Probability Statistics And Data 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 Probability Statistics And Data PDF full book. Access full book title Probability Statistics And Data.
Author | : Darrin Speegle |
Publisher | : CRC Press |
Total Pages | : 644 |
Release | : 2021-11-26 |
Genre | : Business & Economics |
ISBN | : 1000504514 |
Download Probability, Statistics, and Data Book in PDF, ePub and Kindle
This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included, using calculus when appropriate, but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized, quantified using simulations, and demonstrated with real data. The book compares parametric and non-parametric methods through simulation, allowing for a thorough investigation of testing error and power. The text builds R skills from the outset, allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers, so that you are working with current, real data, which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr, ggplot2, tidyr, stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University, and the majority of the more than 400 exercises have been classroom tested.
Author | : Norman Matloff |
Publisher | : CRC Press |
Total Pages | : 295 |
Release | : 2019-06-21 |
Genre | : Business & Economics |
ISBN | : 0429687117 |
Download Probability and Statistics for Data Science Book in PDF, ePub and Kindle
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Author | : F.M. Dekking |
Publisher | : Springer Science & Business Media |
Total Pages | : 488 |
Release | : 2006-03-30 |
Genre | : Mathematics |
ISBN | : 1846281687 |
Download A Modern Introduction to Probability and Statistics Book in PDF, ePub and Kindle
Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books
Author | : Michael J. Evans |
Publisher | : Macmillan |
Total Pages | : 704 |
Release | : 2004 |
Genre | : Mathematics |
ISBN | : 9780716747420 |
Download Probability and Statistics Book in PDF, ePub and Kindle
Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.
Author | : Przemyslaw Grzegorzewski |
Publisher | : Springer Science & Business Media |
Total Pages | : 372 |
Release | : 2013-12-11 |
Genre | : Mathematics |
ISBN | : 3790817732 |
Download Soft Methods in Probability, Statistics and Data Analysis Book in PDF, ePub and Kindle
Classical probability theory and mathematical statistics appear sometimes too rigid for real life problems, especially while dealing with vague data or imprecise requirements. These problems have motivated many researchers to "soften" the classical theory. Some "softening" approaches utilize concepts and techniques developed in theories such as fuzzy sets theory, rough sets, possibility theory, theory of belief functions and imprecise probabilities, etc. Since interesting mathematical models and methods have been proposed in the frameworks of various theories, this text brings together experts representing different approaches used in soft probability, statistics and data analysis.
Author | : Ruma Falk |
Publisher | : A K Peters/CRC Press |
Total Pages | : 264 |
Release | : 1993-04-15 |
Genre | : Mathematics |
ISBN | : |
Download Understanding Probability and Statistics Book in PDF, ePub and Kindle
Author | : Maurits Kaptein |
Publisher | : Springer Nature |
Total Pages | : 342 |
Release | : 2022-02-02 |
Genre | : Computers |
ISBN | : 3030105318 |
Download Statistics for Data Scientists Book in PDF, ePub and Kindle
This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.
Author | : Roy Frieden |
Publisher | : Springer Science & Business Media |
Total Pages | : 496 |
Release | : 2012-12-06 |
Genre | : Science |
ISBN | : 3642566995 |
Download Probability, Statistical Optics, and Data Testing Book in PDF, ePub and Kindle
Scientists and engineers in optics are increasingly confronted with problems that are of a random nature and that require a working knowledge of probability and statistics for their solution. This book develops these subjects within the context of optics, using a problem-solving approach. All methods are explicitly derived and can be traced back to three simple axioms given at the outset. This third edition contains many new applications to optical and physical phenomena, including a method of exactly estimating probability laws.
Author | : Hossein Pishro-Nik |
Publisher | : |
Total Pages | : 746 |
Release | : 2014-08-15 |
Genre | : Probabilities |
ISBN | : 9780990637202 |
Download Introduction to Probability, Statistics, and Random Processes Book in PDF, ePub and Kindle
The book covers basic concepts such as random experiments, probability axioms, conditional probability, and counting methods, single and multiple random variables (discrete, continuous, and mixed), as well as moment-generating functions, characteristic functions, random vectors, and inequalities; limit theorems and convergence; introduction to Bayesian and classical statistics; random processes including processing of random signals, Poisson processes, discrete-time and continuous-time Markov chains, and Brownian motion; simulation using MATLAB and R.
Author | : Darrin Speegle |
Publisher | : CRC Press |
Total Pages | : 513 |
Release | : 2021-11-25 |
Genre | : Business & Economics |
ISBN | : 1000504166 |
Download Probability, Statistics, and Data Book in PDF, ePub and Kindle
This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included, using calculus when appropriate, but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized, quantified using simulations, and demonstrated with real data. The book compares parametric and non-parametric methods through simulation, allowing for a thorough investigation of testing error and power. The text builds R skills from the outset, allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers, so that you are working with current, real data, which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr, ggplot2, tidyr, stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University, and the majority of the more than 400 exercises have been classroom tested. The exercises in the book have been added to to the free and open online homework system myopenmath (https://www.myopenmath.com/) which may be useful to instructors.