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Statistical science as organized in formal academic departments
is relatively new. With a few exceptions, most Statistics and
Biostatistics departments have been created within the past 60
years. This book consists of a set of memoirs, one for each
department in the U.S. created by the mid-1960s. The memoirs
describe key aspects of the department s history -- its founding,
its growth, key people in its development, success stories (such as
major research accomplishments) and the occasional failure story,
PhD graduates who have had a significant impact, its impact on
statistical education, and a summary of where the department stands
today and its vision for the future. Read here all about how
departments such as at Berkeley, Chicago, Harvard, and Stanford
started and how they got to where they are today. The book should
also be of interests to scholars in the field of disciplinary
history. "
For courses in introductory statistics. Statistics: The Art and
Science of Learning from Data takes a conceptual approach, helping
students understand what statistics is about and learning the right
questions to ask when analysing data, rather than just memorising
procedures. This book takes the ideas that have turned statistics
into a central science in modern life and makes them accessible,
without compromising the necessary rigor. Students will enjoy
reading this book, and will stay engaged with its wide variety of
real-world data in the examples and exercises. The authors believe
that it's important for students to learn and analyse both
quantitative and categorical data. As a result, the text pays
greater attention to the analysis of proportions than many other
introductory statistics texts. Concepts are introduced first with
categorical data, and then with quantitative data.
Shows the elements of statistical science that are highly relevant
for students who plan to become data scientists less emphasis on
probability theory and methods of probability such as
combinatorics, derivations of probability distributions of
transformations of random variables (except for explanations of t,
chi-squared, and F constructions) Formal statements and proofs of
theorems, and decision theory Introduces some modern topics that do
not normally appear in "math stat" texts but are especially
relevant for data scientists, such as generalized linear models for
non-normal responses (e.g., logistic regression) Bayesian and
regularized fitting of models (e.g., showing an example using the
lasso), classification and clustering, and implementing methods
with modern software (R and Python)
Take your first steps into learning statistics, and understand the
fascinating science of analysing data. Statistics: The Art and
Science of Learning from Data, Global Edition, 5th edition by
Agresti, Franklin, and Klingenberg is the ideal introduction to the
discipline that will familiarise you with the world of statistics
and data analysis. Ideal for students who study introductory
courses in statistics, this text takes a conceptual approach and
will encourage you to learn how to analyse data the right way by
enquiring and searching for the right questions and information
rather than just memorising procedures. Enjoyable and accessible,
yet informative and without compromising the necessary rigour, this
edition will help you engage with the science in modern life,
delivering a learning experience that is effective in statistical
thinking and practice. Key features include: Greater attention to
the analysis of proportions compared to other introductory
statistics texts. Introduction to key concepts, presenting the
categorical data first, and quantitative data after. A wide variety
of real-world data in the examples and exercises New sections and
updated content will enhance your learning and understanding.
Pearson MyLab (R) Students, if Pearson Pearson MyLab Statistics is
a recommended/mandatory component of the course, please ask your
instructor for the correct ISBN. Pearson MyLab Statistics should
only be purchased when required by an instructor. Instructors,
contact your Pearson representative for more information. This
title is a Pearson Global Edition. The Editorial team at Pearson
has worked closely with educators around the world to include
content which is especially relevant to students outside the United
States.
Statistical science as organized in formal academic departments is
relatively new. With a few exceptions, most Statistics and
Biostatistics departments have been created within the past 60
years. This book consists of a set of memoirs, one for each
department in the U.S. created by the mid-1960s. The memoirs
describe key aspects of the department's history -- its founding,
its growth, key people in its development, success stories (such as
major research accomplishments) and the occasional failure story,
PhD graduates who have had a significant impact, its impact on
statistical education, and a summary of where the department stands
today and its vision for the future. Read here all about how
departments such as at Berkeley, Chicago, Harvard, and Stanford
started and how they got to where they are today. The book should
also be of interests to scholars in the field of disciplinary
history.
A valuable overview of the most important ideas and results in
statistical modeling Written by a highly-experienced author,
Foundations of Linear and Generalized Linear Models is a clear and
comprehensive guide to the key concepts and results of
linearstatistical models. The book presents a broad, in-depth
overview of the most commonly usedstatistical models by discussing
the theory underlying the models, R software applications,and
examples with crafted models to elucidate key ideas and promote
practical modelbuilding. The book begins by illustrating the
fundamentals of linear models, such as how the model-fitting
projects the data onto a model vector subspace and how orthogonal
decompositions of the data yield information about the effects of
explanatory variables. Subsequently, the book covers the most
popular generalized linear models, which include binomial and
multinomial logistic regression for categorical data, and Poisson
and negative binomial loglinear models for count data. Focusing on
the theoretical underpinnings of these models, Foundations ofLinear
and Generalized Linear Models also features: * An introduction to
quasi-likelihood methods that require weaker distributional
assumptions, such as generalized estimating equation methods * An
overview of linear mixed models and generalized linear mixed models
with random effects for clustered correlated data, Bayesian
modeling, and extensions to handle problematic cases such as high
dimensional problems * Numerous examples that use R software for
all text data analyses * More than 400 exercises for readers to
practice and extend the theory, methods, and data analysis * A
supplementary website with datasets for the examples and exercises
An invaluable textbook for upper-undergraduate and graduate-level
students in statistics and biostatistics courses, Foundations of
Linear and Generalized Linear Models is also an excellent reference
for practicing statisticians and biostatisticians, as well as
anyone who is interested in learning about the most important
statistical models for analyzing data.
Statistical methods applied to social sciences, made accessible to
all through an emphasis on concepts Statistical Methods for the
Social Sciences introduces statistical methods to students majoring
in social science disciplines. With an emphasis on concepts and
applications, this book assumes no previous knowledge of statistics
and only a minimal mathematical background. It contains sufficient
material for a two-semester course. The 5th Edition uses examples
and exercises with a variety of "real data." It includes more
illustrations of statistical software for computations and takes
advantage of the outstanding applets to explain key concepts, such
as sampling distributions and conducting basic data analyses. It
continues to downplay mathematics-often a stumbling block for
students-while avoiding reliance on an overly simplistic
recipe-based approach to statistics.
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