statistical techniques in business and economics
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Lind/Marchal/Wathen is a perennial market best seller due to its comprehensive coverage of statistical concepts and methods delivered in a student friendly, step-by-step format. The text presents concepts clearly and succinctly with a conversational writing style and illustrates concepts through the liberal use of business-focused examples that are relevant to the current world of a college student. Known as a eoestudente(tm)s text,e Linde(tm)s supporting pedagogy includes self reviews, cumulative exercises, and coverage of software applications including Excel, Minitab, and MegaStat for Excel. The new 15th edition puts more emphasis on the interpretation of data and results and supports Linde(tm)s student-centric, step-by-step approach with McGraw-Hille(tm)s industry leading online assessment resource Connect Business Statistics.
Over the years, we received many compliments on this text and understand that it’s a favorite among students. We accept that as the highest compliment and continue to work very hard to maintain that status. The objective of Statistical Techniques in Business and Economics is to provide students majoring in management, marketing, finance, accounting, economics, and other fields of business administration with an introductory survey of descriptive and inferential statistics. To illustrate the application of statistics, we use many examples and exercises that focus on business applications, but also relate to the current world of the college student. A previous course in statistics is not necessary, and the mathematical requirement is first-year algebra. In this text, we show beginning students every step needed to be successful in a basic statistics course. This step-by-step approach enhances performance, accelerates preparedness, and significantly improves motivation. Understanding the concepts, seeing and doing plenty of examples and exercises, and comprehending the application of statistical methods in business and economics are the focus of this book. The first edition of this text was published in 1967. At that time, locating relevant business data was difficult. That has changed! Today, locating data is not a problem. The number of items you purchase at the grocery store is automatically recorded at the checkout counter. Phone companies track the time of our calls, the length of calls, and the identity of the person called. Credit card companies maintain information on the number, time and date, and amount of our purchases. Medical devices automatically monitor our heart rate, blood pressure, and temperature from remote locations. A large amount of business information is recorded and reported almost instantly. CNN, USA Today, and MSNBC, for example, all have websites that track stock prices in real time. Today, the practice of data analytics is widely applied to “big data.” The practice of data analytics requires skills and knowledge in several areas. Computer skills are needed to process large volumes of information. Analytical skills are needed to evaluate, summarize, organize, and analyze the information. Critical thinking skills are needed to interpret and communicate the results of processing the information. Our text supports the development of basic data analytical skills. In this edition, we added a new section at the end of each chapter called Data Analytics. As you work through the text, this section provides the instructor and student with opportunities to apply statistical knowledge and statistical software to explore several business environments. Interpretation of the analytical results is an integral part of these exercises. A variety of statistical software is available to complement our text. Microsoft Excel includes an add-in with many statistical analyses. Megastat is an add-in available for Microsoft Excel. Minitab and JMP are stand-alone statistical software available to download for either PC or MAC computers. In our text, Microsoft Excel, Minitab, and Megastat are used to illustrate statistical software analyses. When a software application is presented, the software commands for the application are available in Appendix C. We use screen captures within the chapters, so the student becomes familiar with the nature of the software output. Because of the availability of computers and software, it is no longer necessary to dwell on calculations. We have replaced many of the calculation examples with interpretative ones, to assist the student in understanding and interpreting the statistical results. In addition, we place more emphasis on the conceptual nature of the statistical topics. While making these changes, we still continue to present, as best we can, the key concepts, along with supporting interesting and relevant examples. WHAT’S NEW IN THE SEVENTEENTH EDITION? We have made many changes to examples and exercises throughout the text. The section on “Enhancements” to our text details them. The major change to the text is in response to user interest in the area of data analytics. Our approach is to provide instructors and students with the opportunity to combine statistical knowledge, computer and statistical software skills, and interpretative and critical thinking skills. A set of new and revised exercises is included at the end of chapters 1 through 18 in a section titled “Data Analytics.” In these sections, exercises refer to three data sets. The North Valley Real Estate sales data set lists 105 homes currently on the market. The Lincolnville School District bus data lists information on 80 buses in the school district’s bus fleet. The authors designed these data so that students will be able to use statistical software to explore the data and find realistic relationships in the variables. The Baseball Statistics for the 2016 season is updated from the previous edition. The intent of the exercises is to provide the basis of a continuing case analysis. We suggest that instructors select one of the data sets and assign the corresponding exercises as each chapter is completed. Instructor feedback regarding student performance is important. Students should retain a copy of each chapter’s results and interpretations to develop a portfolio of discoveries and findings. These will be helpful as students progress through the course and use new statistical techniques to further explore the data. The ideal ending for these continuing data analytics exercises is a comprehensive report based on the analytical findings. We know that working with a statistics class to develop a very basic competence in data analytics is challenging. Instructors will be teaching statistics. In addition, instructors will be faced with choosing statistical software and supporting students in developing or enhancing their computer skills. Finally, instructors will need to assess student performance based on assignments that include both statistical and written components. Using a mentoring approach may be helpful. We hope that you and your students find this new feature interesting and engaging.
All statistical concepts are illustrated with solved applied examples immediately upon introduction. Modern computing applications (Excel, Minitab and MegaStat) are introduced, but the text maintains a focus on presenting statistics concepts as applied in business as opposed to technology or programming methods.
Lind/Marchal/Wathen is a perennial market best seller due to its comprehensive coverage of statistical concepts and methods delivered in a student friendly, step-by-step format. The text presents concepts clearly and succinctly with a conversational writing style and illustrates concepts through the liberal use of business-focused examples that are relevant to the current world of a college student. Known as a “student's text,” Lind's supporting pedagogy includes self-reviews, cumulative exercises, and coverage of software applications including Excel, Minitab, and MegaStat for Excel. And now, McGraw-Hill’s adaptive learning component, LearnSmart, provides assignable modules that help students master chapter core concepts and come to class more prepared. In addition, resources within Connect Plus help students solve problems and apply what they’ve learned. Lind’s real-world examples, comprehensive coverage, and superior pedagogy combine with a complete digital solution to help students achieve higher outcomes in the course.
The Fifth Edition of Basic Statistics for Business and Economics is a shorter version of Lind/Marchal/Wathen's Statistical Techniques in Business and Economics, 12e. The authors of this text continue to provide a student-oriented approach to business statistics. In this book you will find step-by-step solved examples, realistic exercises, and up-to-date technology and illustrations. Book jacket.
This brand new book in statistics aims to provide an introduction to the key methods and techniques essential to a typical statistics syllabus, whilst also helping students to develop the skills needed to analyse, interpret and prepare data for use in business, economics and related disciplines. Covering the essential methods required at undergraduate level, the book is structured into four parts that deal with descriptive statistics, probability, sample theory and inferential statistics, taking students from the basics through to more advanced topics such as multiple linear regression. Every chapter contains clear descriptions of each technique, illustrated with numerous worked examples to aid students in understanding how to practice statistical methods. The real data used in the examples is drawn from European sources. The text also contains longer case examples set in a European business context, to show how statistics is used everyday in the business environment. Finally, each chapter concludes with a variety of exercises to test studentsâ€™ ability to apply the theory and attain a high level of competence in using statistics. This comprehensive book is ideal for student of statistics at undergraduate level taking an introductory module in the topic.
This text is aimed at commerce and social science students who have already completed a first semester course in mathematics and applied statistics.