Study Guide and Solutions Manual for Statistics for Managers Using Microsoft Excel

Study Guide and Solutions Manual for Statistics for Managers Using Microsoft Excel book cover

Study Guide and Solutions Manual for Statistics for Managers Using Microsoft Excel

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  • New or revised Using Statistics case scenarios in seven chapters of the 9th Edition. These business scenarios begin each chapter, showing how statistics is used in accounting, finance, information systems, management, or marketing. Scenarios are then used throughout the chapter to provide an applied context for the concepts, to bring students from knowing to applying. 
  • New Tableau Guides in each chapter explain how to use the data visualization software Tableau Public as a complement to Microsoft® Excel® for visualizing data. The text offers Tableau Public results for selected methods in which Tableau can enhance or complement Excel results.  
  • A new Business Analytics chapter (Chapter 17) provides a complete introduction to the field of business analytics. The chapter defines terms and categories that introductory business statistics students may encounter in other courses or outside the classroom.
    • Includes a new Consider This feature, “What’s My Major If I Want to Be a Data Miner?”
  • Exercises have been reviewed, updated and replaced in this edition.
  • Tabular summaries now guide readers to reaching conclusions and making decisions based on statistical information. Found in Chapters 10 through 13, this change not only adds clarity to the purpose of the statistical method being discussed but better illustrates the role of statistics in business decision-making processes.

Also available with MyLab Statistics 

  • Pearson eText is a simple-to-use, mobile-optimized, personalized reading experience available within MyLab It lets students highlight and take notes all in one place — even when offline.
  • Excel Grader Projects: Excel Projects in MyLab™ Statistics allow students to analyze data using actual Microsoft Excel spreadsheet software. Each Excel project focuses on a key concept in the business statistics course and asks students to answer questions about a data set provided in Excel. Excel project questions are auto-graded and provide immediate feedback so students can identify any conceptual or procedural mistakes made in the problem solving process. 23 separate statistical topics are covered.
  • Personal Inventory Assessments are a collection of online exercises designed to promote self-reflection and engagement in students. These 33 assessments include topics such as a Stress Management Assessment, Diagnosing Poor Performance and Enhancing Motivation, and Time Management Assessment. 

Check out the preface for a complete list of features and what’s new in this edition.

David M. Levine is Professor Emeritus of Statistics and Computer Information Systems at Baruch College (City University of New York). He received B.B.A. and M.B.A. degrees in statistics from City College of New York and a Ph.D. from New York University in industrial engineering and operations research. He is nationally recognized as a leading innovator in statistics education and is the co-author of 14 books, including such best-selling statistics textbooks as Statistics for Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications, Business Statistics: A First Course, and Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab.

 

He also is the co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics, currently in its second edition, Six Sigma for Green Belts and Champions and Design for Six Sigma for Green Belts and Champions, and the author of Statistics for Six Sigma Green Belts, all published by FT Press, a Pearson imprint, and Quality Management, third edition, McGraw-Hill/Irwin. He is also the author of Video Review of Statistics and Video Review of Probability, both published by Video Aided Instruction, and the statistics module of the MBA primer published by Cengage Learning. He has published articles in various journals, including Psychometrika, The American Statistician, Communications in Statistics, Decision Sciences Journal of Innovative Education, Multivariate Behavioral Research, Journal of Systems Management, Quality Progress, and The American Anthropologist, and he has given numerous talks at the Decision Sciences Institute (DSI), American Statistical Association (ASA), and Making Statistics More Effective in Schools and Business (MSMESB) conferences. David Levine has also received several awards for outstanding teaching and curriculum development from Baruch College.

 

David F. Stephan is an independent instructional technologist. He was an Instructor/Lecturer of Computer Information Systems at Baruch College (City University of New York) for over 20 years and also served as an Assistant to the Provost and to the Dean of the School of Business & Public Administration for computing. He pioneered the use of computer classrooms for business teaching, devised interdisciplinary multimedia tools, and created techniques for teaching computer applications in a business context. He also conducted the first large-scale controlled experiment to show the benefit of teaching Microsoft Excel in a business case context to undergraduate students.

 

An avid developer, he created multimedia courseware while serving as the Assistant Director of a Fund for the Improvement of Postsecondary Education (FIPSE) project at Baruch College. Stephan is also the originator of PHStat, the Pearson Education statistical add-in for Microsoft Excel and a co-author of Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics and Practical Statistics by Example Using Microsoft Excel and Minitab. He is currently developing ways to extend the instructional materials that he and his co-authors develop to mobile and cloud computing platforms as well as develop social-media facilitated means to support learning in introductory business statistics courses.

 

David Stephan received a B.A. in geology from Franklin and Marshall College and a M.S. in computer methodology from Baruch College (City University of New York).

 

Kathryn A. Szabat is Associate Professor and Chair of Business Systems and Analytics at LaSalle University. She teaches undergraduate and graduate courses in business statistics and operations management. She also teaches as Visiting Professor at the Ecole Superieure de Commerce et de Management (ESCEM) in France.

 

Szabat’s research has been published in International Journal of Applied Decision Sciences, Accounting Education, Journal of Applied Business and Economics, Journal of Healthcare Management, and Journal of Management Studies. Scholarly chapters have appeared in Managing Adaptability, Intervention, and People in Enterprise Information Systems; Managing, Trade, Economies and International Business; Encyclopedia of Statistics in Behavioral Science; and Statistical Methods in Longitudinal Research.

 

She has provided statistical advice to numerous business, non-business, and academic communities. Her more recent involvement has been in the areas of education, medicine, and nonprofit capacity building.

 

Kathryn Szabat received a B.S. in mathematics from State University of New York at Albany and M.S. and Ph.D. degrees in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania.

Student-focused learning aids

  • An integrated five-step approach makes it easier for students to follow the progression of applying statistics: Define, Collect, Organize, Visualize, Analyze.
  • First Things First sets the context for explaining what statistics is (not what students may think), while ensuring that they understand why learning business statistics is important today. This chapter is especially helpful for instructors using course management tools, including hybrid or online courses; this chapter is designed for distribution before the first class begins.
  • REVISED – Tabular summaries guide readers to reaching conclusions and making decisions based on statistical information. Found in Chapters 10 through 13, this change not only adds clarity to the purpose of the statistical method being discussed but better illustrates the role of statistics in business decision-making processes.
  • Student Tips in the margin reinforce hard-to-master concepts and provide quick study tips for mastering important details.
  • LearnMore references reinforce important points and direct students to additional learning resources. 
  • Additional self-study opportunities are provided in an Appendix that offers answers to the “Self-Test” problems and most of the even-numbered problems in the book.

Focus on data interpretation and application 

  • Analyzing data with a focus on software results: Using software is essential to learning statistics. Software should model business best practices and be integrated into the statistical learning process. Reusable templates and model solutions are emphasized over building unaudited solutions from scratch that may contain errors. Using preconstructed and previously validated solutions not only models best practice but reflects regulatory requirements that businesses face today. This text emphasizes data analysis through interpretation of the results from Microsoft® Excel®:
    • Excel content includes end-of-chapter Excel Guides; in-depth Excel step-by-step instructions; Excel Guide workbooks; PHStat, a statistics add-in system for Excel; and multiple appendices devoted to Excel. 
    • Software instruction sets are complete and contain known starting points. Vague instructions that present statements such as “Use command X” that presume students can figure out how to “get to” command X are distracting to learning. Instruction sets are provided that have a known starting point, typically in the form of “open to a specific worksheet in a specific workbook.” 
    • NEW – Tableau Guides in each chapter explain how to use the data visualization software Tableau Public as a complement to Microsoft Excel for visualizing data. The text offers Tableau Public results for selected methods in which Tableau can enhance or complement Excel results.  
  • REVISED – Using Statistics business scenarios begin each chapter. Scenarios are then used throughout the chapter to provide an applied context for the concepts, to bring students from knowing to applying. In the 9th Edition, seven chapters offer new or revised case scenarios.
    • Help students see the relevance of statistics to their own careers by using examples from the functional areas that may become their areas of specialization. Every statistical method is discussed using an example from a functional area, such as accounting, finance, management, or marketing, and explaining the application of methods to specific business activities. 
  • NEW – Business Analytics chapter (Chapter 17) provides a complete introduction to the field of business analytics. The chapter defines terms and categories that introductory business statistics students may encounter in other courses or outside the classroom.
    • NEW – Includes a new Consider This feature, “What’s My Major If I Want to Be a Data Miner?”
  • Getting Ready to Analyze Data in the Future: The final chapter helps students understand how to make decisions about which statistical methods to use in real world problems. This capstone chapter brings the issues in First Things First scenarios full circle, and gives students the ability to apply business statistics to the real world.
  • Consider This essays in every chapter reinforce important concepts, examine side issues, or answer typical student questions that arise while studying business statistics, such as “What is so ‘normal’ about the normal distribution?”
  • End-of-chapter cases include a business case that continues through most chapters. Several cases that reoccur throughout the book.
    • Case Studies offer realistic business scenarios to apply fundamental statistical and analytical concepts. 
    • Digital Cases ask students to examine interactive PDF documents and sift through claims and information in order to discover the data most relevant to a business case scenario. Students determine whether the conclusions and claims are supported by the data, and in doing so, they learn how to identify common misuses of statistical information.
    • The Instructor’s Solutions Manual provides instructional tips for using cases as well as solutions to the Digital Cases.
  • Software integration and flexibility: Software instructions feature chapter examples and were personally written by the authors. With modularized Workbook, PHStat, and Analysis Toolbook instructions where applicable, both instructors and students can switch among these instruction sets as they use this book with no loss of statistical learning.

Check out the preface for a complete list of features and what’s new in this edition.


Also available with MyLab Statistics 

MyLab™ Statistics is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab Statistics personalizes the learning experience and improves results for each student. With MyLab Statistics and StatCrunch® integrated web-based statistical software, students learn the skills they need to interact with data in the real world. Learn more about MyLab Statistics.

Teach your course your way

  • All data sets are available to download in the MyLab Statistics course or via the Pearson Math and Statistics Resource SiteThese are available in Excel®, JMP, and Minitab formats and contain the data used in chapter examples or named in problems and end-of-chapter cases. 
  • Technology¿-specific video tutorials  and study cards provide students with support no matter which statistical software they use. The videos address how to use StatCrunch, Excel, Excel with PHStat, Excel with XLStat Minitab, R, and TI 83/84 calculators to complete exercises. There are also study cards available in MyLab Statistics for all listed software options, in addition to JMP.
  • Learning Catalytics™ is a student response tool that uses students’ smartphones, tablets, or laptops to engage them in more interactive tasks and thinking. It helps to foster student engagement and peer-¿to-¿peer learning, generate class discussion, and guide lectures with real¿-time analytics. 

Empower each learner

  • NEW – Pearson eText is a simple-to-use, mobile-optimized, personalized reading experience available within MyLab It lets students highlight and take notes all in one place – even when offline.
  • NEW – Personal Inventory Assessments are a collection of online exercises designed to promote self-reflection and engagement in students. These 33 assessments include topics such as a Stress Management Assessment, Diagnosing Poor Performance and Enhancing Motivation, and Time Management Assessment. 

  • Question Help consists of homework and practice questions to give students unlimited opportunities to master concepts. Learning aids walk students through the problem – giving them assistance when they need it most.
  • The Study Plan gives students personalized recommendations, practice opportunities, and learning aids to help them stay on track.
  • Getting Ready for Statistics Questions: This question library contains more than 450 exercises that cover the relevant algebraic topics for a given section. These can be made available to students for extra practice or assigned as a prerequisite to other assignments.
  • Improve student results: When you teach with MyLab, student performance often improves. That’s why instructors have chosen MyLab for over 15 years, touching the lives of over 50 million students.

Deliver trusted content

  • NEW – Excel Grader Projects: Excel Projects in MyLab Statistics allow students to analyze data using actual Microsoft Excel software. Each Excel project focuses on a key concept in the business statistics course and asks students to answer questions about a data set provided in Excel. Excel project questions are auto-graded and provide immediate feedback so students can identify any conceptual or procedural mistakes made in the problem solving process. 23 separate statistical topics are covered.
  • StatCrunch: This powerful, web-¿based statistical software is integrated into MyLab Statistics, so students can quickly and easily analyze any data set, including those from their text and MyLab Statistics exercises. In addition, MyLab Statistics includes access to www.StatCrunch.com, a web-¿based community where users can access tens of thousands of shared data sets, create and conduct online surveys, pull data from almost any web page, and perform complex analyses using the powerful statistical software.
  • StatCrunch Reports get students hands-on with statistical procedures by guiding them through real data analysis in StatCrunch. When results are generated with just a few clicks, students can spend more time interpreting and communicating results. StatCrunch Reports are integrated into the text and are now accompanied by assignable questions in MyLab Statistics.
  • StatCrunch Projects in MyLab Statistics provide opportunities for students to explore data beyond the classroom. In each project, students analyze a large data set in StatCrunch and answer corresponding, assignable questions for immediate feedback. StatCrunch Projects span the entire curriculum or focus on certain key concepts. Questions from each project can also be assigned individually. 
  • Conceptual Question Library: A library of 1000 conceptual questions in the Assignment Manager requires students to apply their statistical understanding. 
  • StatTalk Videos: ¿ Hosted by fun-¿loving statistician Andrew Vickers, this video series demonstrates important statistical concepts through interesting stories and real-life events. Videos include assessment questions and an instructor’s guide.

F. First Things First

  • FTF.1 Think Differently About Statistics
  • FTF.2 Business Analytics: The Changing Face of Statistics
  • FTF.3 Starting Point for Learning Statistics
  • FTF.4 Starting Point for Using Software
  • FTF.5 Starting Point for Using Microsoft Excel

1. Defining and Collecting Data

  • 1.1 Defining Variables
  • 1.2 Collecting Data
  • 1.3 Types of Sampling Methods
  • 1.4 Data Cleaning
  • 1.5 Other Data Preprocessing Tasks
  • 1.6 Types of Survey Errors

2. Organizing and Visualizing Variables

  • 2.1 Organizing Categorical Variables
  • 2.2 Organizing Numerical Variables
  • 2.3 Visualizing Categorical Variables
  • 2.4 Visualizing Numerical Variables
  • 2.5 Visualizing Two Numerical Variables
  • 2.6 Organizing a Mix of Variables
  • 2.7 Visualizing a Mix of Variables
  • 2.8 Filtering and Querying Data 
  • 2.9 Pitfalls in Organizing and Visualizing Variables

3. Numerical Descriptive Measures

  • 3.1 Measures of Central Tendency
  • 3.2 Measures of Variation and Shape
  • 3.3 Exploring Numerical Variables
  • 3.4 Numerical Descriptive Measures for a Population
  • 3.5 The Covariance and the Coefficient of Correlation
  • 3.6 Descriptive Statistics: Pitfalls and Ethical Issues

4. Basic Probability

  • 4.1 Basic Probability Concepts
  • 4.2 Conditional Probability
  • 4.3 Ethical Issues and Probability
  • 4.4 Bayes’ Theorem
  • 4.5 Counting Rules

5. Discrete Probability Distributions

  • 5.1 The Probability Distribution for a Discrete Variable
  • 5.2 Binomial Distribution
  • 5.3 Poisson Distribution
  • 5.4 Covariance of a Probability Distribution and Its Application in Finance
  • 5.5 Hypergeometric Distribution

6. The Normal Distribution and Other Continuous Distributions

  • 6.1 Continuous Probability Distributions
  • 6.2 The Normal Distribution
  • 6.3 Evaluating Normality
  • 6.4 The Uniform Distribution
  • 6.5 The Exponential Distribution
  • 6.6 The Normal Approximation to the Binomial Distribution

7. Sampling Distributions

  • 7.1 Sampling Distributions
  • 7.2 Sampling Distribution of the Mean
  • 7.3 Sampling Distribution of the Proportion
  • 7.4 Sampling from Finite Populations

8. Confidence Interval Estimation

  • 8.1 Confidence Interval Estimate for the Mean (σ Known)
  • 8.2 Confidence Interval Estimate for the Mean (σ Unknown)
  • 8.3 Confidence Interval Estimate for the Proportion
  • 8.4 Determining Sample Size
  • 8.5 Confidence Interval Estimation and Ethical Issues
  • 8.6 Application of Confidence Interval Estimation in Auditing
  • 8.7 Estimation and Sample Size Determination for Finite Populations
  • 8.8 Bootstrapping

9. Fundamentals of Hypothesis Testing: One-Sample Tests

  • 9.1 Fundamentals of Hypothesis Testing
  • 9.2 t Test of Hypothesis for the Mean (σ Unknown)
  • 9.3 One-Tail Tests
  • 9.4 Z Test of Hypothesis for the Proportion
  • 9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues
  • 9.6 Power of the Test

10. Two-Sample Tests

  • 10.1 Comparing the Means of Two Independent Populations
  • 10.2 Comparing the Means of Two Related Populations
  • 10.3 Comparing the Proportions of Two Independent Populations
  • 10.4 F Test for the Ratio of Two Variances
  • 10.5 Effect Size

11. Analysis of Variance

  • 11.1 One-Way ANOVA
  • 11.2 Two-Way ANOVA
  • 11.3 The Randomized Block Design
  • 11.4 Fixed Effects, Random Effects, and Mixed Effects Models

12. Chi-Square and Nonparametric Tests

  • 12.1 Chi-Square Test for the Difference Between Two Proportions
  • 12.2 Chi-Square Test for Differences Among More Than Two Proportions
  • 12.3 Chi-Square Test of Independence
  • 12.4 Wilcoxon Rank Sum Test for Two Independent Populations
  • 12.5 Kruskal-Wallis Rank Test for the One-Way ANOVA
  • 12.6 McNemar Test for the Difference Between Two Proportions (Related Samples)
  • 12.7 Chi-Square Test for the Variance or Standard Deviation
  • 12.8 Wilcoxon Signed Ranks Test for Two Related Populations

13. Simple Linear Regression

  • 13.1 Simple Linear Regression Models
  • 13.2 Determining the Simple Linear Regression Equation
  • 13.3 Measures of Variation
  • 13.4 Assumptions of Regression
  • 13.5 Residual Analysis
  • 13.6 Measuring Autocorrelation: The Durbin-Watson Statistic
  • 13.7 Inferences About the Slope and Correlation Coefficient
  • 13.8 Estimation of Mean Values and Prediction of Individual Values
  • 13.9 Potential Pitfalls in Regression

14. Introduction to Multiple Regression

  • 14.1 Developing a Multiple Regression Model
  • 14.2 Evaluating Multiple Regression Models
  • 14.3 Multiple Regression Residual Analysis
  • 14.4 Inferences About the Population Regression Coefficients
  • 14.5 Testing Portions of the Multiple Regression Model
  • 14.6 Using Dummy Variables and Interaction Terms
  • 14.7 Logistic Regression
  • 14.8 Cross-Validation

15. Multiple Regression Model Building

  • 15.1 The Quadratic Regression Model
  • 15.2 Using Transformations in Regression Models
  • 15.3 Collinearity
  • 15.4 Model Building
  • 15.5 Pitfalls in Multiple Regression and Ethical Issues

16. Time-Series Forecasting

  • 16.1 Time-Series Component Factors
  • 16.2 Smoothing an Annual Time Series
  • 16.3 Least-Squares Trend Fitting and Forecasting
  • 16.4 Autoregressive Modeling for Trend Fitting and Forecasting
  • 16.5 Choosing an Appropriate Forecasting Model
  • 16.6 Time-Series Forecasting of Seasonal Data
  • 16.7 Index Numbers

17. Business Analytics

  • 17.1 Business Analytics Overview
  • 17.2 Descriptive Analytics
  • 17.3 Decision Trees
  • 17.4 Clustering
  • 17.5 Association Analysis
  • 17.6 Text Analytics
  • 17.7 Prescriptive Analytics

18. Getting Ready to Analyze Data in the Future

  • 18.1 Analyzing Numerical Variables
  • 18.2 Analyzing Categorical Variables

19. Statistical Applications in Quality Management (online)

  • 19.1 The Theory of Control Charts
  • 19.2 Control Chart for the Proportion: The p Chart
  • 19.3 The Red Bead Experiment: Understanding Process Variability
  • 19.4 Control Chart for an Area of Opportunity: The c Chart
  • 19.5 Control Charts for the Range and the Mean
  • 19.6 Process Capability
  • 19.7 Total Quality Management
  • 19.8 Six Sigma

20. Decision Making

  • 20.1 Payoff Tables and Decision Trees
  • 20.2 Criteria for Decision Making
  • 20.3 Decision Making with Sample Information
  • 20.4 Utility

Appendices

Additional information

Dimensions 1.00 × 8.30 × 10.80 in
Imprint

Format

ISBN-13

ISBN-10

Author

, ,

Subjects

statistics, mathematics, higher education, business statistics