Mathematics for social scientists
著者
書誌事項
Mathematics for social scientists
Sage, c2016
大学図書館所蔵 全3件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
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
「Nielsen BookData」 より