Book picks similar to
Further Mathematics for Economic Analysis by Knut Sydsæter
economics
mathematics
math
textbook
Bayes' Rule: A Tutorial Introduction to Bayesian Analysis
James V. Stone - 2013
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, intuitive visual representations of real-world examples are used to show how Bayes' rule is actually a form of commonsense reasoning. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to gain an intuitive understanding of Bayesian analysis. As an aid to understanding, online computer code (in MatLab, Python and R) reproduces key numerical results and diagrams.Stone's book is renowned for its visually engaging style of presentation, which stems from teaching Bayes' rule to psychology students for over 10 years as a university lecturer.
Mathematical Statistics and Data Analysis
John A. Rice - 1988
The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts which are set in abstract settings.
Numerical Linear Algebra
Lloyd N. Trefethen - 1997
The clarity and eloquence of the presentation make it popular with teachers and students alike. The text aims to expand the reader's view of the field and to present standard material in a novel way. All of the most important topics in the field are covered with a fresh perspective, including iterative methods for systems of equations and eigenvalue problems and the underlying principles of conditioning and stability. Presentation is in the form of 40 lectures, which each focus on one or two central ideas. The unity between topics is emphasized throughout, with no risk of getting lost in details and technicalities. The book breaks with tradition by beginning with the QR factorization - an important and fresh idea for students, and the thread that connects most of the algorithms of numerical linear algebra.
The Theoretical Minimum: What You Need to Know to Start Doing Physics
Leonard Susskind - 2013
In this unconventional introduction, physicist Leonard Susskind and hacker-scientist George Hrabovsky offer a first course in physics and associated math for the ardent amateur. Unlike most popular physics books—which give readers a taste of what physicists know but shy away from equations or math—Susskind and Hrabovsky actually teach the skills you need to do physics, beginning with classical mechanics, yourself. Based on Susskind's enormously popular Stanford University-based (and YouTube-featured) continuing-education course, the authors cover the minimum—the theoretical minimum of the title—that readers need to master to study more advanced topics.An alternative to the conventional go-to-college method, The Theoretical Minimum provides a tool kit for amateur scientists to learn physics at their own pace.
Business Law
Lee Mei Pheng - 2009
The authors' comprehensive experience in legal practice, banking and teaching have enabled them to provide a condensed and easy to understand coverage of business law principles and areas of interest related thereto.
Organizational Communication: Approaches and Processes
Katherine Miller - 2005
Her writing style and consistent use of examples and case studies results in a text that undergraduates students will find easy to understand.
Introductory Statistics
Prem S. Mann - 2006
The realistic content of its examples and exercises, the clarity and brevity of its presentation, and the soundness of its pedagogical approach have received the highest remarks from both students and instructors. Now this bestseller is available in a new 6th edition.
Principles of Marketing
Philip Kotler - 1980
The 11th edition of this text continues to build on four major marketing themes: building and managing profitable customer relationships, building and managing strong brands to create brand equity, harnessing new marketing technologies in the digital age, and marketing in a socially responsible way around the globe.
All the Mathematics You Missed
Thomas A. Garrity - 2001
This book will offer students a broad outline of essential mathematics and will help to fill in the gaps in their knowledge. The author explains the basic points and a few key results of all the most important undergraduate topics in mathematics, emphasizing the intuitions behind the subject. The topics include linear algebra, vector calculus, differential and analytical geometry, real analysis, point-set topology, probability, complex analysis, set theory, algorithms, and more. An annotated bibliography offers a guide to further reading and to more rigorous foundations.
Research Methods for Business Students
Mark N.K. Saunders - 2006
Using real-life case studies and written with a student-centered approach, this new edition provides students with the necessary knowledge and skills to enable them to undertake a piece of business research making the best use of IT where appropriate.
Principles and Practice of Structural Equation Modeling
Rex B. Kline - 1998
Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.
Abnormal Psychology: Clinical Perspectives on Psychological Disorders
Richard P. Halgin - 1998
In Richard Halgin and Susan Krauss Whitbourne’s Abnormal Psychology: Clinical Perspectives on Psychological Disorders, students are shown the human side of Abnormal Psychology. Through the wide
Statistical Methods for Psychology
David C. Howell - 2001
This book has two underlying themes that are more or less independent of the statistical hypothesis tests that are the main content of the book. The first theme is the importance of looking at the data before formulating a hypothesis. With this in mind, the author discusses, in detail, plotting data, looking for outliers, and checking assumptions (Graphical displays are used extensively). The second theme is the importance of the relationship between the statistical test to be employed and the theoretical questions being posed by the experiment. To emphasize this relationship, the author uses real examples to help the student understand the purpose behind the experiment and the predictions made by the theory. Although this book is designed for students at the intermediate level or above, it does not assume that students have had either a previous course in statistics or a course in math beyond high-school algebra.
Hands-On Machine Learning with Scikit-Learn and TensorFlow
Aurélien Géron - 2017
Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details