161. Calculus And Probability Online course by Stefan Waner and Steven R. Costenoble http://people.hofstra.edu/faculty/Stefan_Waner/cprob/cprobintro.html

Calculus Applied to Probability and Statistics for Liberal Arts and Business Majors A Complete Text Resource on the World Wide Web by Stefan Waner and Steven R. Costenoble Table of Contents 1. Continuous Random Variables and Histograms Exercises 2. Probability Density Functions: Uniform, Exponential, Normal, and Beta Exercises 3. Mean, Median, Variance and Standard Deviation Exercises 4. You're the Expert Creating a Family Trust Back to Main Page Introduction Y ou are a financial planning consultant at a neighborhood bank. A 22-year-old client asks you the following question: "I would like to set up my own insurance policy by opening a trust account into which I can make monthly payments starting now, so that upon my death or my ninety-fifth birthday - whichever comes sooner - the trust can be expected to be worth $500,000. How much should I invest each month?" Assuming a 5% rate of return on investments, how should you respond? To answer the question on the previous page, we must know something about the probability of the client's dying at various ages. There are so many possible ages to consider (particularly since we should consider the possibilities month by month) that it would be easier to treat his age at death as a continuous variable, one that can take on any real value (between 22 and 95 in this case). The mathematics needed to do probability and statistics with continuous variables is calculus. The material on statistics in this resource will be readily accessible to you if you are familar with derivatives and integrals (through Chapter 6 of

162. Theory Of Probability And Mathematical Statistics Available in print format, Theory of probability and Mathematical Statistics Theory of probability and Mathematical Statistics http://www.ams.org/journals/tpms/

Journals Home Search Author Info Subscribe ... Help ISSN: 1547-7371(e) 1061-0022(p) Recently posted articles Most recent issue All issues About this journal Journal overview Editorial board Copying and reprinting Publishing cycle and indexing notes Subscription information Subscribe from the AMS Bookstore Electronic Journals Online Subscription Agreement Comments: webmaster@ams.org Privacy Statement Search the AMS

163. Search Sci.stat.math Teachers and students of probability and statistics will find others who share their interests at the sci.stat.math discussion list. http://mathforum.org/discussions/epi-search/sci.stat.math.html

The Math Forum discussions have moved. Math Forum Discussions Please update your bookmarks.

164. Http://www.i-journals.org/ps The URL of probability Surveys has changed. Please visit http//www.ijournals.org/ps. http://www.vtex.lt/ejournals/ps/

The URL of Probability Surveys has changed. Please visit http://www.i-journals.org/ps

165. Probability Distributions Modeling the probability of success for a binomial distribution. Technical Details Open applet Top of page. The F Distribution is used in many situations, http://www.stat.vt.edu/~sundar/java/applets/Distributions.html

Probability Distributions Distributions: Binomial Distribution Poisson Distribution Negative Binomial Distribution Geometric Distribution ... F Distribution Specialty Functions Gamma Function Beta Function The Binomial Distribution is used in finite sampling problems where each observation is one of two possible outcomes ("success" or "failure"). The binomial distribution has two parameters: n = the sample size, and = P("success"). Example: To assure quality of a product, a random sample of size 25 is drawn from a process. The number of defects ( X ) found in the sample is recorded. The random variable X follows a binomial distribution with n = 25 and = P(product is defective). Technical Details Open applet Top of page The Poisson Distribution is used for modeling rates of occurrence. The Poisson distribution has one parameter: = the rate (mean). Example: A process that creates fabric is monitored. If the number of defects ( X ) per meter of fabric exceeds 5 then the process is stopped for diagnosis. The random variable X follows a Poisson distribution with = number of defects per meter of fabric.

166. Olivers Homepage A comprehensive course in probability by Oliver Knill http://www.math.harvard.edu/~knill/index.html

Olivers Homepage Oliver Knill Harvard University One Oxford Street Cambridge, MA 02138, USA Department of Mathematics Office: SciCen 434 (Cam) Tel: (617) 495 5549 Email: document.write(String.fromCharCode(107,110,105,108,108)) .harvard.edu Vita Vita Math publications preprints old pages seminar handouts ... PITF Summer 2004 Teaching Math21a (Fall 2005) Maths21a (Summer 2005) Math118r (Spring 2005) Math21b (Fall 2004) ... CCP Greens theorem Etc Skating near Boston Charles river Running Websites Random and Silly ... Mac OS X Stuff Search math.harvard.edu: Last upload of entire page: Thu Sep 15 22:37:48 EDT 2005

167. Weather Underground: US 12Hr probability of Precip. Ending 800 AM EDT August 10, 2005 Dew Point Prevalent Weather; 12Hr probability of Precip. Sky Cover http://www.wunderground.com/ndfdimage/viewimage?type=pop12®ion=us

168. "DAU STAT REFRESHER MODULE" An interactive module covering basic probability, random variables, moments, distributions, data analysis including regression, moving averages, exponential smoothing, and clustering. Defense Acquisition University. http://cne.gmu.edu/modules/dau/stat/

This module is an interactive tutorial which gives a comprehensive view of Probability and Statistics. This interactive module covers basic probability, random variables, moments, distributions, data analysis including regression, moving averages, exponential smoothing, and clustering. The Probability and Statistics refresher module is intended for use by DAU course participants. for assistance. The weblike arrangement of the tutorial enables the user to browse randomly from topic to topic. A knowledgeable user will be able to skip subtopics or proceed directly to an area of particular interest. CAUTION TO THE NOVICE USER: The weblike form of this tutorial tempts the user to follow each link offered and, thus, lose the thread of the subtopic originally begun. So it is advisable to limit random browsing from topic to topic when first using the tutorial. The subway lines are provided to help the user stay on the topic thread. In addition, note that the browser provides a way to backtrack through the pages seen using the "Back" button. The user is strongly encouraged to return to this page, the "Stat Home Page", from any point in the tutorial to leave a comment for the developers of the tutorial. (Click on "Comments" below.) The "house" button for quick return to this page is found on the subway map and on each individual page.

169. Probability probability provides a mathematical description of randomness. The mathematical study of randomness is called probability theory. http://www.stat.tamu.edu/stat30x/notes/node51.html

Next: Experiments and Events Up: ProbabilityRandom Variables, and Previous: ProbabilityRandom Variables, and Probability Probability provides a mathematical description of randomness. A phenomenon is called random if the outcome of an experiment is uncertain. However, random phenomena often follow recognizable patterns. This long-run regularity of random phenomena can be described mathematically. The mathematical study of randomness is called probability theory Experiments and Events Properties of Probability Some Set Theory Conditional Probability and Independence ... Relative Frequency Interpretation of Probability Jan Lethen Wed Nov 13 16:20:46 CST 1996

170. Rudolf Carnap, The Function And Design Of Explications; & Boardman's Notes. Excerpt from the 1950 paper by Carnap. http://www.lawrence.edu/fac/boardmaw/Carnap_Explica2.html

[reposted March 24, 1997] Boardman's Notes: The Criteria of Adequacy of An Explication, illustrated by a discussion of Ayer's Language, Truth and Logic [Original pagination is shown in square brackets; except for this, comments surrounded by brackets are Carnap's.] FROM pages 3-15 of: Logical Foundations of Probability by RUDOLF CARNAP [p. 3:] § 2. On the Clarification of an Explicandum By the procedure of explication we mean the transformation of an inexact, prescientific concept, the explicandum, into a new exact concept, the explicatum. Although the explicandum cannot be given in exact terms, it should be made as clear as possible by informal explanations and examples. The task of explication consists in transforming a given more or less inexact concept into an exact one or, rather, in replacing the first by the second. We call the given concept (or the term used for it) the explicandum , and the exact concept proposed to take the place of the first (or the term proposed for it) the explicatum . The explicandum may belong to everyday language or to a previous stage in the development of scientific language. The

171. Center For Applied Probability Center for Applied probabilityColumbia University in the City of New York. http://www.cap.columbia.edu/

172. Let's Make A Deal Math: Probability What is the probability of winning? The purpose of this WebQuest is to introduce you to Design a game of probability using either a spinner or dice. http://www.cyberbee.com/probability/mathprob.html

Linda D. Resch Introduction Task Process Resources ... Conclusion Introduction "Heads, you go first; tails, I do." "I need to roll a seven to win." "If I can spin and land on a green slot the jackpot is mine." How many times have you played a game and hoped to win? What is the probability of winning? The purpose of this WebQuest is to introduce you to some interesting problems and learn about probable outcomes. At the conclusion of the WebQuest you will be able to explain why some choices are better than others. Benchmarks: Grade 6 Strand 8: Data Analysis and Probability SLC 24: Make predictions of outcomes of experiments based upon theoretical probabilitities and explain actual outcomes. Strand 4: Data Analysis and Probability SLC 14: Represent and interpret the possible outcomes for a mathematical situation and calculate probabilities. Task Your task is to solve and analyze several problems about probability. After you have completed this task and understand how probability works, design a game or activity that will show others what you have learned. Your final project will include: A tally sheet showing the results of coin flipping A printed copy showing your results while playing one of the Monty Hall simulation games An explanation about whether or not you should switch your door selection during the Monty Hall game A probability game Answers to the Skill Level and Concept Level questions Skill Level What is the definition of probability?

173. Numeracy In The News - Overview Of Site Features sections on data collection and sampling, creating graphs, probability and inference. Also, includes special topic articles and teacher spreadsheet programs. http://ink.news.com.au/mercury/mathguys/mercindx.htm

Numeracy in the News Overview of Site Introduction and Guide New Articles Spreadsheet for Teachers Aspects of Numeracy Topics of Articles Crime Diet Entertainment and Tourism Environment and Science ... Traffic and Transport Articles by Year Also visit the more general INK site Last updated: March 2002

174. Statistics Glossary - Probability The probability of the event can be defined as the limiting value of the relative The addition rule is a result used to determine the probability that http://www.cas.lancs.ac.uk/glossary_v1.1/prob.html

Probability Contents Outcome Sample Space Event Relative Frequency ... Back to Main Contents Outcome An outcome is the result of an experiment or other situation involving uncertainty. The set of all possible outcomes of a probability experiment is called a sample space. Sample Space The sample space is an exhaustive list of all the possible outcomes of an experiment. Each possible result of such a study is represented by one and only one point in the sample space, which is usually denoted by S. Examples 1. Experiment Rolling a die once: 2. Experiment Tossing a coin: 3. Experiment Measuring the height (cms) of a girl on her first day at school: Sample space S = the set of all possible real numbers Event An event is any collection of outcomes of an experiment. Formally, any subset of the sample space is an event. Any event which consists of a single outcome in the sample space is called an elementary or simple event. Events which consist of more than one outcome are called compound events. Set theory is used to represent relationships among events. In general, if A and B are two events in the sample space S, then:

175. Probability Theory In Linguistics II Integrated Frameworks Symposium at the LSA meeting in Atlanta, Georgia. Includes slides, handouts and abstracts. http://turing.wins.uva.nl/~rens/lsa03.html

176. PROBABILITY THEORY -- THE LOGIC OF SCIENCE Online textbook by E.T. Jaynes in PS format. http://omega.albany.edu:8008/JaynesBook

PROBABILITY THEORY: THE LOGIC OF SCIENCE by E. T. Jaynes Wayman Crow Professor of Physics Washington University St. Louis, MO 63130, U.S.A. Dedicated to the Memory of Sir Harold Jeffreys, who saw the truth and preserved it. Fragmentary Edition of June 1994 First three chapters in DJVU Short Contents PREFACE COMMENTS General comments (BY OTHERS, NOT E.T. Jaynes') about the book and maxent in general. PART A - PRINCIPLES AND ELEMENTARY APPLICATIONS Chapter 1 Plausible Reasoning Chapter 2 Quantitative Rules: The Cox Theorems Fig. 2-1 Chapter 3 Elementary Sampling Theory ... Chapter 18 The A Distribution and Rule of Succession PART B ADVANCED APPLICATIONS Chapter 19 Physical Measurements Chapter 20 Regression and Linear Models Chapter 21 Estimation with Cauchy and tDistributions Chapter 22 Time Series Analysis and Autoregressive Models ... Chapter 30 Conclusions APPENDICES Appendix A Other Approaches to Probability Theory Appendix B Formalities and Mathematical Style Appendix C Convolutions and Cumulants Appendix D Dirichlet Integrals and Generating Functions Appendix E The Binomial Gaussian Hierarchy of Distributions Appendix F Fourier Analysis Appendix G Infinite Series Appendix H Matrix Analysis and Computation Appendix I Computer Programs REFERENCES List of references To transfer all the chapters (Adobe's pdf format) at once from bayes.wustl.edu

177. Statistics And Probability Letters Contents and abstracts from vol.41 (1999). http://www.elsevier.com/inca/publications/store/5/0/5/5/7/3/

Home Site map Regional Sites Advanced Product Search ... Products Journal information Product description Editorial board Audience Abstracting/indexing ... Special issues and supplements For Authors Guide for authors Subscription information Bibliographic and ordering information Conditions of sale Dispatch dates Journal related information Contact the publisher Impact factor Most downloaded articles Other journals in same subject area ... Select your view Editor: R.A. Johnson See editorial board for all editors information Feedback We are always interested in receiving suggestions on the journal. Also, if you have publication proposals for books or new journals in the field or suggestions for any other kind of cooperation, please do not hesitate to contact us. Sweitze Roffel, Senior Publishing Editor, E-mail: S.Roffel@elsevier.com New related journal: Statistical Methodology ContentsDirect is Elsevier's free e-mail alerting service which delivers content information to subscribers' desktops. Sign up to receive an alert each time an issue of Statistical Methodology becomes available.

178. Probability Theory In Linguistics Workshop held in Washington, DC, by the Linguistic Society of America, covering probabilistic approaches to a number of subfields. Handouts available in PDF format. http://turing.wins.uva.nl/~rens/lsa.html

179. AutoClass C - General Information An unsupervised Bayesian classification system that seeks a maximum posterior probability classification. http://ic-www.arc.nasa.gov/ic/projects/bayes-group/autoclass/autoclass-c-program

AutoClass C - General Information Contents What Is AutoClass What Is AutoClass C Update History Limitations Theoretical Questions ... Obtaining AutoClass C for Unix and Windows platforms - (new version 3.3.4 Windows stand-alone executable) - 06 May 02 Obtaining AutoClass C for Unix and Windows platforms - (version 3.3.4) - 24 Jan 02 Obtaining AutoClass C for DOS platforms - (version 2.7) Information on SNOB (a related classification program) Bayesian Learning Group What Is AutoClass AutoClass is an unsupervised Bayesian classification system that seeks a maximum posterior probability classification. Key features: determines the number of classes automatically; can use mixed discrete and real valued data; can handle missing values; processing time is roughly linear in the amount of the data; cases have probabilistic class membership; allows correlation between attributes within a class; generates reports describing the classes found; and predicts "test" case class memberships from a "training" classification. AutoClass uses only vector valued data, in which each instance to be classified is represented by a vector of values, each value characterizing some attribute of the instance. Values can be either real numbers, normally representing a measurement of the attribute, or they can be discrete, one of a countable attribute dependent set of such values, normally representing some aspect of the attribute.

180. Www.math.uah.edu/stat/objects/index.xhtml http://www.math.uah.edu/stat/objects/index.xhtml

var baseURL = "../"; Virtual Laboratories Probability/Statistics Object Library Packages Devices Distributions Experiments Downloads library.jar library.zip Help Project Information Hello World Tutorial Introduction The Probability/Statistics Object Library contains applets and the components of the applets for use by teachers and students of probability and statistics. These objects (both executable files and source code) can be downloaded, modified if desired, and reused. The objects are divided into three packages . In the list on the left, click on the name of a package to see a list of the objects in the package. Applets The applets in this project are small, self-contained programs that run in web pages. They are intended to illustrate concepts and techniques from probability and statistics in an interactive, dynamic way. A teacher or student can download an applet, drop it in a web page, and then add other elements of her own choice (such as expository text, data sets, and graphics). The applets in the library contain no explicit mathematical exposition and thus can be used by students at various levels (with appropriate guidance from the instructor). The applets are intended to be small "micro worlds" where students can run virtual versions of random experiments and play virtual versions of statistical games. No knowledge of programming in general or Java in particular is required to use the applets. The applets are the top level objects in the library, and are in the

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