Introductory Statistics and Random Phenomena

The present book is based on a course developed as partofthe large NSF-funded GatewayCoalitionInitiativeinEngineeringEducationwhichincludedCaseWest ern Reserve University, Columbia University, Cooper Union, Drexel University, Florida International University, New Jersey Institute ofTechnology, Ohio State University, University ofPennsylvania, Polytechnic University, and Universityof South Carolina. The Coalition aimed to restructure the engineering curriculum by incorporating the latest technological innovations and tried to attract more and betterstudents to engineering and science. Draftsofthis textbookhave been used since 1992instatisticscoursestaughtatCWRU, IndianaUniversity, Bloomington, and at the universities in Gottingen, Germany, and Grenoble, France. Another purpose of this project was to develop a courseware that would take advantage ofthe Electronic Learning Environment created by CWRUnet-the all fiber-optic Case Western Reserve University computer network, and its ability to let students run Mathematica experiments and projects in their dormitory rooms, and interactpaperlessly with the instructor. Theoretically,onecould try togothroughthisbook withoutdoing Mathematica experimentsonthecomputer,butitwouldbelikeplayingChopin's Piano Concerto in E-minor, or Pink Floyd's The Wall, on an accordion. One would get an idea ofwhatthe tune was without everexperiencing the full richness andpowerofthe entire composition, and the whole ambience would be miscued.

Includes supplementary material: sn.pub/extras

Résumé
This work integrates traditional statistical data analysis with new computational simulation and experimental capabilities. It explains modern data analysis and demonstrates computational experiments, developing an understanding of the effect of random phenomena using Mathematica.

Contenu
I Descriptive Statistics-Compressing Data.- 1 Why One Needs to Analyze Data.- 2 Data Representation and Compression.- 3 Analytic Representation of Random Experimental Data.- II Modeling Uncertainty.- 4 Algorithmic Complexity and Random Strings.- 5 Statistical Independence and Kolmogorov's Probability Theory.- 6 Chaos in Dynamical Systems: How Uncertainty Arises in Scientific and Engineering Phenomena.- III Model Specification-Design of Experiments.- 7 General Principles of Statistical Analysis.- 8 Statistical Inference for Normal Populations.- 9 Analysis of Variance.- A Uncertainty Principle in Signal Processing and Quantum Mechanics.- B Fuzzy Systems and Logic.- C A Critique of Pure Reason.- D The Remarkable Bernoulli Family.- F Tables.