Probability & Statistics ModulesTo find out more about what the version numbers mean, click on any of the indicators.
Conditional probability as applied to any application with transition probabilities, and how it is used in detection/decision theory.
Authors: Kristin Bennett, Jill Richie, Elisa Barney Smith Keywords: probability Learning Level: College Platform: Project Links base technical requirements Applets: [ Full Metadata ]
This module is about continuous random variables, specifically the Uniform, Normal (Gaussian), and Exponential families of random variables. By completing this module, you will learn more about these random variables, including their probability density functions, cumulative density functions, and how the samples from the distributions are used to estimate the population (or theoretical) distribution. In addition, certain real-life applications of these distributions will be introduced.
Author: Kristin Bennett, Jill Richie, Elisa Barney Smith Keywords: probability Learning Level: College Platform: Project Links base technical requirements Applets: Continuous Random Variable Applet [ Full Metadata ]
Most businesses -- retailers, wholesalers, manufacturers -- need to keep a supply of goods readily available for sale or use. Generally, companies want to keep as little capital as possible tied up in inventory, but at the same time they don't want to incur losses (in profits, customer base, or reputation) by not being able to meet demand. The question then becomes: How do companies decide when to order new stock and how much to order? This module introduces one methodology for inventory control : the Economic Order Quantity Model.
Authors: Tom Willemain, Jill Richie Keywords: probability Learning Level: College Platform: Project Links base technical requirements Applets: Economic Order Quantity (EOQ) [ Full Metadata ]
The mean and the variance of a random variable are just two numbers that are the statistics of data or the shape of the probability density function. In the context of additive noise they determine how the resulting signal appears and how well that 'noisy' signal can be used in the application. This module explores the roles that the mean and the variance play in the analysis of the application. The applications that are used are digital images and binary signal detection.
Author: Kristin Bennett, Jill Richie, Elisa Barney Smith, Seth Goodchild Keywords: probability, signal processing, digital image processing Learning Level: College Platform: Project Links base technical requirements Applets: Image Magnification, Image Noise , Continuous Random Variable Applet, Regression Analysis , Signal Detection [ Full Metadata ]
Random Variable Relations
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