random variable Click Insert Function (fx) Select Statistical as.

Discrete and Continuous Random Variables

Random Variables and Probability Distribution (2)

Continuous Random Variables

Uniform Distributions and Random Variables

Probability Review for Financial Engineers

Introduction to Probability & Statistics The Central Limit Theorem

Binomial Distributions

Exponential Probabilities

Warm Up Imagine you are rolling 2 six-sided dice. 1) What is the probability to roll a sum of 7? 2) What is the probability to roll a sum of 6 or 7? 3)

Lecture 23 Section Mon, Oct 25, 2004

Discrete Distributions

Discrete Distributions

Chapter 6 Some Continuous Probability Distributions.

Continuous Random Variable Normal Distribution

Uniform Distributions and Random Variables

I flip a coin two times. What is the sample space?

Modeling Discrete Variables

Discrete Distributions.

AP Statistics Chapter 16 Notes.

7.1: Discrete and Continuous Random Variables

M248: Analyzing data Block A UNIT A3 Modeling Variation.

Discrete Distributions

Section 7.1 Discrete and Continuous Random Variables

Continuous Random Variables

6.1 Construct and Interpret Binomial Distributions

Moments of Random Variables

Modeling Discrete Variables

Presentation transcript:

Suppose you roll two dice, and let X be sum of the dice. Then X is A continuous random variable A discrete random variable A discreet random variable Both Neither

A continuous random variable can take any value between zero and one. True False

Let the random variable X be a random number with the uniform density curve given below. P (0.7 < X < 1.1) has value 0.30 0.40 0.70

Let X be a standard normal random variable Let X be a standard normal random variable. Which of the following probabilities is the smallest? P(-2<X<-1) P(0<X<2) P(X<1) P(X>2)

Consider the following probability histogram for a discrete random variable X. What is P(X < 3)? 0.10 0.25 0.35 0.65

Answers B, B, A (there is no area from 1 to 1.1), D, C