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You are watching: A second course in statistics regression analysis


For the second half of a two-semester introductory statistics sequence because that undergraduates or a graduate food in applied regression analysis.

Gives college student the background and confidence to apply regression evaluation techniques

A 2nd Course in Statistics: Regression Analysis, 8 hours Edition is a extremely readable teaching message that explains principles in a logical, intuitive manner with worked-out examples. Applications come engineering, sociology, psychology, science, and business are demonstrated throughout; genuine data and also scenarios extracted from news articles, journals, and also actual consulting difficulties are offered to use the concepts, enabling students to get experience using the techniques outlined in the text.

Seven case studies throughout the message invite students to focus on particular problems, and also are suitable for course discussion. The 8 hours Edition incorporates several an extensive changes, additions, and enhancements to the instance studies, data sets, and also software tutorials.


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Readability – The authors’ goal is to create a teaching text rather than a reference. Concepts are explained in a logical, intuitive manner with worked-out examples. Emphasis on model building – Fundamental to any regression analysis, model building is introduced in Chapters 4 - 8 and then emphasized throughout. Focus on development of regression skills – The use of regression analysis is emphasized as a tool in solving problems, in addition to its basic concepts and methodology. In turn, students develop the ability to apply regression analysis to appropriate real-life situations. Real data-based examples and exercises:    Many worked examples illustrate important aspects of model construction, data analysis, and the interpretation of results.   Nearly every exercise is based on data and research extracted from a news article, magazine, or journal.  Exercises are located at the ends of key sections and at the ends of chapters. 30% new and updated exercises include interesting topics pulled from biology, business, and history.   Seven case studies address real-life research problems and are suitable for class discussion. While working through these, students can see how regression analysis is used to answer practical questions, and can then formulate appropriate statistical models for the analysis and interpretation of sample data. New and updated case studies: Case Study 2: Modeling Sale Prices of Residential Properties is updated with current data The new Case Study 7: Voice Versus Face Recognition—Does One Follow the Other? now follows the chapter on analysis of variance.   Data sets – The Data Sets website provides complete data sets that are associated with the case studies, exercises and examples.  These can be used by instructors and students to practice model-building and data analyses. Extensive use of statistical software – Online tutorials are provided for any of four popular statistical software packages (SAS, SPSS, MINITAB, and R). Printouts associated with the respective software packages are presented and discussed throughout the text. Updated statistical software output—All statistical software printouts shown now reflect reflect the most recent versions of Minitab, SAS, and SPSS. Updated statistical software tutorials—The text’s online resource provides updated instructions on how to use the Windows versions of SAS, SPSS, MINITAB, and R.  Step-by-step instructions and screenshots for each method presented in the text are shown. End-of-chapter summaries – Important points are reinforced through flow graphs (which aid in selecting the appropriate statistical method) and boxed notes with key words, formulas, definitions, lists, and key concepts. Updated and new sections – Chapter 9, Special Topics in Regression: Section 9.6 on logistic regression has been expanded A new section (Section 9.7) on Poisson regression has been added In addition to ridge regression, Section 9.8 now includes a discussion of Lasso regression. Streamlined, revised narrative throughout based on user feedback.