Statistics And Probability With Applications Fourth Edition Pdf

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Skewed distributions are transversal and ubiquitous to all scientific disciplines. They have captured the attention of many researchers, as a deep understanding of their underlying probabilistic mechanisms is crucial in many fields. The right choice of the probability distribution for a non-normal stochastic process and the proper interpretation of its parameters can be very challenging and of enormous importance in fields such as physics, chemistry, biology, and social sciences.

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Title: A Simplified Discrete Model for Analyzing the Human Response Times to Visual Stimuli
Authors: Aina Noverques Medina; Marcos Orellana; Jos Guerra Carmenate; Miguel E. Iglesias Martnez; Juan Carlos Castro Palacio; Pedro Fernndez de Crdoba.
Affiliation: Universitat Politcnica de Valncia (UPV), Spain.
Abstract: In this paper, we streamline the model proposed in a previous study for representing the distribution of human response times to visual stimuli. We employ a Rayleigh distribution to depict the response time distribution within a group. Additionally, we introduce a discrete model to accurately compute the unique parameter B of the distribution. The obtained results quantitatively improve the previous work results considering the correlations values.

Title: Gumbel-Logistic Unit Distribution with Application in Telecommunications Data Modelling
Authors: Vladica S. Stojanović^1,a, Mihailo Jovanović^1,b, Brankica Pažun^2 and Zlatko Langović3
Affiliation: 1^Department of Informatics & Computer Sciences, University of Criminal Investigation and Police Studies, Belgrade, Serbia 2^Department of Informatics, Mathematics and Statistics, Faculty of Engineering Management, Belgrade, Serbia 3^Department of Business Economy, Faculty of Hotel Management and Tourism, University of Kragujevac, Vrnjačka Banja, Serbia
Abstract: The manuscript deals with a new two-parameter unit stochastic distribution, obtained by transforming the Gumbel distribution, using generalized logistic mapping, into a unit interval. The distribution obtained in this way is called the Gumbel-Logistic Unit (abbreviated GLU) distribution and its basic stochastic properties are examined in detail. Among others, it is shown that the GLU distribution, unlike the Gumbel one which is always positively asymmetric, can take both asymmetric forms. Also, the procedure for estimating parameters based on quantiles, along with the asymptotic properties of the obtained estimators and the study of their numerical simulation, is given. Finally, the application of the GLU distribution in modeling some real-world data related to telecommunications is discussed.

This book includes introduction to data, summarizing data (numerical and graphical), probability, distributions of random variables, inference for categorical and numeric data, linear regression, multiple linear regression and logistic regression. This covers almost all the topics that should cover in an introductory statistics course. I could not find counting techniques in this book.

This book contains basic concepts in statistics. These concepts will not change within a short period of time. Questions/examples in this book cover a wide range of area. Examples and questions can be update as time changes. I do not think this book needs any major changes recently.

Overall clarity of this book is admirable. Topic lineup and teaching methods are different from person to person. There were some sections I would love to see a better explanation or more examples. For example, inference is something that students struggle a lot. I would like to see a better explanation on hypothesis test before we move on to hypothesis testing on proportions or means.

This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. Also, the convenient sample is covered. If the volunteer sample is covered also that would be great because it is very common nowadays. The chapters are well organized and many real data sets are analyzed.

Overall, the text is well-written and explained along with real-world data examples. In addition, it is easy to follow. Each chapter starts with a very interesting paragraph or introduction that explains the idea of the chapter and what will be covered and why.

The way the chapters are broken up into sections and the sections are broken up into subsections makes it easy to select the topics that need to be covered in a course based on the number of weeks of the course. It is easy to skip some topics with no lack of consistency or confusion.

The book is well organized and structured. Having a free pdf version and a hard copy for a few dollars is great. The resources on the website also are well organized and easy to access and download. Choosing the population proportion rather than the population mean to be covered in the foundation for inference chapter is a good idea because it is easier for students to understand compared to the population mean.

This book has both the standard selection of topics from an introductory statistics course along with several in-depth case studies and some extended topics. In particular, the malaria case study and stokes case study add depth and real-world meaning to the topics covered, and there is a thorough coverage of distributions. If you are looking for deep mathematical comprehensiveness of exercises, this may not be the right book, but for most introductory statistics students who are not pursuing deeper options in math/stat, this is very comprehensive.

I find the content to be quite relevant. Almost every worked example and possible homework exercise in the book is couched in real-world situation, nearly all of which are culturally, politically, and socially relevant. I do think there are some references that may become obsolete or lost somewhat quickly; however, I think a diligent editorial team could easily update data sets and questions to stay current.

The writing in this book is above average. However, there are some sections that are quite dense and difficult to follow. The writing could be slightly more inviting, and concept could be more readily introduced via accessible examples more often. Words like "clearly" appear more than are warranted (ie: ever). As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. Jargon is introduced adequately, though.

This book is highly modular. I teach at an institution with 10-week terms and I found it relatively easy to subdivide the material in this book into a digestible 10 weeks (I am not covering the entire book!). Many OERs (and published textbooks) are difficult to convert from a typical 15-week semester to a 10-week term, but not this one! This book is easy to follow and the roadmap at the front for the instructor adds additional ease.