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This book is specially designed to refresh and elevate the level of
understanding of the foundational background in probability and
distributional theory required to be successful in a graduate-level
statistics program. Advanced undergraduate students and
introductory graduate students from a variety of quantitative
backgrounds will benefit from the transitional bridge that this
volume offers, from a more generalized study of undergraduate
mathematics and statistics to the career-focused, applied education
at the graduate level. In particular, it focuses on growing fields
that will be of potential interest to future M.S. and Ph.D.
students, as well as advanced undergraduates heading directly into
the workplace: data analytics, statistics and biostatistics, and
related areas.
This book is specially designed to refresh and elevate the level of
understanding of the foundational background in probability and
distributional theory required to be successful in a graduate-level
statistics program. Advanced undergraduate students and
introductory graduate students from a variety of quantitative
backgrounds will benefit from the transitional bridge that this
volume offers, from a more generalized study of undergraduate
mathematics and statistics to the career-focused, applied education
at the graduate level. In particular, it focuses on growing fields
that will be of potential interest to future M.S. and Ph.D.
students, as well as advanced undergraduates heading directly into
the workplace: data analytics, statistics and biostatistics, and
related areas.
This textbook is designed to give an engaging introduction to
statistics and the art of data analysis. The unique scope includes,
but also goes beyond, classical methodology associated with the
normal distribution. What if the normal model is not valid for a
particular data set? This cutting-edge approach provides the
alternatives. It is an introduction to the world and possibilities
of statistics that uses exercises, computer analyses, and
simulations throughout the core lessons. These elementary
statistical methods are intuitive. Counting and ranking features
prominently in the text. Nonparametric methods, for instance, are
often based on counts and ranks and are very easy to integrate into
an introductory course. The ease of computation with advanced
calculators and statistical software, both of which factor into
this text, allows important techniques to be introduced earlier in
the study of statistics. This book's novel scope also includes
measuring symmetry with Walsh averages, finding a nonparametric
regression line, jackknifing, and bootstrapping . Concepts and
techniques are explored through practical problems. Quantitative
reasoning is at the core of so many professions and academic
disciplines, and this book opens the door to the most modern
possibilities.
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