商務(wù)統(tǒng)計(jì)學(xué)

前言

本書(shū)是為商科學(xué)生而寫(xiě)的，它將回答一個(gè)簡(jiǎn)單的問(wèn)題：“怎樣才能做出更好的決策？”作為企業(yè)家和顧問(wèn)，應(yīng)該知道為了在今天這樣的競(jìng)爭(zhēng)環(huán)境下生存和發(fā)展，統(tǒng)計(jì)學(xué)是至關(guān)重要的。作為教育工作者，我們看到了向商科學(xué)生講授統(tǒng)計(jì)學(xué)的方式與商業(yè)決策制定中統(tǒng)計(jì)學(xué)的使用方式之間的脫節(jié)。本書(shū)將試圖通過(guò)介紹統(tǒng)計(jì)方法來(lái)縮短理論與實(shí)踐之間的距離。所以對(duì)學(xué)生來(lái)說(shuō)，統(tǒng)計(jì)方法既重要又有趣。根據(jù)數(shù)據(jù)做出一個(gè)商業(yè)決策有一個(gè)故事要講，統(tǒng)計(jì)學(xué)在其中所扮演的角色是幫助聽(tīng)清楚這個(gè)故事。像其他教材一樣，本書(shū)將講授如何計(jì)算一個(gè)特定的統(tǒng)計(jì)量或檢驗(yàn)，并且強(qiáng)調(diào)定義和公式。但是，與其他教材不同的是，本書(shū)也將講解“為什么”，并堅(jiān)持在商業(yè)決策的背景下給出結(jié)果。學(xué)生們將會(huì)了解到，為了做出更好的商業(yè)決策，應(yīng)該如何進(jìn)行統(tǒng)計(jì)思考、如何有效地表達(dá)分析結(jié)果并將決策告知他人。在寫(xiě)作本書(shū)時(shí)，我們知道當(dāng)今時(shí)代的統(tǒng)計(jì)學(xué)是用技術(shù)來(lái)實(shí)踐的。這種見(jiàn)解的結(jié)果是：從對(duì)方程形式（比計(jì)算形式更喜歡直覺(jué)形式）的選擇中得到的一切東西，都運(yùn)用到了對(duì)真實(shí)數(shù)據(jù)的廣泛使用中。但是更重要的是，對(duì)技術(shù)價(jià)值的理解，使本書(shū)將重點(diǎn)集中于講授統(tǒng)計(jì)思維而不是計(jì)算上。書(shū)中幾百個(gè)例子關(guān)注的不是“怎么找出答案”，而是“如何思考答案以及它如何有助于制定出一個(gè)更好的決策”。對(duì)統(tǒng)計(jì)思維的關(guān)注將書(shū)中的各章聯(lián)系起來(lái)。初級(jí)商務(wù)統(tǒng)計(jì)學(xué)課程包含大量的新術(shù)語(yǔ)、概念和方法，但是它們有一個(gè)核心部分：通過(guò)理解數(shù)據(jù)告訴如何更加了解這個(gè)世界，怎樣做出更好的決策。從這個(gè)角度來(lái)看，學(xué)生們能夠知道從數(shù)據(jù)中得出推斷的許多方式都是相同的核心概念的一些應(yīng)用。

內(nèi)容概要

　　 統(tǒng)計(jì)學(xué)是一門(mén)工具性學(xué)科，在眾多的學(xué)科領(lǐng)域有著廣泛的應(yīng)用。本書(shū)將統(tǒng)計(jì)學(xué)的概念與方法應(yīng)用于商務(wù)領(lǐng)域，從應(yīng)用層面對(duì)統(tǒng)計(jì)學(xué)的基本方法進(jìn)行了系統(tǒng)的講解。全書(shū)包括探索和收集數(shù)據(jù)、理解數(shù)據(jù)和分布、探索變量間的關(guān)系以及為決策建立模型四部分內(nèi)容，共24章，將方法的講解與商務(wù)領(lǐng)域中的現(xiàn)實(shí)案例緊密結(jié)合起來(lái)，讓讀者掌握如何利用統(tǒng)計(jì)方法解決商務(wù)中的實(shí)際問(wèn)題。本書(shū)還將統(tǒng)計(jì)軟件與統(tǒng)計(jì)方法的應(yīng)用結(jié)合起來(lái)，詳細(xì)介紹各種統(tǒng)計(jì)方法在Excel、Minitab、JMP、SPSS和DataDesk等軟件中的操作實(shí)現(xiàn)步驟。    本書(shū)可作為大學(xué)本科生和研究生的教材，也可供從事工商管理和經(jīng)濟(jì)分析的人士參考。

作者簡(jiǎn)介

作者：（美國(guó)）夏普（Norean Radke Sharpe） （美國(guó)）德維克斯（Richard D.De Veaux） （美國(guó)）維爾曼（Paul F.Velleman）

書(shū)籍目錄

Part I  Exploring and Collecting Data  Chapter 1  Statistics and Variation       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     Chapter 3  Surveys and Sampling       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     The GfK Roper Reports Worldwide Survey   Chapter 4 Displaying and Describing Categorical Data     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   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     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 143Part 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   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   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 424Part 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. 624Part 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 SummaryAppendixes  A Answers A-1   B Photo Acknowledgments A-37  C Tables and Selected Formulas A-41  D Index A-57

章節(jié)摘錄

插圖：Selecting a sample to represent the population fairly is more difficult than it sounds.Polls or surveys most often fail because t11e sample fails to represent part ofthe population.The wav the sample is drawn may overlook subgroups that are hardto find.For example，a telephone survey may get no responses from people withcaller ID and may favor other groups，such as the retired or tlle homebound，who would be more likely to be near their Dhones when the interviewer calls.Samplesthat over-or underemphasize some characteristics of the population are said to bebiased.the corresponding characteristics of the population it is trying to represent.Conclusions based on biased samples are inherently flawed.There is usually no way to fixbias after the sample is drawn and no way to salvage useful information from it. That are the basic techniques for making sure that a sample is representative？To make the sample as representative as possible，you might be tempted to hand-pick t}1e individuals included in the sample.But the best strategY is to do some-thing quite different：We should select individuals for the sample at random.

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《商務(wù)統(tǒng)計(jì)學(xué)(英文版)》特點(diǎn)：1.強(qiáng)調(diào)統(tǒng)計(jì)知識(shí)和開(kāi)發(fā)統(tǒng)計(jì)思維；2.使用真實(shí)數(shù)據(jù)；3.強(qiáng)調(diào)概念的理解而不僅僅是獲取知識(shí)的過(guò)程；4.培養(yǎng)主動(dòng)學(xué)習(xí)；5.在理解概念和分析數(shù)據(jù)時(shí)使用軟件技術(shù)；6.強(qiáng)調(diào)對(duì)統(tǒng)計(jì)結(jié)果的分析過(guò)程。

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