{"product_id":"quantitative-methods-9780470496343","title":"Quantitative Methods","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn accessible introduction to the essential quantitative methods for making valuable business decisions  \u003cp\u003eQuantitative methods-research techniques used to analyze quantitative data-enable professionals to organize and understand numbers and, in turn, to make good decisions. \u003ci\u003eQuantitative Methods: An Introduction for Business Management\u003c\/i\u003e presents the application of quantitative mathematical modeling to decision making in a business management context and emphasizes not only the role of data in drawing conclusions, but also the pitfalls of undiscerning reliance of software packages that implement standard statistical procedures. With hands-on applications and explanations that are accessible to readers at various levels, the book successfully outlines the necessary tools to make smart and successful business decisions.\u003c\/p\u003e \u003cp\u003eProgressing from beginner to more advanced material at an easy-to-follow pace, the author utilizes motivating examples throughout to aid readers interested i\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePart I. Motivations and Foundations\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Quantitative Methods: Should we Bother?.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 A decision problem without uncertainty: product mix.\u003c\/p\u003e \u003cp\u003e1.2 The role of uncertainty.\u003c\/p\u003e \u003cp\u003e1.3 Endogenous vs. exogenous uncertainty: Are we alone?.\u003c\/p\u003e \u003cp\u003e1.4 Quantitative models and methods.\u003c\/p\u003e \u003cp\u003e1.5 Quantitative analysis and problem solving.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Calculus.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 A motivating example: economic order quantity.\u003c\/p\u003e \u003cp\u003e2.2 A little background.\u003c\/p\u003e \u003cp\u003e2.3 Functions.\u003c\/p\u003e \u003cp\u003e2.4 Continuous functions.\u003c\/p\u003e \u003cp\u003e2.5 Composite functions.\u003c\/p\u003e \u003cp\u003e2.6 Inverse functions.\u003c\/p\u003e \u003cp\u003e2.7 Derivatives.\u003c\/p\u003e \u003cp\u003e2.8 Rules for calculating derivatives.\u003c\/p\u003e \u003cp\u003e2.9 Using derivatives for graphing functions.\u003c\/p\u003e \u003cp\u003e2.10 Higher-order derivatives and Taylor expansions.\u003c\/p\u003e \u003cp\u003e2.11 Convexity and optimization.\u003c\/p\u003e \u003cp\u003e2.12 Sequences and series.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Linear Algebra.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 A motivating example: binomial option pricing.\u003c\/p\u003e \u003cp\u003e3.2 Solving systems of linear equations.\u003c\/p\u003e \u003cp\u003e3.3 Vector algebra.\u003c\/p\u003e \u003cp\u003e3.4 Matrix algebra.\u003c\/p\u003e \u003cp\u003e3.5 Linear spaces.\u003c\/p\u003e \u003cp\u003e3.6 Determinant.\u003c\/p\u003e \u003cp\u003e3.7 Eigenvalues and eigenvectors.\u003c\/p\u003e \u003cp\u003e3.8 Quadratic forms.\u003c\/p\u003e \u003cp\u003e3.9 Calculus in multiple dimensions.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Elementary Probability and Statistics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Descriptive Statistics: On the Way to Elementary Probability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 What is Statistics?.\u003c\/p\u003e \u003cp\u003e4.2 Organizing and representing raw data.\u003c\/p\u003e \u003cp\u003e4.3 Summary measures.\u003c\/p\u003e \u003cp\u003e4.4 Cumulative frequencies and percentiles.\u003c\/p\u003e \u003cp\u003e4.5 Multidimensional data.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Probability Theories.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Different concepts of probability.\u003c\/p\u003e \u003cp\u003e5.2 The axiomatic approach.\u003c\/p\u003e \u003cp\u003e5.3 Conditional probability and independence.\u003c\/p\u003e \u003cp\u003e5.4 Total probability and Bayes’ theorems.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Discrete Random Variables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Random variables.\u003c\/p\u003e \u003cp\u003e6.2 Characterizing discrete distributions.\u003c\/p\u003e \u003cp\u003e6.3 Expected value.\u003c\/p\u003e \u003cp\u003e6.4 Variance and standard deviation.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Continuous Random Variables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Building intuition: from discrete to continuous random variables.\u003c\/p\u003e \u003cp\u003e7.2 Cumulative distribution and probability density functions.\u003c\/p\u003e \u003cp\u003e7.3 Expected value and variance.\u003c\/p\u003e \u003cp\u003e7.4 Mode, median, and quantiles.\u003c\/p\u003e \u003cp\u003e7.5 Higher-order moments, skewness, and kurtosis.\u003c\/p\u003e \u003cp\u003e7.6 A few useful continuous probability distributions.\u003c\/p\u003e \u003cp\u003e7.7 Sums of independent random variables.\u003c\/p\u003e \u003cp\u003e7.8 Miscellaneous applications.\u003c\/p\u003e \u003cp\u003e7.9 Stochastic processes.\u003c\/p\u003e \u003cp\u003e7.10 Probability spaces, measurability, and information.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Dependence, Correlation, and Conditional Expectation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Joint and marginal distributions.\u003c\/p\u003e \u003cp\u003e8.2 Independent random variables.\u003c\/p\u003e \u003cp\u003e8.3 Covariance and correlation.\u003c\/p\u003e \u003cp\u003e8.4 Jointly normal variables.\u003c\/p\u003e \u003cp\u003e8.5 Conditional expectation.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Inferential Statistics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Random samples and sample statistics.\u003c\/p\u003e \u003cp\u003e9.2 Confidence intervals.\u003c\/p\u003e \u003cp\u003e9.3 Hypothesis testing.\u003c\/p\u003e \u003cp\u003e9.4 Beyond the mean of one population.\u003c\/p\u003e \u003cp\u003e9.5 Checking the fit of hypothetical distributions: the chi-square test.\u003c\/p\u003e \u003cp\u003e9.6 Analysis of variance.\u003c\/p\u003e \u003cp\u003e9.7 Monte Carlo simulation.\u003c\/p\u003e \u003cp\u003e9.8 Stochastic convergence and the law of large numbers.\u003c\/p\u003e \u003cp\u003e9.9 Parameter estimation.\u003c\/p\u003e \u003cp\u003e9.10 Some more hypothesis testing theory.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Simple Linear Regression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Least squares method.\u003c\/p\u003e \u003cp\u003e10.2 The need for a statistical framework.\u003c\/p\u003e \u003cp\u003e10.3 The case of a non-stochastic regressor.\u003c\/p\u003e \u003cp\u003e10.4 Using regression models.\u003c\/p\u003e \u003cp\u003e10.5 A glimpse of stochastic regressors and heteroskedastic errors.\u003c\/p\u003e \u003cp\u003e10.6 A vector space look at linear regression.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Time Series Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Before we start: Framing the forecasting process.\u003c\/p\u003e \u003cp\u003e11.2 Measuring forecasting errors.\u003c\/p\u003e \u003cp\u003e11.3 Time series decomposition.\u003c\/p\u003e \u003cp\u003e11.4 Moving average.\u003c\/p\u003e \u003cp\u003e11.5 Heuristic exponential smoothing.\u003c\/p\u003e \u003cp\u003e11.6 A glance at advanced time series modeling.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Models for Decision Making.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Deterministic Decision Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 A taxonomy of optimization models.\u003c\/p\u003e \u003cp\u003e12.2 Building linear programming models.\u003c\/p\u003e \u003cp\u003e12.3 A repertoire of model formulation tricks.\u003c\/p\u003e \u003cp\u003e12.4 Building integer programming models.\u003c\/p\u003e \u003cp\u003e12.5 Nonlinear programming concepts.\u003c\/p\u003e \u003cp\u003e12.6 A glance at solution methods.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Decision Making under Risk.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13.1 Decision trees.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.2 Risk aversion and risk measures.\u003c\/p\u003e \u003cp\u003e13.3 Two-stage stochastic programming models.\u003c\/p\u003e \u003cp\u003e13.4 Multi-stage stochastic linear programming with recourse.\u003c\/p\u003e \u003cp\u003e13.5 Robustness, regret, and disappointment.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Multiple Decision Makers, Subjective Probability, and Other Wild Beasts.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 What is uncertainty?.\u003c\/p\u003e \u003cp\u003e14.2 Decision problems with multiple decision makers.\u003c\/p\u003e \u003cp\u003e14.3 Incentive misalignment in supply chain management.\u003c\/p\u003e \u003cp\u003e14.4 Game theory.\u003c\/p\u003e \u003cp\u003e14.5 Braess' paradox for traffic networks.\u003c\/p\u003e \u003cp\u003e14.6 Dynamic feedback effects and herding behavior.\u003c\/p\u003e \u003cp\u003e14.7 Subjective probability: the Bayesian view.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Advanced Statistical Modeling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Introduction to Multivariate Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Issues in multivariate analysis.\u003c\/p\u003e \u003cp\u003e15.2 An overview of multivariate methods.\u003c\/p\u003e \u003cp\u003e15.3 Matrix algebra and multivariate analysis.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Advanced Regression Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Multiple linear regression by least squares.\u003c\/p\u003e \u003cp\u003e16.2 Building, testing, and using multiple linear regression models.\u003c\/p\u003e \u003cp\u003e16.3 Logistic regression.\u003c\/p\u003e \u003cp\u003e16.4 A glance at nonlinear regression.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Dealing with Complexity: Data Reduction and Clustering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 The need for data reduction.\u003c\/p\u003e \u003cp\u003e17.2 Principal component analysis (PCA).\u003c\/p\u003e \u003cp\u003e17.3 Factor analysis.\u003c\/p\u003e \u003cp\u003e17.4 Cluster analysis.\u003c\/p\u003e \u003cp\u003eFor further reading.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402342670679,"sku":"9780470496343","price":108.86,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470496343.jpg?v=1730480125","url":"https:\/\/bookcurl.com\/products\/quantitative-methods-9780470496343","provider":"Book Curl","version":"1.0","type":"link"}