Statistics for Managers Using Microsoft Excel, Global Edition

Series
Pearson
Author
David M. Levine / David F. Stephan / Kathryn A. Szabat  
Publisher
Pearson
Edition
9
Language
English
Total pages
752
Pub.-date
August 2020
ISBN13
9781292338248
ISBN
1292338245
Related Titles



Description

This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States.

 

For one-semester courses in Introduction to Business Statistics.

 

The gold standard in learning Microsoft Excel for business statistics

Statistics for Managers Using Microsoft® Excel®9th Edition, Global Edition helps students develop the knowledge of Excel needed in future careers. The authors present statistics in the context of specific business fields, and now include a full chapter on business analytics. Guided by principles set forth by ASA’s Guidelines for Assessment and Instruction (GAISE) reports and the authors’ diverse teaching experiences, the text continues to innovate and improve the way this course is taught to students. Current data throughout gives students valuable practice analyzing the types of data they will see in their professions, and the authors’ friendly writing style includes tips and learning aids throughout. 

 

The book also integrates PHStat, a statistical add-in that bolsters the functions of Excel. Extensive instructor and student resources are provided, including two online-only chapters, as well as the Digital Cases referenced in the text. 

 

Pearson MyLab Statistics is not included. Students, if Pearson MyLab Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Pearson MyLab Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

Features

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®:

o 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.  

o 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 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.” 

o NEW - Tableau Guides in each chapter explain how to use Tableau Public, the data visualization software, 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, Global Edition, seven chapters offer new or revised case scenarios.

o 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.

o 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.

o Case Studies offer realistic business scenarios to apply fundamental statistical and analytical concepts. 

o 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.

o 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.

 

Pearson MyLab Statistics is not included. Students, if Pearson MyLab Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Pearson MyLab Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

 

Teach your course your way

·    All data sets are available to download in the Pearson 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 Pearson 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.

·    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 Pearson 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 Pearson MyLab Statistics, so students can quickly and easily analyze any data set, including those from their text and Pearson MyLab Statistics exercises. In addition, Pearson 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 Pearson MyLab Statistics.

·    StatCrunch Projects in Pearson 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.

New to this Edition

·    New or revised Using Statistics case scenarios in seven chapters of the 9th Edition, Global 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.

o 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.

 

Pearson MyLab Statistics is not included. Students, if Pearson MyLab Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Pearson MyLab Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

·    Pearson eText is a simple-to-use, mobile-optimized, personalized reading experience available within MyLab.

·    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.

Table of Contents

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 73

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 (online)

20.1 Payoff Tables and Decision Trees

20.2 Criteria for Decision Making

20.3 Decision Making with Sample Information

20.4 Utility 

 

Appendices 

Indices

Credits