PPT-Part V: Continuous Random Variables

http rchsbowmanwordpresscom20091129 statisticsnotesE28093propertiesofnormaldistribution2 Chapter 23 Probability Density Functions http divisbyzerocom20091202 anappletillustratingacontinuousnowheredifferentiablefunction. RANDOM VARIABLES Definition usually denoted as X or Y or even Z and it is th e numerical outcome of a random process Example random process The number of heads in 10 tosses of a coin Example The number 5 rating Jake Blanchard. Spring 2010. Uncertainty Analysis for Engineers. 1. Introduction. We’ve discussed single-variable probability distributions. This lets us represent uncertain inputs. But what of variables that depend on these inputs? How do we represent their uncertainty?. QSCI 381 – Lecture 12. (Larson and Farber, Sect 4.1). Learning objectives. Become comfortable with variable definitions. Create and use probability distributions. Random Variables-I. A . http://www-users.york.ac.uk/~pml1/bayes/cartoons/cartoon08.jpg. 1. Comparison of Named Distributions. discrete. continuous. Bernoulli,. Binomial, Geometric, Negative Binomial, Poisson, Hypergeometric, Discrete Uniform. Expected Value. Airline overbooking. Pooling . blood . samples. Variance and Standard . Deviation . Independent Collections. Optimization. DECS 430-A. Business Analytics . I: Class 2. Random Variables. http://www-users.york.ac.uk/~pml1/bayes/cartoons/cartoon08.jpg. 1. Comparison of Named Distributions. discrete. continuous. Bernoulli,. Binomial, Geometric, Negative Binomial, Poisson, Hypergeometric, Discrete Uniform. Random Variables. Definition:. A rule that assigns one (and only one) numerical value to each simple event of an experiment; or. A function that assigns numerical values to the possible outcomes of an experiment.. 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. . 4.1 - . Probability Density Functions. 4.2 - Cumulative Distribution . Func. tions. and. . . . Expected Values. . . 4.3 - The Normal Distribution. . 4.4 - . The Exponential and Gamma Distributions. Random Variables. Definition:. A rule that assigns one (and only one) numerical value to each simple event of an experiment; or. A function that assigns numerical values to the possible outcomes of an experiment.. adding . constants to random variables, multiplying random variables by constants, and adding two random variables together. AP Statistics B. pp. 373-74. 1. Pp. 373-74 are just plain hard. I don’t like the way they are written. Section 6.1. Discrete & Continuous Random Variables. After this section, you should be able to…. APPLY the concept of discrete random variables to a variety of statistical settings. CALCULATE and INTERPRET the mean (expected value) of a discrete random variable. Nisheeth. Random Variables. 2. Informally, a random variable (. r.v.. ) . denotes possible outcomes of an event. Can be discrete (i.e., finite many possible outcomes) or continuous. Some examples of discrete . Section 6.1. Discrete and Continuous. Random Variables. Discrete and Continuous Random Variables. USE the probability distribution of a discrete random variable to CALCULATE the probability of an event..