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Description
By: Jason Abrevaya
A modern introduction to probability and statistics for economics and business undergraduates, using the R programming language.
Designed for an introductory course in probability and statistics for economics and business undergraduates, this comprehensive textbook introduces students to the R statistical programming language. While covering the standard topics found in traditional textbooks, Jason Abrevaya takes a modern approach that directly integrates R, highlights the use of simulation methods, and provides a general treatment of statistical inference for asymptotically normal estimators. Coverage emphasizes concepts that are useful to economists and data analysts, including general statistical-inference results that apply well beyond averages and variances. The book offers a higher level of mathematical rigor than traditional business statistics textbooks to prepare students for future coursework and for a professional climate where employers increasingly emphasize competence in data science and statistics.
A modern introduction to probability and statistics for economics and business undergraduates, using the R programming language.
Designed for an introductory course in probability and statistics for economics and business undergraduates, this comprehensive textbook introduces students to the R statistical programming language. While covering the standard topics found in traditional textbooks, Jason Abrevaya takes a modern approach that directly integrates R, highlights the use of simulation methods, and provides a general treatment of statistical inference for asymptotically normal estimators. Coverage emphasizes concepts that are useful to economists and data analysts, including general statistical-inference results that apply well beyond averages and variances. The book offers a higher level of mathematical rigor than traditional business statistics textbooks to prepare students for future coursework and for a professional climate where employers increasingly emphasize competence in data science and statistics.
- Introduces students to the R statistical programming language
- Uses real-world examples and datasets related to economics and business
- Provides extensive coverage of simulation methods
- Focuses on large-sample (asymptotic) results
- Is classroom-tested at Emory University, the University of Texas at Austin, Princeton University, and elsewhere
- Suits undergraduate and graduate students in business, economics, data science, and statistics with knowledge of calculus
- Offers companion website and extensive instructor resources
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