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Author: Rafael Perera
Publisher: John Wiley & Sons
ISBN: 1444358499
Category : Medical
Languages : en
Pages : 128
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Book Description
This concise book will help you to interpret the statistical evidence provided by quantitative studies and to plan how to work with data in your own clinical research. Following the successful format of the Toolkit series, Statistics Toolkit guides the reader through statistical concepts using flowcharts, diagrams and real life examples to reflect concepts in a simple and practical manner. Sections include: Clear explanation of basic concepts in the context of clinical research Demonstration of how data are described, displayed and interpreted in different formats Practical glossary and key to the symbols used in statistics and a discussion of the software tools The book offers a handy, quick reference that has an easy-to-follow structure throughout, making it ideal for health care professionals and students.
Author: Rafael Perera
Publisher: John Wiley & Sons
ISBN: 1444358499
Category : Medical
Languages : en
Pages : 128
Get Book Here
Book Description
This concise book will help you to interpret the statistical evidence provided by quantitative studies and to plan how to work with data in your own clinical research. Following the successful format of the Toolkit series, Statistics Toolkit guides the reader through statistical concepts using flowcharts, diagrams and real life examples to reflect concepts in a simple and practical manner. Sections include: Clear explanation of basic concepts in the context of clinical research Demonstration of how data are described, displayed and interpreted in different formats Practical glossary and key to the symbols used in statistics and a discussion of the software tools The book offers a handy, quick reference that has an easy-to-follow structure throughout, making it ideal for health care professionals and students.
Author: Gayathri Rajagopalan
Publisher: Apress
ISBN: 9781484263983
Category : Computers
Languages : en
Pages : 240
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Book Description
Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts – programming with Python, data analysis and visualization, and statistics. You'll start with an introduction to Python – the syntax, functions, conditional statements, data types, and different types of containers. You'll then review more advanced concepts like regular expressions, handling of files, and solving mathematical problems with Python. The second part of the book, will cover Python libraries used for data analysis. There will be an introductory chapter covering basic concepts and terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like Matplotlib and Seaborn. Case studies will be included as examples to help readers understand some real-world applications of data analysis. The final chapters of book focus on statistics, elucidating important principles in statistics that are relevant to data science. These topics include probability, Bayes theorem, permutations and combinations, and hypothesis testing (ANOVA, Chi-squared test, z-test, and t-test), and how the Scipy library enables simplification of tedious calculations involved in statistics. What You'll Learn Further your programming and analytical skills with Python Solve mathematical problems in calculus, and set theory and algebra with Python Work with various libraries in Python to structure, analyze, and visualize data Tackle real-life case studies using Python Review essential statistical concepts and use the Scipy library to solve problems in statistics Who This Book Is For Professionals working in the field of data science interested in enhancing skills in Python, data analysis and statistics.
Author: Gareth James
Publisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617
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Book Description
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Author: Måns Thulin
Publisher:
ISBN: 9781032497457
Category : Mathematics
Languages : en
Pages : 0
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Book Description
The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.
Author: Matthew Thompson
Publisher: John Wiley & Sons
ISBN: 0470657588
Category : Medical
Languages : en
Pages : 114
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Book Description
Diagnostic Tests Toolkit Diagnostic Tests Toolkit Finding the evidence for diagnostic tests Establishing an evidence-based methodology to assess the effectiveness of diagnostic tests has posed problems for many years. Now that the framework is in place health professionals can find and appraise the evidence for themselves. With Diagnostic Tests Toolkit clinicians and junior researchers can interpret the evidence for the effectiveness of different types of diagnostic tests, or develop their own research using the successful ‘step-by-step’ format of the Toolkit series. Written by renowned clinical researchers, this is the first basic guide to evidence-based diagnosis. It is equally valuable to starters in clinical research and those needing a quick refresher on the core elements of evidence-based diagnosis.
Author: Ralph Kimball
Publisher: John Wiley & Sons
ISBN: 1118082141
Category : Computers
Languages : en
Pages : 464
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Book Description
This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.
Author: Mark Jay Kiemele
Publisher:
ISBN: 9781880156063
Category : Production management
Languages : en
Pages : 0
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Book Description
Author: Vaclav Brezina
Publisher: Cambridge University Press
ISBN: 1107125707
Category : Foreign Language Study
Languages : en
Pages : 317
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Book Description
A comprehensive and accessible introduction to statistics in corpus linguistics, covering multiple techniques of quantitative language analysis and data visualisation.
Author: Keith McNulty
Publisher: CRC Press
ISBN: 1000427897
Category : Business & Economics
Languages : en
Pages : 272
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Book Description
Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work.
Author: Jesus Salcedo
Publisher: John Wiley & Sons
ISBN: 1119005574
Category : Computers
Languages : en
Pages : 532
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Book Description
Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.
