INDICE: Preface. Part I. Motivations and Foundations. 1. Quantitative Methods: Should we Bother? 1.1 A decision problem without uncertainty: product mix.1.2 The role of uncertainty. 1.3 Endogenous vs. exogenous uncertainty: are wealone? 1.4 Quantitative models and methods. 1.5 Quantitative analysis and problem solving. 2. Calculus. 2.1 A motivating example: Economic Order Quantity. 2.2 A little background. 2.3 Functions. 2.4 Continuous functions. 2.5 Composite functions. 2.6 Inverse functions. 2.7 Derivatives. 2.8 Rules for calculatingderivatives. 2.9 Using derivatives for graphing functions. 2.10 Higher-order derivatives and Taylor expansions. 2.11 Convexity and optimization. 2.12 Sequences and series. 2.13 Definite integral. 3. Linear Algebra. 3.1 A motivating example: Binomial option pricing. 3.2 Solving systems of linear equations. 3.3 Vector algebra. 3.4 Matrix algebra. 3.5 Linear spaces. 3.6 Determinant. 3.7 Eigenvalues and eigenvectors. 3.8 Quadratic forms. 3.9 Calculus in multiple dimensions. Part II. Elementary Probability and Statistics. 4. Descriptive Statistics: on the Way to Elementary Probability. 4.1 What is Statistics? 4.2 Organizing and representing raw data. 4.3 Location measures: Mean, median, and mode. 4.4 Cumulative frequencies and percentiles. 4.5 Multidimensional data. 5. Probability Theories. 5.1 Different concepts of probability. 5.2 The axiomatic approach. 5.3 Conditional probability and independence. 5.4 Total probability andBayes theorems. 6. Discrete Random Variables. 6.1 Random variables. 6.2 Characterizing discrete distributions. 6.3 Expected value. 6.4 Variance and standard deviation. 6.5 A few useful discrete distributions. 7. Continuous Random Variables. 7.1 Building intuition: From Discrete to continuous random variables. 7.2 Cumulative distribution and probability density functions. 7.3 Expected value and variance. 7.4 Mode, median, and quantiles. 7.5 Higher-order moments, skewness, and kurtosis. 7.6 A few useful continuous probability distributions. 7.7 Sums of independent random variables. 7.8 Miscellaneous applications. 7.9 Stochastic processes. 7.10 Probability spaces, measurability, and information.8. Dependence, Correlation, and Conditional Expectation. 8.1 Joint and marginal distributions. 8.2 Independent random variables. 8.3 Covariance and correlation. 8.4 Jointly normal variables. 8.5 Conditional expectation. 9. Inferential Statistics. 9.1 Random samples and sample statistics. 9.2 Confidence intervals. 9.3 Hypothesis testing. 9.4 Testing hypotheses about the difference in themean of two populations. 9.5 Checking the fit of hypothetical distributions: the chi-square test. 9.6 Analysis of variance. 9.7 Monte Carlo simulation. 9.8Stochastic convergence and the law of large numbers. 9.9 Parameter estimation. 9.10 Some more hypothesis testing theory. 10
- ISBN: 978-0-470-49634-3
- Editorial: John Wiley & Sons
- Encuadernacion: Cartoné
- Páginas: 800
- Fecha Publicación: 11/03/2011
- Nº Volúmenes: 1
- Idioma: Inglés