商務統計學（英文版）

內容簡介

統計學是一門工具性學科，在眾多的學科領域有著廣泛的套用。本書將統計學的概念與仔灑恥方法套用於商務領域，從組拳戒囑套用層面對統計學的基本方法進行了系束少捉統的講解。全書包括探索和收集數據、理解數據和分布、探索頁刪愉變數間的關係以及為決策建立模型四部分內容，共24章，將方法的講解與商務領域中的現實案例緊密結合起來，讓讀者掌握如何利用統計方法解決商務中的實際問題。采檔宙府狼祝本書還將統計軟體與統計方法的套用結合起來，詳細介紹各種統計方法在Excel、Minitab、JMP、SPSS和DataDesk等軟體中的操作實現步驟。市旬斷

圖書目錄

Contents

Part I Exploring and Collecting Data

Chapter 1 Statistics and Variation 3

1.1 So, What Is Statistics? · 1.2 How Will This Book Help?

Chapter 2 Data 9

2.1 What Are Data? · 2.2 Variable Types · 2.3 Where, How, and When

Mini Case Study Project: Credit Card Bank 22

Chapter 3 Surveys and Sampling 27

3.1 Three Ideas of Sampling · 3.2 A Census—Does It Make Sense? · 3.3 Populations and Parameters · 3.4 Simple Random Sample (SRS) · 3.5 Other Sample Designs ·

3.6 Defining the Population · 3.7 The Valid Survey

Mini Case Study Projects: Market Survey Research 47

The GfK Roper Reports Worldwide Survey 47

Chapter 4 Displaying and Describing Categorical Data 53

4.1 The Three Rules of Data Analysis · 4.2 Frequency Tables · 4.3 Charts · 4.4 Contingency Tables

Mini Case Study Project: KEEN Footwear 74

Chapter 5 Randomness and Probability 85

5.1 Random Phenomena and Probability · 5.2 The Nonexistent Law of Averages · 5.3 Different Types of Probability · 5.4 Probability Rules · 5.5 Joint Probability and Contingency

Tables · 5.6 Conditional Probability · 5.7 Constructing Contingency Tables

Mini Case Study Project: Market Segmentation 103

Chapter 6 Displaying and Describing Quantitative Data 111

6.1 Displaying Distributions · 6.2 Shape · 6.3 Center · 6.4 Spread of the Distribution · 6.5 Shape, Center, and Spread—A Summary · 6.6 Five-Number Summary and Boxplots · 6.7 Comparing Groups · 6.8 Identifying Outliers · 6.9 Standardizing · 6.10 Time Series Plots ·

*6.11 Transforming Skewed Data

Mini Case Study Projects: Hotel Occupancy Rates 143,

Value and Growth Stock Returns 143

Part II Understanding Data and Distributions 157

Chapter 7 Scatterplots, Association, and Correlation 159

7.1 Looking at Scatterplots · 7.2 Assigning Roles to Variables in Scatterplots · 7.3 Understanding Correlation · *7.4 Straightening Scatterplots · 7.5 Lurking Variables and Causation

Mini Case Study Projects: *Fuel Efficiency 181, The U.S. Economy and Home Depot Stock Prices 182

Chapter 8 Linear Regression 193

8.1 The Linear Model · 8.2 Correlation and the Line · 8.3 Regression to the Mean · 8.4 Checking the Model · 8.5 Learning More from the Residuals · 8.6 Variation in the Model and R2 · 8.7 Reality Check: Is the Regression Reasonable?

Mini Case Study Projects: Cost of Living 213, Mutual Funds 213

Chapter 9 Sampling Distributions and the Normal Model 223

9.1 Modeling the Distribution of Sample Proportions · 9.2 Simulations · 9.3 The Normal Distribution · 9.4 Practice with Normal Distribution Calculations · 9.5 The Sampling Distribution for Proportions · 9.6 Assumptions and Conditions · 9.7 The Central Limit Theorem—The Fundamental Theorem of Statistics · 9.8 The Sampling Distribution of the Mean · 9.9 Sample

Size—Diminishing Returns · 9.10 How Sampling Distribution Models Work

Mini Case Study Project: Real Estate Simulation 247

Chapter 10 Confidence Intervals for Proportions 255

10.1 A Confidence Interval · 10.2 Margin of Error: Certainty vs. Precision · 10.3 Critical Values ·

10.4 Assumptions and Conditions · *10.5 A Confidence Interval for Small Samples · 10.6 Choosing the Sample Size

Mini Case Study Projects: Investment 272,

Forecasting Demand 272

Chapter 11 Testing Hypotheses about Proportions 279

11.1 Hypotheses · 11.2 A Trial as a Hypothesis Test · 11.3 P-values · 11.4 The Reasoning of Hypothesis Testing · 11.5 Alternative Hypotheses · 11.6 Alpha Levels and Significance · 11.7 Critical Values · 11.8 Confidence Intervals and Hypothesis Tests · 11.9 Two Types of Errors ·

*11.10 Power

Mini Case Study Projects: Metal Production 305,

Loyalty Program 305

Chapter 12 Confidence Intervals and Hypothesis Tests for Means 313

12.1 The Sampling Distribution for the Mean · 12.2 A Confidence Interval for Means · 12.3 Assumptions and Conditions · 12.4 Cautions About Interpreting Confidence Intervals · 12.5 One-Sample t-Test · 12.6 Sample Size · *12.7 Degrees of Freedom—Why n – 1?

Mini Case Study Projects: Real Estate 333, Donor Profiles 333

Chapter 13 Comparing Two Means 343

13.1 Testing Differences Between Two Means · 13.2 The Two-Sample t-Test · 13.3 Assumptions and Conditions · 13.4 A Confidence Interval for the Difference Between Two Means · 13.5 The Pooled t-Test · *13.6 Tukey’s Quick Test

Mini Case Study Project: Real Estate 364

Chapter 14 Paired Samples and Blocks 375

14.1 Paired Data · 14.2 Assumptions and Conditions · 14.3 The Paired t-Test · 14.4 How the Paired t-Test Works

Mini Case Study Projects: A Taste Test (Data Collection and Analysis) 389, Consumer Spending Patterns (Data Analysis) 389

Chapter 15 Inference for Counts: Chi-Square Tests 401

15.1 Goodness-of-Fit Tests · 15.2 Interpreting Chi-Square Values · 15.3 Examining the Residuals · 15.4 The Chi-Square Test of Homogeneity · 15.5 Comparing Two Proportions · 15.6 Chi-Square Test of Independence

Mini Case Study Projects: Health Insurance 424,

Loyalty Program 424

Part III Exploring Relationships Among Variables 435

Chapter 16 Inference for Regression 437

16.1 The Population and the Sample · 16.2 Assumptions and Conditions · 16.3 The Standard Error of the Slope · 16.4 A Test for the Regression Slope · 16.5 A Hypothesis Test for Correlation · 16.6 Standard Errors for Predicted Values · 16.7 Using Confidence and Prediction Intervals

Mini Case Study Projects: Frozen Pizza 461,

Global Warming? 461

Chapter 17 Understanding Residuals 473

17.1 Examining Residuals for Groups · 17.2 Extrapolation and Prediction · 17.3 Unusual and Extraordinary Observations · 17.4 Working with Summary Values · 17.5 Autocorrelation · 17.6 Linearity · 17.7 Transforming (Re-expressing) Data · 17.8 The Ladder of Powers

Mini Case Study Projects: Gross Domestic Product 497,

Energy Sources 498

Chapter 18 Multiple Regression 509

18.1 The Multiple Regression Model · 18.2 Interpreting Multiple Regression Coefficients · 18.3 Assumptions and Conditions for the Multiple Regression Model · 18.4 Testing the Multiple Regression Model · 18.5 Adjusted R2, and the F-statistic · *18.6 The Logistic Regression Model

Mini Case Study Project: Golf Success 536

Chapter 19 Building Multiple Regression Models 547

19.1 Indicator (or Dummy) Variables · 19.2 Adjusting for Different Slopes—Interaction Terms · 19.3 Multiple Regression Diagnostics · 19.4 Building Regression Models · 19.5 Collinearity · 19.6 Quadratic Terms

Mini Case Study Project: Paralyzed Veterans of America 577

Chapter 20 Time Series Analysis 589

20.1 What Is a Time Series? · 20.2 Components of a Time Series · 20.3 Smoothing Methods · 20.4 Simple Moving Average Methods · 20.5 Weighted Moving Averages · 20.6 Exponential Smoothing Methods · 20.7 Summarizing Forecast Error · 20.8 Autoregressive Models · 20.9 Random Walks · 20.10 Multiple Regression-based Models · 20.11 Additive and Multiplicative Models · 20.12 Cyclical and Irregular Components · 20.13 Forecasting with Regressionbased

Models · 20.14 Choosing a Time Series Forecasting Method · 20.15 Interpreting Time Series Models: The Whole Foods Data Revisited

Mini Case Study Projects: Intel Corporation 624,

Tiffany & Co. 624

Part IV Building Models for Decision Making 637

Chapter 21 Random Variables and Probability Models 639

21.1 Expected Value of a Random Variable · 21.2 Standard Deviation of a Random Variable ·

21.3 Properties of Expected Values and Variances · 21.4 Discrete Probability Models · 21.5 Continuous Random Variables

Mini Case Study Project: Investment Options 668

Chapter 22 Decision Making and Risk 675

22.1 Actions, States of Nature, and Outcomes · 22.2 Payoff Tables and Decision Trees · 22.3 Minimizing Loss and Maximizing Gain · 22.4 The Expected Value of an Action · 22.5 Expected Value with Perfect Information · 22.6 Decisions Made with Sample Information · 22.7 Estimating Variation · 22.8 Sensitivity · 22.9 Simulation · 22.10 Probability Trees · *22.11 Reversing the Conditioning: Bayes’s Rule · 22.12 More Complex Decisions

Mini Case Study Projects: Texaco-Pennzoil 693,

Insurance Services, Revisited 694

Chapter 23 Design and Analysis of Experiments and Observational Studies 699

23.1 Observational Studies · 23.2 Randomized, Comparative Experiments · 23.3 The Four Principles of Experimental Design · 23.4 Experimental Designs · 23.5 Blinding and

Placebos · 23.6 Confounding and Lurking Variables · 23.7 Analyzing a Design in One Factor—The Analysis of Variance · 23.8 Assumptions and Conditions for ANOVA · *23.9 Multiple Comparisons · 23.10 ANOVA on Observational Data · 23.11 Analysis of Multifactor Designs

Mini Case Study Project: A Multifactor Experiment 736

Chapter 24 Introduction to Data Mining 747

24.1 Direct Marketing · 24.2 The Data · 24.3 The Goals of Data Mining · 24.4 Data Mining Myths · 24.5 Successful Data Mining · 24.6 Data Mining Problems · 24.7 Data Mining Algorithms · 24.8 The Data Mining Process · 24.9 Summary

Appendixes

A Answers A-1

B Photo Acknowledgments A-37

C Tables and Selected Formulas A-41

D Index A-57

商務統計學（英文版）

基本介紹

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