• Statistics A First Course Pearson
• Business Statistics A First Course

. Preparation for the Business World: the authors’ main goal is to prepare students to be successful in the business world by providing them with statistical tools to make informed decisions. Real data are essential for demonstrating the relevance of statistics in business.

Hundreds of exercises, examples, and applications are based on current events and well-known companies, engaging readers with compelling data and scenarios. Students learn from the authors’ consulting experience and see how statistical thinking is vital to modern business decision making.

Chapter Openers present a statistical issue in a managerial setting from a well-known company. These scenarios use real data to demonstrate how statistics relates to business disciplines such as marketing, finance, and economics.

Brief Cases at the end of each chapter use real data and ask students to investigate a question or make a business decision. Students are asked to define the objective, plan the process, complete the analysis, and report their conclusion. Data for these projects are available on the included CD-ROM and the companion website, and are formatted for multiple software programs. Case Studies at the end of each part of the text give students experience working through in-depth applications using real data (found on the CD-ROM) and scenarios from actual companies. A focus on checking assumptions and conditions when using statistical procedures is emphasized throughout the text and examples. Ethics in Action vignettes in every chapter illustrate the judgment needed in statistical analysis.

BusinessStatistics AFirst Course. Zicklin SchoolofBusiness, BaruchCollege, City University ofNewYork. 1.2 Statistics: Fundamentalfor Business 4. Business statistics: a first course (pdf) by norean r. Sharpe (ebook) Professors Norean Sharpe (Georgetown University), Dick De Veaux (Williams.

Students learn to identify ethically challenging issues and to propose ethically and statistically sound solutions. Questions are included for study and reflection. What Can Go Wrong? Sections near the end of each chapter prepare students with the tools to detect common statistical errors and offer practice in debunking misuses of statistics. Practice and Support: study tools throughout the text prepare students to analyze and interpret data.

For Example—short, illustrative examples are added to most sections to solidify understanding of newly learned statistical methods and concepts. Plan/Do/Report Guided Examples provide a model to help students approach and solve any business statistics problem. Reports are frequently presented in the form of a business memo, helping students become familiar with framing and communicating results in a business setting. Just Checking questions throughout each chapter ask students to stop and think about what they’ve just read. These questions involve little to no calculation. Answers are provided at the end of the chapter so students can check their work. What Have We Learned?

Sections at the end of each chapter provide an overview of the chapter’s concepts through annotated learning objectives and a list of boldface (new) terms and their definitions. Exercises are included within each chapter and progress in difficulty and complexity.

Each exercise set starts with single-concept exercises, identified by section. Then, the exercises incorporate a straightforward application of the chapter ideas. Next, they tackle larger problems that ask students to work through the logic of a complete analysis.

Finally, students are asked to synthesize and incorporate their own ideas. Basic Section Exercises have been added to ensure that students are comfortable with the requisite skills before moving on to more involved exercises. Technology Integration: optional coverage helps students use real statistics software, so they can spend less time learning commands and more time learning the concepts. Technology Help chapter sections offer guidance on using the most common statistics packages (Excel ® 2010, XLStat for Pearson, Minitab ®, JMP ®, and SPSS ®) to practice concepts in the chapters and get started with the technology of their choice. MyStatLab ™: Thecourse that accompanies this new edition includes increased and updated coverage from the text. More algorithmically-generated questions are now available. These are closely aligned with the book exercises to give students more diverse practice opportunities.

A library of 1,000 Conceptual Questions requires students to apply their statistical understanding to conceptual business questions. Technology tutorial videos are available for select exercises to support software usage. Business Insight Videos show students how statistical methods have been used to solve problems in the business world by companies including Southwest Airlines and Starwood Hotels. Assignable questions are available.

StatTalk Videos present statistical concepts through a series of fun, brief, real-world examples, with assignable questions. For Example: short, illustrative examples have been added to most sections to solidify understanding of newly learned statistical methods and concepts. Section Exercises: Each chapter’s exercises now begin with single-concept exercises that target section topics. This makes it easier for students to check their understanding of each topic as they learn it. Recent Data and new examples: The authors teach with real data whenever possible. To keep examples and exercises fresh, they’ve updated data throughout the book. New examples reflect stories in the news and recent economic and business events.

Redesigned Chapter Summaries: What Have We Learned chapter summaries are redesigned to specify learning objectives and place key concepts and skills within those objectives. This makes them even more effective as help for students preparing for exams. Brief Cases: Each chapter ends with one or two small case studies, now called Brief Cases. Case Studies are included at the end of each part of the text giving students experience working through in-depth applications using real data (found on the CD-ROM) and scenarios from actual companies.

Some sections are reorganized and combined for improved flow of material. Streamlined Technology Help with additional Excel ® coverage: Technology Help sections are now in easy-to-follow bulleted lists. Excel screenshots and coverage of Excel 2010 appear throughout the book where appropriate. Increased coverage of Excel includes screenshots and, in Technology Help sections, guidance for using Excel 2010 to demonstrate how to use Excel to perform statistical analysis. Technology Help sections have been updated to reflect the latest technology releases.

Technology Help now includes step-by-step guidance for XLSTAT ™ for Pearson, an Excel add-in offered (bundled) with the textbook. XLSTAT ™ for Pearson is an Excel add-in that enhances the analytical capabilities of Excel. Developed in 1993, XLSTAT is used by leading businesses and universities around the world.

XLSTAT is compatible with all Excel versions (except Mac 2008) and is compatible with both Windows ® and Mac ® systems. XLSTAT for Pearson is available for bundling or standalone purchase. Visit for more information. Increased MyStatLab coverage includes more of the textbook questions, including all of the basic exercises, to give students the practice they need to gain understanding and mastery of the concepts.

Table of Contents Preface Index of Applications PART I. EXPLORING AND UNDERSTANDING DATA 1. Stats Starts Here! 1.1 What Is Statistics? 1.2 Data 1.3 Variables 2.

Displaying and Describing Categorical Data 2.1 Summarizing and Displaying a Single Categorical Variable 2.2 Exploring the Relationship Between Two Categorical Variables 3. Displaying and Summarizing Quantitative Data 3.1 Displaying Quantitative Variables 3.2 Shape 3.3 Center 3.4 Spread 3.5 Boxplots and 5-Number Summaries 3.6 The Center of Symmetric Distributions: The Mean 3.7 The Spread of Symmetric Distributions: The Standard Deviation 3.8 Summary—What to Tell About a Quantitative Variable 4. Understanding and Comparing Distributions 4.1 Comparing Groups with Histograms 4.2 Comparing Groups with Boxplots 4.3 Outliers 4.4 Timeplots: Order, Please! 4.5 Re-expressing Data: A First Look 5. The Standard Deviation as a Ruler and the Normal Model 5.1 Standardizing with z-Scores 5.2 Shifting and Scaling 5.3 Normal Models 5.4 Finding Normal Percentiles 5.5 Normal Probability Plots Review of Part I.

Statistics A First Course Pearson

Exploring and Understanding Data PART II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES 6. Scatterplots, Association, and Correlation 6.1 Scatterplots 6.2 Correlation 6.3 Warning: Correlation ≠ Causation 6.4 Straightening Scatterplots 7. Linear Regression 7.1 Least Squares: The Line of 'Best Fit' 7.2 The Linear Model 7.3 Finding the Least Squares Line 7.4 Regression to the Mean 7.5 Examining the Residuals 7.6 R2—The Variation Accounted for by the Model 7.7 Regression Assumptions and Conditions 8. Regression Wisdom 8.1 Examining Residuals 8.2 Extrapolation: Reaching Beyond the Data 8.3 Outliers, Leverage, and Influence 8.4 Lurking Variables and Causation 8.5 Working with Summary Values Review of Part II. Exploring Relationships Between Variables PART III.

GATHERING DATA 9. Understanding Randomness 9.1 What is Randomness? 9.2 Simulating By Hand 10.

Sample Surveys 10.1 The Three Big Ideas of Sampling 10.2 Populations and Parameters 10.3 Simple Random Samples 10.4 Other Sampling Designs 10.5 From the Population to the Sample: You Can't Always Get What You Want 10.6 The Valid Survey 10.7 Common Sampling Mistakes, or How to Sample Badly 11. Experiments and Observational Studies 11.1 Observational Studies 11.2 Randomized, Comparative Experiments 11.3 The Four Principles of Experimental Design 11.4 Control Treatments 11.5 Blocking 11.6 Confounding Review of Part III Gathering Data PART IV. RANDOMNESS AND PROBABILITY 12. From Randomness to Probability 12.1 Random Phenomena 12.2 Modeling Probability 12.3 Formal Probability 13. Probability Rules!

13.1 The General Addition Rule 13.2 Conditional Probability and the General Multiplication Rule 13.3 Independence 13.4 Picturing Probability: Tables, Venn Diagrams and Trees 13.5 Reversing the Conditioning and Bayes' Rule 14. Random Variables and Probability Models 14.1 Expected Value: Center 14.2 Standard Deviation 14.3 Combining Random Variables 14.4 The Binomial Model 14.5 Modeling the Binomial with a Normal Model.14.6 The Poisson Model 14.7 Continuous Random Variables Review of Part IV Randomness and Probability PART V. FROM THE DATA AT HAND TO THE WORLD AT LARGE 15. Sampling Distribution Models 15.1 Sampling Distribution of a Proportion 15.2 When Does the Normal Model Work? Assumptions and Conditions 15.3 The Sampling Distribution of Other Statistics 15.4 The Central Limit Theorem: The Fundamental Theorem of Statistics 15.5 Sampling Distributions: A Summary 16. Confidence Intervals for Proportions 16.1 A Confidence Interval 16.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 16.3 Margin of Error: Certainty vs.

Business Statistics A First Course

Precision 16.4 Assumptions and Conditions 17. Testing Hypotheses About Proportions 17.1 Hypotheses 17.2 P-Values 17.3 The Reasoning of Hypothesis Testing 17.4 Alternative Alternatives 17.5 P-Values and Decisions: What to Tell About a Hypothesis Test 18.

Inferences About Means 18.1: Getting Started: The Central Limit Theorem (Again) 18.2: Gosset's t 18.3 Interpreting Confidence Intervals 18.4 A Hypothesis Test for the Mean 18.5 Choosing the Sample Size 19. More About Tests and Intervals 19.1 Choosing Hypotheses 19.2 How to Think About P Values 19.3 Alpha Levels 19.4 Practical vs. Statistical Significance 19.5 Critical Values Again 19.6 Errors 19.7 Power Review of Part V From the Data at Hand to the World at Large PART VI. LEARNING ABOUT THE WORLD 20. Comparing Groups 20.1 The Variance of a Difference 20.2 The Standard Deviation of the Difference Between Two Proportions 20.3 Assumptions and Conditions for Comparing Proportions 20.4 The Sampling Distribution of the Difference between Two Proportions 20.5 Comparing Two Means 20.6 The Two-Sample t-Test: Testing for the Difference Between Two Means 20.7 The Two Sample z-Test: Testing for the Difference between Proportions 20.8 The Pooled t-Test: Everyone into the Pool? 20.9 Pooling 21. Paired Samples and Blocks 21.1 Paired Data 21.2 Assumptions and Conditions 21.3 Confidence Intervals for Matched Pairs 21.4 Blocking 22.

Comparing Counts 22.1 Goodness-of-Fit Tests 22.2 Chi-Square Test of Homogeneity 22.3 Examining the Residuals 22.4 Chi-Square Test of Independence 23. Inferences for Regression 23.1 The Population and the Sample 23.2 Assumptions and Conditions 23.3 Intuition About Regression Inference 23.4 Regression Inference 23.5 Standard Errors for Predicted Values 23.6 Confidence Intervals for Predicted Values 23.7. Logistic Regression Review of Part VI. Learning About the World PART VII. INFERENCE WHEN VARIABLES ARE RELATED 24. Analysis of Variance 24.1 Testing Whether the Means of Several Groups Are Equal 24.2 The ANOVA Table 24.3 Plot the Data. 24.4 Comparing Means 25.

Multiple Regression 25.1 Two Predictors 25.2 What Multiple Regression Coefficients Mean 25.3 The Multiple Regression Model 25.4 Multiple Regression Inference 25.5 Comparing Multiple Regression Models Appendices A. Photo Acknowledgments C. Tables and Selected Formulas.Indicates an optional chapter. About the Author(s) As a researcher of statistical problems in business and a professor at a business school, Norean Radke Sharpe (Ph.D. University of Virginia) understands the challenges and specific needs of the business student.

She is currently teaching at the McDonough School of Business at Georgetown University, where she is also Senior Associate Dean and Director of Undergraduate Programs. Prior to joining Georgetown, she taught business statistics and operations research courses to both undergraduates and MBA students for fourteen years at Babson College. Before moving into business education, she taught statistics for several years at Bowdoin College and conducted research at Yale University. Norean is coauthor of the recent text, A Casebook for Business Statistics: Laboratories for Decision Making, and has authored more than 30 articles-primarily in the areas of statistics education and women in science. Norean currently serves as Associate Editor for the journal Cases in Business, Industry, and Government Statistics.

Her research focuses on business forecasting and statistics education. She is also co-founder of DOME Foundation, Inc., a nonprofit foundation that works to increase Diversity and Outreach in Mathematics and Engineering for the greater Boston area.

She has been active in increasing the participation of women and underrepresented students in science and mathematics for several years and has two children of her own. De Veaux (Ph.D. Stanford University) is an internationally known educator, consultant, and lecturer. Dick has taught Statistics at a business school (The Wharton School of the University of Pennsylvania), an engineering school (Princeton University), and a liberal arts college (Williams College).

He is an internationally known lecturer in data mining and is a consultant for many Fortune 500 companies in a wide variety of industries. While at Princeton, he won a Lifetime Award for Dedication and Excellence in Teaching. Since 1994, he has been a Professor of Statistics at Williams College, although he returned to Princeton for the academic year 2006–2007 as the William R. Visiting Professor of Distinguished Teaching. Dick holds degrees from Princeton University in Civil Engineering and Mathematics, and from Stanford University in Dance Education and Statistics, where he studied with Persi Diaconis. His research focuses on the analysis of large data sets and data mining in science and industry.

Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality and is a Fellow of the American Statistical Association. Dick is well known in industry, having consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. He was named the “Statistician of the Year” for 2008 by the Boston Chapter of the American Statistical Association for his contributions to teaching, research, and consulting. In his spare time he is an avid cyclist and swimmer. He also is the founder and bass for the Doo-wop group, “Diminished Faculty,” and is a frequent soloist with various local choirs and orchestras. Dick is the father of four children.

Velleman (Ph.D. Princeton University) has an international reputation for innovative statistics education. He designed the Data Desk® software package and is also the author and designer of the award-winning ActivStats® statistics package, for which he received the EDUCOM Medal for innovative uses of computers in teaching statistics and the ICTCM Award for Innovation in Using Technology in College Mathematics.

He is the founder and CEO of Data Description, Inc. , which supports both of these programs. He also developed the Internet site, Data and Story Library (DASL) , which provides data sets for teaching Statistics. Paul co-authored (with David Hoaglin) the book ABCs of Exploratory Data Analysis. Paul has taught Statistics at Cornell University on the faculty of the School of Industrial and Labor Relations since 1975.

His research often focuses on statistical graphics and data analysis methods. Paul is a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. Paul’s experience as a professor, entrepreneur, and business leader brings a unique perspective to the book. Richard De Veaux and Paul Velleman have authored successful books in the introductory college and AP High School market with Dave Bock, including (Pearson, 2014), (Pearson, 2010), and (Pearson, 2012).