| Description |
1 online resource |
| Contents |
880-01 Cover ; Title Page; Copyright Page; Contents; Preface; Acknowledgments; Chapter 1 -- Business analytics is making decisions; Introduction; 1.1 -- Business Analytics is making decisions subject to uncertainty; 1.2 -- Components of Business Analytics; 1.3 -- Uncertainty€=€probability€=€stochastic; 1.4 -- What is simulation?; 1.4.1 -- Why Use Simulation?; 1.4.2 -- Simulation Applications; 1.5 -- Monte Carlo simulation and random variables; 1.6 -- Simulation terminology; 1.7 -- Probability as relative frequency; 1.8 -- Overview of simulation process; 1.9 -- Random number generation in Excel |
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880-01/(S 8.6.1 -- Small (n ≤ 30) Samples: Use Student's t-Distribution -- Chapter 9 -- Simulation fit and significance: chi-square and ANOVA -- Introduction -- 9.1 -- Conditional Probabilities-again -- 9.1.1 -- Examples of Conditional Probability Estimation Procedures -- 9.2 -- Conditional probability for groups -- 9.2.1 -- Examples of ANOVA and Chi-Square Situations -- 9.3 -- Chi-square (χ2): are the probability distributions the same-- 9.3.1 -- Chi-Square: Actual Frequencies Versus Expected Frequencies -- 9.4 -- Analysis of Variance: are the groups' averages the same-- 9.4.1 -- Conducting an ANOVA: p-Value Again -- 9.4.2 -- Why is it Called Analysis of VARIANCE if Compares Averages-- 9.4.3 -- An Approximate Comparison of More Than Two Groups -- 9.4.4 -- What if Groups Are, or Are Not, Significantly Different-- 9.5 -- ANOVA versus chi-square: Likert scale -- Chapter 10 -- Regression -- Introduction -- 10.1 -- Overview of regression -- 10.1.1 -- Basic Linear Model -- 10.2 -- Measures of fit and significance -- 10.2.1 -- Standard Error of the Slope: -- 10.2.2 -- t-stat -- 10.2.3 -- Standard Error of the Estimate -- 10.2.4 -- Coefficient of Determination: r 2 -- 10.3 -- Multiple regression -- 10.4 -- Nonlinear regression: polynomials -- 10.4.1 -- Nonlinear Models: Polynomials -- 10.4.2 -- Nonlinear Models: Nonlinear (Logarithmic) Transformations -- 10.5 -- Indicator variables -- 10.6 -- Interaction terms -- 10.7 -- Regression pitfalls -- 10.7.1 -- Nonlinearity -- 10.7.2 -- Extrapolation Beyond the Relevant Range -- 10.7.3 -- Correlation ≠ Causality -- 10.7.4 -- Reverse Causality -- 10.7.5 -- Omitted-Variable Bias -- 10.7.6 -- Serial Correlation -- 10.7.7 -- Multicollinearity -- 10.7.8 -- Data Mining -- 10.7.9 -- Heteroscedasticity -- 10.8 -- Review of regression -- Chapter 11 -- Forecasting -- Introduction -- 11.1 -- Overview of forecasting -- 11.2 -- Measures of accuracy |
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1.10 -- Extra practiceChapter 2 -- Decision-making and simulation; Introduction; 2.1 -- Introduction to decision-making; 2.1.1 -- Define the Problem; 2.1.2 -- Identify and Weight the Criteria; 2.1.3 -- Generate Alternatives; 2.1.4 -- Evaluate Each Alternative; 2.1.5 -- Compute the Optimal Decision; 2.2 -- Probability: the measure of uncertainty; 2.3 -- Where do the probabilities come from?; 2.4 -- Elements of probability; 2.5 -- Probability notation; 2.6 -- Examples of simulation and decision-making; Chapter 3 -- Decision Trees; Introduction; 3.1 -- Decision trees and expected value |
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3.2 -- Properties of decision trees3.2.1 -- Linear Transforms; 3.3 -- Overview of the decision making process; 3.4 -- Sensitivity analysis; 3.5 -- Expected value of perfect information; 3.6 -- Summary of the decision analysis process; Chapter 4 -- Probability: measuring uncertainty; Introduction; 4.1 -- Probability: measuring likelihood; 4.2 -- Probability distributions; 4.3 -- General probability rules; 4.4 -- Conditional probability and Bayes' theorem; Further exercises: common interview questions regarding probability; Chapter 5 -- Subjective Probability Distributions; Introduction |
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5.1 -- Subjective probability distributions-probability from€experience5.2 -- Two-point estimation: uniform distribution; 5.2.1 -- Discrete Uniform Distribution; 5.3 -- Three-point estimation: triangular distribution; 5.3.1 -- Simulating a Symmetric Triangular Distribution; 5.3.2 -- Simulating an Asymmetric Triangular Distribution; 5.4 -- Five-point estimates for subjective probability distributions; 5.4.1 -- Simulating a Five-Point Distribution; 5.4.2 -- Other Estimates for Subjective Probability Distributions; Chapter 6 -- Empirical probability distributions; Introduction |
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6.1 -- Empirical probability distributions-probability from data6.2 -- Discrete empirical probability distributions; 6.3 -- Continuous empirical probability distributions; Chapter 7 -- Theoretical probability distributions; Introduction; 7.1 -- Theoretical/classical probability; 7.2 -- Review of notation for probability distributions; 7.3 -- Discrete theoretical distributions; 7.3.1 -- Uniform Distribution; 7.3.2 -- Discrete Uniform Distribution; 7.3.3 -- Continuous Uniform Distribution; 7.3.4 -- Bernoulli Distribution; 7.3.5 -- Binomial Distribution; 7.3.6 -- Poisson distribution |
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7.4 -- Continuous probability distributions |
| Summary |
Introduction to Business Analytics Using Simulation employs an innovative strategy to teach business analytics. It uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on the uncertainty and variability of business, this comprehensive book provides a better foundation for business analytics than standard introductory business analytics books. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making Explains the processes needed to develop, report, and analyze business data Describes how to use and apply business analytics software Includes 50 caselettes, quizzes for each exercise set, a quiz generator spreadsheet, and a sample syllabus |
| Notes |
Includes index |
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Online resource, title from PDF title page (EBSCO), viewed September 12, 2016 |
| Subject |
Business intelligence.
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Business -- Computer simulation
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Industrial management -- Statistical methods
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BUSINESS & ECONOMICS -- Industrial Management.
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BUSINESS & ECONOMICS -- Management Science.
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Business -- Computer simulation
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Business intelligence
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Industrial management -- Statistical methods
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| Form |
Electronic book
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| ISBN |
9780128104866 |
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0128104864 |
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