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Real Analysis With Proof Strategies

Typically undergraduates see real analysis as one of the most diﬃcult courses that a mathematics major is required to take. The main reason for this perception is twofold: Students must comprehend new abstract concepts and learn to deal with these concepts on a level of rigor and proof not previously encountered. A key challenge for an instructor of real analysis is to ﬁnd a way to bridge the gap between a student’s preparation and the mathematical skills that are required to be successful in such a course. Real Analysis: With Proof Strategies provides a resolution to the bridging-the-gap problem. The book not only presents the fundamental theorems of real analysis but also shows the reader how to compose and produce the proofs of these theorems. The detail rigor and proof strategies oﬀered in this textbook will be appreciated by all readers. Features Explicitly shows the reader how to produce and compose the proofs of the basic theorems in real analysis Suitable for junior or senior undergraduates majoring in mathematics. | Real Analysis With Proof Strategies

GBP 42.99

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Introductory Mathematical Analysis for Quantitative Finance

Introductory Mathematical Analysis for Quantitative Finance is a textbook designed to enable students with little knowledge of mathematical analysis to fully engage with modern quantitative finance. A basic understanding of dimensional Calculus and Linear Algebra is assumed. The exposition of the topics is as concise as possible since the chapters are intended to represent a preliminary contact with the mathematical concepts used in Quantitative Finance. The aim is that this book can be used as a basis for an intensive one-semester course. Features:Written with applications in mind and maintaining mathematical rigor. Suitable for undergraduate or master's level students with an Economics or Management background. Complemented with various solved examples and exercises to support the understanding of the subject.

GBP 39.99

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Biomarker Analysis in Clinical Trials with R

The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book will guide you as you begin the journey into communicating understanding and synthesizing biomarker data. From the Foreword Jared Christensen Vice President Biostatistics Early Clinical Development Pfizer Inc. Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R the book helps students researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers. Features:Analysis of pharmacodynamic biomarkers for lending evidence target modulation. Design and analysis of trials with a predictive biomarker. Framework for analyzing surrogate biomarkers. Methods for combining multiple biomarkers to predict treatment response. Offers a biomarker statistical analysis plan. R code data and models are given for each part: including regression models for survival and longitudinal data as well as statistical learning models such as graphical models and penalized regression models.

GBP 39.99

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Estimands Estimators and Sensitivity Analysis in Clinical Trials

The concepts of estimands analyses (estimators) and sensitivity are interrelated. Therefore great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language providing technical details real-world examples and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface such as between causal inference and clinical trialists as well as among clinicians statisticians and regulators when it comes to communicating decision-making objectives assumptions and interpretations of evidence. This book lays out a path toward bridging some of these gaps. It offers¿ A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges¿ A thorough treatment of intercurrent events (ICEs) i. e. postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1)¿ Details on how estimands integrated into a principled study development process lay a foundation for coherent specification of trial design conduct and analysis needed to overcome the issues caused by ICEs:¿ A perspective on the role of the intention-to-treat principle¿ Examples and case studies from various areas¿ Example code in SAS and R¿ A connection with causal inference¿ Implications and methods for analysis of longitudinal trials with missing dataTogether the authors have offered the readers their ample expertise in clinical trial design and analysis from an industrial and academic perspective. | Estimands Estimators and Sensitivity Analysis in Clinical Trials

GBP 39.99

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SAS Software Companion for Sampling Design and Analysis Third Edition

The SAS® Software Companion for Sampling: Design and Analysis designed to be read alongside Sampling: Design and Analysis Third Edition by Sharon L. Lohr (SDA; 2022 CRC Press) shows how to use the survey selection and analysis procedures of SAS® software to perform calculations for the examples in SDA. No prior experience with SAS software is needed. Chapter 1 tells you how to access the software introduces basic features and helps you get started with analyzing data. Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA with code output and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors. Features of the SAS software procedures are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book you will know how to use SAS software to select and analyze almost any type of probability sample. All code is available on the book website and is easily adapted for your own survey data analyses. The website also contains all data sets from the examples and exercises in SDA to help you develop your skills through analyzing survey data from social and public opinion research public health crime education business agriculture and ecology | SAS® Software Companion for Sampling Design and Analysis Third Edition

GBP 28.99

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R Companion for Sampling Design and Analysis Third Edition

The R Companion for Sampling: Design and Analysis designed to be read alongside Sampling: Design and Analysis Third Edition by Sharon L. Lohr (SDA; 2022 CRC Press) shows how to use functions in base R and contributed packages to perform calculations for the examples in SDA. No prior experience with R is needed. Chapter 1 tells you how to obtain R and RStudio introduces basic features of the R statistical software environment and helps you get started with analyzing data. Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA with code output and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors. R features and functions are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book you will know how to use R to select and analyze almost any type of probability sample. All R code and data sets used in this book are available online to help you develop your skills analyzing survey data from social and public opinion research public health crime education business agriculture and ecology. | R Companion for Sampling Design and Analysis Third Edition

GBP 28.99

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Understanding Regression Analysis A Conditional Distribution Approach

Understanding Regression Analysis unifies diverse regression applications including the classical model ANOVA models generalized models including Poisson Negative binomial logistic and survival neural networks and decision trees under a common umbrella - namely the conditional distribution model. It explains why the conditional distribution model is the correct model and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books this one from the outset takes a realistic approach that all models are just approximations. Hence the emphasis is to model Nature’s processes realistically rather than to assume (incorrectly) that Nature works in particular constrained ways. Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed just-in-time within chapters Simple mathematical explanations (baby proofs) of key concepts Clear explanations and applications of statistical significance (p-values) incorporating the American Statistical Association guidelines Use of data-generating process terminology rather than population Random-X framework is assumed throughout (the fixed-X case is presented as a special case of the random-X case) Clear explanations of probabilistic modelling including likelihood-based methods Use of simulations throughout to explain concepts and to perform data analyses This book has a strong orientation towards science in general as well as chapter-review and self-study questions so it can be used as a textbook for research-oriented students in the social biological and medical and physical and engineering sciences. As well its mathematical emphasis makes it ideal for a text in mathematics and statistics courses. With its numerous worked examples it is also ideally suited to be a reference book for all scientists. | Understanding Regression Analysis A Conditional Distribution Approach

GBP 39.99

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Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources the Bayesian approach provides a flexible framework for drug development. Despite its advantages the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners Bayesian Analysis with R for Drug Development: Concepts Algorithms and Case Studies describes a wide range of Bayesian applications to problems throughout pre-clinical clinical and Chemistry Manufacturing and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical clinical and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang Ph. D. is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books 15 book chapters and over 90 peer-reviewed papers on diverse scientific and statistical subjects including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick Ph. D. is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences having developed and taught courses in several areas including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences. | Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

GBP 38.99

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Computational Genomics with R

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming to machine learning and statistics to the latest genomic data analysis techniques. The text provides accessible information and explanations always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary it requires different starting points for people with different backgrounds. For example a biologist might skip sections on basic genome biology and start with R programming whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics supervised and unsupervised learning techniques that are important in data modeling and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics such as heatmaps meta-gene plots and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets such as RNA-seq ChIP-seq and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology Max Delbrück Center Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

GBP 42.99

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Spatio-Temporal Methods in Environmental Epidemiology

Spatio-Temporal Methods in Environmental Epidemiology with R like its first Edition explores the interface between environmental epidemiology and spatio-temporal modelling. It links recent developments in spatio-temporal theory with epidemiological applications. Drawing on real-life problems it shows how recent advances in methodology can assess the health risks associated with environmental hazards. The book's clear guidelines enable the implementation of the methodology and estimation of risks in practice. New additions to the second edition include : a thorough exploration of the underlying concepts behind knowledge discovery through data; a new chapter on extracting information from data using R and the Tidyverse; additional material on methods for Bayesian computation including the use of NIMBLE and Stan; new methods for performing spatio-temporal analysis and an updated chapter containing further topics. Throughout the book there are new examples and the presentation of R code for examples has been extended. Along with these additions the book now has a GitHub site (https://spacetime-environ. github. io/stepi2) that contains data code and further worked examples. Features • Explores the interface between environmental epidemiology and spatio temporal modelling; • Incorporates examples that show how spatio-temporal methodology can inform societal concerns about the effects of environmental hazards on health; • Uses a Bayesian foundation on which to build an integrated approach to spatio-temporal modelling and environmental epidemiology; • Discusses data analysis and topics such as data visualization mapping wrangling and analysis • Shows how to design networks for monitoring hazardous environmental processes networks and the ill-effects of preferential sampling; • Through the listing and application of code shows the power of R tidyverse NIMBLE and Stan and other modern tools in performing complex data analysis and modelling. Representing a continuing important direction in environmental epidemiology this book - in full color throughout - underscores the increasing need to consider dependencies in both space and time when modelling epidemiological data. Readers will learn how to identify and model patterns in spatio-temporal data and how to exploit dependencies over space and time to reduce bias and inefficiency when estimating risks to health.

GBP 39.99

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Time Series A First Course with Bootstrap Starter

Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness (ii) computational illustration and implementation and (iii) conciseness and accessibility to upper-level undergraduate and M. S. students. Basic theoretical results are presented in a mathematically convincing way and the methods of data analysis are developed through examples and exercises parsed in R. A student with a basic course in mathematical statistics will learn both how to analyze time series and how to interpret the results. The book provides the foundation of time series methods including linear filters and a geometric approach to prediction. The important paradigm of ARMA models is studied in-depth as well as frequency domain methods. Entropy and other information theoretic notions are introduced with applications to time series modeling. The second half of the book focuses on statistical inference the fitting of time series models as well as computational facets of forecasting. Many time series of interest are nonlinear in which case classical inference methods can fail but bootstrap methods may come to the rescue. Distinctive features of the book are the emphasis on geometric notions and the frequency domain the discussion of entropy maximization and a thorough treatment of recent computer-intensive methods for time series such as subsampling and the bootstrap. There are more than 600 exercises half of which involve R coding and/or data analysis. Supplements include a website with 12 key data sets and all R code for the book's examples as well as the solutions to exercises. | Time Series A First Course with Bootstrap Starter

GBP 38.99

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Surrogates Gaussian Process Modeling Design and Optimization for the Applied Sciences

Surrogates: a graduate textbook or professional handbook on topics at the interface between machine learning spatial statistics computer simulation meta-modeling (i. e. emulation) design of experiments and optimization. Experimentation through simulation human out-of-the-loop statistical support (focusing on the science) management of dynamic processes online and real-time analysis automation and practical application are at the forefront. Topics include:Gaussian process (GP) regression for flexible nonparametric and nonlinear modeling. Applications to uncertainty quantification sensitivity analysis calibration of computer models to field data sequential design/active learning and (blackbox/Bayesian) optimization under uncertainty. Advanced topics include treed partitioning local GP approximation modeling of simulation experiments (e. g. agent-based models) with coupled nonlinear mean and variance (heteroskedastic) models. Treatment appreciates historical response surface methodology (RSM) and canonical examples but emphasizes contemporary methods and implementation in R at modern scale. Rmarkdown facilitates a fully reproducible tour complete with motivation from application to and illustration with compelling real-data examples. Presentation targets numerically competent practitioners in engineering physical and biological sciences. Writing is statistical in form but the subjects are not about statistics. Rather they’re about prediction and synthesis under uncertainty; about visualization and information design and decision making computing and clean code. | Surrogates Gaussian Process Modeling Design and Optimization for the Applied Sciences

GBP 38.99

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Flexible Imputation of Missing Data Second Edition

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes like deletion or mean imputation only work under highly restrictive conditions which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data. | Flexible Imputation of Missing Data Second Edition

GBP 38.99

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A First Course in Machine Learning

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings and goes all the way to the frontiers of the subject such as infinite mixture models GPs and MCMC. —Devdatt Dubhashi Professor Department of Computer Science and Engineering Chalmers University Sweden This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade. —Daniel Barbara George Mason University Fairfax Virginia USA The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling inference and prediction providing ‘just in time’ the essential background on linear algebra calculus and probability theory that the reader needs to understand these concepts. —Daniel Ortiz-Arroyo Associate Professor Aalborg University Esbjerg Denmark I was impressed by how closely the material aligns with the needs of an introductory course on machine learning which is its greatest strength…Overall this is a pragmatic and helpful book which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months. —David Clifton University of Oxford UK The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process MCMC and mixture modeling provide an ideal basis for practical projects without disturbing the very clear and readable exposition of the basics contained in the first part of the book. —Gavin Cawley Senior Lecturer School of Computing Sciences University of East Anglia UK This book could be used for junior/senior undergraduate students or first-year graduate students as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective. —Guangzhi Qu Oakland University Rochester Michigan USA

GBP 39.99

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Environmental and Ecological Statistics with R

Emphasizing the inductive nature of statistical thinking Environmental and Ecological Statistics with R Second Edition connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature the book explains the approach to solving a statistical problem covering model specification parameter estimation and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment and using several core examples throughout the book the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models including linear and nonlinear models classification and regression trees generalized linear models and multilevel models. It also discusses the use of simulation for model checking and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development it eases the transition from scientific hypothesis to statistical model.

GBP 39.99

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Linux The Textbook Second Edition

Choosen by BookAuthority as one of BookAuthority's Best Linux Mint Books of All TimeLinux: The Textbook Second Edition provides comprehensive coverage of the contemporary use of the Linux operating system for every level of student or practitioner from beginners to advanced users. The text clearly illustrates system-specific commands and features using Debian-family Debian Ubuntu and Linux Mint and RHEL-family CentOS and stresses universal commands and features that are critical to all Linux distributions. The second edition of the book includes extensive updates and new chapters on system administration for desktop stand-alone PCs and server-class computers; API for system programming including thread programming with pthreads; virtualization methodologies; and an extensive tutorial on systemd service management. Brand new online content on the CRC Press website includes an instructor’s workbook test bank and In-Chapter exercise solutions as well as full downloadable chapters on Python Version 3. 5 programming ZFS TC shell programming advanced system programming and more. An author-hosted GitHub website also features updates further references and errata. Features New or updated coverage of file system sorting regular expressions directory and file searching file compression and encryption shell scripting system programming client-server–based network programming thread programming with pthreads and system administration Extensive in-text pedagogy including chapter objectives student projects and basic and advanced student exercises for every chapter Expansive electronic downloads offer advanced content on Python ZFS TC shell scripting advanced system programming internetworking with Linux TCP/IP and many more topics all featured on the CRC Press website Downloadable test bank work book and solutions available for instructors on the CRC Press website Author-maintained GitHub repository provides other resources such as live links to further references updates and errata | Linux The Textbook Second Edition

GBP 38.99

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Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods the book covers many general topics: Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures including sensitivity to priorsFrequentist properties of Bayesian methodsCase studies covering advanced topics illustrate the flexibility of the Bayesian approach:Semiparametric regression Handling of missing data using predictive distributionsPriors for high-dimensional regression modelsComputational techniques for large datasetsSpatial data analysisThe advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code motivating data sets and complete data analyses are available on the book’s website. Brian J. Reich Associate Professor of Statistics at North Carolina State University is currently the editor-in-chief of the Journal of Agricultural Biological and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh Professor of Statistics at North Carolina State University has over 22 years of research and teaching experience in conducting Bayesian analyses received the Cavell Brownie mentoring award and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.

GBP 39.99

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Advanced Survival Models

Survival data analysis is a very broad field of statistics encompassing a large variety of methods used in a wide range of applications and in particular in medical research. During the last twenty years several extensions of classical survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions such as frailty models (in case of unobserved heterogeneity or clustered data) cure models (when a fraction of the population will not experience the event of interest) competing risk models (in case of different types of event) and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models cure models competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used and how they are linked to specific research questions Focuses on the understanding of the models their implementation and their interpretation with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

GBP 42.99

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Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems aims to introduce high school students to all the necessary topics that frequently emerge in international Math Olympiad competitions. In addition to introducing the topics the book will also provide several repetitive-type guided problems to help develop vital techniques in solving problems correctly and efficiently. The techniques employed in the book will help prepare students for the topics they will typically face in an Olympiad-style event but also for future college mathematics courses in Discrete Mathematics Graph Theory Differential Equations Number Theory and Abstract Algebra. Features: Numerous problems designed to embed good practice in readers and build underlying reasoning analysis and problem-solving skills Suitable for advanced high school students preparing for Math Olympiad competitions

GBP 24.99

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Business Financial Planning with Microsoft Excel

Business Finance Planning with Microsoft® Excel® shows how to visualize plan and put into motion an idea for creating a start-up company. Microsoft Excel is a tool that makes it easier to build a business financial planning process for a new business venture. With an easy-to follow structure the book flows as a six-step process: Presenting a case study of a business start-up Creating goals and objectives Determining expenses from those goals and objectives Estimating potential sales revenue based on what competitors charge their customers Predicting marketing costs Finalizing the financial analysis with a of financial statements. Written around an IT startup case study the book presents a host of Excel worksheets describing the case study along with accompanying blank forms. Readers can use these forms in their own businesses so they can build parts of their own business plans as they go. This is intended to be a practical guide that teaches and demonstrates by example in the end presenting a usable financial model to build and tweak a financial plan with a set of customizable Excel worksheets. The book uses practical techniques to help with the planning processing. These include applying a SWOT (strengths weaknesses opportunities and threats) matrix to evaluate a business idea and SMART (Specific Measurable Achievable Relevant and Time-Bound) objectives to link together goals. As the book concludes readers will be able to develop their own income statement balance sheet and the cash-flow statement for a full analysis of their new business ideas. Worksheets are available to download from: https://oracletroubleshooter. com/business-finance-planning/app/ | Business Financial Planning with Microsoft Excel

GBP 34.99

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Python Packages

Python Packages introduces Python packaging at an introductory and practical level that’s suitable for those with no previous packaging experience. Despite this the text builds up to advanced topics such as automated testing creating documentation versioning and updating a package and implementing continuous integration and deployment. Covering the entire Python packaging life cycle this essential guide takes readers from package creation all the way to effective maintenance and updating. Python Packages focuses on the use of current and best-practice packaging tools and services like poetry cookiecutter pytest sphinx GitHub and GitHub Actions. Features: The book’s source code is available online as a GitHub repository where it is collaborated on automatically tested and built in real time as changes are made; demonstrating the use of good reproducible and clear project workflows. Covers not just the process of creating a package but also how to document it test it publish it to the Python Package Index (PyPI) and how to properly version and update it. All concepts in the book are demonstrated using examples. Readers can follow along creating their own Python packages using the reproducible code provided in the text. Focuses on a modern approach to Python packaging with emphasis on automating and streamlining the packaging process using new and emerging tools such as poetry and GitHub Actions.

GBP 35.99

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R Markdown Cookbook

This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. Those new to R Markdown will appreciate the short practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose ‘hidden’ features support customization and demonstrate the many new and varied applications of the software. After reading this book users will learn how to: Enhance your R Markdown content with diagrams citations and dynamically generated text Streamline your workflow with child documents code chunk references and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python D3 and more into your analysis | R Markdown Cookbook

GBP 28.99

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The Effect An Introduction to Research Design and Causality

The Effect: An Introduction to Research Design and Causality is about research design specifically concerning research that uses observational data to make a causal inference. It is separated into two halves each with different approaches to that subject. The first half goes through the concepts of causality with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do? Key Features: • Extensive code examples in R Stata and Python • Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects simulation and power analysis new cutting-edge methods and uncomfortable ignored assumptions • An easy-to-read conversational tone • Up-to-date coverage of methods with fast-moving literatures like difference-in-differences | The Effect An Introduction to Research Design and Causality

GBP 29.99

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Cure Models Methods Applications and Implementation

Cure Models: Methods Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models including estimation inference and software. This book is useful for statistical researchers and graduate students and practitioners in other disciplines to have a thorough review of modern cure model methodology and to seek appropriate cure models in applications. The prerequisites of this book include some basic knowledge of statistical modeling survival models and R and SAS for data analysis. The book features real-world examples from clinical trials and population-based studies and a detailed introduction to R packages SAS macros and WinBUGS programs to fit some cure models. The main topics covered include the foundation of statistical estimation and inference of cure models for independent and right-censored survival data cure modeling for multivariate recurrent-event and competing-risks survival data and joint modeling with longitudinal data statistical testing for the existence and difference of cure rates and sufficient follow-up new developments in Bayesian cure models applications of cure models in public health research and clinical trials. | Cure Models Methods Applications and Implementation

GBP 35.99

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Community College Mathematics Past Present and Future

This book explores the rich history of community college math with a specific focus on gatekeeper math classes. Gatekeeper math classes include courses such as college algebra introduction to statistics and all developmental math classes. For community colleges successful completion of these classes is imperative for student retention. This book presents a decade-by-decade analysis of the history of community college mathematics. The author employs a mix of conceptual empirical and quantitative research. The empirical research stems from interviews with 30 community college faculty members from seven community colleges. From the 1970s to the pandemic in the early 2020s the book explores math curricula as well as trends initiatives teaching practices and mandates that have impacted community college math. The positives and negatives of such trends initiatives and mandates are presented along with suggestions on how to apply such knowledge going forward. The author addresses the key questions: How can we build a future model for community college gatekeeper math classes that is both successful and sustainable? Additionally how can we learn from the past and the present to build such a model? This book will be ideal for students in graduate programs focusing on community college leadership or developmental education leadership as well as all those hoping to improve success rates in community college mathematics programs. | Community College Mathematics Past Present and Future

GBP 24.99

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Brand

Merchant

Price (EUR)

Real Analysis With Proof Strategies

Introductory Mathematical Analysis for Quantitative Finance

Biomarker Analysis in Clinical Trials with R

Estimands Estimators and Sensitivity Analysis in Clinical Trials

SAS Software Companion for Sampling Design and Analysis Third Edition

R Companion for Sampling Design and Analysis Third Edition

Understanding Regression Analysis A Conditional Distribution Approach

Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Computational Genomics with R

Spatio-Temporal Methods in Environmental Epidemiology

Time Series A First Course with Bootstrap Starter

Surrogates Gaussian Process Modeling Design and Optimization for the Applied Sciences

Flexible Imputation of Missing Data Second Edition

A First Course in Machine Learning

Environmental and Ecological Statistics with R

Linux The Textbook Second Edition

Bayesian Statistical Methods

Advanced Survival Models

Introduction to Math Olympiad Problems

Business Financial Planning with Microsoft Excel

Python Packages

R Markdown Cookbook

The Effect An Introduction to Research Design and Causality

Cure Models Methods Applications and Implementation

Community College Mathematics Past Present and Future

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