Curve Ball PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Curve Ball PDF full book. Access full book title Curve Ball by Jim Albert. Download full books in PDF and EPUB format.
Author: Jim Albert
Publisher:
ISBN:
Category : Baseball
Languages : en
Pages :
Get Book Here
Book Description
Author: Jim Albert
Publisher:
ISBN:
Category : Baseball
Languages : en
Pages :
Get Book Here
Book Description
Author: Benedict Gross
Publisher: Cambridge University Press
ISBN: 1108641822
Category : Mathematics
Languages : en
Pages : 213
Get Book Here
Book Description
In a world where we are constantly being asked to make decisions based on incomplete information, facility with basic probability is an essential skill. This book provides a solid foundation in basic probability theory designed for intellectually curious readers and those new to the subject. Through its conversational tone and careful pacing of mathematical development, the book balances a charming style with informative discussion. This text will immerse the reader in a mathematical view of the world, giving them a glimpse into what attracts mathematicians to the subject in the first place. Rather than simply writing out and memorizing formulas, the reader will come out with an understanding of what those formulas mean, and how and when to use them. Readers will also encounter settings where probabilistic reasoning does not apply or where intuition can be misleading. This book establishes simple principles of counting collections and sequences of alternatives, and elaborates on these techniques to solve real world problems both inside and outside the casino. Pair this book with the HarvardX online course for great videos and interactive learning: https://harvardx.link/fat-chance.
Author: Kathryn Kozak
Publisher: Lulu.com
ISBN: 1329757254
Category : Education
Languages : en
Pages : 459
Get Book Here
Book Description
Statistics With Technology, Second Edition, is an introductory statistics textbook. It uses the TI-83/84 calculator and R, an open source statistical software, for all calculations. Other technology can also be used besides the TI-83/84 calculator and the software R, but these are the ones that are presented in the text. This book presents probability and statistics from a more conceptual approach, and focuses less on computation. Analysis and interpretation of data is more important than how to compute basic statistical values.
Author: Abraham de Moivre
Publisher:
ISBN:
Category : Annuities
Languages : en
Pages : 374
Get Book Here
Book Description
Author: David Diez
Publisher:
ISBN: 9781943450046
Category :
Languages : en
Pages :
Get Book Here
Book Description
The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources.
Author: Roman Vershynin
Publisher: Cambridge University Press
ISBN: 1108415199
Category : Business & Economics
Languages : en
Pages : 299
Get Book Here
Book Description
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Author: Daniel Navarro
Publisher: Lulu.com
ISBN: 1326189727
Category : Computers
Languages : en
Pages : 617
Get Book Here
Book Description
"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Author: Michael Kaplan
Publisher: Penguin
ISBN: 1440684510
Category : Mathematics
Languages : en
Pages : 337
Get Book Here
Book Description
A compelling journey through history, mathematics, and philosophy, charting humanity’s struggle against randomness Our lives are played out in the arena of chance. However little we recognize it in our day-to-day existence, we are always riding the odds, seeking out certainty but settling—reluctantly—for likelihood, building our beliefs on the shadowy props of probability. Chances Are is the story of man’s millennia-long search for the tools to manage the recurrent but unpredictable—to help us prevent, or at least mitigate, the seemingly random blows of disaster, disease, and injustice. In these pages, we meet the brilliant individuals who developed the first abstract formulations of probability, as well as the intrepid visionaries who recognized their practical applications—from gamblers to military strategists to meteorologists to medical researchers, from blackjack to our own mortality.
Author: Jim Albert
Publisher: CRC Press
ISBN: 1351030132
Category : Mathematics
Languages : en
Pages : 553
Get Book Here
Book Description
Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
Author: David F. Anderson
Publisher: Cambridge University Press
ISBN: 110824498X
Category : Mathematics
Languages : en
Pages : 447
Get Book Here
Book Description
This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work.
