Nelson Higher Education

Higher Education

MindTap Business Statistics, 1 term (6 months) Instant Access for Anderson/Sweeney/Williams/Camm/Cochran's Statistics for Business and Economics, Revised, 12th Edition

  • David R. Anderson
  • Dennis J. Sweeney
  • Thomas A. Williams
  • Jeffrey D. Camm
  • James J. Cochran
  • ISBN-10: 1305576144
  • ISBN-13: 9781305576148
  • Mixed Media
  • COPYRIGHT: 2015 Published
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Overview

About the Product

MindTap Business Statistics for Anderson/Sweeney/Williams/Camm/Cochran's Statistics for Business and Economics, Revised, 12th Edition is the digital learning solution that helps instructors engage and transform today's students into critical thinkers. Through paths of dynamic assignments and applications that you can personalize, real-time course analytics, and an accessible reader, MindTap helps you turn cookie cutter into cutting edge, apathy into engagement, and memorizers into higher-level thinkers. MindTap Business Statistics lays out assets in a learning path designed to engage students with videos that highlight the content's relevance, provide meaningful animated examples and practice opportunities, and teach students to interpret data and make informed business decisions with simulations. MindTap ultimately gives students a roadmap to successfully master decision making in business statistics -- and allows you to assess their analytical skills with confidence. Access to this product is valid for 6 months of usage. Attention: This MindTap contains Flash-based content. Due to the year-end retirement of Adobe Flash, we will no longer support this unique digital solution after November 30th. Please contact your Cengage Sales Representative to find an alternative Flash free MindTap. https://www.cengage.com/repfinder/

Features

  • WHY DOES THIS MATTER? This one-minute intro video is designed to pique students' interest by showing them how real companies use the statistical methods covered in the chapter to help them make informed decisions.

  • SECTION APPROACH WITH MEDIA-RICH ASSETS & PRACTICE: Students report that they're more likely to read or skim material when it's broken into more manageable chunks for them. Each chapter's content is broken out into digestible sections in the learning path, which mirrors the structure of the Anderson/Sweeney/Williams titles. The sections can contain readings, embedded self-check quizzing, animated examples for more visual learners, and Show Me the Solution videos that align with Self-Test problems from the book. Each section concludes with opportunities for practice with problems crafted to enhance the progression of learning. These non-graded practice assignments include rich feedback and offer multiple attempts with algorithmic content.

  • QUIZZING & ASSIGNMENTS: Once students have had the opportunity to practice what they've learned in each section, they tie it all together and complete a multiple-choice, chapter-level quiz to pinpoint areas of strength and weakness. The learning path then progresses to the chapter-level graded homework assignment. This assignment includes selected problems from the book's end-of-chapter materials and offers a mix of evaluating both methods and application.

  • INTERPRETING THE RESULTS: For students, seeing the relevance of what they're learning to the real world of business helps them stay more engaged in what they're learning. Instructors also report that they'd like to see students be able to bridge the gap between understanding calculations and being able to use data to make informed business decisions. To close each chapter, the last item in the chapter learning path asks students to do just that – interpret data in order to make business decisions. Using the same company example introduced in the chapter's opening video, this decision-making assignment puts students in an environment that requires them to interpret and make decisions based on the data provided in the company scenario. This activity brings the learning path full circle and helps students make a connection to how the study of business statistics applies to the real world of business.

  • Seamlessly deliver appropriate content and technology assets from a number of providers to students, as they need them.

  • Break course content down into movable objects to promote personalization, encourage interactivity and ensure student engagement.

  • Customize the course – from tools to text – and make adjustments 'on the fly,' making it possible to intertwine breaking news into their lessons and incorporate today's teachable moments.

  • Bring interactivity into learning through the integration of multimedia assets (apps from Cengage Learning and other providers), numerous in-context exercises and supplements, student engagement will increase leading to better student outcomes.

  • Track students' use, activities and comprehension in real-time, which provides opportunities for early intervention to influence progress and outcomes. Grades are visible and archived so students and instructors always have access to current standings in the class.

  • Assess knowledge throughout each section: after readings, in activities, homework, and quizzes.

  • Automatically grade of all homework and quizzes.

Reviews

"I'm convinced that the higher course ratings and grades were, in large part, a result of students' engagement. It was an unmitigated success."
— Robert Black, SUNY Maritime College

About the Author

David R. Anderson

Dr. David R. Anderson is a leading author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He has served as head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. He was also coordinator of the college’s first Executive Program. In addition to introductory statistics for business students, Dr. Anderson has taught graduate-level courses in regression analysis, multivariate analysis, and management science. He also has taught statistical courses at the Department of Labor in Washington, D.C. Dr. Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the co-author of ten well-respected textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his B.S., M.S., and Ph.D. degrees from Purdue University.

Dennis J. Sweeney

Dennis J. Sweeney is Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a BSBA degree from Drake University and his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow. Professor Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney served as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. Professor Sweeney has published more than 30 articles and monographs in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger and Cincinnati Gas & Electric have funded his research, which has been published in Management Science, Operations Research, Mathematical Programming, Decision Sciences and other journals. Professor Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.

Thomas A. Williams

N/A

Jeffrey D. Camm

Dr. Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he served on the faculty of the University of Cincinnati. He has also served as a visiting scholar at Stanford University and as a visiting Professor of Business Administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 40 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in numerous professional journals, including Science, Management Science, Operations Research and Interfaces. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of Interfaces. In 2016, Dr. Camm received the George E. Kimball Medal for service to the operations research profession and in 2017 he was named an INFORMS Fellow.

James J. Cochran

James J. Cochran is Associate Dean for Research, Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 40 papers in the development and application of operations research and statistical methods. He has published in several journals, including Management Science, The American Statistician, Communications in Statistics—Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, Interfaces and Statistics and Probability Letters. He received the 2008 INFORMS Prize for the Teaching of Operations Research Practice, 2010 Mu Sigma Rho Statistical Education Award and 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005, was named a Fellow of the American Statistical Association in 2011 and was named a Fellow of INFORMS in 2017. He received the Founders Award in 2014, the Karl E. Peace Award in 2015 from the American Statistical Association and the INFORMS President’s Award in 2019. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has served as operations research consultant to numerous companies and not-for-profit organizations.

Table of Contents

LEARNING PATH
WHY DOES THIS MATTER? This one-minute intro video is designed to pique students' interest by showing them how real companies use the statistical methods covered in the chapter to help them make informed decisions.

SECTION APPROACH WITH MEDIA-RICH ASSETS & PRACTICE: Students report that they're more likely to read or skim material when it's broken into more manageable chunks for them. Each chapter's content is broken out into digestible sections in the learning path, which mirrors the structure of the Anderson/Sweeney/Williams titles. The sections can contain readings, embedded self-check quizzing, animated examples for more visual learners, and Show Me the Solution videos that align with Self-Test problems from the book. Each section concludes with opportunities for practice with problems crafted to enhance the progression of learning. These non-graded practice assignments include rich feedback and offer multiple attempts with algorithmic content.

QUIZZING & ASSIGNMENTS: Once students have had the opportunity to practice what they've learned in each section, they tie it all together and complete a multiple-choice, chapter-level quiz to pinpoint areas of strength and weakness. The learning path then progresses to the chapter-level graded homework assignment. This assignment includes selected problems from the book's end-of-chapter materials and offers a mix of evaluating both methods and application.

INTERPRETING THE RESULTS: For students, seeing the relevance of what they're learning to the real world of business helps them stay more engaged in what they're learning. Instructors also report that they'd like to see students be able to bridge the gap between understanding calculations and being able to use data to make informed business decisions. To close each chapter, the last item in the chapter learning path asks students to do just that – interpret data in order to make business decisions. Using the same company example introduced in the chapter's opening video, this decision-making assignment puts students in an environment that requires them to interpret and make decisions based on the data provided in the company scenario. This activity brings the learning path full circle and helps students make a connection to how the study of business statistics applies to the real world of business.