Quantitative Methods A Short Course

Written specifically to support shorter courses in statistics and quantitative methods, this book provides revision for basic mathematical numerical skills, examines some simple statistical techniques, and introduces some basic quantitiative modelling techniques.

A well-balanced and accessible introduction to the elementary quantitative methods and Microsoft® Office Excel® applications used to guide business decision making Featuring quantitative techniques essential for modeling modern business situations, Introduction to Quantitative Methods in Business: With Applications Using Microsoft® Office Excel® provides guidance to assessing real-world data sets using Excel. The book presents a balanced approach to the mathematical tools and techniques with applications used in the areas of business, finance, economics, marketing, and operations. The authors begin by establishing a solid foundation of basic mathematics and statistics before moving on to more advanced concepts. The first part of the book starts by developing basic quantitative techniques such as arithmetic operations, functions and graphs, and elementary differentiations (rates of change), and integration. After a review of these techniques, the second part details both linear and nonlinear models of business activity. Extensively classroom-tested, Introduction to Quantitative Methods in Business: With Applications Using Microsoft® Office Excel® also includes: Numerous examples and practice problems that emphasize real-world business quantitative techniques and applications Excel-based computer software routines that explore calculations for an assortment of tasks, including graphing, formula usage, solving equations, and data analysis End-of-chapter sections detailing the Excel applications and techniques used to address data and solutions using large data sets A companion website that includes chapter summaries, Excel data sets, sample exams and quizzes, lecture slides, and an Instructors’ Solutions Manual Introduction to Quantitative Methods in Business: With Applications Using Microsoft® Office Excel® is an excellent textbook for undergraduate-level courses on quantitative methods in business, economics, finance, marketing, operations, and statistics. The book is also an ideal reference for readers with little or no quantitative background who require a better understanding of basic mathematical and statistical concepts used in economics and business. Bharat Kolluri, Ph.D., is Professor of Economics in the Department of Economics, Finance, and Insurance at the University of Hartford. A member of the American Economics Association, his research interests include econometrics, business statistics, quantitative decision making, applied macroeconomics, applied microeconomics, and corporate finance. Michael J. Panik, Ph.D., is Professor Emeritus in the Department of Economics, Finance, and Insurance at the University of Hartford. He has served as a consultant to the Connecticut Department of Motor Vehicles as well as to a variety of health care organizations. In addition, Dr. Panik is the author of numerous books, including Growth Curve Modeling: Theory and Applications and Statistical Inference: A Short Course, both published by Wiley. Rao N. Singamsetti, Ph.D., is Associate Professor in the Department of Economics, Finance, and Insurance at the University of Hartford. A member of the American Economics Association, his research interests include the status of war on poverty in the United States since the 1960s and forecasting foreign exchange rates using econometric methods.

Provides students with all the tools they need to pass the typical Quantitative Methods course. This title includes chapters that focus on a selection of statistical techniques, illustrated with examples from across business, marketing, economics, finance, and public administration, that may appeal to students across the business spectrum.

This exciting new core textbook offers a clear and practical introduction to quantitative methods, taking a project-based approach. The author's extensive knowledge and straightforward writing style ensure that students are steered through the process step-by-step, from developing research questions and preparing data for analysis, to explaining how to present data in appropriate formats, avoid bias, and write up results and reports. Featuring a comprehensive pedagogical framework and companion website, readers are encouraged to follow practice analyses as they go, with examples given in both SPSS and Excel, and templates are provided for students' own research. In addition to covering the research project, chapters also cover the essential mathematical and statistical analyses that are a logical consequence of posing a quantitative research methods question. This is the perfect text for all social science students studying introductory modules on quantitative methods, research methods or statistics at undergraduate or postgraduate level. It also functions as an effective guide for undergraduate and postgraduate students faced with an independent research project.

Swaps, futures, options, structured instruments - a wide range of derivative products is traded in today's financial markets. Analyzing, pricing and managing such products often requires fairly sophisticated quantitative tools and methods. This book serves as an introduction to financial mathematics with special emphasis on aspects relevant in practice. In addition to numerous illustrative examples, algorithmic implementations are demonstrated using "Mathematica" and the software package "UnRisk" (available for both students and teachers). The content is organized in 15 chapters that can be treated as independent modules. In particular, the exposition is tailored for classroom use in a Bachelor or Master program course, as well as for practitioners who wish to further strengthen their quantitative background.

"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--

A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causality. To ensure a thorough understanding of all key concepts, Statistical Inference provides numerous examples and solutions along with complete and precise answers to many fundamental questions, including: How do we determine that a given dataset is actually a random sample? With what level of precision and reliability can a population sample be estimated? How are probabilities determined and are they the same thing as odds? How can we predict the level of one variable from that of another? What is the strength of the relationship between two variables? The book is organized to present fundamental statistical concepts first, with later chapters exploring more advanced topics and additional statistical tests such as Distributional Hypotheses, Multinomial Chi-Square Statistics, and the Chi-Square Distribution. Each chapter includes appendices and exercises, allowing readers to test their comprehension of the presented material. Statistical Inference: A Short Course is an excellent book for courses on probability, mathematical statistics, and statistical inference at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and practitioners who would like to develop further insights into essential statistical tools.

Research Methodology is meant to provide a broad guideline to facilitate and steer the whole of a research activity in any discipline. With the ambit and amount of research increasing by the day, the need for Research Methodology is being widely appreciated. Against this backdrop, we notice the dearth of well-written books on the subject. A Guide to Research Methodology attempts a balance between the generic approach to research in any domain and the wide array of research methods which are to be used in carrying out different tasks in any research. Discussions on these research methods appropriate in various disciplines have focused on the research tasks, keeping in mind the fact that a single such task like a comparison among alternatives may involve several methods from seemingly distinct areas. Unique features of this volume, as will be evident to a discerning reader, include: A detailed discussion on problem areas for research in several domains An illustrative and ampliated list of research problems drawn from different disciplines which can be pursued by interested research workers A comprehensive delineation of Research Design supported by illustrations An elaborate engagement with models with a note on model uncertainty Focus on recent and emerging models, methods and techniques A novel treatment of data analysis where the nature of data and the objective(s) of analysis justify drawing upon a variety of techniques for analysis This book will serve the purpose of a pre-PhD or a Master-level course-work for students of any discipline with a basic knowledge of quantitative analysis. In fact, anyone aspiring to take up meaningful research work will find the content useful and interesting.