A Mathematics Course for Political and Social Research / David A. Siegel, Will H. Moore.
- Other records:
- Course Book
- Princeton, NJ : Princeton University Press, 
1 online resource (451 p.)
- Mathematics -- Study and teaching (Higher)
Social sciences -- Mathematical models.
- Electronic books.
- Political science and sociology increasingly rely on mathematical modeling and sophisticated data analysis, and many graduate programs in these fields now require students to take a "math camp" or a semester-long or yearlong course to acquire the necessary skills. Available textbooks are written for mathematics or economics majors, and fail to convey to students of political science and sociology the reasons for learning often-abstract mathematical concepts. A Mathematics Course for Political and Social Research fills this gap, providing both a primer for math novices in the social sciences and a handy reference for seasoned researchers. The book begins with the fundamental building blocks of mathematics and basic algebra, then goes on to cover essential subjects such as calculus in one and more than one variable, including optimization, constrained optimization, and implicit functions; linear algebra, including Markov chains and eigenvectors; and probability. It describes the intermediate steps most other textbooks leave out, features numerous exercises throughout, and grounds all concepts by illustrating their use and importance in political science and sociology. Uniquely designed and ideal for students and researchers in political science and sociology Uses practical examples from political science and sociology Features "Why Do I Care?" sections that explain why concepts are useful Includes numerous exercises Complete online solutions manual (available only to professors, email david.siegel at duke.edu, subject line "Solution Set") Selected solutions available online to students
List of Figures
List of Tables
Part I. Building Blocks
Chapter One. Preliminaries Math
Chapter Two. Algebra Review
Chapter Three. Functions, Relations, and Utility
Chapter Four. Limits and Continuity, Sequences and Series, and More on Sets
Part II. Calculus in One Dimension
Chapter Five. Introduction to Calculus and the Derivative
Chapter Six. The Rules of Differentiation
Chapter Seven. The Integral
Chapter Eight. Extrema in One Dimension
Part III. Probability
Chapter Nine. An Introduction to Probability
Chapter Ten. An Introduction to (Discrete) Distributions
Chapter Eleven. Continuous Distributions
Part IV. Linear Algebra
Chapter Twelve. Fun with Vectors and Matrices
Chapter Thirteen. Vector Spaces and Systems of Equations
Chapter Fourteen. Eigenvalues and Markov Chains
Part V. Multivariate Calculus and Optimization
Chapter Fifteen. Multivariate Calculus
Chapter Sixteen. Multivariate Optimization
Chapter Seventeen. Comparative Statics and Implicit Dierentiation
- Description based upon print version of record.
Includes bibliographical references and index.
Description based on online resource; title from PDF title page (publisher's Web site, viewed 08. Jul 2019)
- Siegel, David A., author.
- Publisher Number:
- 10.1515/9781400848614 doi
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