Mathematics for social scientists

著者

    • Kropko, Jonathan

書誌事項

Mathematics for social scientists

Jonathan Kropko

Sage, c2016

大学図書館所蔵 件 / 全3件

この図書・雑誌をさがす

注記

Includes index

内容説明・目次

内容説明

Written for social science students who will be working with or conducting research, Mathematics for Social Scientists offers a non-intimidating approach to learning or reviewing math skills essential in quantitative research methods. The text is designed to build students' confidence by presenting material in a conversational tone and using a wealth of clear and applied examples. Author Jonathan Kropko argues that mastering these concepts will break students' reliance on using basic models in statistical software, allowing them to engage with research data beyond simple software calculations.

目次

Part I: ALGEBRA, PRECALCULUS, AND PROBABILITY 1. Algebra Review Numbers Fractions Exponents Roots Logarithms Summations and Products Solving Equations and Inequalities 2. Sets and Functions Set Notation Intervals Venn Diagrams Functions Polynomials 3. Probability Events and Sample Spaces Properties and Probability Functions Counting Theory Sampling Problems Conditional Probability Bayes' Rule PART II: CALCULUS 4. Limits and Derivatives What is a Limit? Continuity and Asymptotes Solving Limits The Number e Point Estimates and Comparative Statics Definitions of the Derivative Notation Shortcuts for Finding Derivatives The Chain Rule 5. Optimization Terminology Finding Maxima and Minima The Newton-Raphson Method 6. Integration Informal Definitions of an Integral Riemann Sums Integral Notation Solving Integrals Advanced Techniques for Solving Integrals Probability Density Functions Moments 7. Multivariate Calculus Multivariate Functions Multivariate Limits Partial Derivatives Multiple Integrals PART III: LINEAR ALGEBRA 8. Matrix Notation and Arithmetic Matrix Notation Types of Matrices Matrix Arithmetic Matrix Multiplication Geometric Representation of Vectors and Transformation Matrices Elementary Row and Column Operations 9. Matrix Inverses, Singularity, and Rank Inverse of a (2 x 2) Matrix Inverse of a Larger Square Matrix Multiple Regression and the Ordinary Least Squares Estimator Singularity, Rank, and Linear Dependency 10. Linear Systems of Equations and Eigenvalues Nonsingular Coefficient Matrices Singular Coefficient Matrices Homogeneous Systems Eigenvalues and Eigenvectors Statistical Measurement Models

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