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    Data Driven Business Decisions (Statistics in Practice) (Hardback) By (author) Chris J. Lloyd

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    DescriptionA hands-on guide to the use of quantitative methods and software for making successful business decisions The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel(r), uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel(r); translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including: Use of the Excel(r) functions Solver and Goal Seek Partial correlation and auto-correlation Interactions and proportional variation in regression models Seasonal adjustment and what it reveals Basic portfolio theory as an introduction to correlations Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel(r) add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. The enclosed CD contains the complete book in electronic format, including all presented data, supplemental material on the discussed case files, and links to exercises and solutions. Data Driven Business Decisions is an excellent book for MBA quantitative analysis courses or undergraduate general statistics courses. It also serves as a valuable reference for practicing MBAs and practitioners in the fields of statistics, business, and finance.


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    Title
    Data Driven Business Decisions
    Authors and contributors
    By (author) Chris J. Lloyd
    Physical properties
    Format: Hardback
    Number of pages: 512
    Width: 174 mm
    Height: 256 mm
    Thickness: 30 mm
    Weight: 1,120 g
    Language
    English
    ISBN
    ISBN 13: 9780470619605
    ISBN 10: 0470619600
    Classifications

    BIC E4L: BUS
    B&T Book Type: NF
    Nielsen BookScan Product Class 3: S4.0T
    B&T Merchandise Category: TXT
    B&T Modifier: Region of Publication: 01
    B&T Modifier: Academic Level: 01
    B&T Modifier: Subject Development: 10
    BIC subject category V2: KJQ, PBT
    B&T General Subject: 180
    Ingram Subject Code: BE
    BISAC V2.8: BUS019000
    B&T Modifier: Text Format: 06
    BISAC V2.8: BUS061000
    Warengruppen-Systematik des deutschen Buchhandels: 17840
    LC subject heading:
    B&T Approval Code: A48404238, A48403048
    DC22: 658.4033
    LC subject heading: ,
    DC22: 658.4/033
    Libri: WIRT1088, WIRT9300, WIRU3500
    LC subject heading:
    LC classification: HD30.23 .L593 2011
    LC subject heading: , ,
    Thema V1.0: KJMD, KCH
    Publisher
    John Wiley and Sons Ltd
    Imprint name
    Wiley-Blackwell (an imprint of John Wiley & Sons Ltd)
    Publication date
    04 November 2011
    Publication City/Country
    Chicester
    Author Information
    CHRIS J. LLOYD, PhD, is Associate Dean of Research and Professor of Business Statistics in the Melbourne Business School at The University of Melbourne, Australia. Professor Lloyd has extensive international academic and consulting experience in the fields of statistics, data analysis, and market research within both academic and business environments. He has written more than 100 research articles in the areas of categorical data and is the author of Statistical Analysis of Categorical Data, also published by Wiley.
    Back cover copy
    A hands-on guide to the use of quantitative methods and software for making successful business decisionsThe appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel(R), uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty.Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel(R); translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including: Use of the Excel(R) functions Solver and Goal SeekPartial correlation and auto-correlationInteractions and proportional variation in regression modelsSeasonal adjustment and what it revealsBasic portfolio theory as an introduction to correlationsChapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel(R) add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. The enclosed CD contains the complete book in electronic format, including all presented data, supplemental material on the discussed case files, and links to exercises and solutions.Data Driven Business Decisions is an excellent book for MBA quantitative analysis courses or undergraduate general statistics courses. It also serves as a valuable reference for practicing MBAs and practitioners in the fields of statistics, business, and finance.
    Table of contents
    Preface XIII To the Student XV To the Teacher: How to Build a Course Around This Book XVII Chapter 1 How Are We Doing? Data-Driven Views of Business Performance 1 1.1 Setting Out Business Data 2 1.2 Different Kinds of Variables 5 1.3 The Idea of a Distribution 8 1.4 Typical Performance: The Sample Mean 13 1.5 Uncertainty in Performance: SD 15 1.6 Changing Units 18 1.7 Shapes of Distributions 20 Chapter 2 What Stands Out and Why? Who Wins? Data-Driven Views of Performance Dynamics 25 2.1 Different Layouts of Business Data 27 2.2 Comparing Performance across Different Segments 29 2.3 Complex Comparisons: Using Pivotables 30 2.4 Unusually High or Low Outcomes: z-Scores 35 2.5 Homogeneous Peer Groups 39 2.6 Combining Different Performance Measures 41 Chapter 3 Dealing with Uncertainty and Chance 51 3.1 Framing What Could Happen: Outcomes and Events 52 3.2 How Likely Is It? Probability Basics 55 3.3 Market Segments and Behavior; Probability Tables 57 3.4 Example in Health Care: Testing for a Disease 59 3.5 Conditional Probability 61 3.6 How Strong Is the Relationship? Measuring Dependence 66 3.7 Probability Trees 71 Chapter 4 Let the Data Change Your Views: The Bayes Method 79 4.1 The Bayes Method in Pictures 80 4.2 The Bayes Method as an Algorithm 81 4.3 Example 1: A Simple Gambling Game 83 4.4 Example 2: Bayes in the Courtroom 87 4.5 Some Typical Business Applications 90 Chapter 5 Valuing an Uncertain Payoff 97 5.1 What Is a Probability Distribution? 98 5.2 Displaying a Probability Distribution 100 5.3 The Mean of a Distribution 103 5.4 Example: Fines and Violations 104 5.5 Why Use the Mean? 107 5.6 The Standard Deviation of a Distribution 109 5.7 Comparing Two Distributions 112 5.8 Conditional Distributions and Means 114 Chapter 6 Business Problems That Depend on Knowing "How Many" 121 6.1 The Binomial Distribution 123 6.2 The Mean and Standard Deviation 125 6.3 The Negative Binomial Distribution 128 6.4 The Poisson Distribution 132 6.5 Some Typical Business Applications 135 Chapter 7 Business Problems That Depend on Knowing "How Much" 141 7.1 The Normal Distribution 142 7.2 Calculating Normal Probabilities in Excel 145 7.3 Combining Normal Variables 149 7.4 Comparing Two Normal Distributions 152 7.5 The Standard Normal Distribution 153 7.6 Example 3: Dealing with Uncertain Demand 156 7.7 Dealing with Proportional Variation 160 Chapter 8 Making Complex Decisions with Trees 169 8.1 Elements of Decision Trees 171 8.2 Solving the Decision Tree 175 8.3 Multistage Decision Trees 181 8.4 Valuing a Decision Option 186 8.5 The Cost of Uncertainty 188 Chapter 9 Data, Estimation, and Statistical Reliability 195 9.1 Describing the Past and the Future 197 9.2 How Were the Data Generated? 199 9.3 Law of Large Numbers 200 9.4 The Variability of the Sample Mean 201 9.5 The Standard Error of the Mean 204 9.6 The Normal Limit Theorem 208 9.7 Samples and Populations 212 Chapter 10 Managing Mean Performance 219 10.1 Benchmarking Mean Performance 221 10.2 The Statistical Size of a Deviation 224 10.3 Decision Making, Hypothesis Testing, and p-Values 226 10.4 Confidence Intervals 230 10.5 One-Sided and Two-Sided Tests 232 10.6 Using StatproGo 232 10.7 Why Standard Deviation Matters 234 10.8 Assessing Detection Power 235 Chapter 11 Are These Customers Different? Did the Intervention Work? Looking at Changes in Mean Performance 243 11.1 How Variable Is a Difference? 245 11.2 Describing Changes in Mean Performance 247 11.3 Example 2: Is Product Placement Worth It? 249 11.4 Performing the t-Test with StatproGo 255 11.5 Different Standard Deviations 258 11.6 Analyzing Matched-Pairs Data 261 Chapter 12 What Is My Brand Recognition? Will It Sell? Analyzing Counts and Proportions 271 12.1 How Accurate Are Percentages? 272 12.2 Tests and Confidence Intervals for Proportions 277 12.3 Assessing Changes in Proportions 280 12.4 Using StatproGo 284 12.5 Alternative Methods 284 Chapter 13 Using the Relationship between Shares to Build a Portfolio 293 13.1 How to Measure Financial Growth 295 13.2 Risk and Return: Both Matter 298 13.3 Correlation and Industry Structure 300 13.4 The Riskiness of a Portfolio 306 13.5 Balancing Risk and Return 310 13.6 Controlling Risk with TBs 312 Chapter 14 Investigating Relationships between Business Variables 319 14.1 Measuring Association with Correlation 320 14.2 Looking at Complex Relationships 324 14.3 Interpreting Correlations 328 14.4 What Is Autocorrelation? 331 14.5 Untangling Relationships with Partial Correlation 334 Chapter 15 Describing the Effect of a Business Input: Linear Regression 341 15.1 Linear Relationships 342 15.2 The Line of Best Fit 344 15.3 Computing the Least Squares Line 347 15.4 The Regression Model 351 15.5 How Reliable Is the Regression Line? 354 Chapter 16 The Reliability of Regression-Based Decisions 365 16.1 Three Kinds of Questions that Regression Answers 366 16.2 Estimating the Effect of a Change 369 16.3 Estimating the Trend Mean 370 16.4 Prediction 373 16.5 Prediction Errors and What They Tell You 374 Chapter 17 Multicausal Relationships and Multiple Regression 387 17.1 Multilinear Relationships 390 17.2 Multiple Regression 393 17.3 Model Assessment 400 17.4 Prediction and Trend Estimation 404 Chapter 18 Product Features, Nonlinear Relationships, and Market Segments 413 18.1 Accounting for Yes--No Features 415 18.2 Quadratic Relationships 417 18.3 Quadratic Regression 421 18.4 Allowing for Segments and Groups 425 18.5 Automatic Model Selection 429 Chapter 19 Analyzing Data That Is Collected Regularly Over Time 437 19.1 Measuring Growth and Seasonality 439 19.2 How Is the Growth Rate Changing? 443 19.3 Seasonally Adjusting Data 445 19.4 Delayed Effects 448 19.5 Predicting the Future (Using Autoregression) 453 Chapter 20 Extending Regression Models: The Sky Is the Limit 461 20.1 Inputs That Have Varying Effects: Interactions 462 20.2 Inputs That Have Proportional Impacts 470 20.3 Case Study: How Effective Are Catalog Mail-Outs? 474 20.4 More on Time Series 478 Index 485