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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.
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 Statistical Methods for the Social Sciences .
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 you have no previous
knowledge of statistics and only a minimal mathematical background.
It contains sufficient material for a two-semester course. The 6th
Edition gives you 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.
Statistical science's first coordinated manual of methods for
analyzing ordered categorical data, now fully revised and updated,
continues to present applications and case studies in fields as
diverse as sociology, public health, ecology, marketing, and
pharmacy. "Analysis of Ordinal Categorical Data, Second Edition"
provides an introduction to basic descriptive and inferential
methods for categorical data, giving thorough coverage of new
developments and recent methods. Special emphasis is placed on
interpretation and application of methods including an integrated
comparison of the available strategies for analyzing ordinal data.
Practitioners of statistics in government, industry (particularly
pharmaceutical), and academia will want this new edition.
A valuable new edition of a standard reference The use of
statistical methods for categorical data has increased
dramatically, particularly for applications in the biomedical and
social sciences. An Introduction to Categorical Data Analysis,
Third Edition summarizes these methods and shows readers how to use
them using software. Readers will find a unified generalized linear
models approach that connects logistic regression and loglinear
models for discrete data with normal regression for continuous
data. Adding to the value in the new edition is: - Illustrations of
the use of R software to perform all the analyses in the book - A
new chapter on alternative methods for categorical data, including
smoothing and regularization methods (such as the lasso),
classification methods such as linear discriminant analysis and
classification trees, and cluster analysis - New sections in many
chapters introducing the Bayesian approach for the methods of that
chapter - More than 70 analyses of data sets to illustrate
application of the methods, and about 200 exercises, many
containing other data sets - An appendix showing how to use SAS,
Stata, and SPSS, and an appendix with short solutions to most
odd-numbered exercises Written in an applied, nontechnical style,
this book illustrates the methods using a wide variety of real
data, including medical clinical trials, environmental questions,
drug use by teenagers, horseshoe crab mating, basketball shooting,
correlates of happiness, and much more. An Introduction to
Categorical Data Analysis, Third Edition is an invaluable tool for
statisticians and biostatisticians as well as methodologists in the
social and behavioral sciences, medicine and public health,
marketing, education, and the biological and agricultural sciences.
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.
Praise for the Second Edition "A must-have book for anyone
expecting to do research and/or applications in categorical data
analysis." Statistics in Medicine "It is a total delight reading
this book." Pharmaceutical Research "If you do any analysis of
categorical data, this is an essential desktop reference."
Technometrics The use of statistical methods for analyzing
categorical data has increased dramatically, particularly in the
biomedical, social sciences, and financial industries. Responding
to new developments, this book offers a comprehensive treatment of
the most important methods for categorical data analysis.
Categorical Data Analysis, Third Edition summarizes the latest
methods for univariate and correlated multivariate categorical
responses. Readers will find a unified generalized linear models
approach that connects logistic regression and Poisson and negative
binomial loglinear models for discrete data with normal regression
for continuous data. This edition also features: * An emphasis on
logistic and probit regression methods for binary, ordinal, and
nominal responses for independent observations and for clustered
data with marginal models and random effects models * Two new
chapters on alternative methods for binary response data, including
smoothing and regularization methods, classification methods such
as linear discriminant analysis and classification trees, and
cluster analysis * New sections introducing the Bayesian approach
for methods in that chapter * More than 100 analyses of data sets
and over 600 exercises * Notes at the end of each chapter that
provide references to recent research and topics not covered in the
text, linked to a bibliography of more than 1,200 sources * A
supplementary website showing how to use R and SAS; for all
examples in the text, with information also about SPSS and Stata
and with exercise solutions Categorical Data Analysis, Third
Edition is an invaluable tool for statisticians and methodologists,
such as biostatisticians and researchers in the social and
behavioral sciences, medicine and public health, marketing,
education, finance, biological and agricultural sciences, and
industrial quality control.
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.
For courses in introductory statistics. The art and science of
learning from data Statistics: The Art and Science of Learning from
Data takes a conceptual approach,helping students understand what
statistics is about and learning the rightquestions to ask when
analyzing data, rather than just memorizing procedures.This book
takes the ideas that have turned statistics into a central science
inmodern life and makes them accessible, without compromising the
necessaryrigor. Students will enjoy reading this book, and will
stay engaged with itswide variety of real-world data in the
examples and exercises.
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