PPT-Chapter 5 Joint Probability Distributions

Applied Statistics and Probability for Engineers Sixth Edition Douglas C Montgomery George C Runger Chapter 5 Title and Outline 2 5 Joint Probability Distributions 51 Two or More Random Variables. AS91586 Apply probability distributions in solving problems. NZC level 8. Investigate situations that involve elements of chance. calculating and interpreting expected values and standard deviations of discrete random variables. Objective. : . To solve multistep probability tasks with the concept of geometric distributions. CHS Statistics. A . Geometric probability model. . tells us the probability for a random variable that counts the number of . Continuous distributions. Sample size 24. Guess the mean and standard deviation. Dot plot sample size 49. Draw the population distribution you expect. Sample size 93. Sample size 476. Sample size 948. A Brief Introduction. Random Variables. Random Variable (RV): A numeric outcome that results from an experiment. For each element of an experiment’s sample space, the random variable can take on exactly one value. 1. 5. Joint Probability Distributions. 5-1 Two or More Random Variables. 5-1.1 Joint Probability Distributions. 5-1.2 Marginal Probability Distributions. 5-1.3 Conditional Probability Distributions. Probability Terminology. Classical Interpretation. : Notion of probability based on equal likelihood of individual possibilities (coin toss has 1/2 chance of Heads, card draw has 4/52 chance of an Ace). Origins in games of chance.. Section 5-3 – Normal Distributions: Finding Values. A. We have learned how to calculate the probability given an . x. -value or a . z. -score. . In this lesson, we will explore how to find an . More Practical Problems. Jiaping. Wang. Department of Mathematics. 04/24/2013, Wednesday. Problem 1. Suppose we know in a crab farm, 20% of crabs are male. If one day the owner catches . 400 crabs. , what is the chance that more than 25% of the 400 crabs are male?. Continuous Probability Distribution . (pdf) . Definition:. . b. P(a . . X.  . b) = .  . f(x). dx. . . a. For continuous RV X & a. .  b.. II. BINOMIAL DISTRIBUTIONS A. Binomial Experiments 1. A binomial experiment is a probability experiment that satisfies the following conditions: a. The experiment is repeated for a fixed number of independent trials. Copyright © Cengage Learning. All rights reserved. 5 Joint Probability Distributions and Random Samples Copyright © Cengage Learning. All rights reserved. 5.4 The Distribution of the Sample Mean . 3.1 - Random Variables. 3.2 - Probability Distributions for Discrete. Random Variables . 3.3 - Expected Values. 3.4 - . The Binomial Probability Distribution. 3.5 - Hypergeometric and Negative. How . can it be that mathematics, being after all a product of human thought independent of experience, is so admirably adapted to the objects . of reality. Albert Einstein. Some parts of these slides were prepared based on . http://www.alexfb.com/cgi-bin/twiki/view/PtPhysics/WebHome. Probability for two continuous . r.v. .. http://tutorial.math.lamar.edu/Classes/CalcIII/DoubleIntegrals.aspx. Example 1 (class). A man invites his fiancée to a fine hotel for a Sunday brunch. They decide to meet in the lobby of the hotel between 11:30 am and 12 noon. If they arrive a random times during this period, what is the probability that they will meet within 10 minutes? (Hint: do this geometrically).