PPT-Introduction to Probability Distributions

QSCI 381 Lecture 12 Larson and Farber Sect 41 Learning objectives Become comfortable with variable definitions Create and use probability distributions Random VariablesI A . 2/29/2012. Review. When playing roulette at the Bellagio casino in Las Vegas, a gambler is trying to decide whether to bet $5 on the number 13 or to bet $5 that the outcome is any one of these five possibilities: 0 or 00 or 1 or 2 or 3. From Example 8, we know that the expected value of the $5 bet for a single number is -26₵. For the $5 bet that the outcome is 0 or 00 or 1 or 2 or 3, there is a probability of 5/38 of making a net profit of $30 and a 33/38 probability of losing $5.. Understanding the meaning of the terminology we use.. Quick calculations that indicate understanding of the basis of methods.. Many of the possible questions are already sprinkled in the lecture slides.. 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 . Binomial distributions. are models for some categorical variables, typically representing the . number of successes. in a series of . n. independent trials. . The observations must meet these requirements: . Maryam . Aliakbarpour. (MIT). Joint work with: Eric . Blais. (U Waterloo) and . Ronitt. . Rubinfeld. (MIT and TAU). 1. The Problem . 2. R. elevant features in distributions.  . Smokes. Does not regularly exercise . Maryam . Aliakbarpour. (MIT). Joint work with: Eric . Blais. (U Waterloo) and . Ronitt. . Rubinfeld. (MIT and TAU). 1. The Problem . 2. R. elevant features.  . Smokes. Does not regularly exercise . A Year 1 Joint Hurricane . Testbed. Project Update. . Mark DeMaria. 1. , Stan Kidder. 2. , Robert DeMaria. 2. , . Andrea Schumacher. 2. , Daniel Brown. 3. , Michael Brennan. 3. , . Richard Knabb. 4. Applied Statistics and Probability for Engineers. Sixth Edition. Douglas C. Montgomery George C. . Runger. Chapter 5 Title and Outline. 2. 5. Joint Probability Distributions. 5-1 Two or More Random Variables. 4. Introduction. (slide 1 of 3). A key . aspect of solving real business problems is dealing appropriately with uncertainty.. This involves recognizing explicitly that uncertainty exists and using quantitative methods to model uncertainty.. What we learned last class…. We are not good at recognizing/dealing with randomness. Our “random” coin flip results weren’t streaky enough.. If B/G results behave like independent coin flips, we know how many families to EXPECT with 0,1,2,3,4 girls.. © 2017 W.H. Freeman and Company. 1.1-1. When ordering vinyl replacement windows, the following variables are specified for each window. Which of these variables is . quantitative. ?. a. window style: double hung, casement, or awning. 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. John . Rundle . Econophysics. PHYS 255. Probability Distributions. Q: Why should we care about probability distributions? Why not just focus on the data?. A: Outliers. We want to know how probable are the outliers of large market moves, so we can control our exposure and risk. It is also known as the Gaussian distribution and the bell curve. .. The general form of its probability density function is-. Normal Distribution in . Statistics. The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. .