《Probability and Statistics for Business Decisions An Introduction to Managerial Economics Under Unce》

Introduction The Problem of Decision under Uncertainty1

1．The Meaning of Probability2

2．Expected Value and Utility24

3．Random Variables and Probability Distributions50

Part OneThe Use of Probabilities Based Directly on Experience65

4．The Simplest Problems of Inventory Control; Incremental Analysis66

5．Measures of Location: Fractiles and Expectations; Linear Profits and Costs79

6．Assessment of Probabilities by Smoothing Historical Frequencies95

7．Opportunity Loss and the Cost of Uncertainty117

8．Lump-sum Losses; Scrap Allowances133

Part TwoSimple Random Processes and Derived Probabilities159

9．Conditional and Joint Probability160

10．The Bernoulli Process: The Binomial Distribution174

11．The Bernoulli Process: The Pascal Distribution183

12．Conditional Models and Marginal Probability194

13．The Poisson Process: The Poisson Distribution209

14．The Poisson Process: The Gamma Distribution221

15．Min-Max Inventory Control236

16．Measures of Dispersion: The Variance and the Standard Deviation260

17．The Normal Approximation to Distributions of Sums of Random Variables274

18．The Normal Approximation to Empirical Distributions294

19．Waiting Lines306

20．The Monte Carlo Method320

Part ThreeThe Use of Information Obtained by Sampling329

21．Revision of Probabilities in the Light of New Information330

22．Two-action Problems with Linear Costs342

23．Samples from Finite Populations: The Hypergeometric Distribution355

24．Interdependent Decision Problems; Finite vs．Infinite Populations371

25．Samples from Many-valued Populations; Sufficient Statistics384

26．Samples from “Normal” Populations with Known Variance397

27．Samples from “Normal” Populations with Known Mean405

28．Nuisance Parameters:“Normal” Populations with Both ParametersUnknown413

29．Populations of Incompletely Specified Form; “Large-sample Theory”423

30．Normal Prior Distributions435

31．Biased Measurement and Biased Selection458

32．Comparison of Two Unknown Quantities; the Importance of Sample Design486

Part FourThe Value of Additional Information507

33．Evaluation of a Decision to Sample and Then Act; Preposterior Analysis508

34．Two-action Problems with Linear Costs: Expected Loss and the Prior Dis-tribution of the Posterior Mean519

35．Two-action Problems with Linear Costs: Optimal Sample Size536

36．Interdependent Two-action Problems under a Stationary Distribution553

37．Many-action Problems with Proportional Losses; General-purpose Estima-tion574

38．Sequential Decision Procedures590

Part FiveObjectivist Statistics: Tests of Significance and Confidence Intervals605

39．The Classical Theory of Testing Hypotheses606

40．Evaluation of Statistical Decision Rules in Terms of Expected Loss624

41．Tests of Significance as Sequential Decision Procedures644

42．Confidence Intervals661

Appendix669

ContinuousPrior Distributions for the Parameters of Bernoulli and Poisson Processes670

Tables679

Ⅰ．Cumulative Binomial Distribution680

Ⅱ．Unit Normal Probability Distribution702

Ⅲ．Cumulative Unit Normal Distribution704

Ⅳ．Unit Normal Loss Integral706

Ⅴ．Random Digits708

Ⅵ．Square Roots709

Ⅶ．Cube Roots710

Charts711

Ⅰ．Cumulative Poisson and Gamma Distributions711

Ⅱ．Optimal Sample Size: Two-action Problems with Linear Costs712

Ⅲ．Gamma Probability Distribution713

Ⅳ．Unit Normal Distribution: Ratio of Ordinate to Left Tail715

Ⅴ．x/？f Probability Distribution716

Ⅵ．Optimal Sample Size: Many-action Problems with Proportional Losses718

Index of Symbols719

Subject Index723