PPT-Probability and Distributions

A Brief Introduction Random Variables Random Variable RV A numeric outcome that results from an experiment For each element of an experiments sample space the random variable can take on exactly one value. Fred Davies. ASTR 278. 2/23/12. Contents. Eddington Ratio. What does it mean?. How do we measure it?. Contents. Eddington Ratio. What does it mean?. How do we measure it?. Two regimes of measurement. 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.. 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. 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. Stephen Mansour, . PhD. University of Scranton and The Carlisle Group. Dyalog. ’14 . Conference, . Eastbourne. , UK. M. any statistical software packages out there: Minitab, R, Excel, SPSS. Excel has about 87 statistical functions. 6 of them involve the t distribution alone: . 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.. 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.. Diktys. Stratakis. 1. 2. Scott’s Shuffled Distributions. 3. ICOOL-MPI vs. ICOOL Classic. 2 minutes . (MPI) . vs. . 3 hours . (in my fast . laptop) vs. . 5 hours . in my cheap home laptop!. Shuffled and . 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 smb@isa.ulisboa.pt. . Monte Carlo . Simulation. Forestry. . Applications. Applied. . Operations. Research . 2020-2021. 1. What is Monte Carlo? Basic Principles. 2. 3. Random Numbers. 4. Sample Sizes. 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. .