Michael J Panik – författare

Advanced Statistics from an Elementary Point of View is a highly readable text that communicates the content of a course in mathematical statistics without imposing too much rigor. It clearly emphasizes the connection between statistics and probability, and helps students concentrate on statistical strategies without being overwhelmed by calculations.The book provides comprehensive coverage of descriptive statistics; detailed treatment of univariate and bivariate probability distributions; and thorough coverage of probability theory with numerous event classifications.This book is designed for statistics majors who are already familiar with introductory calculus and statistics, and can be used in either a one- or two-semester course. It can also serve as a statistics tutorial or review for working professionals.Students who use this book will be well on their way to thinking like a statistician in terms of problem solving and decision-making. Graduates who pursue careers in statistics will continue to find this book useful, due to numerous statistical test procedures (both parametric and non-parametric) and detailed examples.- Comprehensive coverage of descriptive statistics- More detailed treatment of univariate and bivariate probability distributions- Thorough coverage of probability theory with numerous event classifications

Advanced Statistics from an Elementary Point of View is a highly readable text that communicates the content of a course in mathematical statistics without imposing too much rigor. It clearly emphasizes the connection between statistics and probability, and helps students concentrate on statistical strategies without being overwhelmed by calculations.

The book provides comprehensive coverage of descriptive statistics; detailed treatment of univariate and bivariate probability distributions; and thorough coverage of probability theory with numerous event classifications.

This book is designed for statistics majors who are already familiar with introductory calculus and statistics, and can be used in either a one- or two-semester course. It can also serve as a statistics tutorial or review for working professionals.

Students who use this book will be well on their way to thinking like a statistician in terms of problem solving and decision-making. Graduates who pursue careers in statistics will continue to find this book useful, due to numerous statistical test procedures (both parametric and non-parametric) and detailed examples.

Comprehensive coverage of descriptive statistics More detailed treatment of univariate and bivariate probability distributions Thorough coverage of probability theory with numerous event classifications

In Mathematical Analysis and Optimization for Economists, the author aims to introduce students of economics to the power and versatility of traditional as well as contemporary methodologies in mathematics and optimization theory; and, illustrates how these techniques can be applied in solving microeconomic problems.This book combines the areas of intermediate to advanced mathematics, optimization, and microeconomic decision making, and is suitable for advanced undergraduates and first-year graduate students. This text is highly readable, with all concepts fully defined, and contains numerous detailed example problems in both mathematics and microeconomic applications. Each section contains some standard, as well as more thoughtful and challenging, exercises. Solutions can be downloaded from the CRC Press website. All solutions are detailed and complete.FeaturesContains a whole spectrum of modern applicable mathematical techniques, many of which are not found in other books of this type.Comprehensive and contains numerous and detailed example problems in both mathematics and economic analysis.Suitable for economists and economics students with only a minimal mathematical background.Classroom-tested over the years when the author was actively teaching at the University of Hartford.Serves as a beginner text in optimization for applied mathematics students.Accompanied by several electronic chapters on linear algebra and matrix theory, nonsmooth optimization, economic efficiency, and distance functions available for free on www.routledge.com/9780367759018.

In Mathematical Analysis and Optimization for Economists, the author aims to introduce students of economics to the power and versatility of traditional as well as contemporary methodologies in mathematics and optimization theory; and, illustrates how these techniques can be applied in solving microeconomic problems.This book combines the areas of intermediate to advanced mathematics, optimization, and microeconomic decision making, and is suitable for advanced undergraduates and first-year graduate students. This text is highly readable, with all concepts fully defined, and contains numerous detailed example problems in both mathematics and microeconomic applications. Each section contains some standard, as well as more thoughtful and challenging, exercises. Solutions can be downloaded from the CRC Press website. All solutions are detailed and complete.FeaturesContains a whole spectrum of modern applicable mathematical techniques, many of which are not found in other books of this type.Comprehensive and contains numerous and detailed example problems in both mathematics and economic analysis.Suitable for economists and economics students with only a minimal mathematical background.Classroom-tested over the years when the author was actively teaching at the University of Hartford.Serves as a beginner text in optimization for applied mathematics students.Accompanied by several electronic chapters on linear algebra and matrix theory, nonsmooth optimization, economic efficiency, and distance functions available for free on www.routledge.com/9780367759018.

In Mathematical Analysis and Optimization for Economists, the author aims to introduce students of economics to the power and versatility of traditional as well as contemporary methodologies in mathematics and optimization theory; and, illustrates how these techniques can be applied in solving microeconomic problems.

This book combines the areas of intermediate to advanced mathematics, optimization, and microeconomic decision making, and is suitable for advanced undergraduates and first-year graduate students. This text is highly readable, with all concepts fully defined, and contains numerous detailed example problems in both mathematics and microeconomic applications. Each section contains some standard, as well as more thoughtful and challenging, exercises. Solutions can be downloaded from the CRC Press website. All solutions are detailed and complete.

Features

Contains a whole spectrum of modern applicable mathematical techniques, many of which are not found in other books of this type. Comprehensive and contains numerous and detailed example problems in both mathematics and economic analysis. Suitable for economists and economics students with only a minimal mathematical background. Classroom-tested over the years when the author was actively teaching at the University of Hartford. Serves as a beginner text in optimization for applied mathematics students. Accompanied by several electronic chapters on linear algebra and matrix theory, nonsmooth optimization, economic efficiency, and distance functions available for free on www.routledge.com/9780367759018.

In Mathematical Analysis and Optimization for Economists, the author aims to introduce students of economics to the power and versatility of traditional as well as contemporary methodologies in mathematics and optimization theory; and, illustrates how these techniques can be applied in solving microeconomic problems.

This book combines the areas of intermediate to advanced mathematics, optimization, and microeconomic decision making, and is suitable for advanced undergraduates and first-year graduate students. This text is highly readable, with all concepts fully defined, and contains numerous detailed example problems in both mathematics and microeconomic applications. Each section contains some standard, as well as more thoughtful and challenging, exercises. Solutions can be downloaded from the CRC Press website. All solutions are detailed and complete.

Features

Contains a whole spectrum of modern applicable mathematical techniques, many of which are not found in other books of this type. Comprehensive and contains numerous and detailed example problems in both mathematics and economic analysis. Suitable for economists and economics students with only a minimal mathematical background. Classroom-tested over the years when the author was actively teaching at the University of Hartford. Serves as a beginner text in optimization for applied mathematics students. Accompanied by several electronic chapters on linear algebra and matrix theory, nonsmooth optimization, economic efficiency, and distance functions available for free on www.routledge.com/9780367759018.

A concise, easily accessible introduction to descriptive and inferential techniquesStatistical 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.

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.

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.

Features recent trends and advances in the theory and techniques used to accurately measure and model growth

Growth Curve Modeling: Theory and Applications features an accessible introduction to growth curve modeling and addresses how to monitor the change in variables over time since there is no “one size fits all” approach to growth measurement. A review of the requisite mathematics for growth modeling and the statistical techniques needed for estimating growth models are provided, and an overview of popular growth curves, such as linear, logarithmic, reciprocal, logistic, Gompertz, Weibull, negative exponential, and log-logistic, among others, is included.

In addition, the book discusses key application areas including economic, plant, population, forest, and firm growth and is suitable as a resource for assessing recent growth modeling trends in the medical field. SAS® is utilized throughout to analyze and model growth curves, aiding readers in estimating specialized growth rates and curves. Including derivations of virtually all of the major growth curves and models, Growth Curve Modeling: Theory and Applications also features:

• Statistical distribution analysis as it pertains to growth modeling • Trend estimations • Dynamic site equations obtained from growth models • Nonlinear regression • Yield-density curves • Nonlinear mixed effects models for repeated measurements data

Growth Curve Modeling: Theory and Applications is an excellent resource for statisticians, public health analysts, biologists, botanists, economists, and demographers who require a modern review of statistical methods for modeling growth curves and analyzing longitudinal data. The book is also useful for upper-undergraduate and graduate courses on growth modeling.

Features recent trends and advances in the theory and techniques used to accurately measure and model growth

Growth Curve Modeling: Theory and Applications features an accessible introduction to growth curve modeling and addresses how to monitor the change in variables over time since there is no “one size fits all” approach to growth measurement. A review of the requisite mathematics for growth modeling and the statistical techniques needed for estimating growth models are provided, and an overview of popular growth curves, such as linear, logarithmic, reciprocal, logistic, Gompertz, Weibull, negative exponential, and log-logistic, among others, is included.

In addition, the book discusses key application areas including economic, plant, population, forest, and firm growth and is suitable as a resource for assessing recent growth modeling trends in the medical field. SAS® is utilized throughout to analyze and model growth curves, aiding readers in estimating specialized growth rates and curves. Including derivations of virtually all of the major growth curves and models, Growth Curve Modeling: Theory and Applications also features:

• Statistical distribution analysis as it pertains to growth modeling • Trend estimations • Dynamic site equations obtained from growth models • Nonlinear regression • Yield-density curves • Nonlinear mixed effects models for repeated measurements data

Growth Curve Modeling: Theory and Applications is an excellent resource for statisticians, public health analysts, biologists, botanists, economists, and demographers who require a modern review of statistical methods for modeling growth curves and analyzing longitudinal data. The book is also useful for upper-undergraduate and graduate courses on growth modeling.

Features recent trends and advances in the theory and techniques used to accurately measure and model growthGrowth Curve Modeling: Theory and Applications features an accessible introduction to growth curve modeling and addresses how to monitor the change in variables over time since there is no “one size fits all” approach to growth measurement. A review of the requisite mathematics for growth modeling and the statistical techniques needed for estimating growth models are provided, and an overview of popular growth curves, such as linear, logarithmic, reciprocal, logistic, Gompertz, Weibull, negative exponential, and log-logistic, among others, is included.In addition, the book discusses key application areas including economic, plant, population, forest, and firm growth and is suitable as a resource for assessing recent growth modeling trends in the medical field. SAS® is utilized throughout to analyze and model growth curves, aiding readers in estimating specialized growth rates and curves. Including derivations of virtually all of the major growth curves and models, Growth Curve Modeling: Theory and Applications also features:• Statistical distribution analysis as it pertains to growth modeling• Trend estimations• Dynamic site equations obtained from growth models• Nonlinear regression• Yield-density curves• Nonlinear mixed effects models for repeated measurements dataGrowth Curve Modeling: Theory and Applications is an excellent resource for statisticians, public health analysts, biologists, botanists, economists, and demographers who require a modern review of statistical methods for modeling growth curves and analyzing longitudinal data. The book is also useful for upper-undergraduate and graduate courses on growth modeling.

A well-balanced and accessible introduction to the elementary quantitative methods and Microsoft® Office Excel® applications used to guide business decision makingFeaturing 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 ManualIntroduction 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.

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.

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.

Solutions Manual to accompany Introduction to Quantitative Methods in Business: With Applications Using Microsoft Office Excel

Solutions Manual to accompany Introduction to Quantitative Methods in Business: With Applications Using Microsoft Office Excel

Solutions Manual to accompany Introduction to Quantitative Methods in Business: With Applications Using Microsoft Office Excel

Set includes Introduction to Quantitative Methods in Business: With Applications Using Microsoft Office Excel ISBN 978-1-119-22097-8 and the accompanying Solutions Manual ISBN 978-1-119-22102-9A well-balanced and accessible introduction to the elementary quantitative methods and Microsoft Office Excel applications used to guide business decision makingFeaturing 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 applicationsExcel-based computer software routines that explore calculations for an assortment of tasks, including graphing, formula usage, solving equations, and data analysisEnd-of-chapter sections detailing the Excel applications and techniques used to address data and solutions using large data setsA companion website that includes chapter summaries, Excel data sets, sample exams and quizzes, lecture slides, and an Instructors' Solutions ManualIntroduction 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.

A beginner’s guide to stochastic growth modelingThe chief advantage of stochastic growth models over deterministic models is that they combine both deterministic and stochastic elements of dynamic behaviors, such as weather, natural disasters, market fluctuations, and epidemics. This makes stochastic modeling a powerful tool in the hands of practitioners in fields for which population growth is a critical determinant of outcomes.However, the background requirements for studying SDEs can be daunting for those who lack the rigorous course of study received by math majors. Designed to be accessible to readers who have had only a few courses in calculus and statistics, this book offers a comprehensive review of the mathematical essentials needed to understand and apply stochastic growth models. In addition, the book describes deterministic and stochastic applications of population growth models including logistic, generalized logistic, Gompertz, negative exponential, and linear.Ideal for students and professionals in an array of fields including economics, population studies, environmental sciences, epidemiology, engineering, finance, and the biological sciences, Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling:• Provides precise definitions of many important terms and concepts and provides many solved example problems• Highlights the interpretation of results and does not rely on a theorem-proof approach• Features comprehensive chapters addressing any background deficiencies readers may have and offers a comprehensive review for those who need a mathematics refresher• Emphasizes solution techniques for SDEs and their practical application to the development of stochastic population modelsAn indispensable resource for students and practitioners with limited exposure to mathematics and statistics, Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling is an excellent fit for advanced undergraduates and beginning graduate students, as well as practitioners who need a gentle introduction to SDEs.Michael J. Panik, PhD, is Professor in the Department of Economics, Barney School of Business and Public Administration at the University of Hartford in Connecticut. He received his PhD in Economics from Boston College and is a member of the American Mathematical Society, The American Statistical Association, and The Econometric Society.

A beginner’s guide to stochastic growth modeling

The chief advantage of stochastic growth models over deterministic models is that they combine both deterministic and stochastic elements of dynamic behaviors, such as weather, natural disasters, market fluctuations, and epidemics. This makes stochastic modeling a powerful tool in the hands of practitioners in fields for which population growth is a critical determinant of outcomes.

However, the background requirements for studying SDEs can be daunting for those who lack the rigorous course of study received by math majors. Designed to be accessible to readers who have had only a few courses in calculus and statistics, this book offers a comprehensive review of the mathematical essentials needed to understand and apply stochastic growth models. In addition, the book describes deterministic and stochastic applications of population growth models including logistic, generalized logistic, Gompertz, negative exponential, and linear.

Ideal for students and professionals in an array of fields including economics, population studies, environmental sciences, epidemiology, engineering, finance, and the biological sciences, Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling:

• Provides precise definitions of many important terms and concepts and provides many solved example problems

• Highlights the interpretation of results and does not rely on a theorem-proof approach

• Features comprehensive chapters addressing any background deficiencies readers may have and offers a comprehensive review for those who need a mathematics refresher

• Emphasizes solution techniques for SDEs and their practical application to the development of stochastic population models

An indispensable resource for students and practitioners with limited exposure to mathematics and statistics, Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling is an excellent fit for advanced undergraduates and beginning graduate students, as well as practitioners who need a gentle introduction to SDEs.

Michael J. Panik, PhD, is Professor in the Department of Economics, Barney School of Business and Public Administration at the University of Hartford in Connecticut. He received his PhD in Economics from Boston College and is a member of the American Mathematical Society, The American Statistical Association, and The Econometric Society.

A beginner’s guide to stochastic growth modeling

The chief advantage of stochastic growth models over deterministic models is that they combine both deterministic and stochastic elements of dynamic behaviors, such as weather, natural disasters, market fluctuations, and epidemics. This makes stochastic modeling a powerful tool in the hands of practitioners in fields for which population growth is a critical determinant of outcomes.

However, the background requirements for studying SDEs can be daunting for those who lack the rigorous course of study received by math majors. Designed to be accessible to readers who have had only a few courses in calculus and statistics, this book offers a comprehensive review of the mathematical essentials needed to understand and apply stochastic growth models. In addition, the book describes deterministic and stochastic applications of population growth models including logistic, generalized logistic, Gompertz, negative exponential, and linear.

Ideal for students and professionals in an array of fields including economics, population studies, environmental sciences, epidemiology, engineering, finance, and the biological sciences, Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling:

• Provides precise definitions of many important terms and concepts and provides many solved example problems

• Highlights the interpretation of results and does not rely on a theorem-proof approach

• Features comprehensive chapters addressing any background deficiencies readers may have and offers a comprehensive review for those who need a mathematics refresher

• Emphasizes solution techniques for SDEs and their practical application to the development of stochastic population models

An indispensable resource for students and practitioners with limited exposure to mathematics and statistics, Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling is an excellent fit for advanced undergraduates and beginning graduate students, as well as practitioners who need a gentle introduction to SDEs.

Michael J. Panik, PhD, is Professor in the Department of Economics, Barney School of Business and Public Administration at the University of Hartford in Connecticut. He received his PhD in Economics from Boston College and is a member of the American Mathematical Society, The American Statistical Association, and The Econometric Society.

Guides in the application of linear programming to firm decision making, with the goal of giving decision-makers a better understanding of methods at their disposalUseful as a main resource or as a supplement in an economics or management science course, this comprehensive book addresses the deficiencies of other texts when it comes to covering linear programming theory—especially where data envelopment analysis (DEA) is concerned—and provides the foundation for the development of DEA.Linear Programming and Resource Allocation Modeling begins by introducing primal and dual problems via an optimum product mix problem, and reviews the rudiments of vector and matrix operations. It then goes on to cover: the canonical and standard forms of a linear programming problem; the computational aspects of linear programming; variations of the standard simplex theme; duality theory; single- and multiple- process production functions; sensitivity analysis of the optimal solution; structural changes; and parametric programming. The primal and dual problems are then reformulated and re-examined in the context of Lagrangian saddle points, and a host of duality and complementary slackness theorems are offered. The book also covers primal and dual quadratic programs, the complementary pivot method, primal and dual linear fractional functional programs, and (matrix) game theory solutions via linear programming, and data envelopment analysis (DEA). This book: Appeals to those wishing to solve linear optimization problems in areas such as economics, business administration and management, agriculture and energy, strategic planning, public decision making, and health careFills the need for a linear programming applications component in a management science or economics courseProvides a complete treatment of linear programming as applied to activity selection and usageContains many detailed example problems as well as textual and graphical explanationsLinear Programming and Resource Allocation Modeling is an excellent resource for professionals looking to solve linear optimization problems, and advanced undergraduate to beginning graduate level management science or economics students.

Guides in the application of linear programming to firm decision making, with the goal of giving decision-makers a better understanding of methods at their disposal

Useful as a main resource or as a supplement in an economics or management science course, this comprehensive book addresses the deficiencies of other texts when it comes to covering linear programming theory—especially where data envelopment analysis (DEA) is concerned—and provides the foundation for the development of DEA.

Linear Programming and Resource Allocation Modeling begins by introducing primal and dual problems via an optimum product mix problem, and reviews the rudiments of vector and matrix operations. It then goes on to cover: the canonical and standard forms of a linear programming problem; the computational aspects of linear programming; variations of the standard simplex theme; duality theory; single- and multiple- process production functions; sensitivity analysis of the optimal solution; structural changes; and parametric programming. The primal and dual problems are then reformulated and re-examined in the context of Lagrangian saddle points, and a host of duality and complementary slackness theorems are offered. The book also covers primal and dual quadratic programs, the complementary pivot method, primal and dual linear fractional functional programs, and (matrix) game theory solutions via linear programming, and data envelopment analysis (DEA). This book:

Appeals to those wishing to solve linear optimization problems in areas such as economics, business administration and management, agriculture and energy, strategic planning, public decision making, and health care Fills the need for a linear programming applications component in a management science or economics course Provides a complete treatment of linear programming as applied to activity selection and usage Contains many detailed example problems as well as textual and graphical explanations

Linear Programming and Resource Allocation Modeling is an excellent resource for professionals looking to solve linear optimization problems, and advanced undergraduate to beginning graduate level management science or economics students.